diff --git a/textbook/03/3/DataTypes.ipynb b/textbook/03/3/DataTypes.ipynb
index e385748f..c6ff0c73 100644
--- a/textbook/03/3/DataTypes.ipynb
+++ b/textbook/03/3/DataTypes.ipynb
@@ -8,7 +8,7 @@
     "# Data Types\n",
     "*Evelyn Campbell, Ph.D.*\n",
     "\n",
-    "Python offers a number of different data types that can be manipulated and used by various functions. Some important built-in Python data types include **booleans**, **strings**, **integers**, and **floats**. These data types can be used to build various data structures, such as lists, dictionaries, arrays, and dataframes, which will be covered in Chapters [4](../4/DataStructures.ipynb) and [6](../6/DataFrames.ipynb). Here we will explore each data type and corresponding functions that are useful when working with these data types."
+    "Python offers a number of different data types that can be manipulated and used by various functions. Some important built-in Python data types include booleans, strings, integers, and floats. These data types can be used to build various data structures, such as lists, dictionaries, arrays, and dataframes, which will be covered in Chapters 4 and 6. Here we will explore each data type and corresponding functions that are useful when working with these data types."
    ]
   },
   {
@@ -18,12 +18,12 @@
    "source": [
     "## Booleans\n",
     "\n",
-    "Booleans are a data type that consist of two possible outcomes: `True` or `False`. Under the hood, these values take on a binary value, where `True` is equal to 1 and `False` is equal to 0. Booleans are very commonly used with comparison operators ([discussed more in section 3.4](../3/4/Comparisons.ipynb)), and because they also can have a numeric meaning, they can be used in calculations as well. Let's start with a simple example of a Boolean."
+    "Booleans are a data type that consists of two possible outcomes: `True` or `False`. Under the hood, these values take on a binary value, where `True` is equal to 1 and `False` is equal to 0. Booleans are very commonly used with comparison operators, and because they also can have a numeric meaning, they can be used in calculations as well. Let's start with a simple example of a Boolean."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "id": "0856f3c4",
    "metadata": {},
    "outputs": [
@@ -33,29 +33,19 @@
        "False"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "boolval = 5 < 3\n",
+    "boolval = 2 + 5 < 3 + 1\n",
     "boolval"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "061f80a6",
-   "metadata": {},
-   "source": [
-    "Above, the variable `boolval` is equated to the expression `5 < 3`, which reads \"5 is less than 3.\" Because 5 is not in fact less than 3, the entire statement is `False`, and this Boolean value is assigned to `boolval`.\n",
-    "\n",
-    "Below, we add 5 to the value of `boolval`. Recall that `False` has a numerical value of 0, so essentially, `boolval + 5` is the same as 0 + 5:"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 2,
    "id": "9474bdc6",
    "metadata": {},
    "outputs": [
@@ -65,7 +55,7 @@
        "5"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -75,14 +65,6 @@
     "boolval"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "071ba5da",
-   "metadata": {},
-   "source": [
-    "Using the variable directly in a comparison expression, we can see that the value of `boolval` is less than 10, and thus returns another Boolean value of `True`:"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -109,9 +91,7 @@
    "id": "37df7040",
    "metadata": {},
    "source": [
-    "Python has built-in **functions** that use values and variables as input to perform a task and produce an output. We have already used some basic functions, such as the `print()` function, and we will learn about a few more that are associated with datatypes. Built-in functions will be further discussed in section [section 3.4](../3/5/IntroFunctions.ipynb). \n",
-    "\n",
-    "For now, we will use a few basic functions associated with data types. The `bool()` function converts an input (i.e. a numeric value, string, or even data structures) to a boolean value."
+    "The `bool()` function converts an input (i.e. a numeric value, string, or even data structures) to a boolean value."
    ]
   },
   {
@@ -147,7 +127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 5,
    "id": "96368899",
    "metadata": {},
    "outputs": [
@@ -162,7 +142,7 @@
    ],
    "source": [
     "something = 6542\n",
-    "nothing = 0\n",
+    "nothing = 0 # an empty list\n",
     "print(bool(something))\n",
     "print(bool(nothing))"
    ]
@@ -174,14 +154,12 @@
    "source": [
     "## Strings\n",
     "\n",
-    "A **string** a data type that can consist of **concatenated** alphanumeric and punctuation characters. According to the Merriam-Webster dictionary, to concatenate means *to link together in a series or chain*.\n",
-    "\n",
-    "Strings are recognized by Python through the use of single (' '), double (\" \"), or triple (''' ''') quotation marks. "
+    "Strings are a data type that can consist of concatenated alphanumeric and punctuation characters. Strings are recognized by Python through the use of single (' ') or double (\" \") quotation marks. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "9868b82e",
    "metadata": {},
    "outputs": [
@@ -208,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "4e147421",
    "metadata": {},
    "outputs": [
@@ -226,7 +204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "5b4ec625",
    "metadata": {
     "tags": [
@@ -236,10 +214,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (3546504085.py, line 1)",
+     "evalue": "unterminated string literal (detected at line 1) (3546504085.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  Input \u001b[0;32mIn [9]\u001b[0;36m\u001b[0m\n\u001b[0;31m    print('This isn't easy.')\u001b[0m\n\u001b[0m                    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn [8], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    print('This isn't easy.')\u001b[0m\n\u001b[0m                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unterminated string literal (detected at line 1)\n"
      ]
     }
    ],
@@ -247,75 +225,17 @@
     "print('This isn't easy.')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "101ed527",
-   "metadata": {},
-   "source": [
-    "The above error can be fixed by an **escape sequence**. Escape sequences are string modifiers that allow for the use of certain characters that would otherwise be misinterpreted by Python. Because strings are created by the use of quotes, the escape sequences `\\'` and `\\\"` allow for the use of quotes as part of a string:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "18da1844",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This isn't easy.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print('This isn\\'t easy.')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2dae3eff",
-   "metadata": {},
-   "source": [
-    "Other useful escape sequences include `\\n` and `\\t`. These allow for a new line and tab spacing to be added to a string, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "241848e8",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is the first sentence \n",
-      "This is the second sentence! \tThis is the third sentence?\n"
-     ]
-    }
-   ],
-   "source": [
-    "sentences = '''This is the first sentence \\nThis is the second sentence! \\tThis is the third sentence?'''\n",
-    "print(sentences)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "a46d5107",
    "metadata": {},
    "source": [
-    "Strings can be used in simple additive mathematical operations, like addition and multiplication, resulting in concatenation of the strings:"
+    "Strings can be used in simple additive mathematical operations, like addition and multiplication."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "3b40b622",
    "metadata": {},
    "outputs": [
@@ -345,7 +265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "c57fbd1b",
    "metadata": {},
    "outputs": [
@@ -363,45 +283,12 @@
     "print(words, words)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "ca3e1bc1",
-   "metadata": {},
-   "source": [
-    "Escape sequences also can be used in the `print()` function as an argument or through concatenation:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "74b515d1",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "This is a sentence. \t This isn't easy.\n",
-      "\n",
-      "\n",
-      "This isn't easy.\tThis is a sentence.\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(words, '\\t', 'This isn\\'t easy.')      # Escape sequence used as an argument in the print function\n",
-    "print('\\n')                                  # Escape sequence used to print a blank line\n",
-    "print('This isn\\'t easy.' + '\\t' + words)    # Escape sequence concatenated to strings in the print function"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "396964f1",
    "metadata": {},
    "source": [
-    "When manipulating string variables, data scientists will often use what are called **methods**. A method is piece of code that is associated with a defined variable, as opposed to a **function** which uses defined variables as input arguments for parameters. Functions will be further discussed in the upcoming section.\n",
+    "When manipulating string variables, data scientist will often used what are called *methods*. A method is piece of code that is associated with a defined variable, as opposed to a *function* which uses defined variables as input parameters. Functions will be further discussed in the upcoming section.\n",
     "\n",
     "\n",
     "Some methods can be used on strings to quickly and efficiently alter them. A few include the `.upper()`, `.lower()`, `.capitalize()`, `.title()`, and `.swapcase()` methods. There are many others, but these few are great to start exploring the different ways string variables can be manipulated:"
@@ -495,7 +382,7 @@
    "id": "01d68864",
    "metadata": {},
    "source": [
-    "We can confirm that these are indeed strings by calling the `type()` function on these variables, which can be used on any variable to check its data type."
+    "We can confirm that these are indeed strings by calling these variables into the `type()` function, which can be used on any variable to check its data type."
    ]
   },
   {
@@ -525,7 +412,7 @@
    "id": "c8160dd5",
    "metadata": {},
    "source": [
-    "Keep in mind that when a numerical value is converted to a string, it can no longer be used to perform certain mathematical calculations, such as division, subtraction, or exponentiation."
+    "Keep in mind that when a numerical value is converted to a string, it can no longer be used to perform advanced mathematical calculations, such as division, subtraction, or exponentiation."
    ]
   },
   {
@@ -598,7 +485,7 @@
    "source": [
     "## Integers & Floats\n",
     "\n",
-    "Integers and floats are numerical data types that are often used to perform mathematical operations. Integers consist of whole numbers, while floats consist of whole numbers with floating decimal places. Floats can hold up to 15 significant figures following the decimal point and can be used to obtain more accurate calculations. However, it is easier and faster for a computer to do calculations using integers. Thus, one must weigh the pros and cons of using these data types when doing calculations and writing functions to obtain outcomes that are most aligned with their end goals. Let's take a look at these data types in use.\n"
+    "Integers and floats are numerical data types that are often used to perform mathematical operations. Integers consists of whole numbers, while floats consists of whole numbers with floating decimal places. Floats can hold up to 15 significant figures following the decimal point and can be used to obtain more accurate calculations. However, it is easier and faster for a computer to do calculations using integers. Thus, one must weigh the pros and cons of using these data types when doing calculations and writing functions to obtain outcomes that are most aligned with their end goals. Let's take a look at these data types in use.\n"
    ]
   },
   {
@@ -709,7 +596,7 @@
    "id": "0b7c63c9",
    "metadata": {},
    "source": [
-    "We can see that the conversion of an integer to a float simply adds one significant figure after the decimal place. Moreover, converting a float to an integer rounds the number *down* to the nearest whole number. We can also convert numerical values in strings and boolean data types to integers and floats"
+    "We can see that the conversion of an integer to a float simply adds one significant figure after the decimal place. Moreover, converting a float to an integer rounds the number <u>down</u> to the nearest whole number. We can also convert numerical values in strings and boolean data types to integers and floats"
    ]
   },
   {
@@ -775,16 +662,10 @@
    "id": "e37e3887",
    "metadata": {},
    "source": [
-    "By understanding data types, we can begin to use them in other analyses and functionalities in Python. Next, we will learn how to use data types in comparisons, which can help further down the line in functions ([Chapter 3.5](../3/5/IntroFunctions.ipynb)), for loops ([Chapter 5.3](../../05/3/Control_Statements_Iteration.ipynb)), and subsetting data from DataFrames ([Chapter 5.3](../../06/6/Select_Condition.ipynb))."
+    "## Conclusions\n",
+    "\n",
+    "In this section, we learned about various different data types. These include the `boolean`, `string`, `int`, and `float` data types. As you become more acquainted with Python, you will see the ubiquity of these data types in many data structures, which we will discuss in upcoming chapters. For now, explore these data types and relevant functions to learn how and when these data types can be used. Happy coding!\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "23d5baaa",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
@@ -803,7 +684,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.6"
   }
  },
  "nbformat": 4,
diff --git a/textbook/04/3/Arrays-Intro.ipynb b/textbook/04/3/Arrays-Intro.ipynb
index a07dea81..b9da0849 100644
--- a/textbook/04/3/Arrays-Intro.ipynb
+++ b/textbook/04/3/Arrays-Intro.ipynb
@@ -14,7 +14,7 @@
    "id": "e72d9bce",
    "metadata": {},
    "source": [
-    "An *array* is a data structure that consists of a collection of elements organized into a grid-like shape. In Python, arrays can be one-dimensional, akin to a list, or multidimensional (2D, 3D, etc.). However, unlike a list, an array consists of elements that are all of the same data type. This makes arrays ideal for convenient storage of data elements. Arrays are offered through the `numpy` library, and are often used in conjunction with other Python libraries, such as `pandas`, `scipy`, and `scikit-learn` (linked below). We will explore arrays in this section, along with commonly used functions used with arrays."
+    "An *array* is a data structure that consists of a collection of elements organized into a grid-like shape. In Python, arrays can be one-dimensional, akin to a list, or multidimensional (2D, 3D, etc.). However, unlike a list, an array consists of elements that are all of the same data type. This makes arrays ideal for convienent for storage and fast manipulation of data elements. Arrays are offered through the `NumPy` library, and are often used in conjunction with other Python libraries, such as `pandas`, `SciPy`, and `Scikit-learn`. We will explore arrays in this section, along with commonly used functions used with arrays."
    ]
   },
   {
@@ -22,14 +22,12 @@
    "id": "77369985",
    "metadata": {},
    "source": [
-    "## Constructing arrays\n",
-    "\n",
-    "To make an array, we first need to import `numpy`. We can then build an array from a list using the `np.array()` function."
+    "To make an array, we first need to import `NumPy`. We can then build an array from a list using the `np.array()` function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 2,
    "id": "ab8b729c",
    "metadata": {},
    "outputs": [
@@ -39,7 +37,7 @@
        "array([30, 50, 70, 90])"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -57,12 +55,12 @@
    "id": "0b31777b",
    "metadata": {},
    "source": [
-    "Another way an array can be made is by using the `np.arange()` function. With this function, one can build an array with a given inclusive start value and exclusive stop value as well as a step, which by default is 1."
+    "Another way an array can be made is by using the `np.arange()` function. With this function, one can build an array with a given inclusive start value and exclusive stop value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 3,
    "id": "9f09fedf",
    "metadata": {},
    "outputs": [
@@ -72,7 +70,7 @@
        "array([ 4,  6,  8, 10])"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,7 +85,7 @@
    "id": "ed9c9a2b",
    "metadata": {},
    "source": [
-    "Above, we made a one-dimensional array with four elements. We started the array at 4 and stopped it at 11. Because we specified the step as 2 (default is 1), our array gave us the values 4, 6, 8, and 10 because 11 is exclusive. Therefore, the `np.arange()` function will evenly space out the elements of our array just before the stop value."
+    "Above, we made a one-dimensional array with four elements. We started the array at 4 and stopped it at 11. Because we specified the spacing as 2, our array gave us the values 4, 6, 8, and 10 because 11 is exclusive. Therefore, the `np.arange()` function will evenly space out the elements of our array just before the stop value."
    ]
   },
   {
@@ -95,14 +93,12 @@
    "id": "7e71b117",
    "metadata": {},
    "source": [
-    "## Mathematical operations with arrays\n",
-    "\n",
-    "Arrays also allow for convenient elementwise calculations. For example, we can easily multiply our two arrays to obtain a new array of values. "
+    "As previously stated, arrays are a data structure that allow for fast calculations. For example, we can easily multiply our two arrays to obtain a new array of values. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 4,
    "id": "a8c411f9",
    "metadata": {},
    "outputs": [
@@ -112,7 +108,7 @@
        "array([120, 300, 560, 900])"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,9 +123,7 @@
    "id": "2c972b46",
    "metadata": {},
    "source": [
-    "The resulting array consists of the products of element-by-element multiplication of the first two arrays. Keep in mind that when performing calculations with multiple arrays, the dimensions of the arrays must be *compatible*. \n",
-    "\n",
-    "Performing elementwise operations on arrays of different shapes is called **broadcasting**, and a discussion on array shape compatibility in mathematical operations can be found in the referenced documentation on *Array broadcasting in numpy* below. "
+    "The resulting array consists of the products of element-by-element multiplication of the first two arrays. Keep in mind that when performing calculations with multiple arrays, the dimensions of the arrays must be compatible. A discussion on array shape compatibility in mathematical operations can be found in the referenced documentation on *Array Broadcasting in Numpy* below."
    ]
   },
   {
@@ -137,12 +131,12 @@
    "id": "e7cefaca",
    "metadata": {},
    "source": [
-    "More simply, we can also perform a desired calculation on all elements of an array using scalar values:"
+    "More simply, we can also perform a desired calculation on all elements of an array"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 5,
    "id": "1fa0c046",
    "metadata": {},
    "outputs": [
@@ -152,7 +146,7 @@
        "array([13., 22., 35., 52.])"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -166,14 +160,12 @@
    "id": "5e772d89",
    "metadata": {},
    "source": [
-    "## Reshaping and combining arrays\n",
-    "\n",
     "Arrays can also be reshaped and combined. We can use the `np.reshape()` function to change the first two arrays from a 1-dimensional 1x4 array to a 2-dimensional 2x2 array."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 6,
    "id": "63529f0f",
    "metadata": {},
    "outputs": [],
@@ -285,8 +277,6 @@
    "id": "3b6bc94d",
    "metadata": {},
    "source": [
-    "## Array functions\n",
-    "\n",
     "Construction and reshaping of arrays is an important consideration if you wish to perform aggregate functions on them. Some useful aggregate functions that can be performed on arrays include `np.min()`, `np.max()`, `np.sum()`, and `np.average()`. These functions can be applied to the entire array or across rows and columns."
    ]
   },
@@ -393,608 +383,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ec7f35b1",
-   "metadata": {},
-   "source": [
-    "## Indexing and Slicing\n",
-    "\n",
-    "1D arrays can be indexed similarly to how lists are indexed, as mentioned in the [section 4.1](../../1/Lists).\n",
-    "\n",
-    "\n",
-    "To begin in demonstrating this, let's make a new array of string elements called `flowers`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "c1f4ad88",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily',\n",
-       "       'violet'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers = np.array(['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily', 'violet'])\n",
-    "flowers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a0004ce9",
-   "metadata": {},
-   "source": [
-    "As seen with lists, arrays utilize *zero-indexing*, meaning that the first element of an array has an index of 0. If we wanted to select the third to the sixth (inclusive) element in `flowers`, we need recognize that this corresponds to indexes 2 and 5, respectively.\n",
-    "\n",
-    "<img align=\"center\" src=\"./img/flowers_array3.png\" width=\"80%\"/>\n",
-    "\n",
-    "A single colon (:) can be used to slice a range of elements in an array. The format for simple slicing an array is as follows:\n",
-    "\n",
-    "```\n",
-    "array[start:end]\n",
-    "```\n",
-    "\n",
-    "\n",
-    "\n",
-    "If used between the indices *j* and *k*, slicing the elements of an array will return all elements between *j* and *k*, <u>excluding k</u>.\n",
-    "\n",
-    "In this case, we use 2:6 to slice from the third to the sixth element because we want to include the sixth element (which is located at index 5):\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "id": "9935ef70",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['lilac', 'peony', 'tulip', 'dahlia'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers[2:6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "17633c64",
-   "metadata": {},
-   "source": [
-    "Arrays can also be sliced in intervals. Slicing in intervals takes on the following format:\n",
-    "\n",
-    "```\n",
-    "array[start:end:step]\n",
-    "```\n",
-    "\n",
-    "The step dictates the spacing between values. If we wanted to slice `flowers` starting at the second element to the sixth element (inclusive) in steps of two, we would use the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "67dcfe21",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['rose', 'peony', 'dahlia'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers[1:6:2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83db1e0c",
-   "metadata": {},
-   "source": [
-    "Slices can be made without explicitly indicating the start of end or the slice. In these cases, Python will start slicing with the first element if the beginning is not indicated and will stop at the last element if the end is not indicated:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "b71afccd",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['rose', 'peony', 'dahlia', 'violet'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers[1::2] # Starts at the second element and selects every other element to the end of the array"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "id": "cbb7b21d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['orchid', 'rose', 'lilac', 'peony'], dtype='<U6')"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers[:4] # Selects every element between the first and forth (inclusive) element"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1c87ad78",
-   "metadata": {},
-   "source": [
-    "When arrays become very long, it may be useful to use **negative-indexing**. Negative indexing assigns indices of elements starting from the last element. The very last element of an array has an index of -1, the pentultimate, -2, and so on. If we want the fourth-from-last element of `flowers`, we could use the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "5a6bca2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'tulip'"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers[-4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a6291acf",
-   "metadata": {},
-   "source": [
-    "Negative indexing can be combined with slicing to return elements of an array in a reverse order. For example, we can list all the flowers from `lily` to `lilac` in reverse order using the following:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "id": "873f024a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([], dtype='<U6')"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flowers[6:1:-2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ba581ef8",
-   "metadata": {},
-   "source": [
-    "Here, it's okay that the starting index is greater than the ending index because we indicated the step to go from back to front."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e64cbc23",
-   "metadata": {},
-   "source": [
-    "## Nested arrays\n",
-    "\n",
-    "When working with a multidimensional array, the same rules apply, but we have to keep in mind that the arrays within the a 2D array themselves have indices. Let's construct two more arrays called `fruits` and `pantone` and make a combined array with `flowers`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "id": "a1a87fb8",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily',\n",
-       "        'violet'],\n",
-       "       ['strawberry', 'banana', 'blueberry', 'pineapple', 'cherry',\n",
-       "        'papaya', 'lychee', 'mango'],\n",
-       "       ['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala',\n",
-       "        'ultra violet', 'mustard', 'lapis blue']], dtype='<U12')"
-      ]
-     },
-     "execution_count": 127,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fruits = np.array(['strawberry', 'banana', 'blueberry', 'pineapple', 'cherry', 'papaya', 'lychee', 'mango'])\n",
-    "pantone = np.array(['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala', 'ultra violet', 'mustard', 'lapis blue'])\n",
-    "combined = np.row_stack((flowers, fruits, pantone))\n",
-    "combined"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8bac48ad",
-   "metadata": {},
-   "source": [
-    "We can assess the final dimensions of `combined` using the `shape` method. The `shape` method returns a tuple indicating the rows and columns:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "id": "0f25f043",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(3, 8)"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined.shape"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "064c7724",
-   "metadata": {},
-   "source": [
-    "Now, we are working with an array of arrays that is 3 rows by 8 columns. As such, we have to be able to determine the index of each array to be able to even access the individual elements within them. For instance, if we wanted to identify the 3rd color within `pantone`, we first have to be able to index the array with colors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "id": "67a9b1fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala',\n",
-       "       'ultra violet', 'mustard', 'lapis blue'], dtype='<U12')"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined[2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "216ef0d6",
-   "metadata": {},
-   "source": [
-    "Once we can get that array, getting the 3rd element would be as easy as identifying the index within `pantone`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "id": "dac5a4e3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'saffron'"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined[2][2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f4b4d38c",
-   "metadata": {},
-   "source": [
-    "To this point, all of these slicing mechanisms are interchangable between lists and arrays. However, if we wanted to take a particular index in all arrays of `combined`, the following would work for an array of arrays, but not a list of lists:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "id": "bf623f51",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['lilac', 'blueberry', 'saffron'], dtype='<U12')"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined[:,2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d6a2cea2",
-   "metadata": {},
-   "source": [
-    "Notice that this gives a different result than the following code:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "id": "3a843104",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array(['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala',\n",
-       "       'ultra violet', 'mustard', 'lapis blue'], dtype='<U12')"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined[:][2]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1da7860a",
-   "metadata": {},
-   "source": [
-    "In slicing, a comma separates rows from columns. Hence, `combined[:,2]` is saying \"from all rows of `combined`, take the column at index 2, while `combined[:][2]` is saying \"from all elements in `combined` take the element at index 2.\" This syntax, while subtle, greatly affects the granularity of slicing.\n",
-    "\n",
-    "Ranges can also be used to slice specific subsets of columns and rows, as shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "id": "907a4af3",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([['lilac', 'peony', 'tulip', 'dahlia'],\n",
-       "       ['blueberry', 'pineapple', 'cherry', 'papaya']], dtype='<U12')"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "combined[0:2,2:6]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5a3d5b7c",
-   "metadata": {},
-   "source": [
-    "We see that the above code returned the elements from index 2 to 5 (inclusive) from the `flowers` and `fruits` arrays within `combined`. The `flowers` and `fruits` arrays were returned because they are the elements at 0 and 1 index, respectively, within `combined`."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5d11e234",
-   "metadata": {},
-   "source": [
-    "Using 2-D arrays to store and organize numerical data types can also be very useful. Below, we create a new array of array consisting of a collection of even numbers, numbers divisible by five, and numbers divisible by ten:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "id": "5249d8ad",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 2.,  5., 10.],\n",
-       "       [ 4., 10., 20.],\n",
-       "       [ 6., 15., 30.],\n",
-       "       [ 8., 20., 40.],\n",
-       "       [10., 25., 50.]])"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evens = np.arange(2.0, 11.0, 2)\n",
-    "fives = np.arange(5, 26, 5)\n",
-    "tens = np.arange(10, 51, 10)\n",
-    "\n",
-    "num_comb = np.column_stack((evens, fives, tens))\n",
-    "num_comb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6ea3bf80",
-   "metadata": {},
-   "source": [
-    "We can use slicing to perform operations on specific subsets of  `num_comb`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "id": "b0f0373c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([4634., 4649., 4674.])"
-      ]
-     },
-     "execution_count": 130,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "num_comb[:][-1] + num_comb[3].sum()**2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e47537f0",
+   "id": "6655852a",
    "metadata": {},
    "source": [
-    "Furthermore, these slices can be used as input to functions:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "id": "3a337098",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([  4.,  16.,  36.,  64., 100.])"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.power(num_comb[:,0], 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d3242398",
-   "metadata": {},
-   "source": [
-    "When working with multidimensional arrays, being able to subset portions and do calculations via functions or operations can be an easy way to analyze data, particularly, if the array is organized in a meaningful way. From this, new arrays can be created and appended to existing arrays to update or complete a dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "id": "1974c88f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  2.,   5.,  10.,   4.,   4.],\n",
-       "       [  4.,  10.,  20.,  16.,  16.],\n",
-       "       [  6.,  15.,  30.,  36.,  36.],\n",
-       "       [  8.,  20.,  40.,  64.,  64.],\n",
-       "       [ 10.,  25.,  50., 100., 100.]])"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "squared = np.power(num_comb[:,0], 2)\n",
-    "\n",
-    "num_comb = np.column_stack((num_comb, squared))\n",
-    "num_comb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "140edda6",
-   "metadata": {},
-   "source": [
-    "Arrays are a powerful data type that make for easy and seamless data storage and analysis. The rules around slicing for arrays can be a bit tricky. It's important to practice to get used indexing and to understand how to access arrays within arrays vs *elements* of said arrays."
+    "Documentation on the available aggregate functions offered by `NumPy` is listed below. In the next section, we will learn how to subset and index arrays."
    ]
   },
   {
@@ -1004,17 +396,14 @@
    "source": [
     "## Resources\n",
     "\n",
-    "- <a target=\"_blank\" href=\"https://numpy.org/doc/stable/reference/routines.math.html\">Mathematical aggregate functions by numpy</a>\n",
-    "- <a target=\"_blank\" href=\"https://numpy.org/doc/1.20/user/theory.broadcasting.html\">Array broadcasting in numpy</a>\n",
-    "\n",
-    "- <a target=\"_blank\" href=\"https://scipy.org\">Scipy documentation</a>\n",
-    "- <a target=\"_blank\" href=\"https://scikit-learn.org/stable/\">scikit-learn documentation</a>"
+    "- <a target=\"_blank\" href=\"https://numpy.org/doc/stable/reference/routines.math.html\">Mathematical aggregate functions by NumPy</a>\n",
+    "- <a target=\"_blank\" href=\"https://numpy.org/doc/1.20/user/theory.broadcasting.html\">Array Broadcasting in Numpy</a>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "e3b13b05",
+   "id": "0e5e643e",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/textbook/04/3/img/flowers_array3.png b/textbook/04/3/img/flowers_array3.png
deleted file mode 100644
index 854ec91c..00000000
Binary files a/textbook/04/3/img/flowers_array3.png and /dev/null differ
diff --git a/textbook/04/4/Arrays-Slicing.ipynb b/textbook/04/4/Arrays-Slicing.ipynb
new file mode 100644
index 00000000..b1280730
--- /dev/null
+++ b/textbook/04/4/Arrays-Slicing.ipynb
@@ -0,0 +1,267 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "2eeb5048",
+   "metadata": {},
+   "source": [
+    "# Indexing and Slicing"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "89700eb9",
+   "metadata": {},
+   "source": [
+    "In this section, we will discuss common methods used to access data from arrays. Let's start by making an array of string elements called `flowers`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "ac3fad2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily',\n",
+       "       'violet'], dtype='<U6')"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "flowers = np.array(['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily', 'violet'])\n",
+    "flowers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8b545b5",
+   "metadata": {},
+   "source": [
+    "1D arrays can be indexed similarly to how lists are indexed, as mentioned in the [Lists](../1/Lists) section of Chapter 4.\n",
+    "\n",
+    "A single colon (:) can be used to slice a range of elements. If used between the numbers *j* and *k*, slicing the elements of an array will return all elements between *j* and *k*, **excluding** ***k***:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "70d9d0c6",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['lilac', 'peony', 'tulip', 'dahlia'], dtype='<U6')"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flowers[2:6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8f1e37bc",
+   "metadata": {},
+   "source": [
+    "When working with a multidimensional array, the same rules apply, but we have to keep in mind that the arrays within the a 2D array themselves have indices. Let's construct two more arrays called `fruits` and `pantone` and make a combined array with `flowers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b74bf5a7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['orchid', 'rose', 'lilac', 'peony', 'tulip', 'dahlia', 'lily',\n",
+       "        'violet'],\n",
+       "       ['strawberry', 'banana', 'blueberry', 'pineapple', 'cherry',\n",
+       "        'papaya', 'lychee', 'mango'],\n",
+       "       ['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala',\n",
+       "        'ultra violet', 'mustard', 'lapis blue']], dtype='<U12')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fruits = np.array(['strawberry', 'banana', 'blueberry', 'pineapple', 'cherry', 'papaya', 'lychee', 'mango'])\n",
+    "pantone = np.array(['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala', 'ultra violet', 'mustard', 'lapis blue'])\n",
+    "combined = np.row_stack((flowers, fruits, pantone))\n",
+    "combined"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f8885b61",
+   "metadata": {},
+   "source": [
+    "Now, we are working with an array of arrays. As such, we have to be able to determine the index of each array to be able to even access the individual elements within them. For instance, if we wanted to identify the 3rd color within `pantone`, we first have to be able to index that array:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "da64d3e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['emerald', 'polignac', 'saffron', 'fuchsia rose', 'marsala',\n",
+       "       'ultra violet', 'mustard', 'lapis blue'], dtype='<U12')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7568fc89",
+   "metadata": {},
+   "source": [
+    "Once we can get that array, getting the 3rd element would be as easy as identifying the index within `pantone`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "1f598df5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'saffron'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined[2][2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f28d8e84",
+   "metadata": {},
+   "source": [
+    "To this point, all of these slicing mechanisms are interchangable between lists and arrays. However, if we wanted to take a particular index in all arrays of `combined`, the following would work for an array of arrays, but not a list of lists:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "2fb35250",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['lilac', 'blueberry', 'saffron'], dtype='<U12')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined[:,2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a02ce2a8",
+   "metadata": {},
+   "source": [
+    "Above, we returned an array of the 3rd element in every array of `combined`. Likewise, we could also take a range of indices in all arrays of `combined`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b8967671",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([['lilac', 'peony', 'tulip', 'dahlia'],\n",
+       "       ['blueberry', 'pineapple', 'cherry', 'papaya'],\n",
+       "       ['saffron', 'fuchsia rose', 'marsala', 'ultra violet']],\n",
+       "      dtype='<U12')"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined[:,2:6]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e31be38",
+   "metadata": {},
+   "source": [
+    "The rules around slicing for lists and arrays can be a bit tricky. It's important to practice to get used to the exceptions of zero-indexing and to understand how to access arrays within arrays vs elements of said arrays."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "1a1af0ee75eeea9e2e1ee996c87e7a2b11a0bebd85af04bb136d915cefc0abce"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/textbook/05/3/Control_Statements_Iteration.ipynb b/textbook/05/3/Control_Statements_Iteration.ipynb
index 8bc6a3ef..92907e0e 100644
--- a/textbook/05/3/Control_Statements_Iteration.ipynb
+++ b/textbook/05/3/Control_Statements_Iteration.ipynb
@@ -354,7 +354,7 @@
    "id": "25e09fb1",
    "metadata": {},
    "source": [
-    "In [Section 4.1: Lists](../../1/Lists.html) we created the following list of prime numbers:"
+    "In [Section 4.1: Lists](../../04/1/Lists.html) we created the following list of prime numbers:"
    ]
   },
   {
@@ -545,8 +545,7 @@
    ],
    "source": [
     "import numpy as np\n",
-    "np.arange(4)\n",
-    "range()"
+    "np.arange(4)\n"
    ]
   },
   {
@@ -643,7 +642,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.10.6"
   },
   "vscode": {
    "interpreter": {
diff --git a/textbook/07/1/Functions_to_DataFrames.ipynb b/textbook/07/1/Functions_to_DataFrames.ipynb
index 23efcf4c..d136b5e6 100644
--- a/textbook/07/1/Functions_to_DataFrames.ipynb
+++ b/textbook/07/1/Functions_to_DataFrames.ipynb
@@ -28,7 +28,7 @@
    "id": "92e8b786",
    "metadata": {},
    "source": [
-    "Now that we know the foundations of [DataFrames](../../../06/DataFrames.html) and [functions](../../../03/5/IntroFunctions.html), we can discuss how to use functions directly on columns or rows of our DataFrame."
+    "Now that we know the foundations of [DataFrames](../../06/DataFrames.html) and [functions](../../03/5/IntroFunctions.html), we can discuss how to use functions directly on columns or rows of our DataFrame."
    ]
   },
   {
@@ -159,7 +159,7 @@
     }
    ],
    "source": [
-    "student_scores_df = pd.read_csv('../data/student_scores_data.csv')\n",
+    "student_scores_df = pd.read_csv('../../data/student_scores_data.csv')\n",
     "student_scores_df.head(5)"
    ]
   },
diff --git a/textbook/07/3/Groups.ipynb b/textbook/07/3/Groups.ipynb
index 550368a9..39794364 100644
--- a/textbook/07/3/Groups.ipynb
+++ b/textbook/07/3/Groups.ipynb
@@ -689,7 +689,7 @@
     }
    ],
    "source": [
-    "salary_data = pd.read_csv(\"../data/Data_Science_Jobs_Salaries.csv\")\n",
+    "salary_data = pd.read_csv(\"../../data/Data_Science_Jobs_Salaries.csv\")\n",
     "salary_data.head(5)"
    ]
   },
diff --git a/textbook/07/4/pivots.ipynb b/textbook/07/4/pivots.ipynb
index 0c3f591a..ae05fcc3 100644
--- a/textbook/07/4/pivots.ipynb
+++ b/textbook/07/4/pivots.ipynb
@@ -174,7 +174,7 @@
     }
    ],
    "source": [
-    "salary_data = pd.read_csv(\"../data/Data_Science_Jobs_Salaries.csv\")\n",
+    "salary_data = pd.read_csv(\"../../data/Data_Science_Jobs_Salaries.csv\")\n",
     "\n",
     "salary_data.head(5)"
    ]
diff --git a/textbook/08/3/Set_Based_Similarity.ipynb b/textbook/08/3/Set_Based_Similarity.ipynb
index 8612802a..d36c2acb 100644
--- a/textbook/08/3/Set_Based_Similarity.ipynb
+++ b/textbook/08/3/Set_Based_Similarity.ipynb
@@ -755,7 +755,7 @@
     }
    ],
    "source": [
-    "nametable= pd.read_csv(\"nametable.csv\")\n",
+    "nametable= pd.read_csv(\"../../data/nametable.csv\")\n",
     "nametable"
    ]
   },
diff --git a/textbook/08/5/Reduced_Alphabets.ipynb b/textbook/08/5/Reduced_Alphabets.ipynb
index f18af653..d333cd93 100644
--- a/textbook/08/5/Reduced_Alphabets.ipynb
+++ b/textbook/08/5/Reduced_Alphabets.ipynb
@@ -394,7 +394,7 @@
    ],
    "source": [
     "import pandas as pd\n",
-    "surnames2010 = pd.read_csv(\"Names_2010Census.csv\")\n",
+    "surnames2010 = pd.read_csv(\"../../data/Names_2010Census.csv\")\n",
     "surnames2010"
    ]
   },
@@ -547,7 +547,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "surnames2010 = pd.read_csv(\"Names_2010Census.csv\", na_filter=False )"
+    "surnames2010 = pd.read_csv(\"../../data/Names_2010Census.csv\", na_filter=False )"
    ]
   },
   {
diff --git a/textbook/08/6/Levinshtein.ipynb b/textbook/08/6/Levinshtein.ipynb
index da421122..49c8d658 100644
--- a/textbook/08/6/Levinshtein.ipynb
+++ b/textbook/08/6/Levinshtein.ipynb
@@ -438,7 +438,7 @@
    "outputs": [],
    "source": [
     "import pandas as pd\n",
-    "words = pd.read_csv(\"SINGLE.txt\", header=None)"
+    "words = pd.read_csv(\"../../data/SINGLE.txt\", header=None)"
    ]
   },
   {
@@ -772,7 +772,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "words = pd.read_csv(\"SINGLE.txt\", header=None, dtype=\"str\")"
+    "words = pd.read_csv(\"../../data/SINGLE.txt\", header=None, dtype=\"str\")"
    ]
   },
   {
@@ -854,7 +854,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "words = pd.read_csv(\"SINGLE.txt\", header=None, na_filter=False)"
+    "words = pd.read_csv(\"../../data/SINGLE.txt\", header=None, na_filter=False)"
    ]
   },
   {
diff --git a/textbook/09/1/Intro-to-Matplotlib.ipynb b/textbook/09/1/Libraries.ipynb
similarity index 91%
rename from textbook/09/1/Intro-to-Matplotlib.ipynb
rename to textbook/09/1/Libraries.ipynb
index 3cc53530..37e76683 100644
--- a/textbook/09/1/Intro-to-Matplotlib.ipynb
+++ b/textbook/09/1/Libraries.ipynb
@@ -5,7 +5,7 @@
    "id": "ccaa9364",
    "metadata": {},
    "source": [
-    "# Matplotlib and Pyplot"
+    "# Data Visualization Libraries"
    ]
   },
   {
@@ -13,30 +13,29 @@
    "id": "a7571e9f",
    "metadata": {},
    "source": [
-    "To create data visualizations, we will need to import the necessary libraries and packages. We will largely be using the `matplotlib` library, which is a popular tool for visualizing data from `pandas` dataframes. Within the `matplotlib` library, a module called `pyplot` can be used to allow for customization to figures. Some common customizations include adding a legend, creating a title, setting axis limits and increments, and many others. To utilize the `pyplot` module, it needs to be imported from the `matplotlib` library. Often times, it is imported as the alias `plt`.\n",
+    "To create data visualizations, we will need to import the necessary libraries and packages. We will be using `seaborn` and  `matplotlib` libraries, as well as the `pyplot` package, which are popular tools for visualizing data from `pandas` dataframes. \n",
     "\n",
-    "For our visualizations we will set a *style* - a specific appearance for our graphs. To do this, we will use the `plt.style.use()` function and use the *fast* style for our plots. Other styles can be used, and a link to documentation on the various style options can be found <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\">here</a>.\n",
+    "The `seaborn` library is multifaceted because it offers many visualization style options. While it can be used to create many types of visualizations, data scientists sometimes use both `seaborn` and `matplotlib` to create graphs and charts that are suited for their visualization needs. Here, we will be using a combination of `matplotlib` and `seaborn` to create our visualizations.\n",
     "\n",
-    "Other visualization libraries, such as `seaborn` can be used as well and will be used later in the chapter.\n",
+    "A link to documentation for the visualization libraries used can be found at the end of this section.\n",
     "\n",
-    "For now, let's start by importing our libraries:"
+    "Let's start by importing our libraries:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 1,
    "id": "85ee9e74-01e3-416f-8a12-bda151809cfd",
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
+    "import seaborn as sns\n",
     "\n",
-    "# Imports the pyplot module from matplotlib\n",
     "from matplotlib import pyplot as plt\n",
     "\n",
-    "# Sets the style for visualizations\n",
-    "plt.style.use('fast')"
+    "sns.set_style('whitegrid')"
    ]
   },
   {
@@ -46,12 +45,12 @@
    "source": [
     "To practice making these visualizations, we will be working with data from the World Bank. This data examines the military spending in each country in North America 1960-2020.\n",
     "\n",
-    "Let's again first take a look at this data in its comma-separated values format."
+    "Let's again first take a look at this data in its comma-separated values format. (Then we'll load it as a file.)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 2,
    "id": "27a2775a-747a-41dd-a923-ef51efb68c31",
    "metadata": {
     "tags": [
@@ -140,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 3,
    "id": "3fb13d36",
    "metadata": {},
    "outputs": [
@@ -319,7 +318,7 @@
        "[61 rows x 6 columns]"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -371,10 +370,14 @@
     "## Resources\n",
     "\n",
     "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/matplotlib_configuration_api.html\">Matplotlib documentation</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/index.html\">Matplotlib visualization gallery</a>\n",
     "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/pyplot_summary.html\">Pyplot documentation</a>\n",
+    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org\">Seaborn documentation</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html\">List of styles for plots in matplotlib</a>\n",
     "- <a target=\"_blank\" href=\"https://data.worldbank.org/indicator/MS.MIL.XPND.CD\">World Bank Data on Military Expenditure (in USD - MS.MIL.XPND.CD)</a>\n",
-    "- <a target=\"_blank\" href=\"https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS\">World Bank Data on Military Expenditure (% of GDP - MS.MIL.XPND.GD.ZS)</a>"
+    "- <a target=\"_blank\" href=\"https://data.worldbank.org/indicator/MS.MIL.XPND.GD.ZS\">World Bank Data on Military Expenditure (% of GDP - MS.MIL.XPND.GD.ZS)</a>\n",
+    "\n",
+    "\n",
+    "\n"
    ]
   }
  ],
diff --git a/textbook/09/2/Categorical_Data.ipynb b/textbook/09/2/Categorical_Data.ipynb
new file mode 100644
index 00000000..484b4d0d
--- /dev/null
+++ b/textbook/09/2/Categorical_Data.ipynb
@@ -0,0 +1,920 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "7ea83cf0",
+   "metadata": {},
+   "source": [
+    "# Categorical Data\n",
+    "\n",
+    "Categorical data consider numerical values (_i.e._ a quantitative variable) in the context of a category (_i.e._ a qualitative variable).\n",
+    "\n",
+    "Surveys, like the ones we see on the television show Family Feud or the frequency of people with various eye colors, are examples of categorical data. \n",
+    "\n",
+    "In this chapter, there are two types of categorical data that we consider: ordinal data and nominal data. \n",
+    "\n",
+    "**Ordinal data** consists of data that can be described as having a meaningful order, ranking, or relationship between categories. An inventory that quantifies the number of small, medium, and large shirts in stock is an example of ordinal data because there is a ranked relationship between shirt sizes.\n",
+    "\n",
+    "**Nominal data** can be described as named categories that have no meaningful relationship to one another. Counting the number of people with black, brunette, red, and blonde hair colors in a room is an example of nominal data because hair color has no inherit meaning amongst each other - one hair color is not greater than or less than the others.\n",
+    "\n",
+    "\n",
+    "Categorical data can be visualized using bar graphs, box and whisker plots, and pie charts, and in this section, we will practice making such visualizations.\n",
+    "\n",
+    "Let's load the necessary libraries and read in the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "45d41791-b19e-48cf-917d-c411750c6122",
+   "metadata": {
+    "tags": [
+     "hide_cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n",
+    "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n",
+    "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n",
+    "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n",
+    "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n",
+    "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n",
+    "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n",
+    "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n",
+    "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n",
+    "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n",
+    "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n",
+    "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n",
+    "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n",
+    "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n",
+    "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n",
+    "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n",
+    "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n",
+    "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n",
+    "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n",
+    "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n",
+    "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n",
+    "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n",
+    "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n",
+    "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n",
+    "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n",
+    "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n",
+    "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n",
+    "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n",
+    "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n",
+    "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n",
+    "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n",
+    "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n",
+    "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n",
+    "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n",
+    "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n",
+    "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n",
+    "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n",
+    "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n",
+    "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n",
+    "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n",
+    "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n",
+    "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n",
+    "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n",
+    "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n",
+    "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n",
+    "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n",
+    "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n",
+    "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n",
+    "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n",
+    "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n",
+    "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n",
+    "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n",
+    "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n",
+    "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n",
+    "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n",
+    "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n",
+    "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n",
+    "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n",
+    "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n",
+    "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n",
+    "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n",
+    "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n",
+    "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n",
+    "'''\n",
+    "\n",
+    "from io import StringIO\n",
+    "\n",
+    "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n",
+    "\n",
+    "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f26e1513",
+   "metadata": {},
+   "source": [
+    "## Bar graphs\n",
+    "\n",
+    "Bar graphs are a popular method to visualize categorical data. They're simple, concise, and can condense\n",
+    "large and complex datasets into a visual summary. Most bar graphs depict a categorical element as an\n",
+    "independent variable on the x-axis while the height of the bar corresponds to a numerical variable on the y-axis.\n",
+    "\n",
+    "We will practice making bar graphs using our military dataset in the context of an ordinal variable.\n",
+    "\n",
+    "First, let's create a graph to examine the percent of the GDP (<u>G</u>ross <u>D</u>omestic <u>P</u>roduct) spent on the military in Canada. We will look at the years 2018, 2019 and 2020.\n",
+    "\n",
+    "To do this, we must extract the data for the years of interest from the column containing the data pertaining to GDP percentage of military spending in Canada. We will call this `can_gdp`.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "af3c0e6d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAN-PercentGDP</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2018</th>\n",
+       "      <td>1.324681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2019</th>\n",
+       "      <td>1.278941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2020</th>\n",
+       "      <td>1.415056</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      CAN-PercentGDP\n",
+       "Year                \n",
+       "2018        1.324681\n",
+       "2019        1.278941\n",
+       "2020        1.415056"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n",
+    "\n",
+    "can_gdp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62a27826-df76-4802-9414-f1a2b5931a96",
+   "metadata": {},
+   "source": [
+    "Next, we call `plt.bar()` to create a bar chart using this data. The `plt.bar()` function needs two arguments. The first argument, `x`, is an array of values to be plotted on the x-axis. \n",
+    "\n",
+    "The second argument, `height`, determines the height of the bars (the y-values). \n",
+    "\n",
+    "We will create a list of our years of interest and call it `year_labels` to input as the first argument and use the \"CAN-PercentGDP\" column of `can_gdp` as our second argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "b2be1224",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "year_labels = ['2018', '2019', '2020']\n",
+    "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5748baf9",
+   "metadata": {},
+   "source": [
+    "The above code produced a plot, but this plot needs more descriptive labeling to help others understand the data. \n",
+    "\n",
+    "We need to add axis labels and a title to communicate what is being measured. Aesthetically, we can also reduce the width of each bar to give more room on the graph and more rest for our eyes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "b66c30a8-e66a-4ad1-b6c5-cbe53ee5eef9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"], width=0.25)\n",
+    "\n",
+    "\n",
+    "plt.title('Military Spending in Canada')\n",
+    "\n",
+    "plt.ylabel('Percentage of GDP')\n",
+    "plt.xlabel('Year')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "18931cc9",
+   "metadata": {},
+   "source": [
+    "This plot looks better and is a lot more descriptive.\n",
+    "\n",
+    "Let's add the data from Mexico and the United States.\n",
+    "\n",
+    "To do this, we use the `plt.subplots()` function. This function creates a `figure` object and an `axis` object, which we will name `fig` and `ax`, respectively. More information on the workings of `plt.subplots()` is linked at the end of this section.\n",
+    "\n",
+    "Using this function, we can add data for Canada, Mexico and the United States to the same plot, within the boundaries of the same axes. We do this by calling `ax.bar()`. Note that we want to group our bars by year. To do this we'll set precise positions on the x-axis.\n",
+    "\n",
+    "We will also use `plt.tight_layout()` to automatically adjust the subplot dimensions to give appropriate spacing between the bars and the axes boundaries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "6833fd5b",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n",
+    "mex_gdp = military.loc[[2018, 2019, 2020], ['MEX-PercentGDP']]\n",
+    "usa_gdp = military.loc[[2018, 2019, 2020], ['USA-PercentGDP']]\n",
+    "\n",
+    "index = np.arange(len(year_labels))\n",
+    "\n",
+    "(fig, ax) = plt.subplots()\n",
+    "\n",
+    "ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"], width=0.25)\n",
+    "\n",
+    "ax.bar(index, mex_gdp[\"MEX-PercentGDP\"], width=0.25)\n",
+    "\n",
+    "ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"], width=0.25)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "21c5b1b1",
+   "metadata": {},
+   "source": [
+    "We were able to create a bar plot with all three data sets together. Now, let's add the appropriate titles, axis labels, and other details.\n",
+    "\n",
+    "To add a title to the entire graph, we can call the `plt.title()` function, just as we did before.\n",
+    "\n",
+    "In a subplot, to add axis labels, we have to use the `set_xlabel()` and `set_ylabel()` methods on the axis object, here `ax`.\n",
+    "\n",
+    "We can also label each individual bar with the associated numerical value by calling the `bar_label()` method on `ax`. In order to label the bars, the label must be specified when creating each each bar.\n",
+    "\n",
+    "The location of the legend within the subplot will be set using `ax.legend()`. The y-axis limits are set using `plt.ylim()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "61bdf283",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(fig, ax) = plt.subplots()\n",
+    "\n",
+    "# round(decimals=2): rounds to 2 places after decimal\n",
+    "\n",
+    "can_bar = ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"].round(decimals=2), width=0.25, label='Canada') \n",
+    "mex_bar = ax.bar(index, mex_gdp[\"MEX-PercentGDP\"].round(decimals=2), width=0.25, label='Mexico')\n",
+    "usa_bar = ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"].round(decimals=2), width=0.25, label='USA')\n",
+    "\n",
+    "# Add labels, title, custom x-axis tick labels, etc.\n",
+    "\n",
+    "plt.title(\"Military Spending in North America\", pad=10)\n",
+    "\n",
+    "ax.set_ylabel('Percentage of GDP')\n",
+    "ax.set_xlabel('Year')\n",
+    "\n",
+    "ax.set_xticks(index, year_labels)\n",
+    "\n",
+    "ax.bar_label(can_bar, label_type=\"edge\", padding=4)\n",
+    "ax.bar_label(mex_bar, label_type=\"edge\", padding=4)\n",
+    "ax.bar_label(usa_bar, label_type=\"edge\", padding=4)\n",
+    "\n",
+    "ax.legend(loc=4, bbox_to_anchor=(1.3, 0.5))\n",
+    "\n",
+    "plt.ylim(0, 5)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0a52a3c",
+   "metadata": {},
+   "source": [
+    "Great! Now we have a well annotated, visually appealing graph that depicts an important message about the data: the percentage of the GDP spent on the military for each country for the years 2018-2020.\n",
+    "\n",
+    "From this graph, we can easily see that during this time period, Canada and Mexico contribute a smaller proportion of their GDP to military spending than the United States. This may not have been easily discernable by just looking at our large data table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fbb08191",
+   "metadata": {},
+   "source": [
+    "## Box and whisker plots\n",
+    "\n",
+    "Box and whisker plots are another visualization method for displaying categorical data that is often used in the social sciences and biological sciences.\n",
+    "\n",
+    "Box and whisker plots are a useful data visualization method because they intrinsically display multiple summary statistics simultaneously. The central line of each box within a box and whisker plot is the *median*. The median (also known as the second quartile, $Q_2$) is a value within a dataset that lies within the middle, separating the higher half and the lower half of the dataset. The median is a valuable measure of center for a distribution because it is not greatly affected by outliers, as opposed to the *mean*.\n",
+    "\n",
+    "Box and whisker plots also show the *lower quartile*, *the upper quartile*, the *interquartile range*, *outliers*, the *minimum*, and the *maximum*. The lower quartile $\\left( Q_1\\right)$ is the value where the lowest 25% of the data points within a distribution lie. It is represented by the lower end of the box. \n",
+    "\n",
+    "On the other side, the upper quartile $\\left( Q_3\\right)$ is the value in which the highest 25% of the data resides. It is represented by the higher end of a box.\n",
+    "\n",
+    "\n",
+    "The interquartile range (IQR), is the difference between the upper quartile and the lower quartile. The IQR is used to make the length of a box and is represented by the equation:\n",
+    "\n",
+    "  > $IQR=Q_3-Q_1$\n",
+    "    \n",
+    "\n",
+    "Outliers are data points that are less than $Q_1-1.5×IQR$ or greater than $Q_3+1.5×IQR$. These data points are shown beyond the extremity of the whiskers.\n",
+    "\n",
+    "Lastly, the minimum and maximum values are represented by the lowest and highest values, respectively, <b>that are within the range $Q_1-1.5×IQR$ and $Q_3+1.5×IQR$</b>. Essentially, they are the lowest and highest values that <u>do not</u> qualify as outliers. The lower whisker represents the minimum value, while the upper whisker represents the maximum value.\n",
+    "\n",
+    "Below is a pictorial summary of the major components of a box and whisker plot with an accompanying set of numbers, *A*:\n",
+    "\n",
+    "![](./img/boxandwhisker.png)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4da5c77",
+   "metadata": {},
+   "source": [
+    "We will use a box and whisker plot to examine the percentage GDP spending on the military for each country in the '60s as a way to examine nominal data.\n",
+    "\n",
+    "First, we extract the data of interest:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "76dfd2bc",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>CAN-PercentGDP</th>\n",
+       "      <th>MEX-PercentGDP</th>\n",
+       "      <th>USA-PercentGDP</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Year</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1960</th>\n",
+       "      <td>4.185257</td>\n",
+       "      <td>0.673509</td>\n",
+       "      <td>8.993125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1961</th>\n",
+       "      <td>4.128312</td>\n",
+       "      <td>0.651780</td>\n",
+       "      <td>9.156031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1962</th>\n",
+       "      <td>3.999216</td>\n",
+       "      <td>0.689655</td>\n",
+       "      <td>9.331673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1963</th>\n",
+       "      <td>3.620650</td>\n",
+       "      <td>0.718686</td>\n",
+       "      <td>8.831891</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1964</th>\n",
+       "      <td>3.402063</td>\n",
+       "      <td>0.677507</td>\n",
+       "      <td>8.051281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1965</th>\n",
+       "      <td>2.930261</td>\n",
+       "      <td>0.591270</td>\n",
+       "      <td>7.587247</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1966</th>\n",
+       "      <td>2.683282</td>\n",
+       "      <td>0.576379</td>\n",
+       "      <td>8.435300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1967</th>\n",
+       "      <td>2.747927</td>\n",
+       "      <td>0.545217</td>\n",
+       "      <td>9.417796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1968</th>\n",
+       "      <td>2.543642</td>\n",
+       "      <td>0.548511</td>\n",
+       "      <td>9.268454</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1969</th>\n",
+       "      <td>2.273785</td>\n",
+       "      <td>0.600160</td>\n",
+       "      <td>8.633264</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      CAN-PercentGDP  MEX-PercentGDP  USA-PercentGDP\n",
+       "Year                                                \n",
+       "1960        4.185257        0.673509        8.993125\n",
+       "1961        4.128312        0.651780        9.156031\n",
+       "1962        3.999216        0.689655        9.331673\n",
+       "1963        3.620650        0.718686        8.831891\n",
+       "1964        3.402063        0.677507        8.051281\n",
+       "1965        2.930261        0.591270        7.587247\n",
+       "1966        2.683282        0.576379        8.435300\n",
+       "1967        2.747927        0.545217        9.417796\n",
+       "1968        2.543642        0.548511        9.268454\n",
+       "1969        2.273785        0.600160        8.633264"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "the60s = military.loc[1960:1969, ['CAN-PercentGDP', 'MEX-PercentGDP', 'USA-PercentGDP']]\n",
+    "\n",
+    "the60s"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c3849183",
+   "metadata": {},
+   "source": [
+    "Next, let's plot the data:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "2f74054c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.boxplot(the60s);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1bf41ad3",
+   "metadata": {},
+   "source": [
+    "If we plot this dataframe, the bare minimum graph plots the data within the columns of interest, but doesn't automatically use the column names as categorical indicators on the x-axis. This doesn't allow for easy interpretation of the data. \n",
+    "\n",
+    "Furthermore, while the current plot does the job of displaying the median and interquartile range, adding individual data points will allow for viewers to more easily see the spread of the data. The addition of axis labels, a title, and some color will also enhance this plot and make it more aesthetically pleasing. \n",
+    "\n",
+    "We can accomplish this using a combination of functions from `matplotlib` and `seaborn`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "id": "948bde16",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = sns.boxplot(data=the60s, palette=\"Set2\", linewidth=1)\n",
+    "ax = sns.swarmplot(data=the60s, palette=\"Set2\", linewidth=0.5, edgecolor = \"black\")\n",
+    "plt.xticks(ticks = [0,1,2], labels = ['Canada', 'Mexico', 'United States'])\n",
+    "plt.ylabel(\"Percent of GDP\")\n",
+    "plt.title(\"% GDP spent on the military in North America from 1960-1969\")\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a21d621d",
+   "metadata": {},
+   "source": [
+    "Now that we have proper labeling, we can see the median, upper quartile, and lower quartile of the percentage of the each country's GDP spent on the military. A noticeable observation this plot shows is that Mexico not only spent a small percentage of their GDP on the military (less than 2%), but the percentage of spending during this decade had very little variability. This makes it hard to see what the median, upper quartile, and lower quartile are for Mexico. The issue of being able to visually resolve displays of data is a common one that data scientists encounter. In the next section, we will offer one potential solution for this."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d28b66ff",
+   "metadata": {},
+   "source": [
+    "## Pie charts\n",
+    "\n",
+    "Pie charts are a commonly used visualization method to represent proportions in datasets. Pie charts use *wedges* to represent the numerical value of a proportion corresponding to a categorial variable.\n",
+    "\n",
+    "While pie charts are very common and can be easily interpreted by a layperson audience, they may not be the best way to represent data in certain cases. Firstly, because pie charts use the area of a circle to represent the proportion of a categorical variable, it can be difficult to gauge the numerical value that a wedge represents if the area doesn't appear as an easily discernible fraction (_e.g._ ½, ⅓, ¼). This can be aided with the help of labels and legends that explicitly show the numerical values associated with the wedges of the pie chart. Secondly, if you want to visualize many categorical variables or variables that make up a significantly small proportion of the dataset, it may be difficult to see the variable on a pie chart. Overall, pie charts can be a simple and effective way to communicate proportional categorical data, but before using them, consider what attributes of the data need to be highlighted to help decide if a pie chart is the most appropriate visualization method. \n",
+    "\n",
+    "Now, let's look at the total amount of money spent on the military in the entire North American continent for the year of 2000 and determine what proportion of this total amount came from each country. Because we are looking at a single year (2000), a pie chart is a good way to visualize the proportions of military spending contributed by each country. To do this, we will extract data for the year 2000; we'll call it `usd_2000`. Then, we will  make a pie chart using the `plt.pie()` function. We will use `usd_2000` to determine the wedge sizes and the argument `normalize=True` to normalize the data to 1. We'll also set the figure size, in inches, using `plt.figure()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "90158219",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CAN-USD      8.299385\n",
+       "MEX-USD      3.031455\n",
+       "USA-USD    320.086324\n",
+       "Name: 2000, dtype: float64"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "usd_2000 = military.loc[2000, ['CAN-USD', 'MEX-USD', 'USA-USD']]\n",
+    "\n",
+    "usd_2000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "cdde5f79-2c19-49e8-b883-0e3121fccb57",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "plt.pie(usd_2000, normalize=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7e7cc03f",
+   "metadata": {},
+   "source": [
+    "Now that we have a pie chart, let's add some more detail to it to make it more descriptive.\n",
+    "\n",
+    "We can label the sectors of the chart so that we know which country corresponds to which color. Likewise, we can label the percentage of each sector to know the definitive proportion of each country's contribution to the total amount of money spent on the military in North America.\n",
+    "\n",
+    "To do this, we will create a list called `countries`,  containing the strings \"Canada\", \"Mexico\", and \"USA\". We then assign the `labels` argument within `plt.pie()` to `countries` and add the `autopct` argument, which labels the wedges using the printf style format. More information on that format is linked at the end of this section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "a4f10620",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "countries = ['Canada', 'Mexico', 'USA']\n",
+    "\n",
+    "plt.pie(usd_2000, normalize=True, labels=countries, autopct='%.1f')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0d8962d3",
+   "metadata": {},
+   "source": [
+    "This plot is okay, but it can be better.\n",
+    "\n",
+    "Because the sectors of Mexico and Canada are a lot smaller than the sector for the United States, overlaying the percentages on top of the sector creates spatial issues that can be visually displeasing. Instead, let's add the percentages into a legend along with the labels of each sector. Let's also add a title so others can know what they are looking at when they view this chart."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "d09a15ca",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 720x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "\n",
+    "\n",
+    "patches, text = plt.pie(usd_2000, normalize = True)\n",
+    "labels = ['Canada (2.8 %)', 'Mexico (0.9 %)', 'USA (96.4 %)']\n",
+    "total = usd_2000.sum().round(decimals=1) #finds the sum of usd_2000 and rounds it to 1 position after the decimal\n",
+    "\n",
+    "plt.legend(patches, labels, loc=4, bbox_to_anchor=(0.8, -0.2), fontsize=15)\n",
+    "plt.title(\"Military Spending in North America in 2000\" + \" (\" + str(total) + \" Billion USD)\",  loc = 'center',\n",
+    "         fontsize = 15)\n",
+    "\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c5fa589",
+   "metadata": {},
+   "source": [
+    "Above, we used `plt.pie()` in a way that we had not used it before.\n",
+    "\n",
+    "Under the hood, the `plt.pie()` function returns two default outputs, which we named: `patches` and`text`. `patches` is an object that dictates the size of each wedge. `text` consists of a list of labels for our data. Here, we needed to specifically assign `patches` and `text` objects so we could use `patches` as an argument for the `plt.legend()` function. \n",
+    "\n",
+    "The `plt.legend()` function has two required arguments. The first argument dictates **what** is being labeled. In our case, the wedges of the pie chart (*i.e.* the `patches` object) are being labeled. The second argument dictates **how** things are labeled. Here, we simply created a variable called labels, which consists of the three strings:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "d430985c-cfee-49e0-ae4d-4fc69e193f36",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Canada (2.8 %)', 'Mexico (0.9 %)', 'USA (96.4 %)']"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "labels"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee88547d-89be-493c-b49d-64fbfb03bb72",
+   "metadata": {},
+   "source": [
+    "The other arguments, `bbox_to_anchor` and `fontsize`, are optional when using the `plt.legend()` function.\n",
+    "\n",
+    "The argument `bbox_to_anchor` designates the position in the plotting area where the legend will be, while the `fontsize` argument dictates the font size, in points, of the legend text."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e64c6b88",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "\n",
+    "In this section, we were introduced to the `plt.bar()` and `plt.pie()` functions to construct bar plots and pie charts, respectively.\n",
+    "\n",
+    "The `plt.bar()` function requires `x` and `height` arguments, which can be an array of number values, but other parameters can be included.\n",
+    "\n",
+    "The use of the `plt.boxplot()` function allows us to make a basic box and whisker plot, but does not offer preferred labeling and coloring by default. The `sns.boxplot()` and `sns.swarmplot()` functions are good alternatives that have more customization and flexibility.\n",
+    "\n",
+    "The `plt.pie()` function only requires an `x` argument as an array of values and has other arguments that can be included as well.\n",
+    "\n",
+    "Both of these types of visualizations are used for depicting categorical data.\n",
+    "\n",
+    "As a reminder, when deciding on whether to use a pie chart, consider certain attributes of the data, such as the number of categorical variables or the size of the proportions to be plotted. Below is a list of functions with linked documentation for your reference and further reading:\n",
+    "\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html\">plt.bar( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html\">plt.pie( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/statistics/boxplot_demo.html\">plt.boxplot( )</a> \n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html\">plt.subplots( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html\">plt.title( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylim.html\">plt.ylim( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html\">plt.xlim( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html\">plt.ylabel( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html\">plt.xlabel( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xticks.html\">plt.xticks( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.bar.html\">ax.bar( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html\">ax.legend( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html\">plt.tight_layout( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html\">plt.figure( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html\">plt.show( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/generated/seaborn.boxplot.html\">sns.boxplot( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/generated/seaborn.swarmplot.html\">sns.swarmplot( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals\">Formatted String Literals</a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "253ed247",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/textbook/09/2/Numerical_Data.ipynb b/textbook/09/2/Numerical_Data.ipynb
deleted file mode 100644
index f7e3fa47..00000000
--- a/textbook/09/2/Numerical_Data.ipynb
+++ /dev/null
@@ -1,1234 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "0fb30d10",
-   "metadata": {},
-   "source": [
-    "# Numerical Data\n",
-    "\n",
-    "Numerical data consists of *discrete* and *continuous* number values. Discrete data values can be integers or rational numbers, such as the number of marbles in a jar or shoe sizes. Continuous data values can be rational and irrational numbers, such as height recordings or temperature collections. In this section, we will practice making histograms, scatter plots, and line graphs to represent numerical data.\n",
-    "\n",
-    "Let's load the necessary libraries and read in the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3e479dc7-ce8e-4ca7-ae35-79038cf4bf99",
-   "metadata": {
-    "tags": [
-     "hide_cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "plt.style.use('fivethirtyeight')\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "\n",
-    "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n",
-    "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n",
-    "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n",
-    "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n",
-    "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n",
-    "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n",
-    "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n",
-    "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n",
-    "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n",
-    "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n",
-    "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n",
-    "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n",
-    "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n",
-    "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n",
-    "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n",
-    "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n",
-    "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n",
-    "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n",
-    "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n",
-    "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n",
-    "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n",
-    "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n",
-    "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n",
-    "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n",
-    "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n",
-    "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n",
-    "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n",
-    "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n",
-    "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n",
-    "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n",
-    "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n",
-    "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n",
-    "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n",
-    "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n",
-    "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n",
-    "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n",
-    "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n",
-    "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n",
-    "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n",
-    "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n",
-    "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n",
-    "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n",
-    "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n",
-    "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n",
-    "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n",
-    "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n",
-    "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n",
-    "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n",
-    "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n",
-    "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n",
-    "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n",
-    "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n",
-    "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n",
-    "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n",
-    "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n",
-    "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n",
-    "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n",
-    "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n",
-    "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n",
-    "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n",
-    "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n",
-    "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n",
-    "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n",
-    "'''\n",
-    "\n",
-    "from io import StringIO\n",
-    "\n",
-    "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n",
-    "\n",
-    "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1af7e1d3",
-   "metadata": {},
-   "source": [
-    "## Scatter plots\n",
-    "\n",
-    "Scatter plots can be used to visualize the relationship between two numerical variables. They are most commonly used to visualize two continous numerical variables against each other (in other words, the data takes on values that are between whole number integers). These plots can also be used when data takes on a large number of different discrete integers. We will use a scatter plot to visualize the percentage of the GDP (<u>G</u>ross <u>D</u>omestic <u>P</u>roduct) of Mexico spent on the military versus the absolute dollar amount (in USD) over 1960-2020.\n",
-    "\n",
-    "We'll simply extract the columns for this data and assign them to `mex_gdp` and `mex_usd`, respectively. Then, we can plot this data using the `plt.scatter()` function and use `plt.show()` to display the plot."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "deb40aae",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mex_gdp = military[['MEX-USD']]\n",
-    "\n",
-    "mex_usd = military[['MEX-PercentGDP']]\n",
-    "\n",
-    "plt.scatter(mex_gdp, mex_usd)  # mex_gdp on the x-axis, mex_usd on the y-axis\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4fba0f1f",
-   "metadata": {},
-   "source": [
-    "Looking at this scatter plot out of context, it would be hard to understand what the data means. Let's add some important details to make it clear.\n",
-    "\n",
-    "Firstly, we can add a title using the `plt.title()` function. This function accepts a string argument to be used as the text for the title. It also has an optional `pad` parameter, which dictates the space between the title and the plotting area.\n",
-    "\n",
-    "We can also use `plt.ylabel()` and `plt.xlabel()` to label the y- and x-axes, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "eccd1b1a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(mex_gdp, mex_usd)\n",
-    "\n",
-    "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n",
-    "\n",
-    "plt.ylabel('Spending in USD (Billions)')\n",
-    "plt.xlabel('Percentage of GDP')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "cd5d171b",
-   "metadata": {},
-   "source": [
-    "Now we have a better understanding of the data. \n",
-    "\n",
-    "In addition to this information, we can add a color scheme that will color each data point based on the year of collection. This adds another dimension of analysis, using year as a feature; the context of the spending relationship can be examined over time.\n",
-    "\n",
-    "The `plt.scatter()` function minimally needs two arguments - *x* and *y* - which are array-like variables. Other optional arguments include `c`, which determines how to color the data points; `alpha`, which sets the opacity of the data points; and `cmap` which sets the Colormap used to color the data points. \n",
-    "\n",
-    "The `plt.colorbar()` function displays a scale of the Colormap based on the feature used to color the data, which in our case is the year of collection."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "23a45386",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mex_years = mex_gdp.index\n",
-    "\n",
-    "plt.scatter(mex_gdp, mex_usd, c=mex_years, alpha=0.4, cmap='winter')\n",
-    "\n",
-    "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n",
-    "\n",
-    "plt.ylabel('Spending in USD (Billions)')\n",
-    "plt.xlabel('Percentage of GDP')\n",
-    "\n",
-    "plt.colorbar()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "20146a6e",
-   "metadata": {},
-   "source": [
-    "We used the years of the dataset (which we defined as the index earlier in this chapter) as our `c` argument to color the data points based on the year of collection. We used the *winter* Colormap as our `cmap` argument, but many other Colormaps are available for your choosing. A list of other possible Colormaps to explore can be found <a target=\"_blank\" href=\"https://matplotlib.org/stable/tutorials/colors/colormaps.html\">here</a>."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "30ba35e7",
-   "metadata": {},
-   "source": [
-    "## Line graphs\n",
-    "\n",
-    "Next, we'll examine the use of a line graph as another visualization tool for numerical data. Line graphs are used to visualize sequential numerical data. By using line graphs, we can easily see trends within data over time.\n",
-    "\n",
-    "Let's examine the spending (in USD) on the military in Canada in the 21st century (2000-2020). We can extract this data and call it `can_usd`.\n",
-    "\n",
-    "In Python, visualizations can be made using dataframe methods or by directly calling functions from the `pyplot` library in `matplotlib`. We can quickly create a line graph using `plot()` method on the `can_usd` dataframe:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b26501e3-def6-48f3-80e5-d238e8220f9f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-USD</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2000</th>\n",
-       "      <td>8.299385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2001</th>\n",
-       "      <td>8.375571</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2002</th>\n",
-       "      <td>8.495399</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2003</th>\n",
-       "      <td>9.958246</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2004</th>\n",
-       "      <td>11.336490</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        CAN-USD\n",
-       "Year           \n",
-       "2000   8.299385\n",
-       "2001   8.375571\n",
-       "2002   8.495399\n",
-       "2003   9.958246\n",
-       "2004  11.336490"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "can_usd = military[['CAN-USD']].loc[2000:2020]\n",
-    "\n",
-    "can_usd.head()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "e9499c29",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "can_usd.plot()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d2ccd60",
-   "metadata": {},
-   "source": [
-    "The same plot can be made using the `pyplot` function `plt.plot()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "d5f4f198",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(can_usd)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e199c81c",
-   "metadata": {},
-   "source": [
-    "Notice how the `plot()` method automatically uses the `Year` column to label the x-axis, while the `plt.plot()` function does not. This can simply be remedied using the `plt.xlabel()` function. We can add a y-label as well using `plt.ylabel()`.\n",
-    "\n",
-    "Also notice the increments of the x-axis for both plots. To change these increments to integers, we can create an array consisting of years of the desired increments and then use it as an argument for the `plt.xticks()` function:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "ab3bb392-3509-4adc-b1ae-4460db515862",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2000, 2005, 2010, 2015, 2020])"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "years = np.arange(2000, 2021, 5)\n",
-    "\n",
-    "years"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "88bcffa6-027e-4208-aeab-9c61649b913c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(can_usd)\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('USD (Billions)')\n",
-    "plt.xticks(years)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d90f500d",
-   "metadata": {},
-   "source": [
-    "We can see from the graph that Canada's spending on the military has increased overall since 2000. The country had a period of time (around 2011 to 2017) where military spending was decreasing consistently.\n",
-    "\n",
-    "### Visualizing multiple trends using line graphs\n",
-    "\n",
-    "We can view trends for multiple variables at once. Let's add the data for Mexico as well to see the country's spending in the 21<sup>st</sup> century."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "3eff4b2a",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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HcGaRm5UfnIgCz9cFeK62m+dqA7zeGGSoBO6tQWX1ju6460TDNcEnzK30MXeSj3kTfZww0Uf9+5uorsz+L8TqEi93nV3Ovzxa35uYOKzwiSebeGCJmxMmZv972F99mxIvry4cV9cD08WCmDF9PLC1k2ufa4krq/C7WL64kion192yWfksc2bjbQ1GeLEuwHO1AdbVdrN+T5DO8P6dYQlweKmHEyf5mDvRx7xJPg4t8fSbB6x+v549M+ZN8nPTGeV8/Il9g6E7QsrFqxpYdd7EjIxrS7VXGwK8FjOrtkvgkkNsbFgqjL1PjzGjsK62m399qjHuulSBR/jrooqEmSeKvS7OnJbHmdOisyAHwsprjUGe293Nc3UB1tUG4prTYpX6hBMn+jhxko8TJ/qYM9FHyRi88P/Bmfn8bH4JX4v5cdDQHeHClfWs/OBEJuaPrUS4t/c5Cztrqp9phWPrPWYLC2LGON5uDnLJY/HzcbkF/rSgnLkjaPbyuYW5E6NnUl8k2kFkY0uIdbUBXq4P4HEJcyq9nDjJxyETPOOmiemThxexsz3ML15r6y17zxkM/cCSyoyNvUu2rpDyt019xoZZh46UsSBmDLCzPcxFKxv6dWf/n1NKOWd63qieW0Q4rNTLYaXecdErbzDfmTOBHe1h7tq0r9fehvogZz24h6sPK+TigwuysrflSDyyrTPuc1Tud7FklJ8hk1huf1qMSYKWQISLVtXHDUoF+ObxxVxhv6CTSkT41allLJwanx7r7eYQX3++hcPu2sUnnmzkiR1dRHJ0lui+HTouPjgfv3t8nG1nggUxM651h6MzIvfNgnHVoQVc1ycPoEkOn1u49cxyji3vPzwgEIHl73XyoZUNHHt3LT96eS9bW0MDPEt22tYW4vE+PVAtzVRqWRAz41ZElc+saeKZ3YG48nOn5/Hzk8dXeqR0K/a6WH5OBctm5ZEo2cj29jA/eaWV4+6p5YIV9dyzuYOuYYzRy6Q73+2I6xR0fKWXowcI1iZ57JqYGZdUlW+/0NI7GLnHvIk+blpQhieL0jiNVZV5bm5ZWBG9RvZuB7fVtPNea/9pWxR4cmc3T+7sptTXzEcOLuBj1QXMrsiuMWYR1X69Em325tSzIGbGpRvfaOM3b7bHlVWXeLjr7HIKbLrdtJpW6OZrs4v5yrFFrK0N8JeN7fxjS9eAY+2aA8of3mrnD2+1c2y5l49VF3DxwQWUDqMzSHdYaQ9GaA0q7SGlLRihPai0BqPL23e7OdzbyaR8F5Py3UzMd1HkkWGfkT+zO8DWmBRbeW648EALYqlmQcyMO3dv6uC7L+6NK6vKd3HPogrKU5SN3gzNJcJpk/2cNtnPT+dHWL65k9tq2nmpPjjg9q81Brnu+Ra+u76FRdPyKPQKbTEBqi2ozi1Ce0gZekozP7wbP1VhvluYmO9iUr6LiXnu6H2+m0l50UA3qWddvpvbNsb/KDp/Zv6wgqsZHQtiZlx5amcXn3umKa6s2CvcvaiCmcX275AtSnwurj68kKsPL+SNxiC31bTz102dAw4a7w7Dg+93paQenWHl/bawk8R44GCaiDUlpkdafiaIyM0iUici/4wp+2vMLM9bROSVBPtuEZHXne3Wp6O+Zmx6rSHAxx5vjPtF7nXBbWeWc2yWXV8x+xxV7uVHJ5Xy9kcnc8vCchZN8yfsDJItphe5+UAGZ9keT9L10/PPwI3ArT0FqvrRnmUR+QXQ0n+3XgtVNZfSxZkss7U1xEdWNdDaJ2v8bz5QxhlTbSBqLvC5pTdn5Y72MHc6nUG2DNAZZCBugSKvUOx1UegRirxCoddFkVco8ggdba0E/UXs6QxT1xlhT1c4Lgv9SHysuqBfvkuTGmkJYqq6RkRmDbROoldNLwbOTEddzPjT2BXmolUN1HbGN0V9/8QJXHSQNfnkommFbq6dXcxXjy3i+boAbzeF8Luh0Oui2CtOkHJR6BXnsQu/m0E7adTUNFBdXdH7WFXZG1T2dIap7YywpzNCXWeYuq5IXKCrc9b1dEQ5bbKPLx1tYwzTJRsuAnwAqFXVmgTrFVgpIgr8TlV/n76qmVwXjCiXrm6kpiV+wOznjirki/ZFk/NcIs5s2slvuhMRSnxCic/FISWDb6uqtIWUUIScT5uVa0TTlNrFORN7UFWP7lP+G+BdVf1Fgv2mqeoOEZkErAK+qKprYrdpaWnpfRM1NYlioRmPbt7m4Tdb4693LaoM8d+HBbL+uooxBqqrq3uXS0pK+v3XZvRMTEQ8wIeBExJto6o7nPs6EbkPmAesSbR97BseqZqamlHtbwaWqeP6VlOQm9bWxZV9YLKP2xZPzflcdvZZTT47pqmR6uOa6fPes4G3VXX7QCtFpFBEinuWgcXAPwfa1phYoYjy+WeaCMRcBqvMc/GnheU5H8CMMfukq4v9ncBzwGEisl1EPumsugS4s8+2U0XkYedhFfCMiLwKvAA8pKqPpqPOJrf9+o02NvQZJPuz+SVU2mBmY8aUdPVOvDRB+VUDlO0EljrLm4HZKa2cGXM2Ngf54cvxGTnOn5nHBbPyM1QjY0yqZLo50ZikCkeULzzTHDe+p8wv/MKy0hszJlkQM2PK795q54U98VOr/OSkUiblWzOiMWORBTEzZmzeG+L7L8U3Iy6ZnsdHDrJmRGPGqiGviYmIF5hP9NpUKdAMvAqsU9WRZcQ0JkUiqnzhmaa46TtKfML/nGLNiMaMZQmDmIhUAN8ErgQagbeBVqAY+BJQJiK3AD+2vIYm0256u521tfHNiD+cV8KUAmtGNGYsG+xM7BngJuC4ngHHsURkKnA50YHHR6amesYMbUtriP9cH9+MuGian8sOsbyIxox1gwWx2aoaSLTS6Qr/MxG5IfnVMmZ4VJVrnm2mPbSvGbHYa82IxowXCTt2DBbAROSgnqz0g21nTKrdsrGDp3Z1x5V9/8QSDijKhtzWxphUG1bvRBG5U0ROcZavBt4A3ojJvGFM2m1rC/HdF+OnoTtjip8rD7VmRGPGi+F2sT8L6JlV+atEcx7OA76RikoZMxRV5ctrm+MmuSz0CL861ZoRjRlPhtvm4lPVgIhMA8pV9VkAEalKXdWMSez2dztYvSO+GfE/505gZrE1Ixozngz3P/4VEfkmMBN4CKLzfAF7B93LmBTY1RHmWy/ENyOeOtnHJw8vzFCNjDGZMtzmxE8CxwD5wHecspOB21NRKWMSUVW+sraZvYF9zYj5buF/Ty3DZc2Ixow7wzoTU9VNwGV9yu4B7klFpYxJ5O7NnTy6rSuu7LsnTOCgCdaMaMx4NOz/fBFZDBwHFMWWq+p/JLlOxgyotiPM159vjis7aZKPTx9hzYjGjFfDCmIiciNwMfAE0JHSGhkzAFXl2nXNNHXva0b0u+HG00pxu6wZ0ZjxarhnYpcRzeCxbX9eRERuBs4D6lT1aKfsP4F/BfY4m31LVR8eYN8lwA2AG/ijqv54f+pgctvft3TywNb4ZsRvHT+B6hJvhmpkjMkGw+3YUU80e/3++jOwZIDy/1HV45zbQAHMDfwaOJdofsZLRcTyNI4z9V1hrn0uvjfinEovnz+qKMEexpjxYrhB7BfA7SJyspNyqvc2nJ1VdQ3RTPgjNQ94V1U3O+mt7gKW7cfzmBx23boWGrojvY99Lvj1aWV4rBnRmHFvuM2Jv3Huz+tTrkSb+fbXF0TkCqLZQL6mqk191k8DYpswtwMnjeL1TI55YGsny9/rjCu77rgJHFFmzYjGGBBVHXqrZLxQNGHwgzHXxKqINlMq8H1giqp+os8+FwFLVPVTzuOPAyep6hdit2tpael9EzU1Nal8GyaNdncJV76aR2Nw3xnXYYUR/jy7C4/NSW7MuFBdXd27XFJS0q/5ZUSDa0RkBtGzo+3728mjh6rWxjzvH4AHB9hsBzA95vEBTllCsW94pGpqaka1vxnY/hzX2o4wH314D43BcG+ZR+CPZ0/miHI7C7PPavLZMU2NVB/X4WaxnyIiTwHvAsuBTSKyxpkYc7+IyJSYhx8C/jnAZi8C1SJyoIj4gEuAf+zva5rc0NQd4UMr69ncGo4r/9rsYo6xAGaMiTHcRpnfAK8CZao6BSgDXgZ+O5ydReRO4DngMBHZ7kzh8lMReV1EXgMWAl9xtp0qIg8DqGoI+AKwAngL+JuqvjHsd2dyTmswwkdW1fNmUyiu/IJZ+Vw3uzhDtTLGZKvhNieeRvSaVRBAVdtF5DqGaNrroaqXDlB8U4JtdwJLYx4/DPTrfm/Gns6QctljDazfE4wrXzTNz+9PL7NBzcaYfoZ7JtZEdJxWrMMY3dgxY3oFI8pVTzby9O74icJPnezj1jMr8LktgBlj+hvumdhPgcdE5CZgK9EpWa4GvpuqipnxIxxRPrOmiRV9EvvOqfRy51kV5HssgBljBjbcLPZ/EJGeTPbHAjuBy1R1dSorZ8Y+VeUrzzVzb5+xYEeWerhnUQUTfNaX3hiT2LC72Kvq48DjKayLGWdUle+8uJdbN8bnlD6w2M3ycyopzxvNOHpjzHiQMIiJyLdV9QfO8vcSbWdTsZj99dNXW/n1G21xZdMK3Pz9nEomF1gAM8YMbbAzsQNilqcn3MqY/fDrN9r40cutcWWVeS7+vqSCmcU2waUxZngSfluo6mdjlq9OT3XMeHDrxna+/UJ8VvoJPmH54gqbWsUYMyKDNScON0P95uRVx4x1yzd3cM2zzXFlhR7hnkUVHFvhy0yljDE5a7B2m3eJJucdrH/zaLPYm3FkxbYu/m1NE7Epp30uuOOscuZN8mesXsaY3DVYc6L1bTZJ8/Subq58ooFQTARzC/x5YTlnTM3LXMWMMTnNApVJuTdaXVz6WANdMfl8BfjtB8pYOiM/Y/UyxuS+wa6JPQ0MOdmYqp6e1BqZMeWNxiBfesNPWyj+o/TLk0v5yMEFGaqVMWasGOya2B/TVgszJm1qCfGhlfXsDcVfVv3+3AlcfXhhhmpljBlLBrsmdks6K2LGli2tIZatqKeuMxJXfu3sYr54jE2pYoxJjsGaEz+uqn9xlj+RaDtVvTkVFTO5a2NzkGUr6tnVER/APn1EId8+3gKYMSZ5BmtOvBT4i7P88QTbKGBBzPT6Z2OQC1bUU98VH8Aury7gRyeVIGIZ6Y0xyTNYc2LsxJQLR/MiInIzcB5Qp6pHO2U/A84HAsAm4GpVbR5g3y1AKxAGQqo6dzR1Manzcn2AD62opzkQ34lj6cQQvzqlFJcFMGNMko24i72I+ERkjoiUjmC3PwNL+pStAo5W1WOBjcA3B9l/oaoeZwEse62r7WbZo/0D2FWHFnD9oQGbldkYkxKDBjERmSAivxSRR0XkehGZArwFrAe2i8jZw3kRVV0DNPYpW6mqIefhOuITDpsc8tTObj68soG9wfgA9pkjC/mfU0qx+GWMSZWhzsR+AxwB3A+cAqwGbgCKgG8AP0hSPT4BPJJgnQIrReQlEfm3JL2eSZKV27q4+LF6OvqMA/vasUX8aJ5dAzPGpJaoJh7PLCJ1wEGq2iYiZUAdkKeqYRFxAY2qWjqsFxKZBTzYc00spvzbwFzgwzpAZURkmqruEJFJRJsgv+ic2fVqaWnp3a+mpmY41TFJ8Hi9m2+/4yOk8YHqszMDfGJ6KMFexhgzfNXV1b3LJSUl/X4VDzVxU56qtgGoapOItKlq2HkccQLZfhORq4h2+DhroADmvM4O575ORO4D5gFrBtoW4t/wSNXU1Ixq//Hk7k0dfOudJsJ9/mo/mFfC548qiiuz45p8dkyTz45paqT6uA4VxEREDmRfJvt+j/f3hUVkCXAdcIaqdiTYphBwqWqrs7wYSDjLtEmPWze2c82zzf1ykv3y5FI+YZk4jDFpNFQQKyQ6JUtssNoUszxkbkUAEbkTWABUish24HqivRH9wCrnusk6Vf2MiEwF/uh08a8C7nPWe4A7VPXR4bymSY3fvdnG15+Pn9DSJXDjqaVcVm0BzBiTXoMGsWRNx6Kqlw5QfFOCbXcCS53lzcDsZNTBjN4Nr7dy/fq9cWUegT+cUcaHDrRkvsaY9BvqTMwYVJUfv9LKT15pjSv3uaLzgdl0KsaYTEl4piUiy0XkxMF2FpETRWR58qtlsoWqcv36vf0CWL5buOvsCgtgxpiMGuxM7HfA/4nIBOAp4B2i6Z+KgUOJXuNqBr6T2iqaTImo8vV1Lfzh7fa48iKPcNeiCk6b7M9QzYwxJmqw3IkrgBUiMhc4FzgJKAWagNeAS1T15XRU0qRfOKJ8aW0zt9fEdxwt8Qn3LKrkxEm+DNXMGGP2GfKamKquJ5pmyowT3WHlc083ce97nXHl5X4X951TwewKC2DGmOxgHTtMnF0dYa54vIEX9wTjyqvyXfz9nEqOKPNmqGbGGNOfBTHT6/nabq54opHaPrMxH1Do5v5zKjm4xD4uxpjsYt9KBoA/vd3Odc83E4yPXxw8wc3yxZXMLLaPijEm+9g30zjXHVauW9fMLRv7Z/5afICf359eTqk/KWPejTEm6YYVxERkKfABoJzovGBrVDXR1CkmR+zqCHPl4428sCfQb921xxbzzeOLbTJLY0xWGzSIiYgPeBg4mWgPxZ1E5xf7koisA85V1f7fgCbrvVDXzRWPN7K7z/WvQo/wfx8oY9ksG8RsjMl+Q52JfQWoAA5X1W09hSIyHfg78FXgxymrnUmJW95p59p1/a9/HVjs5vazKjjSeiAaY3LEUBc7LgKuiQ1gAM7jrwIfSVXFTPIFwspX1jZxzdr+AezsaX6eOH+SBTBjTE4Z6kysGngxwboXgEOSWx2TKrs7wlz5RCPP1/Vv/f3qsUV8+/gJdv3LGJNzhpwUU1U7B1qhqp3OPF8my71YF+CKJxrY1RF/+lXgEf7vtDIuONCufxljctNQQcwnIleTeAZna3vKcrdubOfa55oJ9Gk+nFXs5vYzKziq3P6ExpjcNVQQex64Yoj1wyIiNwPnAXWqerRTVg78FZgFbAEuVtWmAfa9kn3Z8v9bVW8Z7uuOV4Gw8s0XWripTwZ6gDOn+rlpQTllNv7LGJPjhprZeUESX+vPwI3ArTFl3wBWq+qPReQbzuOvx+7kBLrrgbmAAi+JyD8GCnYmqrYjzFVPNvJcbf/rX18+pojvzrHrX8aYsWHEP8VFZJGIfEVETh7Jfqq6huhA6VjLgJ6zqluACwbY9Rxglao2OoFrFbBkZLUeP16pD7Dwgbp+AazAI9x8Rhn/ObfEApgxZswQVU28UuROomdKf3QeXwd8n+h8YkcCn1HVvwz7xURmAQ/GNCc2q2qpsyxAU8/jmH2uBfJU9b+dx98FOlX15z3btLS09L6Jmpqa4VZnzHmqwc133vHRFYkPUlP9EX5+ZDfVhYn/1sYYk42qq6t7l0tKSvr9Ah/qmtipwDUAIuIC/h24TFXvFZFziQ50HnYQG4yqqoiM+ls29g2PVE1Nzaj2zxRV5cY32viPt/bS9wAunOrnpjPKKM9zZ6RukLvHNZvZMU0+O6apkerjOlRzYqmq1jnLxwN5RDN1ADwKzBzl69eKyBQA575ugG12ANNjHh/glBkgGFG+vLaZ777YP4B97qhC7l5UkdEAZowxqTRUEKt3mgABFgLPqWrYeVwIhAfca/j+AVzpLF8J3D/ANiuAxSJSJiJlwGKnbNxr7o5w0cqGfhno3QL/75RSfjivFI9d/zLGjGFDNSf+EXhIRFYQ7Wr/xZh1pwNvDfeFnOtrC4BKEdlOtMfhj4G/icgnga3Axc62c4leb/uUqjaKyPfZlznke6rat4PIuLOlNcTFqxrY2BKKK5/gE25ZUM7CaXkZqpkxxqTPUF3sfygiO4h2b79GVe+MWT0R+MVwX0hVL02w6qwBtl0PfCrm8c3AzcN9rbFuXW03l69upKE7fgTzzCI3f11UweGlNoDZGDM+DDmfmDOwuN/gYhtwnBl3b+rg88809cvAMW+ij9vPKmdivl3/MsaMH0PNJ/YX6NdfIEi06e9uVR12c6IZHVXlJ6+08uNXWvutu+igfG48tYw8j13/MsaML0Odib07QJkXOAxYJyKXqepDya+WidUVUr74bBN3b+6fi/nrxxXzjeOKsWTMxpjxaKhrYv+VaJ2InE20Y4YFsRSq7wpz+er+U6j4XHDjaWVcfHBBhmpmjDGZN+Q1sUGsBg5OVkVMf+80B7l4VQNb2+JHMlT4Xdx2VjknV/kzVDNjjMkOowliU4HmJNXD9PHkzi6ueKKRvYH4S5KHlnj469kVHDhhNH86Y4wZG4bq2HHQAMVeolOnfAf4WwrqNO7d8k47X32umXCfLjWnT/Fz68JySm0KFWOMAYbXsUOJnxQzDLxPdB6w76WoXmNWKKK0BCI0dUdo6laauiM09z6OUNMS4t73+nfguOLQAn5xciley8BhjDG9hurYYT/5HRFVOkNKe0jpCCntQec+FIlZVlqDEZpjAlSTE6CaneW+zYNDEeB7cyfwhaOLrAeiMcb0YRdWYlz/jo/2TfV0OIGpN2A59+mW7xZ+f0YZ58/MT/trG2NMLrAgFuOlvS5qu7szXQ0AphS4uPOsCo6r9GW6KsYYk7UsiMUoSEPjqRBN0lvmd0VvPlfvcqnPRalfmF7k4ZwD8iwDhzHGDMGCWIw89+BNhvluocAjFHiFIo+z7BEKvS4KneUir8QFpd5g5dwmeAW3dc4wxpiksCAW4ysHBqmcMikakJzAVOgErQK3BR9jjMk2FsRiHF8Sodrm4TLGmJxhXeiNMcbkrIwGMRE5TEReibntFZEv99lmgYi0xGzzHxmqrjHGmCyT0eZEVX0HOA5ARNzADuC+ATZ9WlXPS2PVjDHG5IBsak48C9ikqlszXRFjjDG5QVTTn4liICJyM7BBVW/sU74AuBfYDuwErlXVN2K3aWlp6X0TNTU1Ka+rMcaY9Kiuru5dLikp6ddFPCuCmIj4iAaoo1S1ts+6CUBEVdtEZClwg6pWx24TG8RGo6amJu6AmeSw45p8dkyTz45paiTzuA4UxLKlOfFcomdhtX1XqOpeVW1zlh8GvCJSme4KGmOMyT7ZEsQuBe4caIWITBYnfbuIzCNa54Y01s0YY0yWyvhgZxEpBBYBn44p+wyAqv4WuAj4rIiEgE7gEs2GNlBjjDEZl/EgpqrtQEWfst/GLN8I3Nh3P2PMOKeKtDTi2rkVOtsJH3kC5BdkulYmzTIexIwxZlA9wWrHluhtp3O/YyvSvrd3s0hJOd2f/S7hI47PYGXHiGAA13tvoxPK0ckHZLo2g7IgZozJDqpIc0NckHLteA/Xzq1Ie+uQu7taGsn7ydcIfOgqgudfDi53Gio9xnR14H3iAbyP3o2ruR6AyJTphI4/jdAJpxE56AhwZUtXiigLYsaYzAh043nhSdwbX+89w5KOtlE9pWgE//Kbcb/zKt2f/jZaUp6kyo5xrc34Vt2H97Hl/X4wuHZtw7frTnwP30mkpIzwcacSmnMq4SPngM+foQrvY0HMGJNW0tyAd/Xf8T7xD6S1ZVTPpb48IlXTcG3fjMT09/K88RKu737KmheHIA11eB/9K94nH0ICXUNu72ppwvXUg3ifehD15xE+Zh6hOacROu5kKCxOQ437syBmjEkL19YavCvuwbNuNRIOjWhf9eURmTaTyNRZRKbNii5POxCtqAKXC/frL+L/3Q9wtTbvez1rXkxIdm7F9/BdeNauSvi3UK8PImEkHB74Obq78Kxfg2f9GtTlInz4cYTnnEZozqnRv0uaWBAzxqROJIz75bX4Vt6D++1Xh9xc/XlOoIoGqcjUmUSmzeoNVomEjzmRzu//kbzffj/udax5MZ7rvbfxPXgH7peejjtzjaWFxQTP/jCBRR8CtwfPa8/j3vAMnlefR7o6BtxHIhE8b27A8+YG/Lf9ivDMakLHn0r4hNMgxSOisiLt1GhZ2qnsZsc1+bL+mHZ24H36Ybwrl+PaszPhZlpQSPD0DxI+ck40WJVPGl3HgXAI399vwfvAbf2+pIfqvZj1x3R/qeJ+cwPeB2/H8+aGhJtFSisILrmY4ILzBx6qEAzgfvsVPC89g/vltb0dP4ay6ZIvUXXuh/e39nEGSjtlZ2LGmKSRPbvwrlqOd83DSGd7wu0iVdMILrqQ4AeWQF4Sx3a5PQQu/CThQ49N3Lx4wZUE/+VjY795MRLBveFZfA/dgXvzW4k3q5pGYOmlhE5dDF5f4ufz+ggfM4/wMfPgii/jeu8dPC8/i/ulZ3Dv3DLgLurLo3XmYaSycdGCmDFmdFRx1byOb8U9uF96BtFIwk1DR84huPgiwrPnp7Sr9qDNi/f9Kdq8+JnvjM3mxVAIz3Or8D10J65d7yfcLDzjEILnXU7oxNNHHtBdLiIHH0Hg4CPgok8hu7fj2fAMnpefxVXzz96z4PAxJ0avraWQBTFjzP4JBfG88CTelffgfu+dhJupx0to/lkEz7mIyIxD0lY9Lauk87pfDNi86Hlzw9jrvdjRhvfJB/GuuhdX456Em4UPm03gvMuiZ1TSr3Vuv+jkAwguvYTg0kuQvU24X16LZ8OzhOYtSMrzD8aCmDEmsfZWXPW7kT27cdXvQup349oTc9+duFt2pLiU0FnLCJ65LHNnPCNpXsxR0lCLd+W9eJ98MGHHC4DQcacQOO8yItVHp7Q+OqGM0BkfJHTGB6MFKZ7j0YKYMeNZVweuPbv7B6d6J2h1JL6ulUj4gIMInvMRQvPPzIrBsDC85kXP4sszWMORc23ZiPeRv+J54QkkMnATrrpc0bPgpZcSmX5QmmuYHhbEjBlPAt14XnwKzzOP4n7/XaRt79D7DFPouJMJnvORaPNckpqpkmmo5sXD399EZPGF6IRStKgEiiagxSVoUfSGJwu+LiMR3K+/EA1eb72ccDP1+Ql+4FyC534UnTgljRVMvyz4qxhjUk3qduJ94h/RXoNJDFxaWExw/lkEF1+ITp6etOdNmUGaF71tLbD85oS7an6hE9Big1vPsnNfXIpOmoqWTUxuIA9043nuMXyP/i2atT+ByIQygmd/iOCZ/wLFpcl7/SxmQcyYsSoSxv3q83gfvx/36y8kHNw6GPV60crJRCqnoBOnEKmcTGTilGjZxMlQVJKVZ11DSdS8OBjpbI8OGxhk3FsPzcsnMmUGkSkzowO2p84gMnUmOmkquEfwtdvWgnf1/XhX34erpSnhZpGpMwksuZjQyWdnTRNuumRFEBORLUArEAZCqjq3z3oBbgCWAh3AVaqaeNSeMeOYtDTiWfMw3icfwFVfO+i26vagFZNiAlP8vU4oy7qs5ckyWPPiaElXJ+733unXa1PdHiJVB6BOUItMnekEuxngz9u3f+0OvCvuxvv0I0igO+HrhI44nuCSiwkfe9KY/TsNJSuCmGOhqiYaAn4uUO3cTgJ+49wbYyA6Vmvj63gfvx/Pi08NmptQC4uj10tOX4pOmT72B/0OxmleDJ58Ns2rH2Siz4O0tURvrc59215o25uUICfhUHRg8ACDgyOVVUSmzIzmgnzt+cRpoVwuQvMWElxyMZEDDxt1nXJdNgWxwSwDbtVojqx1IlIqIlNUdVemK2ZMRnW241m7KtpkuP29QTcNH3g4wbOWETope3oNZgudOpO6+YspSZR2KhKGjjakba8T3PbGBLmYgNfUgGvX+4N2dU/EVV876Jmz5uUTXHB+9PpjGhPsZrtsCWIKrBQRBX6nqr/vs34asC3m8XanzIKYGZfy6rbjX/sgnrUrka7OhNup10fo5LMJnvkvRA48PI01HGNcbnB6KQ7ZgaV3cs+t0Qk9d73fu+xqaRzxS0fKKgkuvojgGR/M2HQn2SwrEgCLyDRV3SEik4BVwBdVdU3M+geBH6vqM87j1cDXVXU9xCcArknxwDpj0koV794m8hp2k1e/C3/9Lgp2b6UwQa66Hl3lVdSfsIDGY08mnF+YnrqaIbm7OvDX7yKvflfv3zSvfhe+5vp+zYcdVdOpm7+Y5iPnoiPpDDLGxCZlztoEwKq6w7mvE5H7gHnAmphNdgCxP38OcMr6GU0W6jGbxTrD7LgOQziE1O10frE7v9x3bXWaphKfacVSl4vwnNMInrmM8JFzKBWhNLW1HlPS9zmdHfcoCAQD3bh2b8e1ayvS2kJ4+sFEDj2GShEq01CjVEr1cc14EBORQsClqq3O8mLge302+wfwBRG5i2iHjha7HmZyUldH9Muqp3lp1/vIzvdx1W4f8USRPSKlFYQWnEfwjPPQ8olJrrBJC5+fyIyDicw4ONM1yTkZD2JAFXBftBc9HuAOVX1URD4DoKq/BR4m2r3+XaJd7K/OUF2NGVgkjLQ0IU31SNMeXE31SHMD0rQHaap3HtfvVxqnREJHzomedR1/anZkkzAmAzL+yVfVzfQ9v6Y3ePUsK/D5dNbLGEIh6GxD2tuQ9lakoxVpaXQCVT2upj1IU0P0cUvjoFOQjIbmFcQNmI1MmcmmiJtZc+en5PWMySUZD2LGpIPU70Ya6pCOnoDUBj2BqcMJVH3LhnktKlkiJeW9A2B1yozegbBaVtkvK0bQOjAZA1gQM2Oc1O/Gf/PP8byxPtNVAUDdbrS8at9ZVUzWBus+bczIWRAzY5MqnmdX4L/tf6P57tLxkkUTiJRWomU9t4lEyip6l7W0Ai0uHbfpgYxJBQtiZuzZ20zen3+B56WnR/U0KgL5hWhhMVpQFL0vLo0JUpVEyirR0kq0tMKyYBiTARbEzJjifnkt/pt/hmtv/4zf4amz0Mqq3qBEQdG+4FRYHH3cE7AKiiC/0M6ajMlyFsTM2NDZgf+OG/GuebjfKvX5CXz0MwTPuiAnpw0xxiRmQczkPNc7r5H3hx/h2tN//Hv4oCPo+rdvolNmZKBmxphUsyBmclcwgG/5n/A+cle/vHPqdhNYdiXB8y4b2SSExpicYv/dJie53n8X/+9+iHv75n7rIlNm0PXpb1nWdmPGAQtiJrdEwngf+Su+e28eMNdgYPGFBD7yb9ZT0JhxwoJYMoRC0Rx5DXXRMUkeL+rzg88HXh/q9UfvfdF7vD7r9bYfpG4neb//Ie6af/ZbFymfSPenvkH4qBMyUDNjTKZYEBuKKrK3CWmsQxrqcDn30eVapGEP0tIw4qnL1eMFnw/1+sDrB5+/d1n9eWjFJCIVVWjlZCKV0Xstq8yd6zuqSOMepH43Bbt2IQVeyCtA8/IhvyA6yeAInsvz1EP477gR6e7qtzp4yiK6P/Yly3hhzDiUI9+IaRAKUbFhDb7X1kSDVGMdroba6BlWMJj0l5NQEEJBhOFnk1CXCy2fiFZMJlI5OS7ARSqq0IpJ4PEmva6DCnTjqt0Rnb2257bzfVy73+8NOIcN9F58eWh+vhPYCqL3+THLefnR5fwC3G9uwPPquv7PUTSBrqu+SvjEBal9j8aYrGVBrIfLxQEr7sS1n3M6pYNEIkh9LdTX4n7n1X7rVSSa2qgnqBVNiGac6B3U6wzwzS/aN6C3oHDowKcKrS37glRMsJL6XSM+CwWQQBcS6IKW/oOShyM0ez7dn/j3aKYMY8y4ZUGsh8tFsLgMf/Oe/do9UlKGllehxSXRM6xgAAIBCAaQYHf0PuDcBwNJrnyUqCJN9dBUP+B1o0TU54/PYOEEPjxeXLU7ohM3tu9NSZ1HSv15dF/6eUILzrOBy8YYC2KxAiXlAwYxzS8kUjEJLZ8UvVZVPgmtqELLJ0bPeMoqo501hksVgoF9AS3QJ8h1tOKqr0UaapE9u6PNmvW7B0yllAwS6I6+dnNDUp9XC4qIVB1AV1cn+USgsxPp6oDuzv06ewsfcnR04HLVtKTW0xiTuzIaxERkOnAr0dmdFfi9qt7QZ5sFwP3Ae07RclX9Xirq03TUPPzHnhgNTOWT0IqJRMonQUFRcl9IJNoF3Ocn9qs8djk80H7dXUhD7b6gtmd39HH9bqS+FldzfXLrOQwqglZUEZkyI3qbGr3XKTPQCWUgQk1NDdXV1ft2ikSi76WrA7o6kK6O6NxdndHlnvue9YTDRA47ltCJZ4ysQ4gxZszL9JlYCPiaqm4QkWLgJRFZpapv9tnuaVU9L9WVaZhzOuWxX7bZxp+HTp1JeOrMgdcHA9EOKfW7o7MNd7RBexvS2R6d5LGjDZx76Wjf93gYMxKrz09k8vTeYKVOsIpUHQD+vJG9D5cL8qMdOSA+eBtjzEhkNIip6i5gl7PcKiJvAdOAvkHMDIfXh1YdQLjqgOHvowpdndHA1hkT5NrbINAdbT6dOhMtn2Rj24wxWSfTZ2K9RGQWcDzw/ACrTxaRV4GdwLWq+kY66zamifSeFSmTMl0bY4wZEdH9uMCe9EqIFAFPAT9Q1eV91k0AIqraJiJLgRtUNa7Nr6WlpfdN1NTUpKPKxhhj0iD2enpJSUm/LskZD2Ii4gUeBFao6i+Hsf0WYK6q9vZiiA1io9GvA4JJCjuuyWfHNPnsmKZGMo/rQEEsoxc5RESAm4C3EgUwEZnsbIeIzCNa5+T2BTfGGJOTMn1N7FTg48DrIvKKU/YtYAaAqv4WuAj4rIiEgE7gEs306aMxxpiskOneic8Ag6ZdUNUbgRvTUyNjjDG5JOPXxJIhWdfEjDHGZK+suyZmjDHGjIYFMWOMMTlrTDQnGmOMGZ/sTMwYY0zOGtNBTESmi8gTIvKmiLwhItc45eUiskpEapz7MqdcRORXIvKuiLwmInNinutKZ/saEbkyU+8pGyT5uIZF5BXn9o9MvadM249jeriIPCci3SJybZ/nWiIi7zjH+xuZeD/ZIMnHdIuIvO58Ttdn4v1ki/04rpc7//evi8haEZkd81yj/6yq6pi9AVOAOc5yMbAROBL4KfANp/wbwE+c5aXAI0S7/c8HnnfKy4HNzn2Zs1yW6feX68fVWdeW6feTDbf9OKaTgBOBHxDNJ9rzPG5gE3AQ4ANeBY7M9PvL5WPqrNsCVGb6PWXDbT+O6yk935fAuTHfq0n5rI7pMzFV3aWqG5zlVqAnS/4y4BZns1uAC5zlZcCtGrUOKBWRKcA5wCpVbVTVJmAVsCR97yS7JPG4GsdIj6mq1qnqi0Cwz1PNA95V1c2qGgDucp5j3EniMTUx9uO4rnW+NwHWAT3TbCTlszqmg1isPlnyqzQ6DQzAbqKTckL0D7EtZrftTlmi8nFvlMcVIE9E1ovIOhG5IPU1zn7DPKaJ2Gd1AKM8phCd9m6liLwkIv+Wmlrmnv04rp8k2ioDSfqsZjrtVFo4WfLvBb6sqnudVIwAqKqKiHXR3A9JOq4zVXWHiBwEPC4ir6vqphRVOevZZzX5knRMT3M+p5OAVSLytqquSVGVc8JIj6uILCQaxE5LZj3G/JmYRLPk3wvcrvumeantac5y7uuc8h3A9JjdD3DKEpWPW0k6rqhqz/1m4Emiv+rGpREe00TssxojScc09nNaB9xHtCls3BrpcRWRY4E/AstUtSeBe1I+q2M6iIkkzJL/D6Cnh+GVwP0x5Vc4venmAy3O6fEKYLGIlDk9bhY7ZeNSso6rczz9znNWEk0IPS5n9d6PY5rIi0C1iBwoIj7gEuc5xp1kHVMRKRSR4p5lov///0x+jXPDSI+riMwAlgMfV9WNMdsn57OaqR4u6bgRPW1V4DXgFee2FKgAVgM1wGNAubO9AL8m2mPmdaLzlvU81yeAd53b1Zl+b2PhuBLttfQ60V5JrwOfzPR7y6FjOpnoNYS9QLOzPMFZt5Roj7FNwLcz/d5y/ZgS7T33qnN7Yzwf0/08rn8EmmK2XR/zXKP+rFrGDmOMMTlrTDcnGmOMGdssiBljjMlZFsSMMcbkLAtixhhjcpYFMWOMMTnLgpgxxpicZUHMmAwTkdtE5E99ys4QkQZLlGzM4CyIGZN51wDnisgiABHJA/4AfE33JVTdbyIyLnKkmvHJgpgxGabRXHJfBH7vpDW6nmgGg7edSQSbReRVEVnQs4+IXC0ib4lIq4hsFpFPx6xbICLbReTrIrIb+BPGjFH2C82YLKCqd4vIJcCdRHNIzgE2AB8HHgXOAu4VkcNVdQ/R5KrnEZ2g9XTgERF5UZ15noimUCoHZmI/Vs0YZmmnjMkSIlKFk0MOyAOOVtWPx6xfAdyhqrcMsO/fgSdU9QbnjG0l0VyKXWmoujEZY7/QjMkSqloL1BNNMjsT+IjTlNgsIs1EE6/2THVxrjORaKOzbilQGfN0eyyAmfHAmhONyU7bgL+o6r/2XeFMX3MvcAVwv6oGnTMxidnMmljMuGBnYsZkp9uA80XkHBFxi0ie02HjAMAH+IE9QEhEziU6x5Ux444FMWOykKpuA5YB3yIarLYB/w64VLUV+BLwN6LzNF3GOJ340hjr2GGMMSZn2ZmYMcaYnGVBzBhjTM6yIGaMMSZnWRAzxhiTsyyIGWOMyVkWxIwxxuQsC2LGGGNylgUxY4wxOcuCmDHGmJz1/wGd8H+5vg4uZwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "mex_usd = military[['MEX-USD']].loc[2000:2020]\n",
-    "\n",
-    "plt.plot(can_usd)\n",
-    "plt.plot(mex_usd)\n",
-    "\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('USD (Billions)')\n",
-    "plt.xticks(years)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "81e3e39f",
-   "metadata": {},
-   "source": [
-    "We can now see that the military spending for both Mexico and Canada is vastly different. However, just looking at this graph out of context, we wouldn't be able to tell which line corresponds to which country. Let's add some descriptive details, such as a legend and a title:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "50c364ea",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(can_usd, label='Canada')\n",
-    "plt.plot(mex_usd, label='Mexico')\n",
-    "\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('USD (Billions)')\n",
-    "plt.xticks(years)\n",
-    "\n",
-    "plt.legend(loc=\"best\")       # Adds a legend to the figure\n",
-    "plt.title(\"Military Spending in Mexico and Canada in the 21st Century\", pad=10)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3ab35d3c",
-   "metadata": {},
-   "source": [
-    "We can see that the overall trend of military spending in Mexico also increased from 2000 to 2020. However, this increase was a lot less drastic than observed in Canada. Mexico's military spending was a steady rise from about \\$3 billion to \\$6 billion over the course of 20 years, while Canada's spending rose from \\$8 billion to about \\$23 billion over the same period of time.\n",
-    "\n",
-    "Let's add data from the United States to see the trends in all North American countries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "18c12059",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "usa_usd = military[['USA-USD']].loc[2000:2020]\n",
-    "\n",
-    "plt.plot(can_usd, label='Canada')\n",
-    "plt.plot(mex_usd, label='Mexico')\n",
-    "plt.plot(usa_usd, label='United States')\n",
-    "\n",
-    "plt.xlabel('Year')\n",
-    "plt.ylabel('USD (Billions)')\n",
-    "plt.xticks(years)\n",
-    "\n",
-    "plt.legend(loc=\"best\")\n",
-    "plt.title(\"Military Spending in North America in the 21st Century\", pad=10)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ae241382",
-   "metadata": {},
-   "source": [
-    "With the addition of the data from the United States, it's difficult to discern the data from Canada and Mexico. Because the spending on the military in the United States was a lot higher, plotting all three datasets on the same graph with the same axis does not allow us to clearly see trends in the other countries.\n",
-    "\n",
-    "To address this, we can graph the data for each country separately with axis limits that are tailored to each country. If we graph this data side by side, we can see the trends in each country while acknowledging that the axis intervals for each country provides a numerical context for cross-comparisons.\n",
-    "\n",
-    "To do this, we use the `plt.subplots()` function. This function creates a `figure` object and `axis` objects, which we will name `fig` and `ax`, respectively. More information on the workings of `plt.subplots()` is linked at the end of this section.\n",
-    "\n",
-    "By using `plt.subplots()`, we can add data for Canada, Mexico and the United States to the same figure by specifying the data assigned to each `ax` object. Here, we will define three `ax` objects: `ax1`, `ax2`, and `ax3`. This will allow us to create <u>three</u> separate plotting areas, bounded by <u>three</u> different axes, that are contained within <u>one</u> figure.\n",
-    "\n",
-    "Once the figure and axes are defined, we can create a title for the entire figure using `fig.suptitle()`. Using the `.plot()` method for each `ax` object, we can specify which data to plot in each axis. We can also specify other plotting features about each axis by using various methods on each axis object, such as the `set_title()`, `set_xlabel()`, `set_ylim()`, and `set_xticks()` methods:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "af08cad2",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 1080x216 with 3 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Defines a figure object and three axis objects\n",
-    "# Sets the dimensions of the figure (1 x 3 axes)\n",
-    "# Sets the figure size (15in x 3in)\n",
-    "(fig, (ax1, ax2, ax3)) = plt.subplots(1, 3, figsize=(15, 3))\n",
-    "\n",
-    "\n",
-    "# Sets title for entire figure\n",
-    "fig.suptitle('Military Spending in North America in the 21st Century', y=1.1, fontsize=15)\n",
-    "\n",
-    "\n",
-    "# Makes plots for each axis\n",
-    "ax1.plot(can_usd, color='blue')              # Plots Canada data to ax1 \n",
-    "ax1.set_title('Canada')                      # Sets title for ax1\n",
-    "ax1.set_ylim([8, 24])                        # Sets y-axis limits for ax1\n",
-    "ax1.set_xlabel('Years')                      # Sets x-axis labels for ax1\n",
-    "ax1.set_ylabel('USD (Billions)')             # Sets y-axis labels for ax1\n",
-    "ax1.set_xticks(years)                        # Sets x-ticks for ax1\n",
-    "\n",
-    "ax2.plot(mex_usd, color='red')               # Plots Mexico data to ax2\n",
-    "ax2.set_title('Mexico')                      # Sets title for ax2\n",
-    "ax2.set_ylim([2.5, 7])                       # Sets y-axis limits for ax2\n",
-    "ax2.set_xlabel('Years')                      # Sets x-axis labels for ax2\n",
-    "ax2.set_ylabel('USD (Billions)')             # Sets y-axis labels for ax2\n",
-    "ax2.set_xticks(years)                        # Sets x-ticks for ax2\n",
-    "\n",
-    "ax3.plot(usa_usd, color='orange')            # Plots U.S. data to ax3\n",
-    "ax3.set_title('United States')               # Sets title for ax3\n",
-    "ax3.set_ylim([300, 800])                     # Sets y-axis limits for ax3\n",
-    "ax3.set_xlabel('Years')                      # Sets x-axis labels for ax3\n",
-    "ax3.set_ylabel('USD (Billions)')             # Sets y-axis labels for ax3\n",
-    "ax3.set_xticks(years)                        # Sets x-ticks for ax3\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "efee0c99",
-   "metadata": {},
-   "source": [
-    "Now that we've created separate subplots, we can see the trends for all three countries over the last 20 years. All three countries seem to have decreased spending around 2011 and 2018. By observing the difference in scale, we can also see that while the trends are similar, the magnitude of spending was very different between Canada, Mexico, and the United States."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5190be46",
-   "metadata": {},
-   "source": [
-    "## Histograms\n",
-    "\n",
-    "Histograms are a great way to view a **distribution** of numerical data. A distribution of a dataset is a visual display of all the values within the dataset when plotted on a graph, showing the frequency of occurence of said values.\n",
-    "\n",
-    "\n",
-    "In histogram plots, a numerical component of data is divided into what are called **bins**. As data points are assigned to their respective bins, the total number of data points in each bin is quantified and plotted, visualizing a distribution of frequencies. In the upcoming exercise, we will explore how to visualize distributions of values in our dataset.\n",
-    "\n",
-    "\n",
-    "Let's examine military spending in the United States from 1960 to 2020. We can look at multiple ranges of dollar amounts spent on the military as our independent variable and organize them into bins. After, we can determine how many fiscal years fall into each of these bins and visualize the distribution.\n",
-    "\n",
-    "First, we will need to extract the data pertaining to the military spending in the United States. We will call it `hist_data`. Then, we will need to determine the minimum and maximum values of this subset of data so that we can determine the range of values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "a45eb875",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "min: 47.34655267\n",
-      "max: 778.2322\n"
-     ]
-    }
-   ],
-   "source": [
-    "hist_data = military[\"USA-USD\"]\n",
-    "\n",
-    "print('min:', hist_data.min())\n",
-    "print('max:', hist_data.max())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b21d2e3e",
-   "metadata": {},
-   "source": [
-    "We see that the minimum amount the United States spent on the military between the years of 1960 and 2020 was about \\$47 billion, while the maximum amount was about \\$780 billion.\n",
-    "\n",
-    "With this information, we will create a range for our bins, named `binnum`, with integers between 0 and 801, so that it is inclusive of all the data values. We make the interval of the range 100, giving us eight evenly spaced bins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "ccfb3ade",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[0, 100, 200, 300, 400, 500, 600, 700, 800]"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "binnum = np.arange(0, 801, 100)\n",
-    "\n",
-    "list(binnum)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55e62707",
-   "metadata": {},
-   "source": [
-    "To graph the distribution of military spending, a histogram can be made by using the `hist()` dataframe method. We can specify the bins so that they are evenly distributed on the x-axis. We can do this by inputting `binnum` as our `bins` argument. If we do not specify the `bin` argument, the data will be divided into 10 bins by default."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "90028e2d",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "hist_data.hist(bins=binnum)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "43e31df5",
-   "metadata": {},
-   "source": [
-    "We can also use the `plt.hist()` funtion to make the same graph:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "daae9915",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD1CAYAAABz79PWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAQZUlEQVR4nO3dfYxldX3H8fenrIiDdABtDWVJF5opjSU+ENZC3VgVtUiNNI1JIbVFS9PEthatDYGY1viftcZq06a2UcRUuz4gPmQTqxQxZBu7riDIwqIXleBuwcVSxkbSqvXbP+6ZcTrCznLP7965Z3m/ksmch3vP/cw9Z+Yz59xzz01VIUlSKz+x2QEkSUcXi0WS1JTFIklqymKRJDVlsUiSmtoyrQUvLy97upkkHeUWFxezfpp7LJKkpiwWSVJTgyiW0Wi02RGOyFByglmnYSg5wazTMJScMP2sgygWSdJwWCySpKYsFklSUxaLJKkpi0WS1JTFIklqymKRJDU1tUu6tLR99wLsPrjZMTa0d8dmJ5CkzeceiySpKYtFktSUxSJJaspikSQ1ZbFIkpqyWCRJTVkskqSmLBZJUlMbFkuSq5McSrJv3fTXJrkryR1J3jq9iJKkITmSPZZrgAvWTkjyAuAi4JlV9YvA29pHkyQN0YbFUlU3AQ+um/wa4C1V9T/dbQ5NIZskaYBSVRvfKNkG7Kqqs7rxW4FPMN6T+W/gT6tq79r7LC8vry647+crb9+90Ov+s7J3x8ObHUGSpm5paWl1eHFxMevnT3oRyi3AycC5wHbgw0nOqEdpqbUhJjKAC1Cu6P2zzshoNDJrY0PJCWadhqHkhOlnnfSssAPAdTX2BeCHwFPbxZIkDdWkxfJx4AUASX4eOBb4dqNMkqQB2/BQWJKdwPOBpyY5ALwJuBq4ujsF+XvApY92GEyS9PiyYbFU1SWPMuuVjbNIko4CvvNektSUxSJJaspikSQ1ZbFIkpqyWCRJTVkskqSmLBZJUlMWiySpKYtFktSUxSJJaspikSQ1ZbFIkpqyWCRJTVkskqSmLBZJUlMbFkuSq5Mc6j7Ua/28NySpJH4ssSQJOLI9lmuAC9ZPTHIa8BLg3saZJEkDtmGxVNVNwIOPMOuvgCsAP5JYkrRqotdYklwEHKyq2xrnkSQNXKo23uFIsg3YVVVnJVkAbgReUlXLSe4Bzqmqb6+9z/Ly8uqCR6NRr5Dbdy/0uv+s7N3x8GZHkKSpW1paWh1eXFzM+vlbJljmzwGnA7clAdgK3JLkOVV1/0YhJrL7YL/7z1Dvn3VGRqORWRsbSk4w6zQMJSdMP+tjLpaquh346ZXxR9tjkSQ9Ph3J6cY7gc8DZyY5kOSy6ceSJA3VhnssVXXJBvO3NUsjSRo833kvSWrKYpEkNWWxSJKaslgkSU1ZLJKkpiwWSVJTFoskqSmLRZLUlMUiSWrKYpEkNWWxSJKaslgkSU1ZLJKkpiwWSVJTFoskqakj+aCvq5McSrJvzbS/THJXki8n+ViSE6eaUpI0GEeyx3INcMG6adcDZ1XVM4CvAlc1ziVJGqgNi6WqbgIeXDftM1X1g27034CtU8gmSRqgFq+x/C7wqQbLkSQdBVJVG98o2Qbsqqqz1k1/I3AO8Bu1bkHLy8ur46PRqFfI7bsXet1/VvbueHizI0jS1C0tLa0OLy4uZv38LZMuOMmrgJcB568vlcOFmMjug/3uP0O9f9YZGY1GZm1sKDnBrNMwlJww/awTFUuSC4ArgF+pKv9NlyStOpLTjXcCnwfOTHIgyWXA3wAnANcnuTXJu6acU5I0EBvusVTVJY8w+T1TyCJJOgr4zntJUlMWiySpKYtFktSUxSJJaspikSQ1ZbFIkpqyWCRJTU18SRcN2/bdC4O5VM7eHZudQNJj4R6LJKkpi0WS1JTFIklqymKRJDVlsUiSmrJYJElNWSySpKYsFklSU0fyCZJXJzmUZN+aaScnuT7JqPt+0nRjSpKG4kj2WK4BLlg37UrghqpaAm7oxiVJ2rhYquom4MF1ky8C3tcNvw/49baxJElDlara+EbJNmBXVZ3VjT9UVSd2wwH+c2V8xfLy8uqCR6NRr5Dbdy/0uv+s7N3x8GZHOGJDeU6HZEjrX+pjaWlpdXhxcTHr5/e+CGVVVZLDttPaEBMZyMUSocHPOisDek6HZCjrfzQambWxoeSE6Wed9KywbyU5BaD7fqhdJEnSkE1aLJ8ELu2GLwU+0SaOJGnojuR0453A54EzkxxIchnwFuDFSUbAi7pxSZI2fo2lqi55lFnnN84iSToK+M57SVJTFoskqSmLRZLUlMUiSWrKYpEkNWWxSJKaslgkSU1ZLJKkpiwWSVJTFoskqSmLRZLUlMUiSWrKYpEkNWWxSJKaslgkSU31KpYkr09yR5J9SXYmOa5VMEnSME1cLElOBf4YOKeqzgKOAS5uFUySNEx9D4VtAZ6UZAuwAPx7/0iSpCGbuFiq6iDwNuBe4D5guao+0yqYJGmYUlWT3TE5Cfgo8JvAQ8BHgGur6v0Ay8vLqwsejUa9Qm7fvdDr/rOyd8fDmx3hiA3lOR2SIa1/qY+lpaXV4cXFxayfv6XHsl8EfKOqHgBIch3wy8D7DxdiIrsP9rv/DPX+WWdlQM/pkAxl/Y9GI7M2NpScMP2sfV5juRc4N8lCkgDnA/vbxJIkDVWf11j2ANcCtwC3d8v6h0a5JEkD1edQGFX1JuBNjbJIko4CvvNektSUxSJJaspikSQ1ZbFIkpqyWCRJTVkskqSmLBZJUlO93sciSdO2fffCIC5BtHfHZieYH+6xSJKaslgkSU1ZLJKkpiwWSVJTFoskqSmLRZLUlMUiSWrKYpEkNdWrWJKcmOTaJHcl2Z/kvFbBJEnD1Ped9+8E/rmqXpHkWGChQSZJ0oBNXCxJFoHnAa8CqKrvAd9rE0uSNFR99lhOBx4A3pvkmcDNwOVV9d31NxyNRj0eBoa0I9T/Z52V4TynQzKc9T+krMPYVodyTTMYX9esz/pfWlo67Pw+xbIFOBt4bVXtSfJO4Ergzx5riA0NZGVBg591Vgb0nA7JUNb/aDQaTFa31emY5vrv8+L9AeBAVe3pxq9lXDSSpMexiYulqu4HvpnkzG7S+cCdTVJJkgar71lhrwU+0J0R9nXg1f0jSZKGrFexVNWtwDltokiSjga+816S1JTFIklqymKRJDVlsUiSmrJYJElNWSySpKb6vo9FawzpWkGSNC3usUiSmrJYJElNWSySpKYsFklSUxaLJKkpi0WS1JTFIklqymKRJDXVu1iSHJPkS0l2tQgkSRq2FnsslwP7GyxHknQU6FUsSbYCvwa8u00cSdLQ9d1jeQdwBfDD/lEkSUeDiS9CmeRlwKGqujnJ8w9329FoNOnDdBZ63l+ajf7b+uwMJ6u//9PQZ/0vLS0ddn6fqxs/F3h5kguB44CfTPL+qnrlYw2xIa8YrIHova3PyGg0GkxWf/+nY5rrf+JDYVV1VVVtraptwMXAZx+pVCRJjy++j0WS1FSTD/qqqs8Bn2uxLEnSsLnHIklqymKRJDVlsUiSmrJYJElNWSySpKYsFklSUxaLJKmpJu9jkTQs23cveKkUTY17LJKkpiwWSVJTFoskqSmLRZLUlMUiSWrKYpEkNWWxSJKamrhYkpyW5MYkdya5I8nlLYNJkoapzxskfwC8oapuSXICcHOS66vqzkbZJEkD1Ocz7++rqlu64f8C9gOntgomSRqmJq+xJNkGPBvY02J5kqTh6n2tsCRPBj4KvK6qvvNItxmNRj0fZaHn/aXp8/pbGpI+f5eXlpYOO79XsSR5AuNS+UBVXTdpiA35yypJTfX+u3wYfc4KC/AeYH9Vvb1dJEnSkPV5jeW5wG8DL0xya/d1YaNckqSBmvhQWFXtBtIwiyTpKOA77yVJTVkskqSmLBZJUlMWiySpKYtFktSUxSJJaspikSQ1ZbFIkpqyWCRJTVkskqSmLBZJUlMWiySpKYtFktSUxSJJaspikSQ1ZbFIkprqVSxJLkjylSR3J7myVShJ0nD1+cz7Y4C/BV4KPB24JMnTWwWTJA3TxB9NDDwHuLuqvg6Q5IPARcCdLYKt9dCrT229SEnSlPQ5FHYq8M014we6aZKkxzFfvJckNdXnUNhB4LQ141u7aQAsLi6mx7IlSQPVZ49lL7CU5PQkxwIXA59sE0uSNFQTF0tV/QD4I+DTwH7gw1V1R6tgMH+nMye5OsmhJPvWTDs5yfVJRt33k7rpSfLXXfYvJzl7hjlPS3JjkjuT3JHk8jnOelySLyS5rcv65m766Un2dJk+1P3zQpInduN3d/O3zSpr9/jHJPlSkl1znvOeJLcnuTXJF7tpc7f+u8c/Mcm1Se5Ksj/JefOYNcmZ3fO58vWdJK+b06yv736f9iXZ2f2ezW5braq5/AKOAb4GnAEcC9wGPH2TMz0POBvYt2baW4Eru+Ergb/ohi8EPgUEOBfYM8OcpwBnd8MnAF9lfEr4PGYN8ORu+AnAni7Dh4GLu+nvAl7TDf8B8K5u+GLgQzPeBv4E+CdgVzc+rznvAZ66btrcrf/u8d8H/F43fCxw4rxmXZP5GOB+4GfnLSvjk6i+ATxpzTb6qlluqzNfIY/hyTkP+PSa8auAq+Yg1zb+f7F8BTilGz4F+Eo3/PfAJY90u03I/AngxfOeFVgAbgF+Cfg2sGX9tsB4D/m8bnhLd7vMKN9W4AbghcCu7g/G3OXsHvMefrxY5m79A4vdH8HMe9Z1+V4C/Os8ZuVHZ+ye3G17u4BfneW2Os9nhQ3ldOanVdV93fD9wNO64bnI3+3WPpvxnsBcZu0OL90KHAKuZ7yn+lCND7euz7OatZu/DDxlRlHfAVwB/LAbf8qc5gQo4DNJbk7y+920eVz/pwMPAO/tDjG+O8nxc5p1rYuBnd3wXGWtqoPA24B7gfsYb3s3M8NtdZ6LZXBqXPm12TlWJHky8FHgdVX1nbXz5ilrVf1vVT2L8R7Bc4Bf2NxEPy7Jy4BDVXXzZmc5Qjuq6mzGV8b4wyTPWztzjtb/FsaHl/+uqp4NfJfx4aRVc5QVgO61iZcDH1k/bx6ydq/xXMS4tH8GOB64YJYZ5rlYDns68xz5VpJTALrvh7rpm5o/yRMYl8oHquq6ec66oqoeAm5kvJt+YpKV0+HX5lnN2s1fBP5jBvGeC7w8yT3ABxkfDnvnHOYEVv9rpaoOAR9jXNjzuP4PAAeqak83fi3jopnHrCteCtxSVd/qxuct64uAb1TVA1X1feA6xtvvzLbVeS6WoZzO/Eng0m74UsavZ6xM/53uzJBzgeU1u8tTlSTAe4D9VfX2Oc/6U0lO7IafxPi1oP2MC+YVj5J15Wd4BfDZ7r/Eqaqqq6pqa1VtY7wtfraqfmvecgIkOT7JCSvDjF8P2Mccrv+quh/4ZpIzu0nnM74s1NxlXeMSfnQYbCXTPGW9Fzg3yUL3t2DlOZ3dtjrLF7wmeBHqQsZnNH0NeOMc5NnJ+Jjl9xn/p3UZ42ORNwAj4F+Ak7vbhvFFOr8G3A6cM8OcOxjvjn8ZuLX7unBOsz4D+FKXdR/w5930M4AvAHczPuTwxG76cd343d38MzZhO3g+PzorbO5ydplu677uWPndmcf13z3+s4AvdtvAx4GT5jjr8Yz/m19cM23usgJvBu7qfqf+EXjiLLfVdAuWJKmJeT4UJkkaIItFktSUxSJJaspikSQ1ZbFIkpqyWCRJTVkskqSmLBZJUlP/B3IOWeY38ESEAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(hist_data, bins=binnum)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d7d71458",
-   "metadata": {},
-   "source": [
-    "When determining the bins for a histogram, the bin size controls the number of bins that will show. A smaller bin size will result in more bins, which will show more granularity of the data, but could make it difficult to see patterns in the data. A larger bin size decreases the visible detail of the data, but could also make it hard to discern useful take aways from the data. While exploring data, it's important to try different bins sizes out to see which display provides the most useful information for your analysis needs.\n",
-    "\n",
-    "Consider the different bin sizes below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "a2713731",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(hist_data, bins=range(0, 801, 200))\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "5844a254",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAD1CAYAAABz79PWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAP8UlEQVR4nO3dbYxmdXnH8e9VVsRBei9oayhLuthMaAixSlgLdWONqEVKpGlMCukDWJomtbVobQjEtMYXTfpgrDZtao2Cptr1AbGaTaxSxJJp6LqCIAuLHlSCuwVXa7lt3BdqvfriPjNMZ5ed9T7XmT1n9/tJJnMeZv7zm3Of2d+chzkbmYkkSVV+7FgHkCQdXywWSVIpi0WSVMpikSSVslgkSaU29TXwdDr1djNJOs5NJpNYu8wjFklSKYtFklRqFMXSNM2xjnBUxpITzNqHseQEs/ZhLDmh/6yjKBZJ0nhYLJKkUhaLJKmUxSJJKmWxSJJKWSySpFIWiySpVG+PdBm6zTfvLx9z9/byISVpdDxikSSVslgkSaUsFklSKYtFklTKYpEklbJYJEmlLBZJUimLRZJUymKRJJVat1gi4qaIOBARew6z7o0RkRHx7H7iSZLG5miOWN4LXLp2YUScDbwCeLQ4kyRpxNYtlsy8E/j2YVb9NXA9kNWhJEnjNdc1loi4AtifmfcV55EkjVxkrn/AERFbgZ2ZeX5ELAB3AK/IzGlEPAJcmJnfWv050+l0ZeCmaUpDV9i2tFA+5u7tB8vHlKShWVxcXJmeTCaxdv08j83/GeAc4L6IANgC3BMRL8zMx9cLMY+maTqPcYil+sfmQ/fvdaP0sk17MpasY8kJZu3DWHJC/1l/5GLJzPuBn1yef6ojFknSielobjfeAdwFnBsR+yLi2v5jSZLGat0jlsy8ap31W8vSSJJGz7+8lySVslgkSaUsFklSKYtFklTKYpEklbJYJEmlLBZJUimLRZJUymKRJJWyWCRJpSwWSVIpi0WSVMpikSSVslgkSaUsFklSKYtFklTKYpEklbJYJEmlLBZJUql1iyUiboqIAxGxZ9Wyv4qIhyLiixHxsYjY3GtKSdJoHM0Ry3uBS9csuw04PzOfB3wZuLE4lyRppNYtlsy8E/j2mmWfzswftLP/AWzpIZskaYQqrrH8NvDJgnEkSceByMz1PyhiK7AzM89fs/xNwIXAr+aagabT6cp80zQlYSttW1ooH3P39oPlY0rS0CwuLq5MTyaTWLt+07wDR8Q1wOXAJWtL5Ugh5tE0TecxDrG0v3a8VnnOnvSyTXsylqxjyQlm7cNYckL/Wecqloi4FLge+MXM9Nd0SdKKo7ndeAdwF3BuROyLiGuBvwVOA26LiHsj4p0955QkjcS6RyyZedVhFr+nhyySpOOAf3kvSSplsUiSSlkskqRSFoskqZTFIkkqZbFIkkpZLJKkUhaLJKmUxSJJKmWxSJJKWSySpFIWiySplMUiSSplsUiSSlkskqRSFoskqZTFIkkqZbFIkkpZLJKkUusWS0TcFBEHImLPqmVnRMRtEdG070/vN6YkaSyO5ojlvcCla5bdANyemYvA7e28JEnrF0tm3gl8e83iK4D3tdPvA36lNpYkaawiM9f/oIitwM7MPL+dfyIzN7fTAfz38vyy6XS6MnDTNHWJi2xbWigfc/f2g+VjStLQLC4urkxPJpNYu35T1y+QmRkRR2yn1SHm0TRN5zEOsbS/drxWec6e9LJNezKWrGPJCWbtw1hyQv9Z570r7BsRcSZA+/5AXSRJ0pjNWyyfAK5up68GPl4TR5I0dkdzu/EO4C7g3IjYFxHXAn8OvDwiGuBl7bwkSetfY8nMq55i1SXFWSRJxwH/8l6SVMpikSSVslgkSaUsFklSKYtFklTKYpEklbJYJEmlLBZJUqnOD6GU+rZtaaH8oaFPvOas0vEkPckjFklSKYtFklTKYpEklbJYJEmlLBZJUimLRZJUymKRJJWyWCRJpSwWSVKpTsUSEW+IiAciYk9E7IiIU6qCSZLGae5iiYizgD8ELszM84GTgCurgkmSxqnrqbBNwDMiYhOwAPxn90iSpDGbu1gycz/wVuBR4DFgmpmfrgomSRqnyMz5PjHidOCjwK8BTwAfAW7JzPcDTKfTlYGbpukctNq2pYXyMXdvP1g+5pj0sU37cqK/VlIXi4uLK9OTySTWru/y2PyXAV/LzG8CRMStwC8A7z9SiHk0TdN5jEMUP4Z9WXnOnoxpm/ahj9epl23aE7PWG0tO6D9rl2ssjwIXRcRCRARwCbC3JpYkaay6XGPZBdwC3APc3471rqJckqSR6vQ/SGbmm4E3F2WRJB0H/Mt7SVIpi0WSVMpikSSVslgkSaUsFklSKYtFklTKYpEklbJYJEmlLBZJUimLRZJUymKRJJWyWCRJpSwWSVIpi0WSVMpikSSVslgkSaUsFklSKYtFklTKYpEklepULBGxOSJuiYiHImJvRFxcFUySNE6bOn7+O4B/ycxXR8TJwEJBJknSiM1dLBExAV4MXAOQmd8DvlcTS5I0VpGZ831ixPOBdwEPAj8H3A1cl5nfBZhOpysDN03TOWi1bUv1B1e7tx8sH3NM+timfTnRXyupi8XFxZXpyWQSa9d3ORW2CbgAeF1m7oqIdwA3AH9ypBDzaJqm8xiHWNpfO16rPGdPxrRN+9DH69TLNu2JWeuNJSf0n7XLxft9wL7M3NXO38KsaCRJJ7C5iyUzHwe+HhHntosuYXZaTJJ0Aut6V9jrgA+0d4R9FXhN90iSpDHrVCyZeS9wYU0USdLxwL+8lySVslgkSaUsFklSKYtFklTKYpEklbJYJEmlLBZJUimLRZJUymKRJJWyWCRJpSwWSVIpi0WSVMpikSSVslgkSaUsFklSKYtFklTKYpEklbJYJEmlOhdLRJwUEV+IiJ0VgSRJ41ZxxHIdsLdgHEnScaBTsUTEFuCXgXfXxJEkjV3XI5a3A9cDP+weRZJ0PIjMnO8TIy4HLsvM10bES4A/zszLl9dPp9OVgZum6Riz3ralhWMd4ajt3n7wWEc4Km5T9aGP/aqP17+v/X+I++ri4uLK9GQyibXrN3UY+0XAqyLiMuAU4Mcj4v2Z+RtHCjGPpmk6j3GIpf214/Wo/HvHbTqabdqTMWXtY7/q5Xvvaf8f474696mwzLwxM7dk5lbgSuAzhysVSdKJxb9jkSSV6nIqbEVmfhb4bMVYkqRx84hFklTKYpEklbJYJEmlLBZJUimLRZJUymKRJJWyWCRJpSwWSVIpi0WSVKrkL+81PtuWFkb10Eip0uab3ff75BGLJKmUxSJJKmWxSJJKWSySpFIWiySplMUiSSplsUiSSlkskqRSFoskqdTcxRIRZ0fEHRHxYEQ8EBHXVQaTJI1Tl0e6/AB4Y2beExGnAXdHxG2Z+WBRNknSCM19xJKZj2XmPe30/wB7gbOqgkmSxqnkGktEbAVeAOyqGE+SNF6Rmd0GiHgm8G/An2XmrcvLp9PpysBN03T6GtuWFjp9/tjt3n6wfEy3af02PdGd6PvUmHTd/xcXF1emJ5NJrF3f6bH5EfE04KPAB1aXypFCzOUEf7x75+13OG7T8jGbpunntepBL1lP8H1qbPrcV7vcFRbAe4C9mfm2ukiSpDHrco3lRcBvAi+NiHvbt8uKckmSRmruU2GZuQQccm5NknRi8y/vJUmlLBZJUimLRZJUymKRJJWyWCRJpSwWSVIpi0WSVMpikSSV6vwQyqey+iGUXW2+2WcQSVKV3dsPlj0r7HAPofSIRZJUymKRJJWyWCRJpSwWSVIpi0WSVMpikSSVslgkSaUsFklSKYtFklTKYpEklepULBFxaUR8KSIejogbqkJJksZr7mKJiJOAvwNeCZwHXBUR51UFkySN06YOn/tC4OHM/CpARHwQuAJ4sCLYak+85qzqISVJPelyKuws4Our5ve1yyRJJzAv3kuSSnU5FbYfOHvV/JZ2GXD4Z/RLko5/XY5YdgOLEXFORJwMXAl8oiaWJGms5i6WzPwB8AfAp4C9wIcz84GqYDC825kj4qaIOBARe1YtOyMibouIpn1/ers8IuJv2uxfjIgLNjDn2RFxR0Q8GBEPRMR1A856SkR8LiLua7O+pV1+TkTsajN9qP3lhYh4ejv/cLt+60Zlbb/+SRHxhYjYOfCcj0TE/RFxb0R8vl02uNe//fqbI+KWiHgoIvZGxMVDzBoR57bbc/ntOxHx+oFmfUP787QnIna0P2cbt69m5iDfgJOArwDPBU4G7gPOO8aZXgxcAOxZtewvgRva6RuAv2inLwM+CQRwEbBrA3OeCVzQTp8GfJnZLeFDzBrAM9vppwG72gwfBq5sl78T+L12+rXAO9vpK4EPbfA+8EfAPwE72/mh5nwEePaaZYN7/duv/z7gd9rpk4HNQ826KvNJwOPATw8tK7ObqL4GPGPVPnrNRu6rG/6C/Agb52LgU6vmbwRuHECurfz/YvkScGY7fSbwpXb6H4CrDvdxxyDzx4GXDz0rsADcA/w88C1g09p9gdkR8sXt9Kb242KD8m0BbgdeCuxs/8EYXM72az7CocUyuNcfmLT/CMbQs67J9wrg34eYlSfv2D2j3fd2Ar+0kfvqkO8KG8vtzM/JzMfa6ceB57TTg8jfHta+gNmRwCCztqeX7gUOALcxO1J9ImenW9fmWcnarp8Cz9qgqG8Hrgd+2M4/a6A5ARL4dETcHRG/2y4b4ut/DvBN4Ob2FOO7I+LUgWZd7UpgRzs9qKyZuR94K/Ao8Bizfe9uNnBfHXKxjE7OKj+PdY5lEfFM4KPA6zPzO6vXDSlrZv5vZj6f2RHBC4GfPbaJDhURlwMHMvPuY53lKG3PzAuYPRnj9yPixatXDuj138Ts9PLfZ+YLgO8yO520YkBZAWivTbwK+MjadUPI2l7juYJZaf8UcCpw6UZmGHKxHPF25gH5RkScCdC+P9AuP6b5I+JpzErlA5l565CzLsvMJ4A7mB2mb46I5dvhV+dZydqunwD/tQHxXgS8KiIeAT7I7HTYOwaYE1j5rZXMPAB8jFlhD/H13wfsy8xd7fwtzIpmiFmXvRK4JzO/0c4PLevLgK9l5jcz8/vArcz23w3bV4dcLGO5nfkTwNXt9NXMrmcsL/+t9s6Qi4DpqsPlXkVEAO8B9mbm2wae9SciYnM7/Qxm14L2MiuYVz9F1uXv4dXAZ9rfEnuVmTdm5pbM3MpsX/xMZv760HICRMSpEXHa8jSz6wF7GODrn5mPA1+PiHPbRZcweyzU4LKuchVPngZbzjSkrI8CF0XEQvtvwfI23bh9dSMveM1xEeoyZnc0fQV40wDy7GB2zvL7zH7TupbZucjbgQb4V+CM9mOD2UM6vwLcD1y4gTm3Mzsc/yJwb/t22UCzPg/4Qpt1D/Cn7fLnAp8DHmZ2yuHp7fJT2vmH2/XPPQb7wUt48q6wweVsM93Xvj2w/LMzxNe//frPBz7f7gP/DJw+4KynMvttfrJq2eCyAm8BHmp/pv4RePpG7qvRDixJUokhnwqTJI2QxSJJKmWxSJJKWSySpFIWiySplMUiSSplsUiSSlkskqRS/wccOFA5gCcVEAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(hist_data, bins=range(0, 801, 50))\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "73ece7f2",
-   "metadata": {},
-   "source": [
-    "Both histograms show the same data in different ways. The top histogram has a larger bin size and from it, we can see that it shows most of the values in the data fall within the range of 0-200. The second one has a smaller bin size, and we can see that most of the values of the data fall within the range of 50-100. The latter gives us a more specific range of where most of the data lie, which can be useful down the line.\n",
-    "\n",
-    "For now, let's stick with the bin size in the latter graph. Now that we have our plot, let's add additional details to make it more informative:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "942043f1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.hist(hist_data, bins=range(0, 801, 50))\n",
-    "plt.title(\"Distribution of Military Spending in the United States from 1960 to 2020\")\n",
-    "plt.ylabel('Counts of Fiscal Years')\n",
-    "plt.xlabel(\"Dollar Amount (USD)\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5e52c962",
-   "metadata": {},
-   "source": [
-    "Awesome! From this plot, we can see that the United States had the highest frequency of fiscal years where \\$50 - \\$100 billion was spent on the military, while the \\$400 - \\$550 billion and \\$150 - \\$200 bins had the lowest frequencies with only 1 year spending those ranges of money."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "778960f9",
-   "metadata": {},
-   "source": [
-    "### Visualizing multiple distributions using histograms\n",
-    "\n",
-    "We can also view multiple distributions on one plot on using multiple plots. Let's look at the distributions of the percentage of GDP spent on the military in Canada and the United States from 1960 to 2020."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "ea5e0162",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-PercentGDP</th>\n",
-       "      <th>USA-PercentGDP</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1960</th>\n",
-       "      <td>4.185257</td>\n",
-       "      <td>8.993125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961</th>\n",
-       "      <td>4.128312</td>\n",
-       "      <td>9.156031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>3.999216</td>\n",
-       "      <td>9.331673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>3.620650</td>\n",
-       "      <td>8.831891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964</th>\n",
-       "      <td>3.402063</td>\n",
-       "      <td>8.051281</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>1.164162</td>\n",
-       "      <td>3.418942</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>1.351602</td>\n",
-       "      <td>3.313381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>1.324681</td>\n",
-       "      <td>3.316249</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>1.278941</td>\n",
-       "      <td>3.427080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>1.415056</td>\n",
-       "      <td>3.741160</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>61 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      CAN-PercentGDP  USA-PercentGDP\n",
-       "Year                                \n",
-       "1960        4.185257        8.993125\n",
-       "1961        4.128312        9.156031\n",
-       "1962        3.999216        9.331673\n",
-       "1963        3.620650        8.831891\n",
-       "1964        3.402063        8.051281\n",
-       "...              ...             ...\n",
-       "2016        1.164162        3.418942\n",
-       "2017        1.351602        3.313381\n",
-       "2018        1.324681        3.316249\n",
-       "2019        1.278941        3.427080\n",
-       "2020        1.415056        3.741160\n",
-       "\n",
-       "[61 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "perc_gdp = military[['CAN-PercentGDP', 'USA-PercentGDP']]\n",
-    "perc_gdp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "fb06b325",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "CAN-PercentGDP    0.989925\n",
-      "USA-PercentGDP    3.085677\n",
-      "dtype: float64\n",
-      "CAN-PercentGDP    4.185257\n",
-      "USA-PercentGDP    9.417796\n",
-      "dtype: float64\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(perc_gdp.min())\n",
-    "print(perc_gdp.max())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5aab7d8f",
-   "metadata": {},
-   "source": [
-    "We see that the minimum values for these two countries is about 0.98%, while the maximum value is about 9.4%. To plot both of these distributions on a single plot, we can create another array called `binnum2` that can include all of the values.\n",
-    "\n",
-    "\n",
-    "We can then make histograms for each country, specifying the labeling and colors for each. We'll also add a legend to show which color corresponds to which country, as well as proper titles and labeling:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "64136357",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "binnum2 = np.arange(1,11, step = 0.5)\n",
-    "\n",
-    "# plotting histograms\n",
-    "\n",
-    "plt.hist(perc_gdp['CAN-PercentGDP'], label='Canada', alpha=0.6, color = 'blue', bins=binnum2)\n",
-    "plt.hist(perc_gdp['USA-PercentGDP'], label='United States', alpha=0.6, color = 'orange', bins=binnum2)\n",
-    "\n",
-    "# labeling\n",
-    "plt.legend(bbox_to_anchor=(1, 1))\n",
-    "plt.title('Distribution of the Percentage of Military Spending from 1960 to 2020')\n",
-    "plt.xlabel('% of GDP')\n",
-    "plt.ylabel('Counts of Fiscal Years')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b901d2d",
-   "metadata": {},
-   "source": [
-    "Additionally, we can use the `plt.subplots()` function to create two separate plots in one figure. By specifying `ax1` and `ax2`, we can use the `hist()` method to create a histogram and set titles and axis labels for each axis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "3b1fef90",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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NpAt/jyPN+HM/afhOkTgGK/8BpB8nU0gNmz+XpH+edF3Bz4AXSD+QbiY1GoiI84H/A85WGrJ1N+kM62D7/h/p4uzf5PJvQfoxNd31EQXK8WRE1HuPjn+TzvA+Len5vGxP4Mj8P3gY6cd2EacBH6N6I/gl0nHrJzX2zgB+HhFnlqQZBxxOGm61Cvli9oiYQrrIflvSWfOnSe/7YBetT6PkGP6UdAz7SLNG1eJPpIvD7yTNCnYxqQezpnsF5WO/Bekz8zxpZqudSq5nqNefSMOntiN9n77O+9eHzc/7s25dApwUEX8sie0k0nEdTxoS9Sbvfx++RbrYfifSLF1fBbau8lk+FviipJck/To3AjYnNcBfIM1IuHk+VtPIJ1i+QfrueFrpHkKvSNo+x/Ic6bqUH5E+Y59g2mvJfkzqVbm/ZNuDayyH2YgxMIWfmZl1sNyz9TiwfUlDt+cp3bjt6xGxTh15nEKazvrQhgXWZEo3uzs+IgabYMDMrGO558PMrENJ2kjSGKWpYQeuB7lhiM16Rh6Ssyfwx6HSdrs8ZHDTPFRoYVIvzfntjsvMbLjc+DAz61xrku4bMDBUautBxp33JKX7tDxHGtM/rs3htIJI9xJ5iTTsagJpeJqZWVfxsCszMzMzM2sJ93yYmZmZmVlLuPFhZmZmZmYt4caHmZmZmZm1hBsfZmZmZmbWEm58mJmZmZlZS7jxYWZmZmZmLeHGh5mZmZmZtYQbH2ZmZmZm1hJufJiZmZmZWUu48WFmZmZmZi3hxoeZmZmZmbWEGx9mZmZmZtYSbnyYmZmZmVlLuPFhZmZmZmYt4caHmZmZmZm1hBsfZmZmZmbWEm58mJmZmZlZS7jxYdYhJD0s6dB2x2FmZsVICkk7NCnv9XL+izQjf7N2cePDeoak+ST9TNL/JL0h6VlJ10jaSdJM7Y7PzMyaS9JVkk4YZPnY/EN/nWFmuSBwbkk+UyXtUmeYhUmaXdJRkvolvS7pRUk3SdqnJM0Jkq6qIe918nsytpExm/kHl/UESYsC1wJTgcOA24C3gbWAA4A7gdvbFZ+ZmXWfiHi6zSH8Hvg0sC9wBzA3sBKwWDuDMqvGPR/WK34HzAqsHBFnRsS9EdEfEacCqwD9kj6bz4q9KGmypKslrV6aST4LtKek0yVNkfS4pIPK0nxF0vicx/OSLpK0dFmaFSRdJ+nNfMZqm/KAJe0r6XZJr0h6WtLZkhZs/FtjZmalSoY8fTb3kL8m6V5Jm5Sle2/YlaSHgRmBk/PyKEm3iqTL8/f5c5LOk7R4WV575zrlNUmXUawBsTXw84j4W0Q8FBF3RMQpEXFkzvMIYDdg3YGYBnpmqtUxubfjP3kfD+XtriqJddu87Rt5yPDRkuYsWb+OpP/menKKpDskbVSgPNYD3PiwEU/SvMCmwHERMbl8fUS8HRGvAqNIjZQ1ST0i/cClkuYr2+Rw4BpgReAnwI8lrV+yflbgh8DKwGeBd4CLJM2S45kduBiYBKwO7AT8P+CDg4R/APAx4HOkiujs4ZXezMzq8Avgx8AKwHjgz5LmqZB2NdL3/X6k4VgDP+SXBa4GrgdWBT6T010habacZivgV8DRpLrlHODnBeJ7Ctg413OV4h+X9z0Q059L1leqYx4DtsrPV8/bfT7Hugupx+WXwLKkOmwD4Pi8fibgAtL7tXJ+HAG8VqA81gsiwg8/RvSD9MUZwOeHud0MwEvA9iXLAvh1WboJwE+q5DNv3m7t/Hp34BVgnpI0y+c0h1bJZ6WcZuF2v6d++OGHH934AK4CThhk+dj8/bpOfr1eeb0BLJCXbVSyLIAdSl5PBXYpy/sU4OyyZbOSfoxvnV9fC5xZluYXOf9FqpRnbeARUmPmTuCPpN4QlaQ5AbiqwHszTR0DrJNfjy1L9zCwR9myT+W08+RHAOu1+3j70ZkP93xYL1ChRNISeTjV/ZJeBl4GRgOLlyW9vez1k6RKaSCfFSWdL+khSVOAR/OqgXyWBSZExEsD20TE3cA0vTK52/8ySY/lfK4ty8fMzJrr9oEnEfEM6Uf+AhVTD2414HN5eNMrkl4BXgBmA/pymmWB68q2u5YhRMR/gaWATwKn5tjOBS6QVLXuq6WOkfSBvP7osvJckpN8ONdtJwCXSbpE0oGSlhmqLNY73PiwXtAPvEv6cq/mQlK387eANUhd388Cs5Sle6vsdZD/lyTNAVyel+1K6nVZLb8uz6ciSYuRhmY9DGxL6qrfMq8unI+ZmU1jMumkUrkx+e8bZcvLv+9h+L+dZgBOJ9UppY+lST/S6xIRUyPiuoj4ZURsBewCbE7qjRhUHXXMQNn3ZdqyrEBqSN2VY/oa6XrKK4B1gbslfWO4ZbORybNd2YgXES9KugTYS9Jvouy6D0kzk663WBbYNCIuy8sXYfDrMKr5KPAB4JCImJDzWYtpe1/uBb4uaUxETMpplmPaCnE1YHZgv4h4PadZZZixmJnZtO4DviRpxoh4p2T56qRejfvrzP8t0kXnpW4GPg48EBEx/SZAqhfWAn5bsmztGmOYkP8O1F+DxVSkjhloeL23bUQ8I+kxYJmI+FO1IHKP/t2kXpLjga8DfxhmWWwEcs+H9Yo9SVPr3pJno1pW0ofzLCU3k7qqnwO+JmlpSWsCZwGvD3M/jwBvAntLWipfiH4sqedjwDhgCnBGnvVqDeCksn315232z8PBtiZNEWxmZrX7Hen7/uQ8A9VSkrYDjgJOHjghVIeHgE9LWkjS/HnZj0knps6QtHr+Tv+0pGMlLZnT/BL4cp6Bqk/SrsCOQ+1MaVbGPSStKmnxXOf8jjShyZUlMX1E0nKS5pc0K8XqmEdIowY2lfRBSQMnyA4B9pF0iKTlJS0jaWtJf8gxfVjS/+UZrxbP9eknSQ0sMzc+rDdExKOkGTf+Rpp141bS+NqvkWYUuRP4Emns7J2kCwSPIc0kMpz9PA/sQJrl6h7SBYMHkL7AB9K8Rpp9az7gRuBM0iwnz5akuRPYG/gG6Qv7ANIMKmZmVqOIeITUwzAP8A/S9/3BpHpgzwbsYn/ScKOHSSe0yL3ga5FmVLyM9J3+J1LPw6Sc5vy87XdzTNsD3yuwv0ty2ouB/wEnkxoWa+f6COBE4CZSnfccsF2ROiZf43IQcCCpLvx7Xn46sA1paNeNOe8jgCfypq+ShmCdDUwE/pr3vVeB8lgPUOUeQDMzMzMzs8Zxz4eZmZmZmbWEGx9mZmZmZtYSbnyYmZmZmVlLdPRUu5MnT/YFKWZmTTJ69OhCN+AcyVzPmJk1z2D1jHs+zMzMzMysJdz4MDMzMzOzluiJxkd/f3+7Q6hbt5eh2+OH7i9Dt8cPLoN1hpFwDF2GzuAydAaXobV6ovFhZmZmZmbt15LGh6RFJV0p6V5J90jaNy+fV9IVkvrz33laEY+ZmZmZmbVeq3o+pgL7R8SywBrAtyQtCxwI/Csi+oB/5ddmZmZmZjYCtaTxERFPRcSt+fkUYAKwMLAVcGpOdiqwdSviMTMzMzOz1mv5NR+SxgIrAeOBBSLiqbzqaWCBVsdjZmZmZmat0dKbDEoaBfwV2C8iXpbev+9IRISkijd7qvUq/tVWWxVYtaZt63XTTTc3NL9umslgMN0eP3R/Gbo9fnAZ6tXX19e2fY9Uoy8a07Z9T95sUtv2bWZWi5Y1PiTNTGp4nBkR5+XFz0haMCKekrQg8Gyl7buxwmxkzP39/V35Hgzo9vih+8vQ7fGDy2BmZtbtWjXblYATgQkRcXTJqguAnfPznYG/tyIeMzMzMzNrvVb1fKwN7AjcJen2vOxg4KfAOZJ2Ax4BtmlRPGZmZmZm1mItaXxExLWAKqxevxUxmJmZmZlZe/kO52ZmZmZm1hJufJiZmZmZWUu48WFmZmZmZi3hxoeZmXU9SSdJelbS3SXLfi7pPkl3Sjpf0pg2hmhmZrjxYWZmI8MpwMZly64Alo+IjwMTgYNaHZSZmU2rUOND0gfy3cmRNKOkXSXtLMmNFzMzq1u99UxEXAO8WLbs8oiYml/eACzS0KDNzGzYijYeLgQGbsn7I+AA4NvAL5sRlJmZ9Zxm1zNfBS5pUF5mZlajovf5WBq4PT/fAVgLeAW4h1Q5mJmZ1aNp9YykQ4CpwJnV0vX39xfOszTtqrUG1gDDibmR23YKl6EzuAydoVPK0NfXV3V90cbHO8AskpYGJkfEo7krfFSd8ZmZmUGT6hlJuwCbA+tHRFRLO1SFOaC/v3/atBNrj69eRWMuN10ZupDL0Blchs7QTWUo2vi4FDgHmA84Oy9bFniiGUGZmVnPaXg9I2lj4LvAuhHxWt0RmplZ3Yo2PnYDdgbeBk7Ly+YHjmhCTGZm1nvqqmcknQWsB8wv6XHgcNLsVrMCV0gCuCEi9mho1GZmNixDNj4kzQhcBmwUEW8OLI+Iq5oYl5mZ9YhG1DMRsd0gi0+sPzozM2ukIWe7ioh3gCUANT8cMzPrNa5nzMx6R9Gpdn8AHC9p8Tz/+gwDj2YGZ2ZmPcP1jJlZDyh6zccJ+e+OJcsEBDBjQyMyM7Ne5HrGzKwHFG18LNHUKMzMrNe5njEz6wGFGh8R8UizAzEzs97lesbMrDcU7flA0pbAuqSpD9+7KDAidmpCXGZm1mNcz5iZjXyFLuSTdDjwh5z+S8ALwEbApKZFZmZmPcP1jJlZbyg6i8hXgc9GxLeBt/LfLYCxzQrMzMx6iusZM7MeULTxMSYi7s7P35I0c0TcSOoeNzMzq5frGTOzHlD0mo8HJC0XEfcAdwPflPQS8FLzQjMzsx7iesbMrAcUbXwcCsyXnx8EnAmMAvZsRlBmZtZzXM+YmfWAolPtXlzyfDzw4aZFZGZmPcf1jJlZbxjOVLsfIc1AskBE7CVpGWDWiLizadGZmVnPcD1jZjbyFZ1q90vANcDCwMB863MBRzcpLjMz6yGuZ8zMekPR2a6OJE2BuAfwTl52B7BCU6IyM7Ne43rGzKwHFG18fBAY6PaOkr8xeHIzM7NhqauekXSSpGcl3V2ybF5JV0jqz3/naWzIZmY2XEUbH7cAO5Yt2xa4sbHhmJlZj6q3njkF2Lhs2YHAvyKiD/hXfm1mZm1U9YLzkjnX9wEul7QbMKeky4ClgQ1bEKOZmY1QjapnIuIaSWPLFm8FrJefnwpcBXyvAWGbmVmNhur5uE7S94F+4CPAb0lzsZ8MfCwi+ovspEJ3+BGSnpB0e35sWmMZzMysezWknqlggYh4Kj9/GligrkjNzKxuQ021uxrwR+ALwK4RcU6N+zkFOA44rWz5ryLiFzXmaWZm3a9R9UxVERGSql4/0t9fvJ1TmnbV2sOq23BibuS2AKtOXK2u7etx89I3AfWXoRO4DJ3BZWicvr6+quurNj4iYiKwnqRvAJdJOg24tyzNSUMFUaE73MzMelyj6pkKnpG0YEQ8JWlB4NlqiYeqMAf09/dPm3ZijdE1QNGYy01Xhlq0udwNKUObuQydwWVoraI3Gfw76cZPXwQeKlkeQK2VAsBeknYCbgb2j4iX6sjLzMy6VzPqmQuAnYGf5r9/rydAMzOr35CND0m7Az8hDZ3aPCLeaNC+fw8cRapYjgJ+CXy1UuLau5La1yHe6O6vTulOq1W3xw/dX4Zujx9chnp14pmxRtQzks4iXVw+v6THgcNJjY5z8kXsjwDbNCpmMzOrzVCzXf2TdIHeZhHR0Gl1I+KZkv38CbiwWvpOrDCH0siYu6k7bTDdHj90fxm6PX5wGUaiRtUzEbFdhVXr15qnmZk13lCzXV0PrNLohgdAHn874HPA3ZXSmpnZiNW0esbMzDrPUBecf78RO6nQHb6epBVJw64eBr7RiH2ZmVn3aFQ9Y2Zm3aHoBed1qdAdfmIr9m1mZmZmZp1hqGFXZmZmZmZmDeHGh5mZmZmZtUTFYVeSliySQUQ82LhwzMysV7ieMTPrPdWu+bifdDG4qqQJYMaGRmQNMWbM6Lbte9KkyW3bt5l1FdczZmY9pmLjIyI8JMvMzJrG9Uz9Rl80pqbtVgWY2MhIzMyK8Re/mZmZmZm1RKGpdiXNBOwJrAvMT0kXeUR8qjmhmZlZr3A9Y2bWG4r2fPyKdBPAa4BVgL8CHwT+3aS4zMyst7ieMTPrAUUbH58HNomIY4Gp+e/WwKebFZiZmfUU1zNmZj2gaONjDuCx/Px1SXNExH3ASs0Jy8zMeozrGTOzHlDomg9gArAacCNwM3CEpJeBJ5oVmJmZ9RTXM2ZmPaBo42Nf4J38/DvA74G5gK83IygzM+s5rmfMzHpAocZHRNxU8rwf2KBpEZmZWc9pZj0j6dvA7qQbFt4F7BoRbzQqfzMzK67QNR+SDpS0Wtmy1SV9tzlhmZlZL2lWPSNpYWAfYNWIWJ50t/Rt68nTzMxqV/SC832Be8uW3Qvs19BozMysVzWznpkJmD3fS2QO4MkG5GlmZjUo2viYBXi7bNlbwGyNDcfMzHpUU+qZiHgC+AXwKPAUMDkiLq8nTzMzq13RC85vId159piSZXsAtzY6IDMz60lNqWckzQNsBSwBTAL+ImmHiDijPG1/f3/hfEvTrlpPgFaTgfd/OMesU7kMncFlaJy+vr6q64s2Pr4NXCFpR+ABYCngQ8Bn64rOzMwsaVY9swHwUEQ8ByDpPGAtYLrGx1AV5oD+/v5p006sM0Ibtr6+vumPQxdyGTqDy9BaRWe7ukfS0sDmwKLAecCFEfFKM4MzM7Pe0MR65lFgDUlzAK8D65PuI2JmZm1QtOeDXAGcDSBpSWB+wI0PMzNriGbUMxExXtK5pOFbU4HbgD/WGaqZmdWo6FS7Z0laKz/fFbgHuEfSbs0MzszMekMz65mIODwiPhIRy0fEjhHxZr15mplZbYrOdlXaTf0d0hja1YEDmxGUmZn1HNczZmY9oOiwq1ki4q18s6Z5I+K/AJIWaF5oZmbWQ1zPmJn1gKKNj9slHQQsDlwE79019uVmBWZmZj3F9YyZWQ8oOuxqN+BjwOzAoXnZmsCZzQjKzMx6jusZM7MeUHSq3QeAr5QtOxc4txlBmZlZb3E9Y2bWGyo2PiTtGBGn5+dfrZQuIk5qRmDWvcaMGT3I0tbcA3jSpMkt2Y+Z1c/1jJlZ76nW87EdcHp+vmOFNAG4UjAzs1q4njEz6zEVGx8RsamkL0XEXyLi060MyszMRj7XM9aNRl80pmF5rQowsXj6yZtNati+zdplqAvOTyx9IenZJsZiZma9x/WMmVkPGarxobLXM9eyE0knSXpW0t0ly+aVdIWk/vx3nlryNjOzrtaQesbMzLrDUI2PGOJ1UacAG5ctOxD4V0T0Af/Cd7E1M+tFjapnzMysCww11e6sko4seT172Wsi4rChdhIR10gaW7Z4K2C9/PxU4Crge0PlZWZmI0pD6hkzM+sOQzU+xgGLlrw+u+x1PWeoFoiIp/Lzp4EF6sjLzMy6UzPrGTMz6zBVGx8RsWsrgoiIkFS1gunv768x99bcX2IwtcfciPzaV+52avR73ur8m63b4weXoV59fX1t2/dgWlXPmJlZZyh0h/MmeUbSghHxlKQFgaoznHRahVlEI2Pu7+/vyveg1Zr5HnX7Mej2+MFlMDMz63ZDXXDeTBcAO+fnOwN/b2MsZmY2QkkaI+lcSfdJmiBpzXbHZGbWq1rS8yHpLNLF5fNLehw4HPgpcI6k3YBHgG1aEYuZmfWcY4FLI+KLkmYB5mh3QGZmvapiz4ekn5c8/0w9O4mI7SJiwYiYOSIWiYgTI+KFiFg/IvoiYoOIeLGefZiZWXdpZD1TZR+jgU+Rb2YYEW9FxKRm7MvMzIZWbdjV10ue/63JcZiZWe9pRT2zBPAccLKk2ySdIGnOJu3LzMyGUG3Y1R2SzgXuZfp52N/j+dfNzKxGrahnZgJWBvaOiPGSjiXd1Pb75QmHMwtZadrenFuwvQbe/3bMHNfO4z36ojFt2/fNS99UcZ1nIWyeVSeuViwdwMTG7rvaMa9mqElVqjU+vkg6K7U4IKadd32A5183M7NataKeeRx4PCLG59fnkhof0yk6C9l0M5Y1uMK3ofX19bVv5rgePd6V3uuRMINfR5ehjZ+3Zr0nFRsfEfEs8EMASTN5LnYzM2ukVtQzEfG0pMckLRMR/wPWJ/W0mJlZGxSa7SoidpU0D7AFsDDwBHChLxI3M7NGaHI9szdwZp7p6kHAJ9PMzNqk0H0+8pzoDwB7AB8HvgHc77nSzcysEZpZz0TE7RGxakR8PCK2joiX6s3TzMxqU/Q+H8cAe0bE2QMLJH0Z+DVQ7EoYMzOzyo7B9YyZ2YhX9A7nSwPnlC07F/hwY8MxM7Me5XrGzKwHFG189APbli37EqmL3MzMrF6uZ8zMekDRYVf7ARdK2gd4BBgL9AGbNycsMzPrMfvhesbMbMQrOtvVdZKWAjYDFgL+AVzs2a7MzKwRXM+YmfWGoj0f5NlBzmhiLGZm1sNcz5iZjXxFr/kwMzMzMzOrixsfZmZmZmbWEm58mJmZmZlZS9TU+JD0aUnrNjoYMzMzcD1jZjZSFWp8SLpa0tr5+feAs4Fxkg5uZnBmZtYbXM+YmfWGoj0fywM35OdfAz4NrAHs0YygzMys57ieMTPrAUWn2p0BiDwHuyLiXgBJ8zQtMjMz6yWuZ8zMekDRxse1wHHAgsD5ALmCeL5JcZmZWW9xPWNm1gOKDrvaBZgE3Akcnpd9BDi28SGZmVkP2gXXM2ZmI17Rno/PRMQ0F/1FxEWSvtiEmMzMrPc0tZ6RNCNwM/BERGzeiDzNzGz4ivZ8nFhh+R8bFYiZmfW0Ztcz+wITGpSXmZnVqGrPh6Ql89MZJC0BqGT1ksAbzQrMzMxGvlbUM5IWATYDfgR8p978zMysdkMNu7ofCFJl8EDZuqeBI5oQk5mZ9Y5W1DPHAN8F5mpAXmZmVoeqjY+ImAHSzZ8iwneaNTOzhmp2PSNpc+DZiLhF0nrV0vb39xfOtzTtqjXGZrUbfdGY9L5PbHckvaPa/8dw/nc6VaeWoZ3fL7W+J319fVXXF7rg3A2P2owZM7qBubl6M7ORq4n1zNrAlpI2BWYD5pZ0RkTsUJ5wqApzQH9//7Rp/QPYekCl/4/p/h+6UEeXoY3fL816Two1PvI43B8BKwKjStdFxGKND8vMzHpJs+qZiDgIOCjvYz3ggMEaHmZm1hpFp9odRxqLuz/wWvPCMTOzHuV6xsysBxRtfCwHrB0R7zYzGDMz61lNr2ci4irgqmblb2ZmQyt6n49rgJWaEYCkhyXdJel2STc3Yx9mZtbxmlbPmJlZ5yja8/EwcKmk80lTH74nIg5rQByfjojnG5CPmZl1p4dpbj1jZmYdoGjjY07gQmBmYNHmhWNmZj3K9YyZWQ8oOtXurk2MIYDLJQXwh4j442CJap9/2VPU9pJmz9PdqfOAF9Xt8YPLUK9OnU6yyfWMmZl1iKJT7S5ZaV1EPFhnDOtExBOSPghcIem+iLimPFGnVpjWWZr5OenoecAL6Pb4wWUYyZpcz5iZWYcoOuzqflIPhUqWRf47Yz0BRMQT+e+zeazv6qQLD83MrHc0rZ4xM7POUXTY1TSzYkn6EHA48J96di5pTmCGiJiSn28IHFlPnmZm1n2aVc+YWfcbfdGYpua/KlS8k/jkzSY1dd+9qGjPxzQi4mlJ+5EO1bg69r8AcL6kgVjGRcSldeRnZmYjQAPrGTMz6yA1NT6yZYA56tl5Hse7Qj15mJnZiFV3PWNmZp2l6AXn/+H9sbeQKoPl8BApMzNrANczZma9oWjPxwllr18F7oiI7p/z0szMOoHrGTOzHlD0gvNTmx2ImZn1LtczZma9YYahk4CkmSX9QNKDkt7If38gaZZmB2hmZiOf6xkzs95QdNjVz0j339gDeARYHPg+MDfw7eaEZmZmPcT1jJlZDyja+PgSsEJEvJBf/0/SrcAduFIwM7P6uZ4xM+sBhYZdMe0dZ4ssNzMzG46m1DOSFpV0paR7Jd0jad968jMzs/oUbXz8BfiHpI0kfVTSxsDfgHOaFpmZmfWSZtUzU4H9I2JZYA3gW5KWrTNPMzOrUdFhV98FDgV+CywEPAGcDfywSXGZmVlvaUo9ExFPAU/l51MkTQAWBu6tK1ozM6tJ0al23wIOyw8zM7OGakU9I2kssBIwfrD1/f3FbylSmnbVOuMy6wbV/j+G879Ti3b+j42+aEwb995etR7Xvr6+quurNj4krQ1sGRHfG2TdT4G/RcQNNUVmZtblxowZXcNWjalGJ02a3JB82q1V9YykUcBfgf0i4uXB0gxVYQ7o7++fNu3EeqMz63yV/j+m+39oBv+PtUWzjutQ13wcDFxTYd3VwCGNDcfMzHpM0+sZSTOTGh5nRsR59eZnZma1G6rxsSJwaYV1VwCrNDQaMzPrNSvSxHpGkoATgQkRcXQ9eZmZWf2GanzMDVS6u+zMwFyNDcfMzHpMs+uZtYEdgc9Iuj0/Nq0zTzMzq9FQF5zfB2wI/H2QdRvm9WZmZrVqaj0TEdfie1KZmXWMoRofvwL+IGlG0kV/70qaAdiaNB3id5ocn5mZjWyuZ8zMekjVxkdEjJP0IeBUYFZJzwPzA28Ch0fEWS2I0czMRijXM2ZmvWXI+3xExNGSTgDWBOYDXgCurzRVoZmZ2XC4njEz6x1FbzL4MnBZk2Mxq1tt910oqnNvJTZS7vlgvcv1jJlZbxhqtiszMzMzM7OGcOPDzMzMzMxaotCwKzMzMzPrXaMvGjPo8lUBJrYyEut27vkwMzMzM7OWcOPDzMzMzMxawo0PMzMzMzNrCTc+zMzMzMysJXzBudkIUOz+Jp17n5LiRkIZzMzMepd7PszMzMzMrCXc+DAzMzMzs5Zw48PMzMzMzFqi7Y0PSRtL+p+k+yUd2O54zMxsZHE9Y2bWOdra+JA0I/BbYBNgWWA7Scu2MyYzMxs5XM+YmXWWds92tTpwf0Q8CCDpbGAr4N5G7WDSpMmNysrMzLpPw+uZvr6+aV5P3mxSHeGZmfWWdg+7Whh4rOT143mZmZlZI7ieMTPrIO1ufJiZmZmZWY9o97CrJ4BFS14vkpcBMHr0aLU8IjMzG0lcz5iZdZB293zcBPRJWkLSLMC2wAVtjsnMzEYO1zNmZh2krY2PiJgK7AVcBkwAzomIe2rJS9JJkp6VdHeF9ZL06zzV4p2SVq498uYoUIb1JE2WdHt+HNbqGKuRtKikKyXdK+keSfsOkqajj0PBMnTscZA0m6QbJd2R4//BIGlmlfTnfAzGSxrbhlArKliGXSQ9V3IMdm9HrNVImlHSbZIuHGRdRx+DkaRR9UyRz2W3qPbZ7BaSHpZ0V/7/v7nd8dRC0hhJ50q6T9IESWu2O6bhkLRMyXfw7ZJelrRfu+MaLknfzv/Td0s6S9Js7Y5puCTtm+O/pyuOQUSMiAfwKWBl4O4K6zcFLgEErAGMb3fMNZRhPeDCdsdZJf4FgZXz87mAicCy3XQcCpahY49Dfl9H5eczA+OBNcrS7Akcn59vC/y53XHXUIZdgOPaHesQ5fgOMG6wz0qnHwM/Bj2eQ34uu+VR7bPZLQ/gYWD+dsdRZxlOBXbPz2cBxrQ7pjrKMiPwNLB4u2MZZtwLAw8Bs+fX5wC7tDuuYZZheeBuYA7S5RT/BD7c7riqPdo97KphIuIa4MUqSbYCTovkBmCMpAVbE10xBcrQ0SLiqYi4NT+fQjrLWD6rTEcfh4Jl6Fj5fX0lv5w5P6Is2VakSg/gXGB9SR0z7r1gGTqapEWAzYATKiTp6GNg0xsJn0so9Nm0FpA0mnTC8USAiHgrIia1Naj6rA88EBGPtDuQGswEzC5pJtIP+CfbHM9wfZR0Ive1SD29VwOfb3NMVY2YxkcBI2W6xTVzt/8lkpZrdzCV5GEkK5HODpbqmuNQpQzQwcchD6m4HXgWuCIiKh6D/EU1GZivpUEOoUAZAL6Qh+6dK2nRQda30zHAd4F3K6zv+GNg0yv4uex0x1D9s9ktArhc0i2Svt7uYGqwBPAccHIeAneCpDnbHVQdtgXOancQwxURTwC/AB4FngImR8Tl7Y1q2O4GPilpPklzkEaYdFqdOI1eanyMBLeSujRXAH4D/K294QxO0ijgr8B+EfFyu+OpxRBl6OjjEBHvRMSKpFl9Vpe0fJtDGrYCZfgHMDYiPg5cwfu9CG0naXPg2Yi4pd2xWGN1+//WCPtsrhMRK5PuXP8tSZ9qd0DDNBNpmPXvI2Il4FXgwPaGVBuliRy2BP7S7liGS9I8pJ7oJYCFgDkl7dDeqIYnIiYA/wdcDlwK3A68086YhtJLjY+q0y12g4h4eaDbPyIuBmaWNH+bw5qGpJlJP9rPjIjzBknS8cdhqDJ0w3EAyF34VwIbl6167xjkbubRwAstDa6gSmWIiBci4s388gRglRaHVs3awJaSHgbOBj4j6YyyNF1zDGx6Vf63Ol2Rz2ZXyGesiYhngfNJd7LvJo8Dj5f0np1Laox0o02AWyPimXYHUoMNgIci4rmIeBs4D1irzTENW0ScGBGrRMSngJdI16t2rF5qfFwA7KRkDVLX2lPtDmo4JH1oYFy4pNVJx69jfrDk2E4EJkTE0RWSdfRxKFKGTj4Okj4gaUx+PjvwWeC+smQXADvn518E/h0RHTN2vUgZyq4T2pJ0bU5HiIiDImKRiBhLGorw74goP5PW0cfAplfwf6ujFfxsdjxJc0qaa+A5sCFp6EnXiIingcckLZMXrQ/c28aQ6rEdXTjkKnsUWEPSHLleX58Oqk+KkvTB/Hcx0vUe49obUXXtvslgw0g6izQL0fySHgcOJ10QSEQcD1xMGgd3P/AasGt7Iq2sQBm+CHxT0lTgdWDbDvvBsjawI3BXHhcNcDCwGHTNcShShk4+DgsCp0qakdQoOiciLpR0JHBzRFxAalydLul+0gQH27Yv3EEVKcM+krYEppLKsEvboi2oy46BTW/Qz2WbY+pVCwDn53NAMwHjIuLS9oZUk72BM/OwpQfpvPpwSLnx91ngG+2OpRYRMV7SuaTh1FOB24A/tjeqmvxV0nzA28C3On3yAnXObyYzMzMzMxvJemnYlZmZmZmZtZEbH2ZmZmZm1hJufJiZmZmZWUu48WFmZmZmZi3hxoeZmZmZmbWEGx/WkSTtIunaktch6cPtjKmTSfqvpJVauL8FJE2QNGur9mlm1kiuZ4bH9Yw1ihsf1hSSHpb0uqQpkiZJuk7SHpI65jMnaQlJ70r6fbtjqUTSVZJ2HyLNFsCUiLgtvz5F0g/L0ozNFetM+fU6+ZhMlvRirlRWy+t2kfSOpFfy4yFJJ0taeiC/fCfbK4GvN7jIZmaFuJ5pDNcz1mod8w9qI9IWETEXsDjwU+B7pJurtczAl2AFOwEvAV/u8jMrewCnF00saW7gQuA3wLzAwsAPgDdLkl0fEaOA0cAGpJsp3iJp+ZI0Z9KlN5YysxHD9UxruJ6xhnHjw5ouIibnuzp/Gdh54ItF0mhJp0l6TtIjkg4tcsZK0maSbpP0sqTHJB1Rsm7gzMtukh4F/l0hD5EqhUNJdwTdomx9SNpTUn8+q3aUpKXyWZyXJZ2T70o7kP5rku7PZ3cukLRQWTwzlaR97yzTQLe/pF9Ieimf/dkkr/sR8EnguHxm6LhByjEL8Bng6qHetxJLA0TEWRHxTkS8HhGXR8Sd5Qnz+gciYs+8jyNKVo8HlpS0+DD2bWbWcK5nXM9Y93Djw1omIm4EHid90UE6IzIaWBJYl/QlvWuBrF7NaccAmwHflLR1WZp1gY8CG1XIYx1gEeBs4Bxg50HSbASsAqwBfBf4I7ADsCiwPLAdgKTPAD8BtgEWBB7J+Rb1CeB/wPzAz4ATJSkiDgH+A+wVEaMiYq9Btu0D3o2Ix4exv4nAO5JOlbSJpHkKbnce7x87ImIqcD+wwjD2bWbWNK5nKnI9Yx3DjQ9rtSeBeSXNCGwLHBQRUyLiYeCXwI5DZRARV0XEXRHxbj6LchapEih1RES8GhGvV8hmZ+CSiHgJGAdsLOmDZWl+FhEvR8Q9wN3A5RHxYERMBi4BBi682x44KSJujYg3gYOANSWNHaos2SMR8aeIeAc4lVSxLFBw2zHAlIJpAYiIl0mVYgB/Ap7LZ9GG2ueTpO7zUlNyDGZmncL1zPRcz1jHcOPDWm1h4EXS2ZeZSWdvBjyS11cl6ROSrszd6JNJY1HnL0v2WJXtZwe+RBpLSkRcDzwKfKUs6TMlz18f5PWo/Hyh0nJExCvAC0XKkj1dsu1r+emoCmnLvQTMVbZsKum9LTUz8G5+EBETImKXiFiEdHZtIeCYIfY1cOxKzQVMKhirmVkruJ6ZnusZ6xhufFjLKM1ysTBwLfA8aQxs6TjOxYAnCmQ1DrgAWDQiRgPHAypLE1W2/xwwN/A7SU9LejrHNViXeBFPUlIOSXMC85HK8mpePEdJ+g8NI+9q5YDUHS1JpRXQo8DYsnRLAI9FxLvT7SDiPuAUUuVQzedI3fPAexdZfhi4Y4jtzMxawvXMe1zPWMdy48OaTtLckjYnjU89I3dlv0MaA/sjSXPli8m+A5xRIMu5gBcj4g1JqzP9maSh7AycBHwMWDE/1gZWkPSxYeYFqTt+V0krKs1m8mNgfEQ8HBHPkSqHHSTNKOmrwFLDyPsZ0ljlQUXEW8A/mXY4wF+BzSRtmPe5EOmCx7MBJH1E0v6SFsmvFyWNK76hPP+8/RKSfgOsR5qtZMDqwMMR8Uj5dmZmreR6xvWMdQ83PqyZ/iFpCqlr+hDgaKa90G9v0hmbB0lnqcaRvqyHsidwZM77MFLlUkg+c7M+cExEPF3yuAW4lBrOSkXEP4Hvk76MnyJ96W9bkuRrwP8jdZEvB1w3jOyPBb6YZyj5dYU0f6BkDHMeO7wd6eLEF4HrSTOGDHyhTyFdfDhe0qukyuBuYP+SPNeU9ArwMnAV6QzeahFxV0ma7UlnA83M2sX1TOJ6xrqGIobqbTOzTifpv6TZSm5r0f4+SJoScaWIeKMV+zQzs/ZxPWON4saHmZmZmZm1hIddmZmZmZlZS7jxYWZmZmZmLeHGh5mZmZmZtYQbH2ZmZmZm1hJufJiZmZmZWUu48WFmZmZmZi3hxoeZmZmZmbWEGx9mZmZmZtYS/x+voLASB2U1aAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 864x216 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(fig, (ax1, ax2)) = plt.subplots(1, 2, figsize=(12, 3))\n",
-    "\n",
-    "plt.suptitle('Distribution of the Percentage of Military Spending from 1960 to 2020', y=1.1)\n",
-    "\n",
-    "ax1.hist(perc_gdp['CAN-PercentGDP'], color = 'blue')\n",
-    "ax1.set_title('Canada')\n",
-    "ax1.set_xlabel('Dollar Amount (USD)')\n",
-    "ax1.set_ylabel('Counts of Fiscal Years')\n",
-    "\n",
-    "ax2.hist(perc_gdp['USA-PercentGDP'], color = 'orange')\n",
-    "ax2.set_title('United States')\n",
-    "ax2.set_xlabel('Dollar Amount (USD)')\n",
-    "ax2.set_ylabel('Counts of Fiscal Years')\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "6e35bfce",
-   "metadata": {},
-   "source": [
-    "Notice with the above use of the `hist()` method, we did not specify the bins for each axis. Thus, each subplot created 10 bins by default to fit the range of the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "886787a1",
-   "metadata": {},
-   "source": [
-    "## Conclusions\n",
-    "\n",
-    "In this section, we learned functions and methods to create histograms, scatter plots, and line graphs as a means of visualizing numerical data.\n",
-    "\n",
-    "The `plt.scatter()` and `plt.plot()` functions require numerical arrays that serve as `x` and `y` arugments. The `plt.hist()` function requires one numerical array of values for plotting distributions of data.\n",
-    "\n",
-    "The `hist()` and `plot()` methods can also be used directly on dataframes to create histograms and line plots, respectively.\n",
-    "\n",
-    "We can also create subplots within a figure using `plt.subplots()`.\n",
-    "\n",
-    "Lastly, we learned about a number of other functions that can be used to enhance and annotate our plots. Documentation for the functions used in this section, and related functions, are listed below:\n",
-    "\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html\">plt.hist( )</a>\n",
-    "\n",
-    "- <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.hist.html\">DataFrame hist method</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\">plt.scatter( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html\">plt.plot( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.html\">DataFrame plot method</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.subplots.html\">plt.subplots( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.suptitle.html\">fig.suptitle( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html\">ax.set_title( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html\">ax.set_ylim( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html\">ax.set_xlim( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html\">ax.set_ylabel( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html\">ax.set_xlabel( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.colorbar.html\">plt.colorbar( )</a>"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "7ea83cf0",
-   "metadata": {},
-   "source": [
-    "# Categorical Data\n",
-    "\n",
-    "Categorical data considers numerical quantities in the context of categorical variables. Surveys, like the ones we see on the television show <a target=\"_blank\" href=\"https://www.youtube.com/watch?v=qqtB4Romq5Y&list=PLAnv4_rGsTE7hsFYS_jyYScpyRr_QjwEY\">Family Feud</a> or the frequency of people with various eye colors, are examples of categorical data. \n",
-    "\n",
-    "\n",
-    "In this chapter, there are two types of categorical data that we consider: ordinal data and nominal data. \n",
-    "\n",
-    "**Ordinal data** consists of data that can be described as having a meaningful order, ranking, or relationship between categories. An inventory that quantifies the number of small, medium, and large shirts in stock is an example of ordinal data because there is a ranked relationship between shirt sizes.\n",
-    "\n",
-    "**Nominal data** can be described as named categories that have no meaningful relationship to one another. Counting the number of people with black, brunette, red, and blonde hair colors in a room is an example of nominal data because hair color has no inherit meaning amongst each other - one hair color is not greater than or less than the others.\n",
-    "\n",
-    "\n",
-    "Categorical data can be visualized using bar graphs and pie charts, and in this section, we will practice making such visualizations.\n",
-    "\n",
-    "Again, we'll load the necessary libraries and data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "45d41791-b19e-48cf-917d-c411750c6122",
-   "metadata": {
-    "tags": [
-     "hide_cell"
-    ]
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import seaborn as sns\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "\n",
-    "plt.style.use('fast')\n",
-    "\n",
-    "\n",
-    "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n",
-    "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n",
-    "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n",
-    "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n",
-    "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n",
-    "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n",
-    "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n",
-    "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n",
-    "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n",
-    "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n",
-    "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n",
-    "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n",
-    "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n",
-    "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n",
-    "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n",
-    "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n",
-    "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n",
-    "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n",
-    "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n",
-    "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n",
-    "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n",
-    "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n",
-    "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n",
-    "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n",
-    "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n",
-    "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n",
-    "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n",
-    "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n",
-    "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n",
-    "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n",
-    "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n",
-    "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n",
-    "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n",
-    "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n",
-    "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n",
-    "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n",
-    "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n",
-    "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n",
-    "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n",
-    "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n",
-    "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n",
-    "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n",
-    "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n",
-    "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n",
-    "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n",
-    "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n",
-    "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n",
-    "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n",
-    "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n",
-    "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n",
-    "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n",
-    "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n",
-    "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n",
-    "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n",
-    "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n",
-    "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n",
-    "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n",
-    "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n",
-    "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n",
-    "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n",
-    "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n",
-    "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n",
-    "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n",
-    "'''\n",
-    "\n",
-    "from io import StringIO\n",
-    "\n",
-    "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n",
-    "\n",
-    "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f26e1513",
-   "metadata": {},
-   "source": [
-    "## Bar graphs\n",
-    "\n",
-    "Bar graphs are a popular method to visualize categorical data. They're simple, concise, and can condense\n",
-    "large and complex datasets into a simple visual summary. Most bar graphs depict a categorical element as an\n",
-    "independent variable on the x-axis while the height of the bar corresponds to a numerical value on the y-axis.\n",
-    "\n",
-    "We will practice making bar graphs using our military dataset in the context of an ordinal variable.\n",
-    "\n",
-    "First, let's create a graph to examine the percent of the GDP spent on the military in Canada. We will look at the years 2018, 2019 and 2020.\n",
-    "\n",
-    "To do this, we must extract the data for the years of interest from the column containing the data pertaining to GDP percentage of military spending in Canada. We will call this `can_gdp`.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "af3c0e6d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-PercentGDP</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>1.324681</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>1.278941</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>1.415056</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      CAN-PercentGDP\n",
-       "Year                \n",
-       "2018        1.324681\n",
-       "2019        1.278941\n",
-       "2020        1.415056"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n",
-    "\n",
-    "can_gdp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62a27826-df76-4802-9414-f1a2b5931a96",
-   "metadata": {},
-   "source": [
-    "To make a bar graph from a dataframe, the `plot.bar()` method can be used. We will use this method on `can_gdp`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "7669818c",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "can_gdp.plot.bar()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c53841ab",
-   "metadata": {},
-   "source": [
-    "We can also use `plt.bar()` to create a bar graph using the `pyplot` library. The `plt.bar()` function needs two arguments. The first argument, `x`, is an array of values to be plotted on the x-axis. \n",
-    "\n",
-    "The second argument, `height`, determines the height of the bars (the y-values). \n",
-    "\n",
-    "We will create a list of our years of interest and call it `year_labels` to input as the first argument and use the \"CAN-PercentGDP\" column of `can_gdp` as our second argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b2be1224",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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4eVXdn+RHgRng8d34bwK7quobSf4Q+FVG3+p/HnhtVX17PT/P2WDgY/YZRo+Hfhh4c1XduK4f5iywguP1fuCl3TqAk6eevpjkIuBdfPfRJX+0Xp9jvRl0SWqEl1wkqREGXZIaYdAlqREGXZIaYdAlqREGXZIaYdAlqRH/B29LNnR+NibcAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "year_labels = ['2018', '2019', '2020']\n",
-    "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5748baf9",
-   "metadata": {},
-   "source": [
-    "The above code produced a plot, but this plot needs more descriptive labeling to help others understand the data. \n",
-    "\n",
-    "We need to add axis labels and a title to communicate what is being measured. Aesthetically, we can also reduce the width of each bar to give more room on the graph and more rest for our eyes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "b66c30a8-e66a-4ad1-b6c5-cbe53ee5eef9",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.bar(year_labels, can_gdp[\"CAN-PercentGDP\"], width=0.25)\n",
-    "\n",
-    "\n",
-    "plt.title('Military Spending in Canada')\n",
-    "\n",
-    "plt.ylabel('Percentage of GDP')\n",
-    "plt.xlabel('Year')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "18931cc9",
-   "metadata": {},
-   "source": [
-    "This plot looks better and is a lot more descriptive.\n",
-    "\n",
-    "Let's add the data from Mexico and the United States.\n",
-    "\n",
-    "To do this, we will once again use the `plt.subplots()` function. This time, we will specify our figure with a single axis called `ax`. Because we want to group our data by year, we can call `ax.bar()` to set precise positions on the x-axis for each country.\n",
-    "\n",
-    "We will also use `plt.tight_layout()` to automatically adjust the subplot dimensions to give appropriate spacing between the bars and the axes boundaries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "6833fd5b",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "can_gdp = military.loc[[2018, 2019, 2020], ['CAN-PercentGDP']]\n",
-    "mex_gdp = military.loc[[2018, 2019, 2020], ['MEX-PercentGDP']]\n",
-    "usa_gdp = military.loc[[2018, 2019, 2020], ['USA-PercentGDP']]\n",
-    "\n",
-    "index = np.arange(len(year_labels))\n",
-    "\n",
-    "(fig, ax) = plt.subplots()\n",
-    "\n",
-    "# Offsets the bars for Canada by -0.25in\n",
-    "ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"], width=0.25) \n",
-    "\n",
-    "# Plots the bars for Mexico in the middle\n",
-    "ax.bar(index, mex_gdp[\"MEX-PercentGDP\"], width=0.25)\n",
-    "\n",
-    "# Offsets the bars for the U.S. by +0.25in\n",
-    "ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"], width=0.25)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "21c5b1b1",
-   "metadata": {},
-   "source": [
-    "We were able to create a bar plot with all three data sets together. Now, let's add the appropriate titles, axis labels, and other details using previously described functions.\n",
-    "\n",
-    "If we assign each country's bar to a variable, we can also label each individual bar with the associated numerical value by calling the `bar_label()` method on `ax`. In order to label the bars, the label must be specified when creating each each bar."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "61bdf283",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "(fig, ax) = plt.subplots()\n",
-    "\n",
-    "\n",
-    "# Creates variable for each country and offsets as appropriate\n",
-    "# Annotates bar with the associated value, rounded to 2 places after the demical\n",
-    "can_bar = ax.bar(index - 0.25, can_gdp[\"CAN-PercentGDP\"].round(decimals=2), width=0.25, label='Canada') \n",
-    "mex_bar = ax.bar(index, mex_gdp[\"MEX-PercentGDP\"].round(decimals=2), width=0.25, label='Mexico')\n",
-    "usa_bar = ax.bar(index + 0.25, usa_gdp[\"USA-PercentGDP\"].round(decimals=2), width=0.25, label='USA')\n",
-    "\n",
-    "\n",
-    "# Add labels and titles for entire figure\n",
-    "plt.title(\"Military Spending in North America\", pad=10)\n",
-    "plt.ylabel('Percentage of GDP')\n",
-    "plt.xlabel('Year')\n",
-    "plt.xticks(index, year_labels)\n",
-    "plt.ylim(0, 5)\n",
-    "\n",
-    "\n",
-    "# Creates legend for the entire figure\n",
-    "plt.legend(loc=4, bbox_to_anchor=(1.3, 0.5))\n",
-    "\n",
-    "\n",
-    "# Add labels for individual bars; gives spacing (padding) between the value and the bar\n",
-    "ax.bar_label(can_bar, label_type=\"edge\", padding=4)\n",
-    "ax.bar_label(mex_bar, label_type=\"edge\", padding=4)\n",
-    "ax.bar_label(usa_bar, label_type=\"edge\", padding=4)\n",
-    "\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "c0a52a3c",
-   "metadata": {},
-   "source": [
-    "Great! Now we have a well annotated, visually appealing graph that depicts an important message about the data: the percentage of the GDP spent on the military for each country for the years 2018-2020.\n",
-    "\n",
-    "From this graph, we can easily see that during this time period, Canada and Mexico contribute a smaller proportion of their GDP to military spending than the United States. This may not have been easily discernable by just looking at our large data table."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "19fadd76",
-   "metadata": {},
-   "source": [
-    "### Horizontal Bar Graphs\n",
-    "\n",
-    "There may be times when you want to present data as a horizontal bar graph. Using the dollar amounts spent in 2020 for each North American country, we can create a horiztonal bar graph to represent nomimal data. First, we need to extract the data for the year of 2020:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "f1baccac",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "year2020 = military.loc[[2020]][['CAN-USD', 'MEX-USD', 'USA-USD']]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b06712a1",
-   "metadata": {},
-   "source": [
-    "From this, we obtain a dataframe with a single row of data with values for three variables. To format this data for plotting, we can tranpose the dataframe using the `transpose()` method:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "2acad7b4",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Year</th>\n",
-       "      <th>2020</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>CAN-USD</th>\n",
-       "      <td>22.754847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MEX-USD</th>\n",
-       "      <td>6.116377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA-USD</th>\n",
-       "      <td>778.232200</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Year           2020\n",
-       "CAN-USD   22.754847\n",
-       "MEX-USD    6.116377\n",
-       "USA-USD  778.232200"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "year2020 = year2020.transpose()\n",
-    "year2020"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "286d0d9e",
-   "metadata": {},
-   "source": [
-    "The dataframe in this format can directly be used for plotting. To create a horiztonal bar graph, we can use the `plt.barh()` function. This function requires arguments for the `y` and `width` parameters. The `y` parameter is the categorical variables to be plotted, which are usually displayed on the x-axis of a regular bar graph. The `width` parameter corresponds to the numerical values that are associated with each categorical variable.\n",
-    "\n",
-    "We will use the index of the `year2020` dataframe as the an argument for the `y` parameter and the values in the `2020` column as an argument for the `width` parameter:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "6d4d4be5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.barh(y = year2020.index, width = year2020[2020])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "245f26fe",
-   "metadata": {},
-   "source": [
-    "In the above graph, the index is used to label the categories on the y-axis. If desired, this can be changed by using a list of the same size as an argument for the `tick_label` parameter. Additional titling and labeling can be added to this graph as well:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "f9d97586",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.barh(y = year2020.index, width = year2020[2020], tick_label = ['Canada', 'Mexico', 'USA'])\n",
-    "plt.title('Military Spending in North American in 2020 (USD)')\n",
-    "plt.xlabel('USD')\n",
-    "plt.ylabel('Country')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9d60ec90",
-   "metadata": {},
-   "source": [
-    "For further customization, the bars can be colored too. To do this, the bar graph will need to be defined as a variable and then each bar color can be set by calling for the index of the bar and using the `set_color()` method like so:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "6643a16c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZoAAAEWCAYAAABfdFHAAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAd3UlEQVR4nO3debxd873/8ddbIolEiIieouqYW0ODpjXUbVGK4JZLTSG0Sidu3VZbOpivan966eBHdeKGooaUqppKqf7akJAQ1FSzDChFqq3E5/fH97tlZTvDPsn+nr1zvJ+Px36cddb42Xutvd/ru9baaysiMDMzK2WZVhdgZmYDm4PGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrCgHTT+TdI6kb+TubSU9VRl2r6RtW1Vbu5D0mKQdcvdXJf240HJekbR2iXk3Q/320e4kTZB0fT8vs9j20Z8kbShpqiT14zKPlPStfllYRPjRhAfwGPAvYExd/7uAADq7mGZb4Klu5ncCcEE/P4evAo8CrwBPAZe08LXcodXrtIf6OvM6vaau/wXACUsw3wDWbWT76GU+5wHzgVVb/Vq18wP4EjATeDlv91/qYj3fDPwd+HN1mwQOBqYBL+X3yreBwZXho4HJwDzgceCAXmq5HNivu20h91vkM6Gn9yvwO+Af+bm9lGs9BhhaGWdYnu5tpV9rt2ia61Fg/9o/kjYBhreiEEmD+zj+wcBBpDfT8sA44LclahtAtpC09ZLOpK/rqpd5jQD2Av4GHNis+Taw3KY9h34kYCKwErAzcISk/SrDLyLtKK4MfA24TNIqedhw4ChgDLAF8GHg6Mq0Z5F2PDuACcDZkjbqsghpVWA74JcNF97Y+/WIiBgJrAp8EdgPuKbWaoqIfwC/ya9BWa3eqxgoD9Je+NeBOyr9TidtoG+0aEh7m6fk7m2p7LHmeexA2uj/BbxG2luZkYd/HLiftJfyF+BTlWm3Je2dfAWYDUwi7a3tXhlnWeA5YLMu6v8BcGYPz+93wDeB20l7SFcCoyvDtwT+H/AiMAPYtm7ak4E/5Nqvp9LyI71hHgeez6/XY+S9Ryp7cSxsSRwMPJGfy9cq81kOOB94Ib9OX6aHFgGVvca8Xs4Cfp1rnAKs0810tTq+Atxc6b9IiwY4DHgY+CtwFbBa3bI/BzxE2kG5Nfebl9f5vpV1+kVgLjAL+Hgv2+FE4Eng88DMumEnAJfmOl8G7gHWB47N838S+Ehl/BWBn+TlPg2cAgzKww7J6/OMvN5Oyf1uq0y/EXBDfv5zgK/m/u8H/pi3lVmkbW9I3Wvz6fzavJjXi7p5vg1vHw28h78HfD93rw/8ExhZGf574NPdTPsF4Fe5ewTp/bt+Zfgk4LQe1tmN3W2b3TzXRt6vn6zr905S62y3Sr8JVLbhUg+3aJrrT8AKkt4taRBpD+KCvs4kIq4FTiU1hZePiLF50FxgN2AFUuicIWnzyqRvJzXZ1wQOB/6XRfdqxwOzIuKubmqfKOlLksbl+utNBD5B2kOaT3pjIml10gf0KXn5RwOXV/b+AA7INb8NGJLHQdKGwNmksFmNtPf4jh5fINgG2IC0F3mcpHfn/seTPmzWBnak73v0+wEnkvZwHwb+u5fx/y+wfu18UpWk7UnBvA/p9XocuLhutD1Ie8MbRsQHc7+xeZ1fkv9/O+kDf3XgUOAsSSv1UNPBpD3xi4F3SXpv3fDdSR96K5H21q8jnatdHTgJ+GFl3PNI63ldYDPgI8AnK8O3IO3wdFD3WkkaCdwIXEtar+uycI97AfBfpNbAVqT1+Nm6OncD3ge8h/Qa7tTDc67X3fbRrbyX/2/AvbnXRsBfIuLlymgzcv+ufLAy7frA/Ih4sMFpNwEe6K3GOo28XxcREU8AU0nPs+Z+YGzXUzSPg6b5JpE+kHckrcSnmzXjiPh1RDwSyS2klkF1o3kdOD4i/hkRr5JCbrykFfLwg3J9Xc37AuBI0hv6FmCupK/UjTYpImZGxDzgG8A+eQM/kHS+4pqIeD0ibiBt0OMr0/4sIh7Mdf0C2DT33xu4OiJujYh/5vm+3stLcWJEvBoRM0hv4NobZR/g1Ih4ISKeIgdhH0yOiNsjYj5wYaXG7rxK+oA9pYthE4CfRsSd+XkdC2wlqbMyzjcj4q/5NenOa8BJEfFaRFxDau1s0NWIkt5JOgTz84iYQ/pgrz8s8vuIuC4/x0uBVUh72q+RwqlT0ihJHaT1d1REzIuIuaTWS/XQ0jMR8f2ImN/Fc9gNmB0R34mIf0TEyxExBSAipkXEn/J0j5HC7UN1058WES/mD8eb6X1dVHW3ffTkBNLn4c/y/8uTDj9W/Q0YWT+hpE+QDl2dXpn2pUamzUaRWpgNa/D92pVnSDuDNS+TdmSKctA03yTS3vshpBZF00jaRdKfJP1V0oukD4IxlVGejXTcFYCIeIZ0eGMvSaOAXUgfoF2KiAsjYgfShv9p4GRJ1T3JJyvdj5MOxY0htaA+JunF2oO0V7lqZfzZle6/k96MkPZ235hvDrHnu6uxL/Oq625Ed/PtyY+BDkm71/VfjfQaARARr5Ce1+p9rO/5HAqN1HUQcH9ETM//XwgcIGnZyjhzKt2vAs9FxILK/+T5r0lav7Mq6/SHpBZpI/WvATzS1QBJ60u6WtJsSS+RWu9j6kZbnHWxWNNKOoIUyLvmnQJIgb5C3agrUBcIkvYgtVx3iYjn+jJtxQu8OYQWkF7/qmVJOx5AQ+/XrqxOOpRZM5I3B2rTOWiaLCIeJx1zHw9csSSzqv4jaSjpypTTgY6IGAVcQzqh2eU02fmkFsfHgD9GRK8trLz3fClwN7BxZdAale53kjb650gfOJMiYlTlMSIiTuttWaRj9G/MV9Jw0uGzxTGLRQ+7rdHdiM0SEf8iHW47mUXXxTOkD2vgjZP0K7NoC7fZt06fCKydP8BnA/9D+gAf3/NkXXqSdI5iTGWdrhAR1cM/PdX/JOkQZlfOJl3FtV5ErEC6eqrfLuutyq2RY4AP51Zwzb2k17IaAGNZeHgMSTsDPyKdB72nMt6DwGBJ63U3bZ27SYfbqp4gHQauWovKzktND+/XRUhaA3gv6VxTzbtJrb6iHDRlHApsn/fOF9cc0mGM2joaAgwFngXmS9qFdMy8N78ENiedHO62hSXpEEm7ShopaZk8/41IJ8VrDszX+w8nHc+/LO8NXwDsLmknSYMkDcvfAentXAvAZcBukraRNCTPd3G3y18Ax0paKZ83OmIx59NXk0iXiu5c6XcR8HFJm+adhFOBKflQUXfm0P2Hc48kbQWsQzrRvml+bAz8nMW4qigiZpEOzX5H0gp5m1hHUv0hru5cDawq6ShJQ/N2tUUeNpJ0aOkVSe8CPtPX+ppB0gTSetkxIv5SHZbPr0wHjs/b856k80WX52m3J7UY94qI2+umnUfayTxJ0ghJHwA+SjeHrUkXTGwuaVil3yXA1yW9I7/2O5DOr12Wl9/I+7X2PIfn9XYl6WKeayqDP0S68qwoB00B+TzK1CWczaX57/OS7swnJf+T9GH6Aunw3FUN1PIq6c2xFj23sF4i7Vk+QbrS59vAZyLitso4k0gniGeTPlj/My/jSdIb6aukIHyS9B2FXreviLiXdPXVz0ktkhdIV1otjpPytI+STkRfRtorLyqH7XFUjn1HxI2k802Xk57XOix6fqMrJwDn50NV+/SxjIOBKyPinoiYXXsA3yUF+ehepu/KRNIOzn2k9XIZix4O7VbeXnckfTjOJl1Btl0efDRp+32Z1CK4pKt59INTSK3MO5S+vPuKpHMqw/cjnXt5ATgN2Dsins3DvkE6t3FNZdrqB/ZnSVdBziXtdHwmb+tvks+n3UR6D9WcRLqK87a8/G8DEyJiZh7eyPv1B5JeJu3AnEnaFneOiNcBcrCNJx31KEoRzW69W7uRdBzpUsvF/l6FpN+RLq1car6FLekzpC/BNboXbtYS+erL84H3Rz99KEs6ElgjIr5cellL45esrA/ynuyhpBPFA5rSF9/WJn1HYz3S909+0NKizBoQEfeRLufuz2V+v7+W5UNnA5ikw0iHsX4TEbe2up5+MIR0ZdTLpEMRV5K+62JmLeRDZ2ZmVpRbNGZmVpTP0WRjxoyJzs7OVpdhZrZUmTZt2nMRsUpP4zhoss7OTqZOXdIrks3M3lokvelLpPV86MzMzIpy0JiZWVEOGjMzK8pBY2ZmRTlozMysKAeNmZkV5aAxM7OiHDRmZlaUv7CZzZgBaslv/JmZtU5HB8ye3ft4S8Itmmz+/N7HMTMbaObMKb8MB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFtXXQSOqUNLOu3wmSjpa0paQpkqZLul/SCXXjnSnpaUlt/RzNzAa6wa0uYAmcD+wTETMkDQI2qA3I4bIn8CTwIeDm1pRoZmZL897+24BZABGxICLuqwzbFrgXOBvYv/9LMzOzmqU5aM4AHpA0WdKnJA2rDNsfuAiYDOwqadmuZiDpcElTJU2FZ/uhZDOzt552D5rorn9EnASMA64HDgCuBZA0BBgP/DIiXgKmADt1M5NzI2JcRIyDVZpevJmZtf85mueBler6jQYeBYiIR4CzJf0IeFbSysDWwCjgHkkAw4FXgav7qWYzM6to6xZNRLwCzJK0PYCk0cDOwG2SdlVOEmA9YAHwIumw2ScjojMiOoG1gB0lDe/v+s3MrM2DJpsIfEPSdOAm4MTckjmIdI5mOjAJmAAMJQXRr2sTR8Q84DZg9/4t28zMABTR3WmQtxZpXMDUVpdhZtbvliQGJE1L57m7tzS0aMzMbCnmoDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWiywYNbXYGZWf/r6Ci/DH+8ZmPHwtSpra7CzGzgcYvGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrCgHjZmZFeWgMTOzohw0ZmZWlIPGzMyKctCYmVlRDhozMyvKQWNmZkX5pprZjDkz0IkCoGNEB7OPnt3iiszMBga3aLL5C+a/0T1n3pwWVmJmNrA4aMzMrCgHjZmZFeWgMTOzohw0ZmZWlIPGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrKiGgkbSyqULMTOzganRFs2fJF0qabwkFa3IzMwGlEaDZn3gXOAg4CFJp0pav1xZZmY2UDQUNJHcEBH7A4cBBwO3S7pF0lZFKzQzs6VaQz8TkM/RHEhq0cwBjgSuAjYFLgXWKlSfmZkt5Rr9PZo/ApOAPSLiqUr/qZLOaX5ZZmY2UPQaNJIGAb+KiJO7Gh4R32p6VWZmNmD0eo4mIhYAW/dDLWZmNgA1euhsuqSrSOdj5tV6RsQVRaoyM7MBo9GgGQY8D2xf6ReAg8bMzHrUaND8OCL+UO0h6QMF6jEzswGm0S9sfr/BfmZmZovosUWTv4y5NbCKpC9UBq0ADCpZmJmZDQy9HTobAiyfxxtZ6f8SsHeposzMbODoMWgi4hbgFknnRcTji7sQSQFcGBEH5v8HA7OAKRGx22LM79PA3yPifxe3JjMz6x+NXgwwVNK5QGd1mojYvtspFjUP2FjSchHxKrAj8HRfCq2KCN+NwMxsKdHoxQCXAncBXwe+VHn0xTXArrl7f+Ci2gBJIyT9VNLtku6S9NHc/7uSjsvdO0m6VdIykk6QdHTuv66kGyXNkHSnpHWU/B9JMyXdI2nfPtZqZmZN0miLZn5EnL2Ey7oYOE7S1cB7gJ8C/5aHfQ24KSI+IWkU6c7QNwLHAndI+j3wPWB8RLxe95M4FwKnRcRkScNI4fkfpBt+jgXG5HncGhGzqhNKOhw4HIAVl/DZmZlZlxpt0fxK0mclrSppdO3RlwVFxN2kQ2/7k1o3VR8BjpE0Hfgd6Qui74yIv5N+luAG4AcR8Uh1IkkjgdUjYnJexj/yNNsAF0XEgoiYA9wCvK+Lms6NiHERMY7hfXk2ZmbWqEZbNAfnv9XDZQGs3cflXQWcDmwLVH8eWsBeEfFAF9NsQrorwWp9XJaZmbWBRn/4bK0uHn0NGUiHy06MiHvq+l8HHFn7mWhJm+W/awJfBDYDdpG0RV1dLwNPSdojjz9U0nDg98C+kgZJWgX4IHD7YtRrZmZLqNEfPpvYVf++Xl6cf8vme10MOhk4E7hb0jLAo5J2B34CHB0Rz0g6FDhPUv0hsIOAH0o6CXgN+BgwGdgKmEFqeX05Imb3pVYzM2sORUTvI0nV280MAz4M3BkRA+ZLm1pNwacW/h/H9/66mJm91UmaFhHjehqnoRZNRBxZN+NRpKvIzMzMetToVWf15gFrNbMQMzMbmBo9R/Mr0rkOSDfTfDfwi1JFmZnZwNHo5c2nV7rnA4/nE/tmZmY9avTy5luAP5Pu4LwS8K+SRZmZ2cDRUNBI2of0PZSPAfsAUyQNmCvOzMysnEYPnX0NeF9EzAXIX4K8EbisVGFmZjYwNHrV2TK1kMme78O0Zmb2FtZoi+ZaSdex8Nb++/LmG2OamZm9SY9BI2ldoCMiviTpP0h3RQb4I+n2/GZmZj3qrUVzJuk3YYiIK4ArACRtkoftXrA2MzMbAHo7z9LRxZ2Wyf06i1RkZmYDSm9BM6qHYcs1sQ4zMxugeguaqZIOq+8p6ZPAtDIlmZnZQNLbOZqjgMmSJrAwWMYBQ4A9C9ZlZmYDRI9BExFzgK0lbQdsnHv/OiJuKl6ZmZkNCI3+Hs3NwM2FazEzswHI3+43M7OiHDRmZlaUg8bMzIpy0JiZWVEOGjMzK8pBkw0etPACvI4RHS2sxMxsYGn0ZwIGvLEdY5l6/NRWl2FmNuC4RWNmZkU5aMzMrCgHjZmZFeWgMTOzohw0ZmZWlIPGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrCgHjZmZFeWgMTOzohw0NTNmgNT74+1vb3WlZmZLFQdNzfz5jY03Z07ZOszMBhgHjZmZFeWgMTOzohw0ZmZWlIPGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrCgHjZmZFeWgMTOzohw0ZmZWlIPGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrCgHjZmZFeWgMTOzohw0ZmZWlIPGzMyKctCYmVlRDhozMyvKQWNmZkU5aMzMrKiiQSPp7ZIulvSIpGmSrpG0fsHlvVJq3mZmtngGl5qxJAGTgfMjYr/cbyzQATxYarlmZtZeSrZotgNei4hzaj0iYgZwl6TfSrpT0j2SPgogqVPS/ZJ+JOleSddLWi4PO0zSHZJmSLpc0vDcfy1Jf8zzOaW2HEnLd7UMMzPrfyWDZmNgWhf9/wHsGRGbk8LoO7n1A7AecFZEbAS8COyV+18REe+LiLHA/cChuf93gbMjYhNgVoPLeIOkwyVNlTT12SV5pmZm1q1WXAwg4FRJdwM3AquTDqcBPBoR03P3NKAzd28s6feS7gEmABvl/h8ALsrdkxpcxhsi4tyIGBcR41ZpxjMzM7M3KXaOBrgX2LuL/hOAVYD3RsRrkh4DhuVh/6yMtwBYLnefB+wRETMkHQJsWxkv+rgMMzPrRyVbNDcBQyUdXush6T3AmsDcHADb5f97MxKYJWlZUojU/AHYL3dX+6+4GMswM7MCigVNRASwJ7BDvrz5XuCbwDXAuHwYbCLw5wZm9w1gCilYquN/Hvhcntfqlf4XLsYyzMysAKU8sHFSTG10ZL9mZmYASJoWEeN6Gsd3BjAzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRXloDEzs6IcNGZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWhqBg9ubLyOjrJ1mJkNMA6amrFjIaL3x+zZra7UzGyp4qAxM7OiHDRmZlaUg8bMzIpy0JiZWVEOGjMzK8pBY2ZmRTlozMysKAeNmZkVpYhodQ1tQdLLwAOtrqMXY4DnWl1EL1xjc7jG5nCNzdFTjWtGxCo9TdzgfVfeEh6IiHGtLqInkqa6xiXnGpvDNTbHW6FGHzozM7OiHDRmZlaUg2ahc1tdQANcY3O4xuZwjc0x4Gv0xQBmZlaUWzRmZlaUg8bMzIpy0ACSdpb0gKSHJR3Twjp+KmmupJmVfqMl3SDpofx3pdxfkr6Xa75b0ub9UN8akm6WdJ+keyV9vg1rHCbpdkkzco0n5v5rSZqSa7lE0pDcf2j+/+E8vLN0jZVaB0m6S9LV7VijpMck3SNpuqSpuV/brOu83FGSLpP0Z0n3S9qqnWqUtEF+/WqPlyQd1U415uX+V36/zJR0UX4fNW97jIi39AMYBDwCrA0MAWYAG7aolg8CmwMzK/2+DRyTu48BvpW7xwO/AQRsCUzph/pWBTbP3SOBB4EN26xGAcvn7mWBKXnZvwD2y/3PAT6Tuz8LnJO79wMu6cf1/QXg58DV+f+2qhF4DBhT169t1nVe7vnAJ3P3EGBUu9VYqXUQMBtYs51qBFYHHgWWq2yHhzRze+y3F7ldH8BWwHWV/48Fjm1hPZ0sGjQPAKvm7lVJXywF+CGwf1fj9WOtVwI7tmuNwHDgTmAL0reaB9evc+A6YKvcPTiPp36o7R3Ab4HtgavzB0u71fgYbw6atlnXwIr5A1LtWmNdXR8B/tBuNZKC5klgdN6+rgZ2aub26ENnC1/kmqdyv3bRERGzcvdsoCN3t7Tu3FzejNRiaKsa8yGp6cBc4AZSi/XFiJjfRR1v1JiH/w1YuXSNwJnAl4HX8/8rt2GNAVwvaZqkw3O/dlrXawHPAj/LhyB/LGlEm9VYtR9wUe5umxoj4mngdOAJYBZp+5pGE7dHB81SJNIuRMuvR5e0PHA5cFREvFQd1g41RsSCiNiU1Gp4P/CuVtZTT9JuwNyImNbqWnqxTURsDuwCfE7SB6sD22BdDyYdaj47IjYD5pEOQ72hDWoEIJ/f+Hfg0vphra4xnx/6KCm4VwNGADs3cxkOGngaWKPy/ztyv3YxR9KqAPnv3Ny/JXVLWpYUMhdGxBXtWGNNRLwI3Exq9o+SVLu3X7WON2rMw1cEni9c2geAf5f0GHAx6fDZd9usxtqeLhExF5hMCu12WtdPAU9FxJT8/2Wk4GmnGmt2Ae6MiDn5/3aqcQfg0Yh4NiJeA64gbaNN2x4dNHAHsF6+wmIIqXl7VYtrqroKODh3H0w6L1LrPzFfpbIl8LdKU7wISQJ+AtwfEf/TpjWuImlU7l6OdA7pflLg7N1NjbXa9wZuynuYxUTEsRHxjojoJG1vN0XEhHaqUdIISSNr3aTzCzNpo3UdEbOBJyVtkHt9GLivnWqs2J+Fh81qtbRLjU8AW0oant/jtdexedtjf50Ia+cH6UqPB0nH8r/WwjouIh0jfY20t3Yo6djnb4GHgBuB0XlcAWflmu8BxvVDfduQmvh3A9PzY3yb1fge4K5c40zguNx/beB24GHS4Yuhuf+w/P/Defja/bzOt2XhVWdtU2OuZUZ+3Ft7X7TTus7L3RSYmtf3L4GV2rDGEaQ9/hUr/dqtxhOBP+f3zCRgaDO3R9+CxszMivKhMzMzK8pBY2ZmRTlozMysKAeNmZkV5aAxM7OiHDRmLSSpU5W7ded+J0g6WtKW+e6405XuTHxCHn6IpGfzbVceknSdpK1b8gTMGjC491HMrEXOB/aJiBmSBgEbVIZdEhFHAEjaDrhC0nYRcX8rCjXriVs0Zu3rbaQv8BLp/m33dTVSRNxM+k33w7sabtZqDhqz9nUG8ICkyZI+JWlYD+PeSZvdPNSsxkFj1lrd3ZojIuIkYBxwPXAAcG0P81GzCzNrFgeNWWs9T7o/V9Vo0o9JERGPRMTZpBsdjpXU3e9+bEa6eahZ23HQmLVQRLwCzJK0PYCk0aTfArlN0q75broA6wELgBfr5yHpQ6TzMz/ql6LN+shXnZm13kTgLEm1n144MSIekfTfwBmS/g7MByZExIKcPftK2ob0c9WPAnv5ijNrV757s5mZFeVDZ2ZmVpSDxszMinLQmJlZUQ4aMzMrykFjZmZFOWjMzKwoB42ZmRX1/wEqgJkCG9d6fgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "barh = plt.barh(y = year2020.index, width = year2020[2020], tick_label = ['Canada', 'Mexico', 'USA'])\n",
-    "\n",
-    "barh[0].set_color('red')\n",
-    "barh[1].set_color('green')\n",
-    "barh[2].set_color('blue')\n",
-    "\n",
-    "plt.title('Military Spending in North American in 2020 (USD)')\n",
-    "plt.xlabel('USD')\n",
-    "plt.ylabel('Country')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ed7a75f6",
-   "metadata": {},
-   "source": [
-    "The same can be done for regular bar graphs made using `plt.bar()`. Depending on your visualization preferences, `plt.bar()` and `plt.barh()` provide multiple options for constructing a bar graph."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d28b66ff",
-   "metadata": {},
-   "source": [
-    "## Pie charts\n",
-    "\n",
-    "Pie charts are a commonly used visualization method to represent proportions in datasets. Pie charts use *wedges* to represent the numerical value of a proportion corresponding to a categorial variable.\n",
-    "\n",
-    "While pie charts are very common and can be easily interpreted by a layperson audience, they may not be the best way to represent data in certain cases. Firstly, because pie charts use the area of a circle to represent the proportion of a categorical variable, it can be difficult to gauge the numerical value that a wedge represents if the area doesn't appear as an easily discernible fraction (_e.g._ ½, ⅓, ¼). This can be aided with the help of labels and legends that explicitly show the numerical values associated with the wedges of the pie chart. Secondly, if you want to visualize many categorical variables or variables that make up a significantly small proportion of the dataset, it may be difficult to see the variable on a pie chart. Overall, pie charts can be a simple and effective way to communicate proportional categorical data, but before using them, consider what attributes of the data need to be highlighted to help decide if a pie chart is the most appropriate visualization method. \n",
-    "\n",
-    "Now, let's look at the total amount of money spent on the military in Mexico from 1970 - 1975 and determine the proportion of the total amount for each year. To do this, we will extract data from the years 1970-1975 and call it `mex70s`. Then, we will  make a pie chart using the `plt.pie()` function. We will use `mex70s` to determine the wedge sizes. We'll also set the figure size, in inches, using `plt.figure()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "63c723fa",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>MEX-USD</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1970</th>\n",
-       "      <td>0.1768</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1971</th>\n",
-       "      <td>0.1912</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1972</th>\n",
-       "      <td>0.2424</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1973</th>\n",
-       "      <td>0.3008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1974</th>\n",
-       "      <td>0.4072</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1975</th>\n",
-       "      <td>0.5048</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      MEX-USD\n",
-       "Year         \n",
-       "1970   0.1768\n",
-       "1971   0.1912\n",
-       "1972   0.2424\n",
-       "1973   0.3008\n",
-       "1974   0.4072\n",
-       "1975   0.5048"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mex_70s = military.loc[np.arange(1970, 1976), ['MEX-USD']]\n",
-    "\n",
-    "mex_70s"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "b156e98f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "plt.pie(x = mex_70s['MEX-USD'])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e12d0bf4",
-   "metadata": {},
-   "source": [
-    "Now that we have a pie chart, let's add some more detail to it to make it more descriptive.\n",
-    "\n",
-    "We can label the sectors of the chart so that we know which year corresponds to which color. Likewise, we can label the percentage of each sector to know the definitive proportion of each years's contribution to the total amount of money spent on the military in Mexico from 1970-1975.\n",
-    "\n",
-    "To do this, we will create an array called `years`,  containing the integers 1970, 1971, 1972, 1973, 1974, and 1975. We then use `years` as an argument for the `labels` parameter within `plt.pie()` and specify formatting for the `autopct` parameter, which labels the wedges using the printf style format. More information on that format can be found <a target=\"_blank\" href=\"https://docs.python.org/3/tutorial/inputoutput.html#formatted-string-literals\">here</a>."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "db7549c6",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "years = np.arange(1970, 1976)\n",
-    "\n",
-    "plt.pie(x = mex_70s['MEX-USD'], labels=years, autopct='%.1f')\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e2b162dd",
-   "metadata": {},
-   "source": [
-    "This plot is okay, but it can be better.\n",
-    "\n",
-    "The percentages may be difficult to see as the labeling competes with the color of the wedge. Instead, let's add the percentages into a legend along with the labels of each sector. Let's also add a title so others can know what they are looking at when they view this chart."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "0d310f32",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 720x432 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 6))\n",
-    "\n",
-    "\n",
-    "patches, text = plt.pie(x = mex_70s['MEX-USD'])\n",
-    "labels = ['1970 (9.7 %)', '1971 (10.5 %)', '1972 (13.3 %)', '1973 (16.5 %)', '1974 (22.3 %)', '1975 (27.7%)']\n",
-    "\n",
-    "# Finds the sum of usd_2000 and rounds it to 1 position after the decimal\n",
-    "total = mex_70s['MEX-USD'].sum().round(decimals=1) \n",
-    "\n",
-    "plt.legend(patches, labels, loc=4, bbox_to_anchor=(1.5, 0.3), fontsize=15)\n",
-    "plt.title(\"Military Spending in Mexico from 1970-1975\" + \" (\" + str(total) + \" Billion USD)\",  loc = 'center',\n",
-    "         fontsize = 15)\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "71eb38e3",
-   "metadata": {},
-   "source": [
-    "Above, we used `plt.pie()` in a way that we had not used it before.\n",
-    "\n",
-    "Under the hood, the `plt.pie()` function returns two default outputs, which we named: `patches` and`text`. The size of each wedge is dictated by the `patches` object. The `text` object consists of a list of labels for our data. Here, we needed to specifically assign `patches` and `text` objects so we could use `patches` as an argument for the `plt.legend()` function. \n",
-    "\n",
-    "The `plt.legend()` function has two required arguments. The first argument dictates **what** is being labeled. In our case, the wedges of the pie chart (*i.e.* the `patches` object) are being labeled. The second argument dictates **how** things are labeled. Here, we simply created a variable called labels, which consists of the six strings for the six wedges:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "id": "b4eb218d",
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['1970 (9.7 %)',\n",
-       " '1971 (10.5 %)',\n",
-       " '1972 (13.3 %)',\n",
-       " '1973 (16.5 %)',\n",
-       " '1974 (22.3 %)',\n",
-       " '1975 (27.7%)']"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "labels"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9721e589",
-   "metadata": {},
-   "source": [
-    "The other arguments, `bbox_to_anchor` and `fontsize`, are optional when using the `plt.legend()` function.\n",
-    "\n",
-    "The argument `bbox_to_anchor` designates the position in the plotting area where the legend will be, while the `fontsize` argument dictates the font size, in points, of the legend text.\n",
-    "\n",
-    "As mentioned previously, labeling can greatly enhance the efficiency of of a pie chart's ability to communicate information. Because some of the wedges are very similar in size, it can be hard to discern the numeric value associated with each year. Labeling each wedge with the percentage and associated category or making a legend that depicts this information leaves less room for ambiguity when it comes to the data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "e64c6b88",
-   "metadata": {},
-   "source": [
-    "## Conclusions\n",
-    "\n",
-    "In this section, we were introduced to the `plt.bar()` and `plt.pie()` functions to construct bar plots and pie charts, respectively.\n",
-    "\n",
-    "The `plt.bar()` function requires `x` and `height` arguments, which can be an array of number values, but other parameters can be included.\n",
-    "\n",
-    "The `plt.pie()` function only requires an `x` argument as an array of values and has other parameters that can be utilized well.\n",
-    "\n",
-    "Both of these types of visualizations are used for depicting categorical data.\n",
-    "\n",
-    "As a reminder, when deciding on whether to use a pie chart, consider certain attributes of the data, such as the number of categorical variables or the size of the proportions to be plotted. Below is a list of functions with linked documentation for your reference and further reading:\n",
-    "\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.bar.html\">plt.bar( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pie.html\">plt.pie( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/statistics/boxplot_demo.html\">plt.boxplot( )</a> \n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.title.html\">plt.title( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylim.html\">plt.ylim( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlim.html\">plt.xlim( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.ylabel.html\">plt.ylabel( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xlabel.html\">plt.xlabel( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.xticks.html\">plt.xticks( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.bar.html\">ax.bar( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.legend.html\">ax.legend( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.tight_layout.html\">plt.tight_layout( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.figure.html\">plt.figure( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.show.html\">plt.show( )</a>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "69a868f4",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/textbook/09/3/Numerical_Data.ipynb b/textbook/09/3/Numerical_Data.ipynb
new file mode 100644
index 00000000..99da178d
--- /dev/null
+++ b/textbook/09/3/Numerical_Data.ipynb
@@ -0,0 +1,779 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "0fb30d10",
+   "metadata": {},
+   "source": [
+    "# Numerical Data\n",
+    "\n",
+    "Numerical data consists of values including integers and rational numbers. Temperature collections, height recordings, and numerical grades in a class are examples of numerical data. In this section, we will practice making histograms, scatter plots, and line graphs to represent numerical data.\n",
+    "\n",
+    "Again, we'll load the necessary libraries and data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "id": "3e479dc7-ce8e-4ca7-ae35-79038cf4bf99",
+   "metadata": {
+    "tags": [
+     "hide_cell"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "sns.set_style('whitegrid')\n",
+    "\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n",
+    "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n",
+    "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n",
+    "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n",
+    "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n",
+    "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n",
+    "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n",
+    "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n",
+    "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n",
+    "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n",
+    "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n",
+    "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n",
+    "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n",
+    "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n",
+    "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n",
+    "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n",
+    "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n",
+    "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n",
+    "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n",
+    "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n",
+    "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n",
+    "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n",
+    "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n",
+    "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n",
+    "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n",
+    "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n",
+    "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n",
+    "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n",
+    "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n",
+    "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n",
+    "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n",
+    "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n",
+    "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n",
+    "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n",
+    "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n",
+    "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n",
+    "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n",
+    "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n",
+    "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n",
+    "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n",
+    "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n",
+    "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n",
+    "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n",
+    "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n",
+    "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n",
+    "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n",
+    "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n",
+    "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n",
+    "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n",
+    "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n",
+    "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n",
+    "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n",
+    "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n",
+    "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n",
+    "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n",
+    "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n",
+    "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n",
+    "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n",
+    "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n",
+    "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n",
+    "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n",
+    "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n",
+    "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n",
+    "'''\n",
+    "\n",
+    "from io import StringIO\n",
+    "\n",
+    "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n",
+    "\n",
+    "military = pd.read_csv(../../data/NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5190be46",
+   "metadata": {},
+   "source": [
+    "## Histograms\n",
+    "\n",
+    "Histograms are a great way to view distributions of numerical data. In histogram plots, a numerical component of data is divided into what are called *bins*. As data points are assigned to their respective bins, the total number of data points in each bin is quantified and plotted, visualizing a distribution of frequencies. In the upcoming exercise, we will explore how to visualize a distribution of values in our dataset.\n",
+    "\n",
+    "\n",
+    "Let's examine military spending in the United States from 1960 to 2020. We can look at multiple ranges of dollar amounts spent on the military as our independent variable and organize them into bins. After, we can determine how many fiscal years fall into each of these bins and visualize the distribution.\n",
+    "\n",
+    "First, we will need to extract the data pertaining to the military spending in the United States. We will call it `hist_data`. Then, we will need to determine the minimum and maximum values of this subset of data so that we can determine the range of values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a45eb875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'min': 47.34655267, 'max': 778.2322}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hist_data = military[\"USA-USD\"]\n",
+    "\n",
+    "{\n",
+    "    'min': hist_data.min(),\n",
+    "    'max': hist_data.max(),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b21d2e3e",
+   "metadata": {},
+   "source": [
+    "We see that the minimum amount the United States spent on the military between the years of 1960 and 2020 was about \\$47 billion, while the maximum amount was about \\$780 billion.\n",
+    "\n",
+    "With this information, we will create a range for our bins, named `binnum`, with integers between 0 and 801, so that it is inclusive of all the data values. We make the interval of the range 100, giving us eight evenly spaced bins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "ccfb3ade",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 100, 200, 300, 400, 500, 600, 700, 800]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "binnum = range(0, 801, 100)\n",
+    "\n",
+    "list(binnum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55e62707",
+   "metadata": {},
+   "source": [
+    "To graph the distribution of military spending, we will make a histogram by calling the `plt.hist()` funtion.\n",
+    "\n",
+    "Minimally, this function needs an input set of values. Additionally, we will specify the bins, so that they are evenly distributed on the x-axis. We do this by inputting `binnum` as our `bins` argument. If we do not specify the `bin` argument, the data will be divided into 10 bins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "daae9915",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(hist_data, bins=binnum)\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6043632d",
+   "metadata": {},
+   "source": [
+    "Now that we have our plot, let's add additional details to make it more understandable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "942043f1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(hist_data, bins=binnum)\n",
+    "plt.title(\"Distribution of Military Spending in the United States from 1960 to 2020\")\n",
+    "plt.ylabel('Counts of Fiscal Years')\n",
+    "plt.xlabel(\"Dollar Amount (USD)\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e52c962",
+   "metadata": {},
+   "source": [
+    "Awesome! From this plot, we can see that the United States had the highest frequency of fiscal years where \\$0 - \\$100 billion was spent on the military. We can also see that the \\$400 - \\$500 billion bin had the lowest frequency with only two years spending that range of money."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1af7e1d3",
+   "metadata": {},
+   "source": [
+    "## Scatter plots\n",
+    "\n",
+    "Next, we'll examine the use of a scatter plot as another visualization tool for numerical data.\n",
+    "\n",
+    "Scatter plots visualize the relationship between two numerical variables. For this exercise, let's examine the percentage of the GDP of Mexico spent on the military versus the absolute dollar amount (in USD) over 1960-2020.\n",
+    "\n",
+    "We'll simply extract the columns for this data and assign them to `mex_gdp` and `mex_usd`, respectively. Then, we can plot this data using the `plt.scatter()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "id": "deb40aae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mex_gdp = military[['MEX-USD']]\n",
+    "\n",
+    "mex_usd = military['MEX-PercentGDP']\n",
+    "\n",
+    "plt.scatter(mex_gdp, mex_usd)  # mex_gdp on the x-axis, mex_usd on the y-axis\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4fba0f1f",
+   "metadata": {},
+   "source": [
+    "Looking at this scatter plot out of context, it would be hard to understand what the data means. Let's add the important details to make it clear:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "id": "bc576939",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(fig, ax) = plt.subplots()\n",
+    "\n",
+    "plt.scatter(mex_gdp, mex_usd)\n",
+    "\n",
+    "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n",
+    "\n",
+    "ax.set_ylabel('Spending in USD (Billions)')\n",
+    "ax.set_xlabel('Percentage of GDP')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd5d171b",
+   "metadata": {},
+   "source": [
+    "Now, we have a better understanding of the data, which depicts the relationship between the percentage of the Mexican GDP spent on the military and the total amount spent in USD from the years 1960 to 2020. \n",
+    "\n",
+    "In addition to this information, we can add a color scheme that will color each data point based on the year of collection. This adds another dimension of analysis, using year as a feature; the context of the spending relationship can be examined over time:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "id": "23a45386",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(fig, ax) = plt.subplots()\n",
+    "plt.title(\"% GDP vs. Absolute Spending on Military in Mexico 1960 - 2020\", pad=10)\n",
+    "ax.set_ylabel('Spending in USD (Billions)')\n",
+    "ax.set_xlabel('Percentage of GDP')\n",
+    "\n",
+    "\n",
+    "mex_years = mex_gdp.index\n",
+    "\n",
+    "plt.scatter(mex_gdp, mex_usd, c=mex_years, alpha=0.4, cmap='winter')\n",
+    "\n",
+    "plt.colorbar()\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20146a6e",
+   "metadata": {},
+   "source": [
+    "The `plt.scatter()` function minimally needs two arguments - *x* and *y* - which are array-like variables. Other optional arguments include `c`, which determines how to color the data points; `alpha`, which sets the opacity of the data points; and `cmap` which sets the Colormap used to color the data points. \n",
+    "\n",
+    "We used the years of the dataset (which we defined as the index earlier in this chapter) as our `c` argument to color the data points based on the year of collection. We used used the 'winter' Colormap as our `cmap` argument, but many other Colormaps are available for your choosing. A list of other possible Colormaps to explore is linked at the end of this section.\n",
+    "\n",
+    "The `plt.colorbar()` function displays a scale of the Colormap based on the feature used to color the data, which in our case is the year of collection."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "30ba35e7",
+   "metadata": {},
+   "source": [
+    "## Line graphs\n",
+    "\n",
+    "Line graphs are used to visualize sequential numerical data. Using line graphs, we can easily see trends within data over time.\n",
+    "\n",
+    "Let's examine the spending (in USD) on the military in Canada in the 21st century (2000-2020).\n",
+    "\n",
+    "This can be done most quickly through the pandas Series."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "b26501e3-def6-48f3-80e5-d238e8220f9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Year\n",
+       "2000     8.299385\n",
+       "2001     8.375571\n",
+       "2002     8.495399\n",
+       "2003     9.958246\n",
+       "2004    11.336490\n",
+       "2005    12.988133\n",
+       "2006    14.809893\n",
+       "2007    17.417140\n",
+       "2008    19.342058\n",
+       "2009    18.936226\n",
+       "2010    19.315689\n",
+       "2011    21.393721\n",
+       "2012    20.452107\n",
+       "2013    18.515731\n",
+       "2014    17.853640\n",
+       "2015    17.937642\n",
+       "2016    17.782776\n",
+       "2017    22.269696\n",
+       "2018    22.729328\n",
+       "2019    22.204408\n",
+       "2020    22.754847\n",
+       "Name: CAN-USD, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "can_usd = military.loc[2000:2020, 'CAN-USD']\n",
+    "\n",
+    "can_usd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "2f9a252d",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "can_usd.plot();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22bd9379-07f0-4900-99b2-bcafee5b2728",
+   "metadata": {},
+   "source": [
+    "To use matplotlib directly, we first extract the years of interest and assign it to the variable `years`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "ab3bb392-3509-4adc-b1ae-4460db515862",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Int64Index([2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010,\n",
+       "            2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020],\n",
+       "           dtype='int64', name='Year')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "years = can_usd.index\n",
+    "\n",
+    "years"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "148a691a-3fd4-4501-800d-f926099108db",
+   "metadata": {},
+   "source": [
+    "Then, we call `plt.plot()` and specifiy the years on the x-axis and the spending on the y-axis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "88bcffa6-027e-4208-aeab-9c61649b913c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(years, can_usd);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d90f500d",
+   "metadata": {},
+   "source": [
+    "We can see from the graph that Canada's spending on the military has increased overall since 2000. The country had a period of time (around 2011 to 2017) where military spending was decreasing consistently.\n",
+    "\n",
+    "Let's add the data for Mexico as well to see the country's spending in the 21<sup>st</sup> century."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "3eff4b2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mex_usd = military.loc[2000:2099, 'MEX-USD']\n",
+    "\n",
+    "plt.plot(years, can_usd)\n",
+    "plt.plot(years, mex_usd);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81e3e39f",
+   "metadata": {},
+   "source": [
+    "We can now see that the military spending for both Mexico and Canada is vastly different. However, just looking at this graph out of context, we wouldn't be able to tell which line corresponds to which country. Let's add some descriptive details, such as line labels, a title, and axis labels. Let's also fix the x-axis ticks to set an interval for every five years."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "50c364ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(years, can_usd, label='Canada')\n",
+    "plt.plot(years, mex_usd, label='Mexico')\n",
+    "\n",
+    "plt.title(\"Military Spending in Mexico and Canada in the 21st Century\", pad=10)\n",
+    "plt.ylabel('USD (Billions)')\n",
+    "plt.xlabel('Year')\n",
+    "plt.xticks(range(2000, 2025, 5))\n",
+    "\n",
+    "plt.legend(loc=\"best\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ab35d3c",
+   "metadata": {},
+   "source": [
+    "We can see that the overall trend of military spending in Mexico also increased from 2000 to 2020. However, this increase was a lot less drastic than observed in Canada. Mexico's military spending was a steady rise from about \\$3 billion to \\$6 billion over the course of 20 years, while Canada's spending rose from \\$8 billion to about \\$23 billion over the same period of time.\n",
+    "\n",
+    "Let's add data from the United States to see the trends in all North American countries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "18c12059",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "usa_usd = military.loc[2000:2099, 'USA-USD']\n",
+    "\n",
+    "plt.plot(years, can_usd, label='Canada')\n",
+    "plt.plot(years, mex_usd, label='Mexico')\n",
+    "plt.plot(years, usa_usd, label='United States')\n",
+    "\n",
+    "plt.title(\"Military Spending in North America in the 21st Century\", pad=10)\n",
+    "plt.ylabel('USD (Billions)')\n",
+    "plt.xlabel('Year')\n",
+    "plt.xticks(range(2000, 2025, 5))\n",
+    "\n",
+    "plt.legend(loc=\"best\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ae241382",
+   "metadata": {},
+   "source": [
+    "With the addition of the data from the United States, it's diffiult to discern the data from Canada and Mexico. Because the spending on the military in the United States was a lot higher, plotting all three datasets on the same graph with the same axis does not allow us to clearly see trends in the other countries.\n",
+    "\n",
+    "To address this, we can graph the data for each country separately with axis limits that are tailored to each country. If we graph this data side by side, we can see the trends in each country while acknowledging that the axis intervals for each country provides a numerical context for cross-comparisons."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "af08cad2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x216 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "(fig, (ax1, ax2, ax3)) = plt.subplots(1, 3, figsize=(15, 3))\n",
+    "\n",
+    "fig.suptitle('Military Spending in North America in the 21st Century', y=1.1, fontsize=15)\n",
+    "\n",
+    "ax1.plot(years, can_usd)\n",
+    "ax1.set_title('Canada')\n",
+    "ax1.set_ylim([8, 24])\n",
+    "ax1.set_xlabel('Years')\n",
+    "ax1.set_ylabel('USD (Billions)')\n",
+    "\n",
+    "ax2.plot(years, mex_usd, color='orange')\n",
+    "ax2.set_title('Mexico')\n",
+    "ax2.set_ylim([2.5, 7])\n",
+    "ax2.set_xlabel('Years')\n",
+    "ax2.set_ylabel('USD (Billions)')\n",
+    "\n",
+    "ax3.plot(years, usa_usd, color='green')\n",
+    "ax3.set_title('United States')\n",
+    "ax3.set_ylim([300, 800])\n",
+    "ax3.set_xlabel('Years')\n",
+    "ax3.set_ylabel('USD (Billions)')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "efee0c99",
+   "metadata": {},
+   "source": [
+    "Now that we've created separate plots, we can see the trends for all three countries over the last 20 years. All three countries seem to have decreased spending around 2011 and 2018. By observing the difference in scale, we can also see that while the trends are similar, the magnitude of spending was very different between Canada, Mexico, and the United States.\n",
+    "\n",
+    "In creating these plots, we used `plt.subplots()` in a way that we had not used it before. Here, we defined our `fig` argument, as well as three `axes` arguments, `ax1`, `ax2`, and `ax3`. This allowed us to create <u>three</u> separate plotting areas, bounded by <u>three</u> different axes, that are contained within <u>one</u> figure."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "886787a1",
+   "metadata": {},
+   "source": [
+    "## Conclusions\n",
+    "\n",
+    "In this section, we learned how to use `plt.hist()`, `plt.scatter()`, and `plt.plot()` to create histograms, scatter plots, and line graphs as a means of visualizing numerical data.\n",
+    "\n",
+    "The `plt.scatter()` and `plt.plot()` functions require numerical arrays that serve as `x` and `y` arugments.\n",
+    "\n",
+    "The `plt.hist()` function requires an array of values for plotting distributions of data.\n",
+    "\n",
+    "We also learned about a number of other functions that can be used to enhance and annotate our plots. Documentation for the functions used in this section, and related functions, are listed below:\n",
+    "\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html\">plt.hist( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.scatter.html\">plt.scatter( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.plot.html\">plt.plot( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.suptitle.html\">fig.suptitle( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_title.html\">ax.set_title( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylim.html\">ax.set_ylim( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlim.html\">ax.set_xlim( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_ylabel.html\">ax.set_ylabel( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.axes.Axes.set_xlabel.html\">ax.set_xlabel( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.colorbar.html\">plt.colorbar( )</a>\n",
+    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/tutorials/colors/colormaps.html\">Colormap options</a>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "37ae5ff6",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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diff --git a/textbook/09/4/other-viz.ipynb b/textbook/09/4/other-viz.ipynb
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index 36644a6b..00000000
--- a/textbook/09/4/other-viz.ipynb
+++ /dev/null
@@ -1,1021 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "02ee0b6e",
-   "metadata": {},
-   "source": [
-    "# Other Visualization Techniques\n",
-    "\n",
-    "In this section, we will introduce other data visualizations that can be used to represent categorical or numerical data. We will discuss another visualization library called `seaborn`. While the `matplotlib` library can be used to create most data visualizations in Python, there are some restrictions when it comes to customization. The `seaborn` library provides many flexible options when creating visualizations. In the upcoming exercises, we will use a combination of `seaborn` and `matplotlib` to make visualizations, including box and whisker plots, heatmaps, and area plots.\n",
-    "\n",
-    "Along with the previous data and libraries we have been using, we will import `seaborn` as a common convention: `sns`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "41e82e0c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "import seaborn as sns\n",
-    "\n",
-    "plt.style.use('fast')\n",
-    "\n",
-    "\n",
-    "NorthAmerica_Military_USD_PercentGDP_Combined_csv = '''\\\n",
-    "Year,CAN-PercentGDP,MEX-PercentGDP,USA-PercentGDP,CAN-USD,MEX-USD,USA-USD\n",
-    "1960,4.18525654,0.673508659,8.993124587,1.702442711,0.084,47.34655267\n",
-    "1961,4.128312243,0.651780326,9.1560315,1.677820881,0.0864,49.87977061\n",
-    "1962,3.999216389,0.689655172,9.331672945,1.671313753,0.0992,54.65094261\n",
-    "1963,3.620650112,0.718685832,8.831891186,1.610091701,0.112,54.56121578\n",
-    "1964,3.402062837,0.677506775,8.051281106,1.657457283,0.12,53.43232706\n",
-    "1965,2.930260659,0.591269841,7.587247177,1.57470454,0.1192,54.56179126\n",
-    "1966,2.683282422,0.576379066,8.435300286,1.614422827,0.1304,66.44275153\n",
-    "1967,2.74792677,0.545217107,9.417795933,1.775500366,0.1336,78.39844224\n",
-    "1968,2.54364188,0.548510764,9.268454275,1.797265817,0.1488,84.32903122\n",
-    "1969,2.27378467,0.600160043,8.633263795,1.770108751,0.18,84.99016543\n",
-    "1970,2.188979696,0.497411659,8.032743584,1.889157918,0.1768,83.407993\n",
-    "1971,2.131485639,0.48765558,6.943069609,2.077659711,0.1912,78.23797989\n",
-    "1972,2.011818438,0.536568089,6.519756924,2.233737031,0.2424,80.70807097\n",
-    "1973,1.832601818,0.544217687,5.893870591,2.363060955,0.3008,81.46979441\n",
-    "1974,1.783813085,0.565744137,5.954111197,2.809465529,0.4072,89.27892034\n",
-    "1975,1.863541853,0.57358422,5.622679096,3.18091549,0.5048,92.08092875\n",
-    "1976,1.765927978,0.598103574,5.191071429,3.581805735,0.531576968,94.71525108\n",
-    "1977,1.8057636,0.534256205,5.155617351,3.752174526,0.437692986,104.665219\n",
-    "1978,1.848887401,0.504834431,4.943087248,3.969158477,0.518287193,113.3820637\n",
-    "1979,1.711245918,0.505297474,4.951991535,4.084145738,0.679663588,126.8799271\n",
-    "1980,1.764448615,0.416107383,5.153537467,4.744402251,0.810422204,143.6883549\n",
-    "1981,1.709915638,0.513301014,5.646541256,5.141128191,1.284948561,176.5588753\n",
-    "1982,1.954343585,0.495419418,6.814057094,6.017321456,0.858130163,221.6735426\n",
-    "1983,2.081196249,0.522866314,6.32114426,6.947104072,0.778556797,223.427165\n",
-    "1984,2.117188855,0.65981906,6.23641653,7.349795764,1.155945373,245.1491683\n",
-    "1985,2.097376234,0.676313139,6.453219205,7.460563318,1.241863652,272.1632293\n",
-    "1986,2.109197118,0.634622463,6.626522658,7.78013674,0.817296612,295.5462238\n",
-    "1987,2.062576371,0.580341889,6.420274023,8.694447168,0.813391574,304.0866487\n",
-    "1988,1.986767119,0.536145374,6.071277702,9.897335684,0.981914646,309.6612693\n",
-    "1989,1.934614309,0.517255829,5.871206008,10.74713469,1.153375828,321.8665588\n",
-    "1990,1.958793742,0.433081035,5.605175294,11.41463185,1.210872502,325.129314\n",
-    "1991,1.895444339,0.435402301,4.883429398,11.3385033,1.459136041,299.3727791\n",
-    "1992,1.8616877,0.469454656,4.970466808,10.78880312,1.824550066,325.033736\n",
-    "1993,1.821753504,0.442785494,4.604350295,10.26882262,2.122980338,316.7194437\n",
-    "1994,1.696680257,0.518830327,4.215264675,9.57737764,2.635284079,308.084\n",
-    "1995,1.554090071,0.450891531,3.860245792,9.176903908,1.562615372,295.8530977\n",
-    "1996,1.403752581,0.476484778,3.554982206,8.615884471,1.882873103,287.9606687\n",
-    "1997,1.246243202,0.458095854,3.405562244,7.945140183,2.184061042,293.1678258\n",
-    "1998,1.256293902,0.450450487,3.201558499,7.748607984,2.263223453,290.9960551\n",
-    "1999,1.241703064,0.460988776,3.085676783,8.21077854,2.652912012,298.0948913\n",
-    "2000,1.11808088,0.44604782,3.112242147,8.299385231,3.031454509,320.0863242\n",
-    "2001,1.137368973,0.442657004,3.123809803,8.375571425,3.229469276,331.8056106\n",
-    "2002,1.120852292,0.421606002,3.447618099,8.495399281,3.172268734,378.4631388\n",
-    "2003,1.115878799,0.405916547,3.827161045,9.958245602,2.960496802,440.5320696\n",
-    "2004,1.107966027,0.364898723,4.016312736,11.33648983,2.854385965,492.9993762\n",
-    "2005,1.110669655,0.355958931,4.090034876,12.98813296,3.123454978,533.203\n",
-    "2006,1.125832408,0.311171936,4.041627237,14.8098928,3.035131019,558.335\n",
-    "2007,1.188901783,0.401163918,4.079655081,17.41713993,4.223037646,589.586\n",
-    "2008,1.248621382,0.390513227,4.463827356,19.3420584,4.334654124,656.756\n",
-    "2009,1.377555631,0.501556275,4.88559968,18.93622605,4.514233914,705.917\n",
-    "2010,1.194338338,0.452734493,4.922641677,19.31568883,4.789031339,738.005\n",
-    "2011,1.193291895,0.465777803,4.840173995,21.39372086,5.498458542,752.288\n",
-    "2012,1.118404598,0.475987281,4.477401219,20.45210711,5.717035575,725.205\n",
-    "2013,1.0023672,0.507919455,4.046678879,18.51573121,6.473144378,679.229\n",
-    "2014,0.989925299,0.513829957,3.69589465,17.85364048,6.758693845,647.789\n",
-    "2015,1.152709374,0.466676122,3.477845166,17.93764189,5.468837812,633.829639\n",
-    "2016,1.164161567,0.495064414,3.418942337,17.78277554,5.33687574,639.856443\n",
-    "2017,1.351602232,0.436510296,3.313381294,22.26969632,5.062076646,646.752927\n",
-    "2018,1.324681094,0.477517407,3.316248808,22.72932758,5.839521271,682.4914\n",
-    "2019,1.27894142,0.52348249,3.427080181,22.20440844,6.650808254,734.3441\n",
-    "2020,1.415055841,0.573651659,3.741160091,22.75484713,6.116376582,778.2322\n",
-    "'''\n",
-    "\n",
-    "from io import StringIO\n",
-    "\n",
-    "NorthAmerica_Military_USD_PercentGDP_Combined_file = StringIO(NorthAmerica_Military_USD_PercentGDP_Combined_csv)\n",
-    "\n",
-    "military = pd.read_csv(NorthAmerica_Military_USD_PercentGDP_Combined_file, index_col='Year')\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "589f64b7",
-   "metadata": {},
-   "source": [
-    "## Box and Whisker Plots\n",
-    "\n",
-    "Box and whisker plots are a useful data visualization method because they intrinsically display multiple summary statistics simultaneously. The central line of each box within a box and whisker plot is the *median*. The median (also known as the second quartile, $Q_2$) is a value within a dataset that lies within the middle, separating the higher half and the lower half of the dataset. The median is a valuable measure of center for a dataset because it is not greatly affected by outliers, as opposed to the *mean*.\n",
-    "\n",
-    "If *A* represents a dataset with the values listed below, the median of *A* can be determined by sorting the numbers from low to high and determining the value that falls into the middle (which in this case is 109):\n",
-    "\n",
-    "<img align=\"center\" src=\"./img/median(odd).png\" width=\"60%\"/>\n",
-    "\n",
-    "\n",
-    "In the case of a dataset with an even number of values, the median can be calculated as the mean of the middle two values, as shown in the example dataset *B* below:\n",
-    "\n",
-    "<img align=\"center\" src=\"./img/median(even).png\" width=\"45%\"/>\n",
-    "\n",
-    "\n",
-    "Box and whisker plots also show the *lower quartile*, *the upper quartile*, the *interquartile range*, *outliers*, the *minimum*, and the *maximum*. The lower quartile $\\left( Q_1\\right)$ is the value where the lowest 25% of the data points within a dataset lie. It is represented by the lower end of the box. \n",
-    "\n",
-    "On the other side, the upper quartile $\\left( Q_3\\right)$ is the value in which the highest 25% of the dataset resides. It is represented by the higher end of a box.\n",
-    "\n",
-    "\n",
-    "The interquartile range (IQR), is the difference between the upper quartile and the lower quartile. The IQR is used to make the length of a box and is represented by the equation:\n",
-    "\n",
-    "  > $IQR=Q_3-Q_1$\n",
-    "    \n",
-    "\n",
-    "Outliers are data points that are less than $Q_1-1.5×IQR$ or greater than $Q_3+1.5×IQR$. These data points are shown beyond the extremity of the whiskers.\n",
-    "\n",
-    "Lastly, the minimum and maximum values are represented by the lowest and highest values, respectively, <b>that are within the range $Q_1-1.5×IQR$ and $Q_3+1.5×IQR$</b>. Essentially, they are the lowest and highest values that <u>do not</u> qualify as outliers. The lower whisker represents the minimum value, while the upper whisker represents the maximum value.\n",
-    "\n",
-    "Below is a pictorial summary of the major components of a box and whisker plot with an accompanying set of numbers, *A*:\n",
-    "\n",
-    "<img align=\"center\" src=\"./img/boxandwhisker.png\" width=\"75%\"/>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0b21bf29",
-   "metadata": {},
-   "source": [
-    "We will use a box and whisker plot to examine the percentage GDP spending on the military for each country in the '60s as a way to examine multiple statistics for each country in this time period.\n",
-    "\n",
-    "First, we extract the data of interest:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "3f2494b4",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-PercentGDP</th>\n",
-       "      <th>MEX-PercentGDP</th>\n",
-       "      <th>USA-PercentGDP</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1960</th>\n",
-       "      <td>4.185257</td>\n",
-       "      <td>0.673509</td>\n",
-       "      <td>8.993125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961</th>\n",
-       "      <td>4.128312</td>\n",
-       "      <td>0.651780</td>\n",
-       "      <td>9.156031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>3.999216</td>\n",
-       "      <td>0.689655</td>\n",
-       "      <td>9.331673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>3.620650</td>\n",
-       "      <td>0.718686</td>\n",
-       "      <td>8.831891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964</th>\n",
-       "      <td>3.402063</td>\n",
-       "      <td>0.677507</td>\n",
-       "      <td>8.051281</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1965</th>\n",
-       "      <td>2.930261</td>\n",
-       "      <td>0.591270</td>\n",
-       "      <td>7.587247</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1966</th>\n",
-       "      <td>2.683282</td>\n",
-       "      <td>0.576379</td>\n",
-       "      <td>8.435300</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1967</th>\n",
-       "      <td>2.747927</td>\n",
-       "      <td>0.545217</td>\n",
-       "      <td>9.417796</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1968</th>\n",
-       "      <td>2.543642</td>\n",
-       "      <td>0.548511</td>\n",
-       "      <td>9.268454</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1969</th>\n",
-       "      <td>2.273785</td>\n",
-       "      <td>0.600160</td>\n",
-       "      <td>8.633264</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      CAN-PercentGDP  MEX-PercentGDP  USA-PercentGDP\n",
-       "Year                                                \n",
-       "1960        4.185257        0.673509        8.993125\n",
-       "1961        4.128312        0.651780        9.156031\n",
-       "1962        3.999216        0.689655        9.331673\n",
-       "1963        3.620650        0.718686        8.831891\n",
-       "1964        3.402063        0.677507        8.051281\n",
-       "1965        2.930261        0.591270        7.587247\n",
-       "1966        2.683282        0.576379        8.435300\n",
-       "1967        2.747927        0.545217        9.417796\n",
-       "1968        2.543642        0.548511        9.268454\n",
-       "1969        2.273785        0.600160        8.633264"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "the60s = military.loc[1960:1969, ['CAN-PercentGDP', 'MEX-PercentGDP', 'USA-PercentGDP']]\n",
-    "\n",
-    "the60s"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "097daad0",
-   "metadata": {},
-   "source": [
-    "It is possible to make boxplots using using `pyplot` or the dataframe method `plot.box()`, as shown below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "34aca023",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Creates a boxplot using pyplot from matplotlib\n",
-    "plt.boxplot(the60s)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "adda1f5f",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Creates a boxplot using the dataframe method plot.box()\n",
-    "the60s.plot.box()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "a12f1be2",
-   "metadata": {},
-   "source": [
-    "Plotting using these approaches, the graphs show data within the columns of interest, but depending on the approach used, we see that the column title may or may not be used as categorical indicators on the x-axis. Furthermore, while these plots do the job of displaying the median and interquartile range, adding individual data points will allow for viewers to more easily see the spread of the data. The addition of axis labels, a title, and some color would also enhance this plot and make it more aesthetically pleasing. \n",
-    "\n",
-    "We can accomplish this using a combination of functions from `matplotlib` and `seaborn`. Using the `sns.boxplot()` and `sns.swarmplot()` functions will allow us to create a boxplot with data points overlayed on top:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "f5f1e20c",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "ax = sns.boxplot(data=the60s, palette=\"Set2\", linewidth=1)\n",
-    "ax = sns.swarmplot(data=the60s, palette=\"Set2\", linewidth=0.5, edgecolor = \"black\")\n",
-    "plt.xticks(ticks = [0,1,2], labels = ['Canada', 'Mexico', 'United States'])\n",
-    "plt.ylabel(\"Percent of GDP\")\n",
-    "plt.title(\"% GDP spent on the military in North America from 1960-1969\")\n",
-    "\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "62687a9d",
-   "metadata": {},
-   "source": [
-    "Now that we have proper labeling, we can see the median, upper quartile, and lower quartile of the percentage of the each country's GDP spent on the military from 1960 to 1969. A noticeable observation this plot shows is that Mexico not only spent a small percentage of their GDP on the military (less than 2%), but the percentage of spending during this decade had very little variability. This makes it hard to see what the median, upper quartile, and lower quartile are for Mexico. The issue of being able to visually resolve displays of data is a common one that data scientists encounter."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "913ed316",
-   "metadata": {},
-   "source": [
-    "## Heatmaps\n",
-    "\n",
-    "A heatmap is a matrix of data points depicted through a color gradient. Heatmaps are a great way to visualize data when you want to look at a multidimensional comparison of many variables. Heatmaps can be made from `matplotlib`, but this process may not be as straightforward to some. On the other hand, `seaborn` has a function dedicated to generations of heatmaps called `sns.heatmap()`. For your reference, both the `matplotlib` and `seaborn` approaches for constructing heatmaps are listed below.\n",
-    "\n",
-    "\n",
-    "We can use a heatmap to visualize variables that have a large number of values, such as the percentage of a country's GDP spent on the military from 1960 to 2020. Because Mexico has not spent over 1% of its GDP on the military in this time frame, we will look at these numbers for the U.S. and Canada. We will do this using the `seaborn` library.\n",
-    "\n",
-    "\n",
-    "To do this, we will first subset our data:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1ab14198",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-PercentGDP</th>\n",
-       "      <th>USA-PercentGDP</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1960</th>\n",
-       "      <td>4.185257</td>\n",
-       "      <td>8.993125</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961</th>\n",
-       "      <td>4.128312</td>\n",
-       "      <td>9.156031</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>3.999216</td>\n",
-       "      <td>9.331673</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>3.620650</td>\n",
-       "      <td>8.831891</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964</th>\n",
-       "      <td>3.402063</td>\n",
-       "      <td>8.051281</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>1.164162</td>\n",
-       "      <td>3.418942</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>1.351602</td>\n",
-       "      <td>3.313381</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>1.324681</td>\n",
-       "      <td>3.316249</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>1.278941</td>\n",
-       "      <td>3.427080</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>1.415056</td>\n",
-       "      <td>3.741160</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>61 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "      CAN-PercentGDP  USA-PercentGDP\n",
-       "Year                                \n",
-       "1960        4.185257        8.993125\n",
-       "1961        4.128312        9.156031\n",
-       "1962        3.999216        9.331673\n",
-       "1963        3.620650        8.831891\n",
-       "1964        3.402063        8.051281\n",
-       "...              ...             ...\n",
-       "2016        1.164162        3.418942\n",
-       "2017        1.351602        3.313381\n",
-       "2018        1.324681        3.316249\n",
-       "2019        1.278941        3.427080\n",
-       "2020        1.415056        3.741160\n",
-       "\n",
-       "[61 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "perc_gdp = military[['CAN-PercentGDP', 'USA-PercentGDP']]\n",
-    "perc_gdp"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "93db9fb9",
-   "metadata": {},
-   "source": [
-    "Next, we will use the `sns.heatmap()` function to generate a heatmap for the subsetted data. We will use the dataframe `perc_gdp` as an argument for the `data` parameter. Other parameters such as `cmap`, `linewidth`, and `linecolor` allow for customization of heatmap. The `cbar_kws` parameter alters components of the color bar, and the `vmin` and `vmax` parameters set the minimum and maximum values of the colorbar, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "4125b38d",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jl/2kuCR57OJ/qLgSy8mH/rV51SUYvmZkZmYZyD61W9JMSbdJmp2Wlcuq2cxsTKrOnXM23ZCOpUep3cBuETElLfNKrNnMzCqQfWq3mZk1Xx1SuwGOSafoviLVYLxpZmajknVqd9q2Wzqtt3la9ujUuINSzawS42qwZC731G4i4r708XHgJGCTIdp3UKqZWQ1lndotaYKkFdMxiwA7ADe+uGUzM6uz0m56Tand04AVJc0FDgImStor7XIabandklqp3QGcExFnS1oKOC91ROOB3wE/KatmM7Mx8aXsrmWd2h0RTwIb97g0MzPLTA0ua5mZWdM1OpuulUVm1s5ZZNZzPkvXNY+MzMysco0eGW3zqY9UXYJl5IIjjwbg20+eUXEllpMDl3pX1SUY9QhKXVTSDEm3S7pV0nsHez0zs8pUHYLqoNQhHUtvglK/DMyLiHWAycAlJdZsZmYVqENQ6keAb6b1z0XE38qq2czMqpF1UKqk5dL2r6X1v5S0Sl8rNjMbTtW5c86mG7XRBqVOACYBl0XERsDlwKGdGndQqplZPeUelPp34CmK6CCAX6b1ndp3UKqZWQ1lHZQaEQGcSTGhAWAr4OZ+1mxmZuXLOig17XcgcLykw4CHgA+XVbOZ2ZjUYOp07rIOSk3r7wbe0sPSzMwsMzWYY2FmZk3X6DigVvyLWTvHv1jP+Sxd1zwyMjOzyjV6ZLTlJz9UdQmWkYt+dCwAB9/8s2oLsawcPPkDVZdgZB6UKmlpSbPblr+lWXVmZvkYp/yXzGUdlBoRj0fElNYC3M2CG2DNzKwh6hCUCoCkdYCVgT+UVbOZmVUj66DUAXYBfp5SGczM8lH1s4r8PKNRG21QartdKPLszMysYXIPSgWKiQ/AhIi4eqj2ndptZlZPWQelth06nRGMipzabWaVUA2WzNUhKBVgZ+AdZdVqZmbVyj4oNW1bs1d1mZlZfhwHZGZmlWt0HFAr/sWsneNfrNdUg6nTufPIyMzMKtfokdE79vp41SVYRs75358AcPAdJ1ZcieXk4LV3q7oEo+GdkZlZP/g0XfeyTu1O66enr6+XdK6kFcuq2czMqpF1andafziwRUSsD1wPfKbEms3MrAK5p3a37h1eSsU4eBng/rJqNjMbi6ozUBuQk5p3andEPAN8CriBohOaDBzV35LNzKxsWad2S1qEojPaEFiN4jTdlzo17qBUM7N6yj21e0o67q70HKNfAJsN0b6DUs2s78ZJ2S+5yz21+z5gsqSV0n7bALf0s2YzMytf9qndkg4Bfi/pGeBu4ENl1WxmZtXIPrU7In7EghGUmVl2fNNr95xNZ2ZmlWt0HFAri8ysnbPIzPLT6M7IzKwffJque43ujN79uU9VXYJl5PQjjgTgqw+fVnEllpP/Wv49VZdg1CMo9f0pJPUmSd8uq14zM6tO7kGpKwDfBbZK618qaasSazYzswrkHpS6JnBHRDyU9vtd2zFmZlmQlP2Su6yDUoE7gXUlrZ5GTzu1HWNmZg2RdVBqRDxMEZT6c+APwBzg2U6NOyjVzKyecg9KJSLOjIg3RMQbgduA24do30GpZtZ3VT+ryM8zGqUxBKW2H7M88GmKSRBmZtYg2QelAodL2iB9/tWI6DgyMjOzeqpDUGqndszMslCH2Wq5c1CqmZlVrtFxQK34F7N2jn8xy0+jOyMzs37wabruNbozes/n9hp+J1tonHbE/wLww+fOr7gSy8lnxm1bdQmGrxmZmVkGskjtlrSbpNlty3OSpqRtG6f975R0hDweNrPMqAb/cpdFandEnBgRUyJiCrAH8JeImJ2OORL4OLB2Wga2aWZmNZdLane76cApAJJWBZaJiCsiIoCfUYSlmplZg+SS2t3u/RS5dQAvo8ita5mb1pmZWYPkktoNgKQ3AE9FxI2DHTwcp3abWRWqflaRn2c0SkOkdrfswoJREcB9wKS2ryeldZ3ad2q3mVkN5ZLa3Vq3M+l6EUBEPAA8JmnTNIvuA8Bv+lmzmZmVL4vU7uQtwL0R8ecBTX2aYmbeEsBv02Jmlo0anAXLXk6p3TOBTQdZPwtYr3eVmZlZbhodB9SKfzFr5/gXs/w0ujMyM+uHcT5P17VGd0bv2/szVZdgGTn18B8C8OuX3VFxJZaTne5bu+oSDAelmplZBuoQlPoNSfdKeqKsWs3MulH1Da2+6XVox9KboNQzgU1KrNPMzCqWdVBqaueKdPOrmZk1VL8nMLSCUn/N0EGpOw6y3swsS3U4DZY7B6WamVnlcg9KHW37Dko1M6uhvp6mk7RyRMwbJih1837WZGbWLZ+l616ZU7tPBi4H1pU0V9JHgemSbgduBe5nBEGpkr6TglaXTO0cXFbNZmZWjToEpR4AHNC7yszMLDdOYDAzs8o1OpuulUVm1s5ZZNZrTZnaLWlf4GNAADcAH46If/XjtT0yMjMzJL0M+BwwNSLWA8ZTzHDui0aPjHbe53NVl2AZ+cVhRwBwwbp/q7gSy8k2t61YdQk5mQAsIekZYEmKiWZ9kXVQqqQlJZ0t6VZJN0n6Vln1mpmNVdUhqCNcng8FSMue7e8hIu4DDgXuAR4AHo2I8/v1PaxDUOqhEfFqYEPgTZLeXmLNZmaN1B4KkJYZ7dslLU8RxbYGsBqwlKTd+1Vf1kGpEfFURFycPn8auAaYVErBZmYLt60pBgIPRcQzwGnAZv168X5PYGgFpcLQQakvigSStBzwTuDCsoozMxuLDE7B9eJ5RvcAm6bLIwK2Am4p9RvXphZBqZImUHRQRwxMaBiwn4NSzczGICKuBE6lOAN1A0X/MGPIg3qoLkGpM4A7IuKwYdp3UKqZ2RhFxEER8eqIWC8i9oiIf/frtbMPSpX0dWBZihuxzMyy05SbXquUdVCqpEnAl4HJwDVp2rc7JTOzhsk6KDUi5gL+k8PMrOEancBgZtYPPkvXvUZ3Rq34F7N2jn8xy4+DUs3MrHKNHhntuu++VZdgGTnpBz8AYNbURv/Y2yhNnTW/6hKMhndGZmb94Knd3cs6tTttO1fSdSpSu38kaXxZNZuZWTXqkNq9c0RsAKwHrESRaWdmZg2SdWp3auex9OkEYFGKx+GamWWj6hDUHgWlVqoWqd2SzgPmAY9TBPmZmVmD1CK1OyLeBqwKLAZs2alxp3abmdVTXVK7iYh/Ab9hwchqsH2c2m1mfTdOyn7JXV87I0krp49DpXaf0rZuoqRV0+cTgO0pQlbNzKxBSrvPKKV2TwNWlDQXOAiYKGmvtMtpDJPaDSwFnCFpMYqO82LaOjAzM2uG3FO7HwRe39vKzMx6qwZnwbLnbDozM6tco+OAWllkZu2cRWaWn0Z3RmZm/VCHm0pz1+jOaDendlubE9NI+U8b+xeHLbDJ1Q51yUH2Qaltx57R3paZmTVHHYJSkfQe4IkSazUzswplH5QqaSKwH/D1Eso0M+uaavAvd3UISv0a8D3gqXJLMzOzqmQdlJquG60VEaePpHEHpZqZ1VPuQalvBKZKmgNcCqwjaeYQ7Tso1cz6rupnFfl5RqM02qDUiDgyIlaLiNWBNwO3R8S0ftZsZmblK3Nq98nA5cC6kuZK+igwXdLtFMnb9zN8UKqZmS0Esg5KHbB9DrBe14WZmfVYHU6D5c5BqWZmVrlGxwGd6KBUG4TjX8zy0+jOyMysH3yWrnuN7oym77tP1SVYRk7+wWEA3LjpMtUWYllZ74rHqi7BqEFQqqSZkm5r27ZyWTWbmVk1ahGUCuzW2h4R80qs2cxs1Kq+odU3vQ6hV0GpZmbWfHUISgU4Jp2i+4rq0MWbmdmoZB2UmuyWTuttnpY9+lWsmdlISOOyX3KXe1AqEXFf+vg4cBKwSaf2ndptZlZPWQelSpogacX0+SLADkDHR487tdvMrJ5Ku88oBaVOA1aUNBc4CJgoaa+0y2kMH5S6GHBe6ojGA78DflJWzWZmVo2sg1Ij4klg495WZmbWW55X1b38r2qZmVnjNToOqBX/YtbO8S9m+Wl0Z2Rm1g8a55NM3Wp0Z7TbvvtWXYJlpPVIkbvf+oqKK7GcvPKSe6ouwfA1IzMzy0AdUrsXlTRD0u2SbpU0WJ6dmVllqk5XcALD0I6lN6ndXwbmRcQ6wGTgkhJrNjOzCtQhtfsjwDdTm89FxN96XKqZmVUs69RuSculdV+TdI2kX0papfQqzcxGoepnFfl5RqM32tTuCcAk4LKI2Ai4HDi0U+MOSjUzq6fcU7v/DjxFkWMH8EtgoyHad1CqmVkN9fU+I0krR8S8YVK7N2+ti4iQdCZF4OpFwFbAzf2s2cxsOHWYrZa73FO7AQ4Ejpd0GPAQ8OGyajYzs2pkndqd1t9N0VGZmVlDeWxpZmaVa3Q2XSuLzKyds8is1+owdTp3HhmZmVnlGj0y2tWp3dbmpDRSvmfaKyuuxHLyipl3V12CUe5suqOBHShy5dZL6zagmM49EZgD7BYRj0najZRTl6xPcT/RXcAf2tZPAk6IiH3KqtvMbLQ8tbt7WQelRsTjrfVp290suAHWzMwaog5BqQBIWgdYmReOlMzMrAH6fc2oFZT6a4YOSt1xkPW7AD+PiCitOjOzMfBsuu7lHpTabmBu3Ys4KNXMrJ5yD0oFnp/4MCEirh6mfQelmpnVUNZBqW2mM8yoyMysKp5N173SvoMpKPVyYF1JcyV9FJgu6XbgVuB+RhaUCkUn5c7IzKyhsg9KTdvW7FFZZmaWoUYnMJiZ9YNn03Wv0Z3RSQ5KtUE4/sUsP77qZmZmlWv0yGj6vvtUXYJl5OQfHAbAX7dap9pCLCsvvfD2rtvwbLrulTmb7mhJ8yTd2LZuA0mXS7pB0pmSlknrd5M0u215TtKUtG162v96SedKWrGsms3MrBpZB6VKmkAx+26LiFgfuB74TIk1m5lZBXIPSlVallIxXWUZivuTzMysQbIOSo2IZyR9CrgBeBK4A9irL5WamY3UOE/t7lbWQamSFgE+BWwIrEZxmu5Lfa3YzMxKl3tQ6pR03F3p0RG/ADbr1L5Tu83M6qmvnZGkldPHoYJS2x+sdx8wWdJK6ettgFs6te/UbjOrgjQu+yV3pV0zSkGp04AVJc0FDgImSmpd8zmNYYJSI+J+SYcAv5f0DMVjxz9UVs1mZlaN7INSI+JHtI2gzMyseRqdwGBm1g8OSu1eozujVvyLWbtexL+YWW/lf1XLzMwar9Ejo93326/qEiwjJ3z/+4CDUu2FHJSaB38HzcyscnVI7X5/Suy+SdK3y6rXzMyqk3tq9wrAd4GtIuK1wEslbVVizWZmo1b1Da1NuOk199TuNYE7IuKh9PXvOhxjZmY11u/uspXaDUOndrfy6e4E1pW0enq20U4djjEzsxrLOrU7Ih6mSO3+OfAHYA7wbKfGHZRqZlZPuad2ExFnRsQbIuKNwG1Ax3mYDko1sypIyn7JXe6p3e3HLA98mmIShJmZNUjWqd3J4ZI2SJ9/NSKc5WJm1jB1SO3u1I6ZWRbqMHU6d/4OmplZ5RqdTdfKIjNr59Rus/w0ujMyM+uHOsxWy12jO6Pd9t236hIsIyf+4AcA3DPtlRVXYjl5xcy7qy7BKPma0SjDUheRdFxaf4ukL7Uds52k2yTdKemLZdZsZmb9V/YEhmMZYVgqRTzQYmn9xsAnUgzQeOB/gbcDk4HpkiaXXLeZ2YhVHYLqoNRhjDIsNYClUgbdEhRRQY8BmwB3RsSfI+Jpiptid8TMzBqjiu6yU1jqqcCTwAPAPcChEfEP4GXAvW3Hz03rzMysIarojDqFpW5CEYK6GrAG8HlJa46mYQelmlkVqs6dczbdGAwRlrorcG5EPBMR84A/AlOB+3jhYyMmpXWDte2gVDOzGup7ZzREWOo9wJZp21IU0UC3AlcBa0taQ9KiFMneZ/S7bjMzK0/ZU7tPBi6neEDeXEkfpZgNdztFR3M/C8JS/5ciSPUmig7omIi4PiLmA58BzgNuAX4RETeVWbeZmfVXqTe9jiYsNSKeoJjQMFg75wDn9LA0M7Oe0bj8p07nzt9BMzOrXKPjgFrxL2btHP9ilp9Gd0ZmZv1Qh6nTuWt0Z7T7fvtVXYJlpPVIkRs2nVhxJZaT113xRNUlGL5mZGZmGSitM+phYveL2jEzy0nVIagOSh3asXSZ2D1EO2Zm1iCldUY9Suzu1I6ZmTVIv8duo03sNjPLXtUhqA5KHb3SErvBqd1mZnXV185oDIndo23fqd1mZjXU185oDIndZmbZq3qmnGfTDaEXid1DtGNmZg1SWgJDDxO7O7VjZmYN0eg4oFb8i1k7x79Yr9Vhtlru8j+RaGZmjdfokdH79/lc1SVYRn5+2BEAnPmKOdUWYll55z2rV12C4ZGRmZllIOugVEkvl3SxpJsl3SRp77LqNTMbq6qnbXtq99COpfug1PnA5yNiMsW9R3tJmlxizWZmVoGsg1Ij4oGIuCa19zhwC/Cysmo2M7Nq1CYoNY2UNgSu7EulZmYjpXH5L5mrRVCqpInAr4B9IuKxTo07KNXMrJ6yD0qVtAhFR3RiRJw2TPsOSjUzq6Gsg1JV3NZ8FHBLRDhOwcyyVPWzivw8oyH0KCj1TcAewJaSZqflHWXVbGZm1cg6KDUiLgXy79LNzKwrjY4DMjPrhzrcVJq7RndGrSwys3bOIjPLj7tzMzOrXKNHRu/b+zNVl2AZOfXwHwJw8Xp+npEtsMWNE7tuY1wNZqvlrtSRUY/CUheX9CdJ16Ww1EPKrNnMzPqv7NN0x9J9WOq/gS0jYgNgCrCdpE1LrtvMzPqo1M6oR2GpkaZ+AyySliizbjMz668qJjCMOixV0nhJs4F5wAUR4bBUM8uGavAvd1V0RqMOS42IZyNiCjAJ2ETSen2v2szMStP3zmgsYaltxz4CXMyLr0MBTu02M6urvndGYwhLXUnScmn9EsA2FNl2L+LUbjOrQtWPFPdjx4fRo7DUVYGLJV2f1l8QEWeVWbeZmfVXqTe99igs9XqKJ7yamVlDNTqBwcysH+rwvKDcNbozasW/mLXrRfyLmfVW/le1zMys8Ro9MnJQqrVrjZQvWf+fFVdiOXnr9Ut03Yb8d33X/B00MzMAJC0n6VRJt6bA6jf267WzT+1uO268pGsleVq3mVk5DqcIH3g1sAFwS79euA6p3S1708dvjJnZSEnKfhnBe1gWeAtwFEBEPJ1Sb/oi+9RuAEmTgO0pOjIzM+u9NYCHgGPSWaifpjScvqhFajdwGHAA8Fz/yjQza4727M607DlglwnARsCREbEhxe/jL/arvipm030EOELSV4AzGDy1e3ngD5J+B0wG5kXE1ZKmDdVw+ubuCbDDDjvwvlLKNzN7oXE1yH6LiBnAjCF2mQvMbXtEz6n0sTOqQ2r3m4B3SZoDnAJsKemEDm07KNXMbAwi4q/AvZLWTau2Am7u1+tnn9odEV+KiEkRsTqwC3BRROze77rNzBYCnwVOTMHUU4D/7tcLl3qaLqV2TwNWlDQXOIgimXuvtMtpvDC1+5iU2i0WpHabmVkfRMRsBjxHrl+yT+0esM9MYGbXhZmZ9VAdHuudu/yvupmZWeM1OpvOqd02mF5kkZlZbzW6MzIz64c6PNY7d43ujJzabe1aI+XfvfrvFVdiOdn61hWqLsEo8ZpRr0JSJc1J62dLmlVWvWZmVp0yx5bH0ruQ1C0iYkpE+E5WM8tO1SGovQhKrVppnVGvQlLNzKz5+n3VbSwhqQGcL+nqQYL9zMysAfrdGX0E+LSkq4GlGTwkdQ3g85LWTNveHBEbAW8H9pL0lk6Nt6fSzprly0tm1h+qwb/c9bUzGkNIKhFxX/o4j+I60yZDtO+gVDOzGuprZzTakFRJS0laum39tsCNA9s1M7N6K+0+o16EpKZTdaenmSATgJMi4tyyajYzGwvf9Nq90jqjXoSkRsSfgQ16XJqZmWXG3bmZmVWu0XFADkq1wTj+xSw/je6MzMz6YVwNpk7nrtGdkYNSrV1rpHzuWg9UXInlZLu7Vq26BKMeQanLSTpV0q1p2xvLqtnMzKpRh6DUwyluiH01xcy6W0qs2cxs1KRx2S+5yzooVdKywFuAo1KbT0fEI2XVbGZm1cg9KHUN4CGKG2KvlfTTlMRgZmYNkntQ6gRgI+DIiNiQosP6Yp9rNjMbUtXPKvLzjEZpDEGpc4G5EXFl2u9Uis5pUE7tNjOrp6yDUiPir8C9ktZN+20F3Nypfad2m5nVU9ZBqWnbZ4ETJS0K/Bn4cFk1m5mNhZys1rWsg1LTttmkZxuZmVkzuTs3M7PKNToOyEGpNhjHv1iv1WG2Wu48MjIzs8o1emT0H3t/tuoSLCO/PPx/ALjwNQ9XXInlZKtblq+6BMMjIzMz6xFJK4712KxTuyWtK2l22/KYpH3KqtnMbCyqDkGtOihV0jslPQTcIGmupM1G20bWqd0RcVtETImIKWn9U+k4MzPLxzeAzSNiVYoA7G+OtoGsU7sHHLsVcFdE3F1OxWZmNkbzI+JWgBTftvRoG+j3BIZWaveveXFq944Uqd1LAvum1O52u1Dk2ZmZZUV+7PjKkvbr9HVEfH+4BnJP7QYgRQG9C/jlUI07KNXMrBI/ofid3loGfj2svo6M0jBuWwBJ6wDbp03Pp3YD8yS1Urv/nLa/HbgmIh4cpv0ZwAyAQw45JHr/DszMbKCIOKTbNrJO7W47dDo+RWdmmRonZb+UTdIWkn4l6aa0nCpp2kiPL3Nq98nA5cC6aarfR4Hpkm6n6Gju54Wp3RNTavdVtKV2p85pG4qUbzMzy4yk7YGjgbMoznTtBpwDHC3pHSNpow6p3U8CK/SwNDMz660vADtFxHVt62ZLmgX8D0XHNKRGxwGZmfWDn2fESwd0RABExPWSVhlJA43ujFpZZGbtnEVm1nNPjnHb8xrdGZmZWV+sJemMQdYLWHOQ9S/S6M7o/ft8ruoSLCM/P+wIAC7bcH7FlVhONru2+1+Dfp4ROw6x7dCRNFBqZyTpaGAHYF5ErJfWbUAxpXsiMAfYLSIek7QIRXbdRqmun0XEN9Mx+wIfo4gNugH4cET8q8zazcxsZCLikm7bKPuq27F0GZYq6WXA54CpqUMbTxENZGZmGZC0o6S92r6+UtKf0/K+kbRRamfUw7DUCcASaduSFPcomZlloerHQ1T9CAngAKD9mtFiwOuBacCnRtJAFfMRW2Gp8OKw1CcpwlLvAQ6NiH9ExH0U5xzvSdsejYjz+1uymZkNYdGIuLft60sj4u8RcQ+w1EgaqKIzGlVYqqTlKTqvNdK2pSTtPljDDko1M6vEC+6XiIjPtH250kga6HtnFBG3RsS2EbExRd7cXWnT82GpETEPaIWlbg38JSIeSkGqpwGDPkUwImZExNSImDp16tTy34yZmQFcKenjA1dK+gTwp5E00Pep3ZJWjoh5Q4SlHt8WlnoYxfWjTSUtCfyT4iF7HvaYWTb8PCP2BX4taVfgmrRuY4prRzuNpIGyp3afTHEBa0VJc4GDKAJRW7MuTuOFYanHpLBU8cKw1FMp3uB84FrSYyLMzKx66WzWZpK2BF6bVp8dEReNtI1SO6MehqUeRNGRmZlZplLnM+IOqF2jExjMzPqhD1OnG6/RnVEr/sWsXS/iX8xsAUmLRcS/u2nD3bmZmXXrcgBJx4+1gUb/ibjLPntXXYJl5JTDikuVs6Y2+sfeRmnqrO6Dc/vxWO/MLZpm0m0m6T0DN0bEsE/q9v+VZmbWrU9SPGp8OeCdA7YFxczpIZXWGfUwsXtv4OMU071/EhGHlVWzmZmNXkRcClwqaVZEHDWWNsq8ZnQs3Sd2r0fREW0CbADsIOlVJdZsZjZqYlz2S58cL+lzkk5Ny2fTYGNYpVXYo8Tu1wBXRsRTETEfuAR40flIMzPLwv9RDCj+Ly0bAUeO5MB+XzNqJXb/mhcndu9Ikcq9JLBvRPxD0o3ANyStQBEF9A4cBWRmlqvXR8QGbV9fJOm6kRzY76ndo0rsjohbgG8D5wPnArPTfoNyareZVUFS9kufPCtprbbvy5oM8Tu7XV87ozEkdhMRR0XExhHxFuBh4PYh2ndqt5lZdb4AXCxppqRLKKKBPj+SA/t6mm4Mid3tx7yC4nrRpv2s2czMRiYiLpS0NrBuWnXbSJMZypza3ZPEbuBX6ZrRM8BeEfFIWTWbmVl3Uudz/bA7DjBkZyRpPHBTRLx6DAX1KrF789G+tplZP/l5Rt0bsjOKiGcl3SbpFelZ5rXSin8xa9eL+Bcz662RnKZbHrhJ0p+AJ1srI+JdpVVlZma1o2La3m7AmhHx1XSt/6URMeyjx0fSGX2l2wKr8v59Pld1CZaR1iNFbtx0mYorsZysd8VjXbfh5xk97/+A5ygmpH0VeBz4FfD64Q4ctjOKiEu6rc7MzBYKb4iIjSRdCxARD0tadCQHDtudS9pU0lWSnpD0tKRnJQ37p4SkoyXNSykKrXUbSLpc0g2SzpS0TFq/qKRj0vrrJE1rO2bjtP5OSUeoj3dvmZnZqDyTJr4FgKSVKEZKwxrJ2PKHwHTgDorcuI9RTMUezrGMPCj14wBp/TbA97Rg3Htk2r52Wga2aWZWqarTFTJKYDiC4nf7ypK+AVwK/PdIDhzRic6IuBMYHxHPRsQxjKBDGGVQ6mSKO3VJCQyPAFMlrQosExFXREQAPwN2GknNZmbWXxFxInAA8E2KrNGdIuKXIzl2JBMYnkrn/GZL+k56gbFeresUlHod8K50o+zLKVJfX04xvJvbdvxc4GVjfG0zMyuRpJcA8yji3lrrFomIZ4Y7diSdyh5pv89QTO1+OQtGNKPVKSj1aIqOZhZFDNBljDBcr50clGpmFRhXg399cg3wEEWG6B3p8zmSrpG08VAHjmQ23d2SlgBWjYhDuqkyIm4FtgWQtA6wfVo/H9i3tZ+kyyjezMPApLYmJgH3DdH+DGAGwCGHHBLd1GpmZqN2AXBqRJwHIGlbisHLMRTTvt/Q6cCRzKZ7J8WjG85NX0+RdMZYqpS0cvr4gqBUSUumgFQkbQPMj4ibI+IB4LE0o0/AB4DfjOW1zcysdJu2OiKAiDgfeGNEXAEsNtSBI7lmdDDF84ZmpsZnS1pjuINGGZS6MnCepOcoRj57tDX1aYqZeUsAv02LmVk2fMfJ8x6QdCBwSvr6/cCDabr3kFO8R9IZPRMRjw74Zg97CmyUQalzWBA5PnDbLGC94cs0M7OK7Uox8Ph1+vqPad14YOehDuzYGUk6B9iLIpduV2B8ek7F5ygmGJiZmT0vIv4GfLbD5juHOnaokdExwHnA8RQjk38DJ6V1Xxt9mf3XyiIza9eLLDKzduN8mg54PnHhAOC1wOKt9RGx5XDHdpzAkG5U2giYSDHr7ecU5wEfphgxmZmZtTsRuBVYAzgEmANcNZIDh7tm9DTFvUWLUXRKtZouvcs+e1ddgmWk9XyreVsPennSFlIr/+62qktokhUi4ihJe6eQ7UskddcZSdoO+D5wBrBRRDw1mookHQ3sAMyLiPXSug0opnNPpOgxd4uIx1LCw4+BqRQzLvaOiJnpmG9QTOlePiImjqYGMzPrq1bSwgOStgfuB14ykgOHus/oy8B/RMQXR9sRJcfSm6DUMymmlpuZZWkcyn7pk69LWhb4PLA/xe/8fUZy4FDXjDaPiJvGWlEvglLT11ekm1/NzCxvD0fEoxFxY0RsEREb8+J+YFD9fjxhKygVBg9KnZBuqN24bZuZmdXD/4xw3YuM5KbXXvoIcISkr1Bci2oPSn0NRVDq3XQRlArsCbDDDjuwSy8qNjMbxsKewCDpjcBmwEqS9mvbtAzFDa/D6uvIKCJujYht09DtZOCutH5+ROwbEVMiYkdgOYqg1NG2PyMipkbE1KlTp/a0djMz62hRiolpEyieyNBaHgPeN5IG+joykrRyRMwbLCgVUEQ82R6U2s/azMxsbNqmcR8bEXePpY3SOqNeBaWmB/rtCiyZ2vlpRBxcVt1mZqPlBIbnLSZpBrA6bf3LSBIYSuuMehiUegBFvISZmeXtlxRnvH7KKK/793sCg5mZNdf8iDhyLAc2ujNqxb+YtXP8i/Wa+n6XTLbOlPRpilCDf7dWRsSw9xo1ujMyM7O++mD6+IW2dQGsOdyBje6Mpu+7T9UlWEZO/sFhADyw5drVFmJZWfWiO6ouoTEiYtingHfS6M7IzKwfPJuukG7T2Q94RUTsmR7Ium5EnDXcsaWd6JR0tKR5km5sW7eBpMsl3SDpTEnLpPWLSjomrb9O0rS0fklJZ0u6VdJNkr5VVr1mZta1YyiSdTZLX98HfH0kB5Z51e1YepPafWhEvBrYEHiTpLeXWLOZmY3dWhHxHdKjJNITH0Y0bCytM+pFandEPBURF6f1TwPXAJPKqtnMzLrytKQlSA9ilbQWbbPqhlKb1G5JywHvBC7sT6lmZiMzTsp+6ZODgHOBl0s6keL39YhCC/rdGX0E+LSkqylC9NpTu+dSpHYfxoDUbkkTKIJVj4iIP3dqXNKekmZJmjVr1qxy3oGZmQ0qIi4A3gN8iOJ39tTWU7uHU5fU7hnAHRFx2DDtO7XbzKwikt5NkcJwdppBN1/STiM5tq+dkaSV08cXpXZLWip9/oLUbklfB5ZlhI+uNTPrN0nZL31yUEQ82voiIh6hOHU3rKxTuyVNAr4M3Apck76hP4yIn5ZVt5mZjdlgA5wR9TNZp3ZHxFxGOC3QzMwqN0vS94H/TV/vBVw9kgOdwGBm1qVx/pu55bPAV4CfU0zvvoCiQxpWozujVhaZWTtnkZn1nqTxwFkRscVYjnfuuZmZdS0ingWek7TsWI5v9Mho1333rboEy8hJP/gBAP98+5RqC7GsLPHb2V230cfZarl7ArhB0gXAk62VEfG54Q4sczbd0cAOwLyIWC+t24BiOvdEYA6wW0Q8JmlR4MfAVOA5YO/WjVKSzgVWTbX+Adgr9cBmZpaX09IyamWOjI4Ffgj8rG3dT4H9I+ISSR+hCEr9Cm1BqelepN9Ken1EPAfsnDosAadSxAidUmLdZmY2BhFxXMqme0VEjOqRylkHpaavH0v7TAAWJQXwmZnlYpzGZb/0g6R3ArMp8umQNEXSGSM5thZBqZLOA+YBj1OMjszMLD8HA5tQDCiIiNmM4JHjUJOg1Ih4G8V1o8WALTs17qBUM7NKPdMeB5Q8N5ID6xKUSkT8C/gNC0ZWg7XvoFQzs+rcJGlXYLyktSX9D8XgYlhZB6VKmihp1bR+ArA9RU6dmVk2xqHslz75LPBaigfqnQQ8yghDrrMOSgWWAs6QtBhFx3kxqQMzM7M8SFoc+CTwKuAG4I0RMX80beQelPog8PreVmZmZj12HPAMxb2gbwdewygf+9PoBAYzs35wAgOTI+J1AJKOAv402gYa3Rm14l/M2vUi/sXMXuCZ1icRMX8snXOjOyMzM+uLDSS1AgoELJG+FhARscxwDTS6M9p9v/2qLsEycsL3vw/AYju9ueJKLCf//vWlXbcxbiE/TRcR47tto7Sp3ZKOljRP0o1t6zaQdLmkGySdKWmZtH5RScek9ddJmjZIe2e0t2VmZs1R5n1GxwLbDVj3U+CL6ULX6RRBqdAWlApsA3wv3YsEgKT3UESTm5lZA2UflCppIrAf8PWyajUz64Zq8C93dQhK/RrwPeCpfhZqZmb9k3VQqqQpwFoRcXqf6zQzsz7q62y6iLgV2BZA0joUWXOk2IjnnxEu6TKKoNS3AlMlzUm1rixpZkRMG6x9SXsCewLssMMO7F7aOzEzW2Bhn03XC1kHpUbEkRGxWkSsDrwZuL1TRwRO7TYzq6vcg1LNzGwhkHVQ6iD7rNd1YWZmPebTdN3r9wQGMzOzF2l0HFAr/sWsXS/iX8ystzwyMjOzyjV6ZPTBz39h+J1soXHc974LwEo7b1txJZaTh35xftdtyH/Xd83fQTMzq1z2qd2SZkq6TdLstKxcVs1mZlaNWqR2A7tFxJS0zCuxZjOzURsnZb/kLvvUbjMza746pHYDHJNO0X1FY3m4upmZZS3r1O60bbd0+m7ztHSMCpK0p6RZkmbNmjWrnHdgZjaApOyX3PW1M4qIWyNi24jYGDgZuCutnx8R+6ZrQjsCy1GkdhMR96WPjwMnAZsM0b6DUs3Maijr1O502m7FtH4RYAfgxkEbNzOz2so9tXuxtH4RYDzwO+AnZdVsZjYWdZitlrusU7sj4kmKyQxmZtZgTmAwM7PKNTqbrpVFZtauF1lkZu3G4dN03fLIyMzMKtfokdFH9z+w6hIsI0cd+m0AVt/tnRVXYjmZc+KZVZdg1CModVFJMyTdLulWSe998auZmVmd1SEo9cvAvIhYhyLD7pISazYzG7Wq0xWcwDCEHgalfgT4Ztr2XET8rayazcwWZpLGS7pW0ln9fu2sg1IlLZe2f03SNZJ+KWmVvlZsZrbw2Bu4pYoXzj0odQIwCbgsIjYCLgcO7dS4g1LNrArjNC77ZTiSJgHbU1xO6bvcg1L/DjxFER0E8EtgoyHad1CqmdnYHAYcADxXxYtnHZQaEQGcSZFxB7AVcHM/azYza4L2M0dp2bNt2w4UE8Wurqq+3INSAQ4Ejpd0GPAQ8OGyajYzGwvVIIEhImYAMzpsfhPFdft3AIsDy0g6ISJ271d9WQelpm13A2/pXWVmZtYuIr4EfAkg3ee5fz87InAckJmZZaDRcUCt+Bezdo5/sV5r0vOMImImMLPfr+uRkZmZVa7RI6OPf+FLVZdgGfnJd78JwLp7vLviSiwntx1/etUlGJkHpUpaWtLstuVvaVadmVk2xknZL7nLOig1Ih5PN8JOiYgpwN0suAHWzMwaog5BqQBIWofifqQ/lFOxmZlVJeug1AHH7gL8PKUymJllQzX4l7vcg1Lb7UKRZ2dmZg2Te1AqUEx8ACYMl5vk1G4zs3rKOii17dDpjGBU5NRuM7N6qkNQKsDOwDvKqtXMrBt1mDqdu+yDUtP2NXtUlpmZZchxQGZmVrlGxwG14l/M2jn+xXpNI3istw3N30EzM6tco0dGDkq1dq2R8pQP71xxJZaT2cf8ouoSjIZ3RmZm/TCuBgkHucs6tTttm57WXy/pXEkrllWzmZlVI+vUbkkTKKaCbxER6wPXA58psWYzM6tA7qndSstSkgQsA9xfVs1mZmMxTvkvucs6tTsingE+BdxA0QlNBo7qb8lmZla2rFO7JS1C0RltCKxGcZqu4xQ5B6WamdVT7qndU9L2u9JzjH4BbDZE+w5KNbO+k5T9krvcU7vvAyZLWik1sQ1wSz9rNjOz8mWd2h0R90s6BPi9pGeAu4EPlVWzmZlVI/vU7oj4EWkEZWZmzeQEBjOzLjmBoXuN7oyc2m2DcRaZWX6c2m1mZpVr9MjIqd3WrjVSfv1HO13OtIXRVUed3HUbdZg6nbs6BKW+P4Wk3iTp22XVa2Zm1ck9KHUF4LvAVhHxWuClkrYqsWYzM6tA7kGpawJ3RMRDab/ftR1jZpaFcVL2S+6yDkoF7gTWlbR6epzETm3HmJlZQ2QdlBoRD1MEpf4c+AMwB3i2U+MOSjUzq6e+zqaLiFuBbQEkrQNsn9bPB/Zt7SfpMoqgVCLiTODMtH5PhuiMImIGMAPgkEMOiVLehJnZAL7ptXu5B6W2H7M88GmKSRBmZtYgWQelJodL2iB9/tWIuL2sms3MrBp1CEr1HYpmljXf9No9xwGZmVnlGh0H5KBUG0wv4l/MrLc8MjIzs8o1emT0iQMclGoL/Pg7xUh56kd2qbgSy8mso0/pug1P7e6eR0ZmZla5MlO7Xy7pYkk3p8TtvdP6l0i6QNId6ePyab0kHSHpzpTSvVFbWx9M+98h6YNl1WxmZtUoc2Q0H/h8REwGNgX2kjQZ+CJwYUSsDVyYvgZ4O7B2WvYEjoSi86K4R+kNwCbAQa0OzMwsB5KyX3JXZmr3AxFxTfr8ceAW4GUUQanHpd2Oowg/Ja3/WRSuAJaTtCrwNuCCiPhHyqq7gBc/msLMzGqsL9eMJK0ObAhcCawSEQ+kTX8FVkmfvwy4t+2wuWldp/VmZtYQpXdGkiYCvwL2iYjH2rdFRAA9CzR1areZVaHqZxX5eUbDkLQIRUd0YkScllY/mE6/kT7OS+vv44XPKpqU1nVa/yIRMSMipkbE1KlTp/bujZiZWanKnE0n4Cjgloj4ftumM4DWjLgPAr9pW/+BNKtuU+DRdDrvPGBbScuniQvbpnVmZtYQZd70+iaK9O0bJM1O6/4f8C3gF5I+CtwN7Jy2nQO8g+Lprk8BHwaIiH9I+hpwVdrvqxEx8HHmZmaV8U2v3SsztftS6PhfaKtB9g9gr0H2JSKOpngarJmZNVCj44Ba8S9m7XoR/2JmvdXozsjMrB9qMFkte43ujByUau0clGqD8Ug5Dw5KNTOzytUlKPVcSY9IOquses3MxqrqG1p90+vQehKUmnyXYpq4mZk1UB2CUomIC4HHy6rVzMyqVYegVDMzazgHpZqZdUk1+Je7OgSljpiDUs3M6qkOQalmZtZw2QelAkj6A/BqYKKkucBHI8LJ3WaWhTpMnc5dXYJSN+9haWZmlhknMJiZWeUanU3n1G4bjLPIrNf8PKPueWRkZmaVa/TIyKnd1q41Ul57j52qLcSycsfxv666BKMGQamSpki6PLVxvaT3l1WzmdlYSMp+yV0dglKfAj4QEa8FtgMOk7RciXWbmVmfZR+UGhG3R8QdqZ37KRIbViqrbjMz67++XDPqMij1+RQGSZsAiwJ3lVyymdmI+abX7tUmKDXl2B0PfDginut5oWZmVplaBKVKWgY4G/hyOoXX6fWc2m1mVkPZB6VKWhQ4neJ60qlDvaZTu83M6qkOQak7A28BVpD0obTuQxHRatPMrFJ1eF5Q7rIPSo2IE4ATeludmZnlxHFAZmZWuUbHATko1Qbj+BfrNU/t7p5HRmZmVrlGj4wclGrtWiPlVd7/toorsZw8+HM/NDoHje6MzMz6wafpuleH1O5XSrpG0uzUzifLqtnMzKpRh9TuB4A3RsQU4A3AFyWtVmLdZmbWZ2XeZ/QAKeQ0Ih6X1J7aPS3tdhwwEziQttRu4ApJrdTuB9qaXQxPujCzzPim1+715Rd7l6ndrVN+16ft306PkjAzs4aoRWp3RNwbEesDrwI+KGmVwfZzUKqZWT3VIrW7JY2IbgQ2H+z1HJRqZlUYp/yX3NUhtXuSpCVSm8sDbwZuK6tuMzPrvzqkdr8G+J6koAhePTQibiixbjMz67M6pHZfAKzf2+rMzHrHs+m652nSZmZWuUbHATm12wbjLDKz/HhkZGZmlWv0yMip3dauNVJe9n1bVlyJ5eTRUy/qug0HpXYv+6DUtvaWkTRX0g/LqtnMzKpRh6DUlq8Bvy+xXjMzq0gtglIlbUyRYXcu4GgFM8uKT9N1L/ugVEnjgO8B+/ejVjMz6786BKV+GjgnIuaO4LUclGpmVkN1CEp9I/AZSXOAQyny67412Os5KNXMqqAa/Mtd9kGpEbFbRLwiIlanOFX3s4j4ImZm1hh1CEo1M7OGyz4odcA+xwLHdlubmVkveTZd9xwHZGZmlWt0HJCDUm0wvYh/MbPeanRnZGbWD/Jpuq41ujNyUKq1a42UJ77nrRVXYjl54rRLqi7BqElQqqRnJc1Oyxll1WxmZtWoS1DqPyNiSlreVWLNZmZWgVoEpZZVo5lZL4yrQcJB7rIPSk2fL54y566QtFP5FZuZWT+VPoFhYFBq+6yTiAhJwwWlArwyIu6TtCZwkaQbIuKukko2M7M+q0NQKhHR+vhnitN6G3Z4Pad2m1nfjZOyX3KXfVCqpOUlLZbaXJEi8+7mwV7Tqd1mZvVUh6DU1wA/lvQcRef5rYgYtDMyM7N6yj4oNSIuA17X2+rMzHon/5Ng+XNQqpmZVa7RcUAOSrXBOP7FLD+N7ozMzPrDJ+q61ejOyEGp1q41Ul76vdOqLcSy8vivZlZdguFrRmZmloG6pHa/QtL5km5J7a1eVt1mZqMlKfsld3VJ7f4Z8N2IeA2wCQtSG8zMrAFK64wi4oGIuCZ9/jjQntp9XNrtOGCn9Pnzqd0RcQWwnKRVUwc2ISIuSG09ERFPlVW3mZn1Xx1Su9cBHpF0mqRrJX1X0vh+1G1mZv1Remc0MLW7fVtKXRgutXsCsDmwP/B6YE3gQx1ey0GpZtZ3qsGSuzqkds8FZkfEnyNiPvBrYCMG4aBUM7N6yj61G7iK4vrRSmm/LemQ2m1mZvWUfWp3RDwraX/gwtTBXQ38pMS6zcxGRbU4EZa37FO707YLgPV7V52ZmeXECQxmZla5RmfTObXbBuMsMuu1GgQcZM8jIzMzq1yjR0Z7fsGp3bbAjO8WI+WJ73lrxZVYTvx8qzxkH5QqaQtJs9uWf0naqay6zcxGr+pbWut/22v2QakRcXFETImIKRT3GD0FnF9i3WZm1mfZB6UOaPZ9wG8dlGpm1ix9uWbUZVDqA23rdgHa0xzMzCrnm167V4eg1FY7qwKvA84bYh8HpZqZ1VAdglJbdgZOj4hnOr2eg1LNzOqpDkGpLdOBk8uq18xsrKqeJ1f/uXQ1CEqF5685vRzwDQFmZg1Ul6DUORSTGczMrIEcB2RmZpVrdBxQK/7FrJ3jX6zX5KTUrnlkZGZmlWv0yOjjDkq1Nj9JI+X523vavy0w4Wzfk5gDj4zMzKxy2ad2p23fSW3ckvbxCVozswbJPrVb0mYU9yytD6wHvB7wA2nMzBqkzPuMHiCFnEbE45LaU7unpd2OA2YCB9KW2g1cIamV2h3A4sCiFPctLQI8WFbdZmaj5aDU7vXlmlE3qd0RcTlwMUXH9gBwXkTc0o+6zcysP7JP7Zb0KuA1FMGpLwO2lLR5h32d2m1mVkN1SO1+N3BFRDwREU8AvwXeONjrObXbzKogKftlBO9h0Eln/VKH1O57gLdKmpA6t7dSPDXWzMx6p9Oks76oQ2r3qcCWwA0Up/TOjYgzS6zbzGyhM8Sks5v78frZp3ZHxLPAJ3pbnZlZ79RhLp2kPSlum2mZEREzOuy7OgsmnfVFo+OAfuKgVBuE419sYZQ6nkE7n3ZDTTork+OAzMwM6DjprC8aPTL66P4HVl2CZeSoQ78NwLyt1624EsvJyr+7reoSsjDEpLO+aHRnZGbWDw1JYBh00llEnNOPF69LUOq3Jd2YlveXVbOZ2cIqIi6NCEXE+hExJS196YigHkGp2wMbAVOANwD7S1qmxLrNzKzPSuuMIuKBiLgmff44xY2qraDU49JuxwE7pc+fD0qNiCuAVlDqZOD3ETE/Ip4Erge2K6tuMzPrv+yDUoHrgO0kLSlpRWALXhgbZGZmNZd9UGpEnE+RznAZcDJwOfBsh9dyUKqZWQ3VISiViPhGupi2DcXNzrcP9noOSjWzKlQdgtqLoNSqZR+UKmm8pBVSm+tTPPH1/LLqNjOz/qtDUOoiwB9Sz/4YsHtEzC+xbjMz67M6BKX+i2JGnZlZlhpy02ulnE1nZmaVa3QcUCuLzKyds8jM8tPozsjMrB98kq57je6M9vzCl6ouwTIyIz3f6q9brVNxJZaTl1446J0i1mc5BaW+WtLlkv4taf8BbW0n6bYUovrFwV7PzMzqK6eg1H8AnwMObW9E0njgfymCVCcD01M7ZmZ5kPJfMpdNUGpEzIuIq4BnBjS1CXBnRPw5Ip4GTkltmJlZQ+QUlNpJpwBVMzNriOyDUkf5Wg5KNTOroZyCUjvpGKA6kINSzawKqsGSu5yCUju5Clhb0hqSFgV2SW2YmVlDZBOUKumlwCxgGeA5SfsAkyPiMUmfAc4DxgNHR8RNJdZtZmZ9llNQ6l8pTsEN1tY5FKneZmbZcVBq9xyUamZmlWt0HFAr/sWsneNfzPLT6M7IzKwffJque43ujByUau1aI+W/b/OaiiuxnKxwwS1Vl2D4mpGZmWWgLqndR0uaJ+nGsuo1MxurqjNQG5CTmn9qd3IssF2JtZqZWYXqkNpNRPyeorMyM7MG6ssEhi5Tu83MMleD82CZc2q3mZlVrg6p3SPm1G4zs3qqQ2q3mZk1XF1Su08GpgErSpoLHBQRR5VYu5nZiPmKUffqkto9vYelmZlZZhodB+SgVBuM41/M8tPozsjMrB9Uh4iDzDW6M/rEAQ5KtQV+/J1ipDx/e8+0tAUmnO3bQHLgoFQzM6tc9kGpndoxM8uFavAvd3UISu3UjpmZNUT2QalDtGNmZg1Rq6DUAe2YmWUh/5Ng+atNUOpQ7bTt46BUM7MaqkVQaod2XsRBqWZm9ZR9UOoQ7ZiZZUI1WPKWfVAqsP5g7UTEOSXWbmZmfVSHoNSh2jEzswZwAoOZmVWu0dl0rSwys3bOIrNec05q9zwyMjOzyjV6ZPSpA/9f1SVYRo789n8DsPR7p1VbiGXl8V/NrLoEox5BqYtL+pOk61I7h5RVs5nZWFQdguqg1KH1Kij138CWEbEBMAXYTtKmJdZtZmZ9Voeg1IiIJ9KXi6RlRBFCZmZWD32ZwNBtUKqk8emG13nABRHhoFQzswapRVBqRDwbEVMobordRNJ6ZdRqZmbVqEVQaktEPAJcDGzX4fWc2m1mVkN1CEpdSdJy6fMlgG2AWwfb16ndZlaFqiNQ6x+TWo+g1FWB4ySNp+g8fxERZ5VYt5mZ9VkdglKvp5j8YGZmDdXoBAYzs35wNl33Gt0ZteJfzNo5/sUsPw5KNTOzyjV6ZOSgVGvXGilPmv6OiiuxnMw92Q+NzoFHRmZmVrnsU7vb2hsv6VpJntZtZtYwdUjtbtmbImzVzMwaJvvUbgBJk4DtgZ+WVa+Z2VhV/awiP89ohLpN7QYOAw4AniujPjMzq1b2qd2SdgDmRcTVI3gtB6WamdVQHVK73wS8S9Ic4BRgS0knDLajg1LNzOop+9TuiPhSREyKiNWBXYCLImL3Eko2M7OKZJ/aPfDUnpmZNU8dUrvb95kJzOy2NjOzXnJQavecwGBmZpVrdDadU7ttMM4iM8uPR0ZmZla5Ro+MPnHAl6ouwTLy4+98E4BX7b5jxZVYTu48YcgJvdYntQhKlTRH0g2SZkvy3axmZg1Tp6DULSJiSkT4blYzy4pqsOSuFkGpZmbWbHUJSg3gfElXS9qznCrNzKwq2QelJm+OiI2At1Oc7ntLh9dyUKqZWQ3VISiViLgvfZwHnA5s0mE/B6WaWd9VfT3I14yG0KugVElLSVq69TmwLXBj7ys2M7OqZB+UCqwInF70bUwAToqIc0us28zM+qwOQamPARv0sDQzM8uM44DMzKxyjY4DasW/mLVz/ItZfhrdGZmZ9YOfZ9S9RndGDkq1dq2R8rp7vLviSiwntx1/etUlGPUJSl1O0qmSbpV0i6Q3llW3mZn1X12CUg8Hzo2IV1PMrLulxLrNzEap6lta63/ba/ZBqZKWBd5CcQMtEfF0RDxSVt1mZtZ/dQhKXQN4CDhG0rWSfpqSGMzMrCHqEJQ6AdgIODIiNgSeZMGpPTOzylV9Aq7+J+nqEZQ6F5gbEVemr0+l6JwGez2ndpuZ1VD2QakpJuheSeumVVsBN3fY16ndZmY1lH1Qajq191ngREmLAn8GPlxi3WZm1md1CEolImYDHuqYmTWUg1LNzKxyjY4DclCqDcbxL2b5aXRnZGbWD3WYOp27RndGH93ftyPZAkcd+i0A1vvg+yquxHJy43GnVl2C4WtGZmaWgexTuyWtK2l22/JYmvZtZpYFKf8ld9mndkfEbRExJSKmABsDTwG+Am1m1iDZp3YPsBVwV0TcXVbdZmbWf3VI7W63C3Byb6szM7Oq1SG1u9XOosC7gF8OsY+DUs3MaqgOqd0tbweuiYgHO+3goFQzs3rKPrW7zXR8is7MMlT1s4qa8DyjWqR2pye7bgN8osR6zcysInVJ7X4SWKF31ZmZWU6cwGBmZpVrdDZdK4vMrJ2zyMzy45GRmZlVrtEjo499wandtsBPv+vUbnsxj5TzkH1Qatq2b2rjRkknS1q8rLrNzEZLUvZL7rIPSpX0srR+akSsB4yniAUyM7OGqEtQ6gRgCUkTgCWB+8uq28zM+i/7oNSIuI9itHQP8ADwaEScX161ZmbWb9kHpaZrSjsCawCrAUtJ2r3Dvg5KNTOroToEpW4N/CUiHoqIZ4DTgM0G29FBqWZm9VSHoNR7gE0lLZna3Iri+pOZWRaqDkF1UOrQehWUeqWkU4FrKGboXQvMKLFuMzPrs7oEpR4EHNS76szMLCeOAzIzs8o1Og6oFf9i1s7xL2b58cjIzMwq1+iR0ce/8KWqS7CM/OS73wRg3T3eXXEllpPbjj+96zbqMFstdx4ZmZlZ5eqS2r13Suy+KU35NjOzBqlDavd6wMeBTYANgB0kvarEus3MrM/qkNr9GuDKiHgqIuYDlwDvKatuM7PRkvJfcpd9ajdwI7C5pBUkLQm8A3h5WbWamVn/ZZ/aHRG3AN8GzgfOBWYDz3Z4Lad2m5nVUB1Su4mIoyJi44h4C/AwcHuH/ZzabWZWQ3VI7UbSyunjKyiuF53U22rNzKxKdUjtfgz4laQVKCY37BURj5RYt5mZ9VldUrs372FpZmY9VYPJatlrdBxQK/7FrF0v4l/MrLccB2RmZpVr9MjoI/sfWHUJlpGjD/02AKvt8o6KK7Gc3H/KOT1oxSfquuWRkZmZVS6noNTdJF0v6QZJl0naoK2t7STdJulOSV/s9JpmZlZPOQWl/gV4a0S8DvgaMANA0njgf4G3A5OB6akdM7MsVJ0752y6IYwhKPWyiHg4rb+CBdO8NwHujIg/R8TTwCmpDTMza4hcg1I/Cvw2ff4y4N62bXPTOjMza4jsglIlbUHRGY16KpyDUs3M6imroFRJ6wM/BXaMiL+n1ffxwkdGTErrXsRBqWZm9ZRNUGoKQT0N2CMi2lO5rwLWlrSGpEWBXVIbZmbWENkEpQL/BawA/F/RjzE/jXLmS/oMcB4wHjg6Im4qsW4zM+uznIJSPwZ8rENb5wC9uE3azKznajBzekQkbQccTvGH/08j4lv9em0nMJiZWeX3dDY6m66VRWbWrjdZZGaN8/w9nQCSWvd03tyPF1cxu9qaTNKeETGj6josL/65WLhI2hPYs23VjPb//pLeB2yXLpkgaQ/gDRHxmX7U59N0C4c9h9/FFkL+uViItN/6kpas/hBxZ2RmZjCKezrL4M7IzMyg4ns6Gz2BwZ6X1XDcsuGfC3te1fd0egKDmZlVzqfpzMyscu6MzMyscu6MuiTppZJOkXSXpKslnSNpnbRtH0n/krRs2/7TJIWkd7atO0vStEHaXl3SPyXNTo9v/5Gkvv83k7TTwDuxJe0n6db0mPjrJH0/pbQjaU5af0Oq++uSFs/pPZUtvc8bB6w7WNL+kjaVdGX6Htwi6eAB+x0m6b6hvi+Snk3H3yjpl5KWLOmtdJR+ljcbsG53SddLuin9XPxU0nJp20xJt6Xtt0r6YWtb2l75e7LqNO6XQD+lZPLTgZkRsVZEbAx8iQUPDJxOMUPlPQMOnQt8eYQvc1dETAHWp4jo2GmEtfVycspO6bVbbX8S2BbYND0m/vUUjwJZou2YLdK2TYA1gR+3bRvTe2qQ44A90/dgPeAXrQ2pA3o3xQMl3zpEG/+MiCkRsR7wNPDJkbxwj38upgHPd0Yp12xf4O0R8VpgI+AyXvgAzd0iYn2K//b/JqX2J2N6T9YM7oy6swXwTET8qLUiIq6LiD9IWguYCPwnRafU7jrgUUnbjPSFImI+xf/Yr5K0kqRfSboqLW+C5//yPl7SH4HjJa0i6fT0F+p1rb9i01+vf0p/hf44ZVIh6QlJ30j7XpGO3wx4F/DdtP9aFB3ppyLikVTb0xHxrYEPT0zbnqD4pbKTpJd0ek8j/T40xMrAAwAR8WxEtMetTANuAo7kxT83nfyB4udiKUlHp/+210raEUDShySdIeki4EJJEyUdk0au10t6b9pvW0mXS7omjUwmpvVzJB2S1t8g6dUqnt78SWDf9HOxOcXPxf4RcV/bezs6Im4bWHBEPA0cALxC0gad3tMI3781gDuj7qwHXN1h2y7AKRT/U60raeDj1b9B0VGNSDplsRVwA0Wq7g8i4vXAeykeSNgyGdg6IqYDRwCXRMQGFH+l3iTpNcD7gTelv8yfBXZLxy4FXJH2/z3w8Yi4jOJegy+k/R8CJkbEX0Zae+qk/gKsPcR7Wpj8ALgt/aHwidYpzGQ6cDLFiHv71qnPTtJI5+0U38MvAxdFxCYUfyh9V9JSadeNgPdFxFuBrwCPRsTr0ijlIkkrUvw8bh0RGwGzgP3aXupvaf2RFB3OHOBHFD+HUyLiD8BrgWtG+k2IiGcp/jB79RDvyRYS7ozKMx04JSKeo3ja7X+0b4yI3wNIevMw7ayl4nlQfwTOjojfAlsDP0zrzwCWaf0VC5wREf9Mn29J8cuj9VfqoxS//DcGrkrHb0VxGg2KUyNnpc+vBlYf7k1Kelv6y3jOwOsHA3cd5j01Tad7JiIivgpMBc4HdgXOBVBxo+E7gF+nDvxK4G0d2lkifQ9nAfdQPMhyW+CLaf1MYHHgFWn/CyLiH+nzrSnSmVsFPQxsSvGHzB/T8R8EXtn2eq0nNY/05+J16efiLknvH2rXYd6TLSR802t3bgLeN3ClpNdRjAIuKC4rsSjFyOCHA3ZtjY7mp+PewIJrK/8FXM+C6yvtxlFcr/nXgNcFeHKYmgUcFxFfGmTbM7HgxrNnGeTnIyIeS6fz1oiIv0TEecB5ks5K7/PFLygtTfEL7HZg2Q7vqWn+Diw/YN1LKH4OiIi7gCMl/QR4SNIKFNdflqN4ICXAksA/JV0HnJna+FE6LfzPgd/DdA3zvQNPi6Wfq5H8XFyQRtSD+Xf6OOjPRXITxQjs4oi4AZgi6Ye88Fpie13jgdcBt6RVL3pPtvDwyKg7FwGLqUjDBUDS+hSnxw6OiNXTshqwmqT2vzSJiPMpfmGtn76+Mp3ymBIRQ8VwnA98tu01p3TY70LgU2mf8Spm9V0IvE/Symn9SwbWNYjHgaXbvv4mxS/S5VIbovgr/EXSiO3/KP7af3iY12mMdK3sAUlbQvF9BrYDLpW0ffqeQfFHy7PAIxSj6Y+1fm6ANYBtgL+3/Vz8iM7OAz7balvShh32uwDYq/WFpOWBK4A3SXpVWreU0qzQIQz2c3GopElt6zp1RIuk/e+NiOuHeR1bCLgz6kIaRbwb2DqdjriJ4n+waRTn/NudTnEdaaBv8MJwwpH4HDA1XXy+mc6zjvYGtpB0A8XplcnpYvl/AudLup7iF9Oqw7zeKcAX0kXxtShO/V0IXJna+CNwbVpaLlYxtflPFKdcPjHK99gEHwC+kk49XQQckkZEe1BcM5oNHE9xzW4xis7q7NbBEfEkcCnwTkbma8AiwPXpZ/FrHfb7OrC8iinU11HMfHwI+BBwcvpvejkDruUM4kzg3a0JDOmJzEcAv1Uxbf8yio72vLZjTkzt30hxjXLHEb43azjHAZmZWeU8MjIzs8q5MzIzs8q5MzIzs8q5MzIzs8q5MzIzs8q5MzIzs8q5MzIzs8r9fzS29nlX1MweAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x1080 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(6,15)) \n",
-    "\n",
-    "sns.heatmap(data = perc_gdp, cmap='BuGn', linewidth=2, linecolor=\"grey\", # colormap, line width, and color specified,\n",
-    "           cbar_kws={'label': 'Percentage of GDP'}, vmin=0, vmax=10)  # color bar labeled, min and max values set\n",
-    "plt.title('Births per year in each state')                              # title added\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a5f744d",
-   "metadata": {},
-   "source": [
-    "Above, we see the matrix of values of the percentage of the GDP spent on the military in the U.S. and Canada from 1960 to 2020. Interestingly, Canada's percentage of the GDP going toward the military appeared to decrease steadily over the years."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "334804bf",
-   "metadata": {},
-   "source": [
-    "## Area Plots\n",
-    "\n",
-    "An area plot is a specialized line graph that can be used to show trends of multiple variables in a dataset over a period of time. In an area plot, data points over time are connected to create a trend line and the region formed under the line is filled with a solid color. A useful adaption of an area plot is that it can be constructed in a way that shows the a proportional relationship of each variable to all variables over time, which can be a great alternative to using multiple pie charts to examine temporal trends.\n",
-    "\n",
-    "We will use an area plot to examine the proportion of USD spent between Canada and Mexico from 1960 to 2020. To do this, we will need to extract the data of interest and then calculate the proportion of money spent for each country. The following code takes the USD spent on the military for Canada and Mexico:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "4be3c8fe",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-USD</th>\n",
-       "      <th>MEX-USD</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1960</th>\n",
-       "      <td>1.702443</td>\n",
-       "      <td>0.084000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961</th>\n",
-       "      <td>1.677821</td>\n",
-       "      <td>0.086400</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>1.671314</td>\n",
-       "      <td>0.099200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>1.610092</td>\n",
-       "      <td>0.112000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964</th>\n",
-       "      <td>1.657457</td>\n",
-       "      <td>0.120000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>17.782776</td>\n",
-       "      <td>5.336876</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>22.269696</td>\n",
-       "      <td>5.062077</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>22.729328</td>\n",
-       "      <td>5.839521</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>22.204408</td>\n",
-       "      <td>6.650808</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>22.754847</td>\n",
-       "      <td>6.116377</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>61 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        CAN-USD   MEX-USD\n",
-       "Year                     \n",
-       "1960   1.702443  0.084000\n",
-       "1961   1.677821  0.086400\n",
-       "1962   1.671314  0.099200\n",
-       "1963   1.610092  0.112000\n",
-       "1964   1.657457  0.120000\n",
-       "...         ...       ...\n",
-       "2016  17.782776  5.336876\n",
-       "2017  22.269696  5.062077\n",
-       "2018  22.729328  5.839521\n",
-       "2019  22.204408  6.650808\n",
-       "2020  22.754847  6.116377\n",
-       "\n",
-       "[61 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "usd = military[['CAN-USD', 'MEX-USD']]\n",
-    "usd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8b013073",
-   "metadata": {},
-   "source": [
-    "Now that we have this data, we can begin to calculate the proportion of USD for each country. First, let's find the total between the two countries. We can do this by using the `apply()` method that we learned about in [Chapter 7](../7/1/Functions_to_DataFrames.ipynb). Because we want to find the total for each year, we need to apply the `np.sum()` function across columns (in other words, `axis=1`):"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "b4eb14b0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>CAN-USD</th>\n",
-       "      <th>MEX-USD</th>\n",
-       "      <th>Total</th>\n",
-       "      <th>CAN-Prop</th>\n",
-       "      <th>MEX-Prop</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Year</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>1960</th>\n",
-       "      <td>1.702443</td>\n",
-       "      <td>0.084000</td>\n",
-       "      <td>1.786443</td>\n",
-       "      <td>0.952979</td>\n",
-       "      <td>0.047021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1961</th>\n",
-       "      <td>1.677821</td>\n",
-       "      <td>0.086400</td>\n",
-       "      <td>1.764221</td>\n",
-       "      <td>0.951027</td>\n",
-       "      <td>0.048973</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1962</th>\n",
-       "      <td>1.671314</td>\n",
-       "      <td>0.099200</td>\n",
-       "      <td>1.770514</td>\n",
-       "      <td>0.943971</td>\n",
-       "      <td>0.056029</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1963</th>\n",
-       "      <td>1.610092</td>\n",
-       "      <td>0.112000</td>\n",
-       "      <td>1.722092</td>\n",
-       "      <td>0.934963</td>\n",
-       "      <td>0.065037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1964</th>\n",
-       "      <td>1.657457</td>\n",
-       "      <td>0.120000</td>\n",
-       "      <td>1.777457</td>\n",
-       "      <td>0.932488</td>\n",
-       "      <td>0.067512</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2016</th>\n",
-       "      <td>17.782776</td>\n",
-       "      <td>5.336876</td>\n",
-       "      <td>23.119651</td>\n",
-       "      <td>0.769163</td>\n",
-       "      <td>0.230837</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2017</th>\n",
-       "      <td>22.269696</td>\n",
-       "      <td>5.062077</td>\n",
-       "      <td>27.331773</td>\n",
-       "      <td>0.814792</td>\n",
-       "      <td>0.185208</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2018</th>\n",
-       "      <td>22.729328</td>\n",
-       "      <td>5.839521</td>\n",
-       "      <td>28.568849</td>\n",
-       "      <td>0.795598</td>\n",
-       "      <td>0.204402</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2019</th>\n",
-       "      <td>22.204408</td>\n",
-       "      <td>6.650808</td>\n",
-       "      <td>28.855217</td>\n",
-       "      <td>0.769511</td>\n",
-       "      <td>0.230489</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2020</th>\n",
-       "      <td>22.754847</td>\n",
-       "      <td>6.116377</td>\n",
-       "      <td>28.871224</td>\n",
-       "      <td>0.788150</td>\n",
-       "      <td>0.211850</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>61 rows × 5 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        CAN-USD   MEX-USD      Total  CAN-Prop  MEX-Prop\n",
-       "Year                                                    \n",
-       "1960   1.702443  0.084000   1.786443  0.952979  0.047021\n",
-       "1961   1.677821  0.086400   1.764221  0.951027  0.048973\n",
-       "1962   1.671314  0.099200   1.770514  0.943971  0.056029\n",
-       "1963   1.610092  0.112000   1.722092  0.934963  0.065037\n",
-       "1964   1.657457  0.120000   1.777457  0.932488  0.067512\n",
-       "...         ...       ...        ...       ...       ...\n",
-       "2016  17.782776  5.336876  23.119651  0.769163  0.230837\n",
-       "2017  22.269696  5.062077  27.331773  0.814792  0.185208\n",
-       "2018  22.729328  5.839521  28.568849  0.795598  0.204402\n",
-       "2019  22.204408  6.650808  28.855217  0.769511  0.230489\n",
-       "2020  22.754847  6.116377  28.871224  0.788150  0.211850\n",
-       "\n",
-       "[61 rows x 5 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "usd['Total'] = usd.apply(np.sum, axis=1)\n",
-    "usd['CAN-Prop'] = usd['CAN-USD']/usd['Total']\n",
-    "usd['MEX-Prop'] = usd['MEX-USD']/usd['Total']\n",
-    "usd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8868b9a1",
-   "metadata": {},
-   "source": [
-    "Now we can begin to make the area plot with the calculated proportions. To do these, we will use the `plt.stackplot()` function. This function first takes an array-like object for the x-values, followed by arrays of the y-values that are to be stacked. The `labels` parameter dictates how each y-value is to be labeled, which can be visualized in the legend using `plt.legend()`:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "f005d3c1",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.stackplot(usd.index, usd['CAN-Prop'], usd['MEX-Prop'], labels=['Canada','Mexico'])\n",
-    "plt.legend(loc='lower left')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dfad8561",
-   "metadata": {},
-   "source": [
-    "Now that we have made the plot, we can improve it by adding a descriptive title. As an aesthetic feature, we can also make this graph so that it takes up the entire plotting area by using `plt.margins`. This function accepts an x and y value, respectively, to indicate where the margins begin on each axis. To get rid of the margins, we will use 0 for each value. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "01ade042",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.stackplot(usd.index, usd['CAN-Prop'], usd['MEX-Prop'], labels=['Canada','Mexico'])\n",
-    "plt.legend(loc='lower left')\n",
-    "plt.margins(0,0)\n",
-    "plt.title('Proportion of Total Military Spending Between Canada and Mexico from 1960 to 2020')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ca499c7e",
-   "metadata": {},
-   "source": [
-    "We can now see the changes in the proportion of military spending between Canada and Mexico from 1960 to 2020. We see that the proportion of spending by Mexico increased over time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b90364f6",
-   "metadata": {},
-   "source": [
-    "## Conclusions\n",
-    "\n",
-    "In this section, we were introduced to a new data visualization library: `seaborn`. The `seaborn` library can make many of the same visualizations available in `matplotlib` and can be used as an alternative in cases where more flexibility is needed. \n",
-    "\n",
-    "We learned how to make box and whisker plots and the multiiple statistics that these plots innately show. Box and whisker plots can be made in both `matplotlib` and `seaborn`, but using `seaborn` to construct these plots allows for an easy way to overlay data points upon the box and whisker plot.\n",
-    "\n",
-    "We also learned about heatmaps and their ability to show multidimensional data.\n",
-    "\n",
-    "Lastly, we learned how to construct area plots as another way to show proportional trends overtime, combining benefits of both a line graph and pie chart.\n",
-    "\n",
-    "Documentation to functions introduced in this section can be found below:\n",
-    "\n",
-    "\n",
-    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/generated/seaborn.boxplot.html\">Seaborn documentaion and user guide</a>\n",
-    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/examples/index.html\">Seaborn data visualization gallery</a>\n",
-    "- <a target=\"_blank\" href=\"https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.plot.box.html\">DataFrame.plot.box( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.boxplot.html\">plt.boxplot( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/generated/seaborn.boxplot.html\">sns.boxplot( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/generated/seaborn.swarmplot.html\">sns.swarmplot( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/gallery/images_contours_and_fields/image_annotated_heatmap.html\">Heatmaps in Matplotlib</a>\n",
-    "- <a target=\"_blank\" href=\"https://seaborn.pydata.org/examples/spreadsheet_heatmap.html\">sns.heatmap( )</a>\n",
-    "- <a target=\"_blank\" href=\"https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.stackplot.html\">plt.stackplot( )</a>\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "31d3ff63",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.9.12"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/textbook/09/data-visualization.md b/textbook/09/data-visualization.md
index d75e8d3d..13791d38 100644
--- a/textbook/09/data-visualization.md
+++ b/textbook/09/data-visualization.md
@@ -6,4 +6,4 @@ Distilling large and complex sets of data into more easily digestible forms is a
 
 The visual that we choose is dependent on the type of data. Two major data types that can be visualized graphically are **numerical data** and **categorical data**.
 
-Numerical data is commonly visualized using <i>histograms</i>, <i>scatter plots</i> and <i>line graphs</i>. Categorical data can be depicted using <i>bar graphs</i> and <i>pie charts</i>. There are a vast number of other methods to visualize these data types, (<i>e.g.</i> box and whisker plots, heatmaps, etc.), but the aforementioned graphs can greatly distill complex datasets and are commonly used throughout multiple disciplines.
+Numerical data is commonly visualized using <i>histograms</i>, <i>scatter plots</i> and <i>line graphs</i>. Categorical data can be depicted using <i>bar graphs</i>, <i>box and whisker plots</i>, and <i>pie charts</i>. There are a vast number of other methods to visualize these data types, (<i>e.g.</i> cartograms, heatmaps, etc.), but the aforementioned graphs are the most commonly used among data scientists.
diff --git a/textbook/10/1/causality.ipynb b/textbook/10/1/causality.ipynb
index 236d7205..f7d37942 100644
--- a/textbook/10/1/causality.ipynb
+++ b/textbook/10/1/causality.ipynb
@@ -140,7 +140,7 @@
     }
    ],
    "source": [
-    "covid = pd.read_csv(\"simpsons_paradox_covid.csv\")\n",
+    "covid = pd.read_csv(\"../../data/simpsons_paradox_covid.csv\")\n",
     "covid.head()"
    ]
   },
diff --git a/textbook/10/3/sampling.ipynb b/textbook/10/3/sampling.ipynb
index ce1af389..fbd86ada 100644
--- a/textbook/10/3/sampling.ipynb
+++ b/textbook/10/3/sampling.ipynb
@@ -147,7 +147,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Or, we can give different elements different chances of being chosen by using the argument `p` which takes in a list of the same size as marbles. Each element in the `p` list corresponds to an element of marbles, and together they must add to 1 (the reasoning for this will be explained further in the [next chapter](../../11/Probability.ipynb)). The last 3 elements are 0 since those marbles have no chance of being chosen. The first two are 0.5 or $\\frac{1}{2}$ since they have a 1 in 2 chance of being chosen. "
+    "Or, we can give different elements different chances of being chosen by using the argument `p` which takes in a list of the same size as marbles. Each element in the `p` list corresponds to an element of marbles, and together they must add to 1 (the reasoning for this will be explained further in the [next chapter](../../11/1/Probability_1_RulesDefinitions.ipynb)). The last 3 elements are 0 since those marbles have no chance of being chosen. The first two are 0.5 or $\\frac{1}{2}$ since they have a 1 in 2 chance of being chosen. "
    ]
   },
   {
diff --git a/textbook/11/4/Probability_4_BirthdayPb_RelaxedAssumptions.ipynb b/textbook/11/4/Probability_4_BirthdayPb_RelaxedAssumptions.ipynb
index df2903bf..dd2ccd48 100644
--- a/textbook/11/4/Probability_4_BirthdayPb_RelaxedAssumptions.ipynb
+++ b/textbook/11/4/Probability_4_BirthdayPb_RelaxedAssumptions.ipynb
@@ -184,7 +184,7 @@
     }
    ],
    "source": [
-    "birth_data = pd.read_csv(\"US_births_2000-2014_SSA.csv\")\n",
+    "birth_data = pd.read_csv(\"../../data/US_births_2000-2014_SSA.csv\")\n",
     "birth_data"
    ]
   },
diff --git a/textbook/12/3/normal.ipynb b/textbook/12/3/normal.ipynb
index d64a3c16..177e2400 100644
--- a/textbook/12/3/normal.ipynb
+++ b/textbook/12/3/normal.ipynb
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"tAzM3r78p08k","tags":["remove-cell"]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"id":"HtrRcRR0uFAM"},"source":["# Normal Distribution"]},{"cell_type":"markdown","metadata":{"id":"q0LoZTYHoCuw"},"source":["Let's consider another distribution by considering real data.\n","\n","Below we load in the Galton Height Data. This dataset was collected from families and contains heights of the father, mother, children. We focus on the father heights, and plot the distribution of heights in a histogram."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":188,"status":"ok","timestamp":1689610841357,"user":{"displayName":"Susanna Lange","userId":"00843628675540967552"},"user_tz":300},"id":"UOdUEyhemMef","outputId":"79220073-def2-4067-bec0-293762a2f8a4"},"outputs":[{"data":{"text/html":["\n","\n","  <div id=\"df-c8b57ff1-3e17-442f-93e2-9b7c00c14e42\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>family</th>\n","      <th>father</th>\n","      <th>mother</th>\n","      <th>midparentHeight</th>\n","      <th>children</th>\n","      <th>childNum</th>\n","      <th>gender</th>\n","      <th>childHeight</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>78.5</td>\n","      <td>67.0</td>\n","      <td>75.43</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>male</td>\n","      <td>73.2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>78.5</td>\n","      <td>67.0</td>\n","      <td>75.43</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>69.2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>78.5</td>\n","      <td>67.0</td>\n","      <td>75.43</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>69.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","    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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n"],"text/plain":["  family  father  mother  midparentHeight  children  childNum  gender  \\\n","0      1    78.5    67.0            75.43         4         1    male   \n","1      1    78.5    67.0            75.43         4         2  female   \n","2      1    78.5    67.0            75.43         4         3  female   \n","3      1    78.5    67.0            75.43         4         4  female   \n","4      2    75.5    66.5            73.66         4         1    male   \n","5      2    75.5    66.5            73.66         4         2    male   \n","\n","   childHeight  \n","0         73.2  \n","1         69.2  \n","2         69.0  \n","3         69.0  \n","4         73.5  \n","5         72.5  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["galton_df = pd.read_csv(\"galton.csv\")\n","galton_df.head(6)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":1567,"status":"ok","timestamp":1689610867384,"user":{"displayName":"Susanna Lange","userId":"00843628675540967552"},"user_tz":300},"id":"6Mi-17cNm1oG","outputId":"93ce9d81-264e-49aa-c749-3c4867921b15"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["bin_size=np.arange(60, 80.5, 1)\n","plt.hist(galton_df[\"father\"], bins = bin_size, density = True)\n","plt.title('Father heights')\n","plt.xlabel('heights (in inches)')\n","plt.ylabel('Probabilities')\n","plt.scatter(galton_df['father'].mean(), 0, color='red', s=60);\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"MO3r-9W6moSJ"},"source":["This is another empirical distribution, but a special one!\n","An empirical *normal* distribution.\n","\n","The values for the normal distribution are continous variables - as heights can take on any real number in a given range."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"ja0QmiNunQY0"},"source":["## Normal Probability Distribution\n","\n","Arguably one of the most important continuous distributions, the normal distribution is a common distribution of heights, weights, and SAT scores, to name a few.\n","This distribution is symmetric and bell-shaped, giving it the nickname \"bell-curve\". The three measures of center: mode, median, and mean, are exactly the same for the normal distribution. Further, the distribution itself is defined entirely in terms of its mean and standard deviation. Notationally, given a random variable $X$ that is normally distributed, we can say $X \\sim N(\\mu,\\sigma)$, where $\\mu$ and $\\sigma$ are the mean and standard deviation of the distribution, respectively.\n","\n","\n","Below is a plot of a normal distribution, with the mean in red on the graph. We use the scipy library and the *normal* function by calling\n","\n","```python\n","stats.norm(µ, σ)\n","```\n","where the arguments correspond to the mean and standard deviation of the normal distribution. To get the corresponding $y$ values, we call the pdf method on a given array of x-values:\n","```python\n","stats.norm(µ, σ).pdf(x)\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":480,"status":"ok","timestamp":1689622686716,"user":{"displayName":"Susanna 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Fx+fj78/f1NjhkeHl5vHPHx8YiLizPeV6lUCAwMbPJ5EZF52HHqMl7+3xHkq/Q9vAo701F/jVaHrcfzsC/rCt56oBdG9qz7vkNE1knUBEgulyMiIgJJSUnGS9l1Oh2SkpIwa9asOu27du2Ko0ePmmybN28eSktL8cEHHyAwMBD29vbw8/NDUlKSMeFRqVRITU3FjBkz6o1DoVBAoeC3PyJrUaauwVs/n8BXe3MAACFtnPHuw2Ho097DpN3x3BK88PVhnMwrxTNfHMADfdpiwZgeUDrZixE2EbUi0YfA4uLiMGXKFPTr1w8DBgzA0qVLUV5ejtjYWADA5MmT0bZtWyQmJsLBwQE9e/Y0eby7uzsAmGyfM2cO3nzzTXTq1AnBwcF47bXXEBAQUGe+ICKyPifzVPjXuv24cLUSAPDkkGC8NLILHOxlddr2CFDi+1lD8MHvZ7Dyz7P49tBF/H22EJ8+0Q9hge6tHDkRtSbRE6AJEyagoKAA8+fPR15eHsLDw7F161ZjEXN2djak0sZdrPbSSy+hvLwcTz31FIqLizF06FBs3boVDg4OLXEKRGQmVFXVeOrzA7hwtRLtPBzxzkNhGNTR66aPUdjJ8NLIroju7ou5Xx/GucJyPL3+AH55fhg8neWtFDkRtTaJIAiC2EGYG5VKBaVSiZKSEri5uYkdDhE1gCAImPXVIfx85BLaujvi5+eGwt2pcQlMmboG93+0C+cKynFnlzb4bEp/SKWSFoqYiJpbYz6/RZ8IkYioOXyZmo2fj1yCnVSCjx7r0+jkBwBcFHZY/lhfKOyk2HGqAKv+OtcCkRKROWACREQW73huCd746QQA4D8ju9Ypdm6Mbv5uSBjTAwDw9rZTOHD+arPESETmhQkQEVm0MnUNZm04BE2NDnd39cG/hgXf9jEnDgjEmLAAaHUCnvvqEIorNM0QKRGZEyZARGSxBEHAq98eRWZhOQKUDljycBgkktuv2ZFIJPi/B3oiyMsJF4srMXfzEbBcksi6MAEiIov1XdpFfJ+WC5lUgmWP9YFHM1615epgj48e6wu5TIrf0/PxRWp2sx2biMTHBIiILJKmRocl204DAObc3QkRHTyb/Tl6tlXi5VFdAQAf/H4aFZqaZn8OIhIHEyAiskib9ufgYnElfFwVmH5HSIs9zxODOqC9pxMKyzT4POV8iz0PEbUuJkBEZHGqqrVY/kcGAGDmnaH1zvLcXOxlUjx3dycAwCd/nkWZmr1ARNaACRARWZyv9mYjT1UFf6UDHh3Q8gsXjwsPQIi3M65WVGPt7swWfz4ianlMgIjIolRqtPg4+SwAYNZdoVDYtVzvj4GdTIrno/W9QJ/uPIeSyuoWf04iallMgIjIonyx5zwKStVo5+GIhyNavvfH4L7eAejk4wJVVQ1W72IvEJGlYwJERBajXF2DlX/qe3+eu6sT5Hat9xYmk0owJ7ozAGD1rkxOjkhk4ZgAEZHFWJeShaJyDYK8nDC+b9tWf/5RPf3Q1c8VpeoarhNGZOGYABGRRSitqsanO/VJx/PRnWAna/23L6lUgrgR+l6gNbuzUFSmbvUYiKh5MAEiIouwfs95FFdUo2MbZ9wf1vq9PwYjuvuiV1slKjRafMZaICKLxQSIiMyeTidgQ+1SFDOGh0Imvf31vppKIpFg5p2hAICv9+dAU6MTLRYiajomQERk9nZlFOLC1Uq4Odjhvt7+YoeDu7v5wMdVgcIyDZLS88UOh4iagAkQEZm9r/bqe3/G923XorM+N5S9TIqH+7UDAGzYy0VSiSwREyAiMmsFpWpsP6HvZWmNWZ8bakK/9gD0vVM5VypEjoaIGosJEBGZtS0HLqBGJ6BPe3d09XMTOxyj9l5OGNbJG4IAbNqXI3Y4RNRITICIyGzpdAI27dMPMU3s317kaOp6tDamr/fnoEbLYmgiS8IEiIjM1p5zRcgqqoCLwg73hYlf/Hy9Ed194eUsx+VSNf44eVnscIioEZgAEZHZ+qp2aGlseACc5HYiR1OX3E6KhyL0xdAbOQxGZFGYABGRWbpSrsG2Y3kAgIkDzG/4y2BCf31hdvKpy8gtrhQ5GiJqKCZARGSWvjl4ARqtDr3aKtGzrVLscG4opI0LBoZ4Qifoa4GIyDIwASIisyMIgnF+HXPu/TEwxPj1vhxodYLI0RBRQzABIiKzsy/rKs4VlMNJLsP94QFih3NLMT384O5kj9ySKuw8XSB2OETUAEyAiMjsbDmgH0oa0zsALgrzK36+noO9DOP76Iuhtxy4IHI0RNQQTICIyKxUa3X4rXbm57F9zL/3x2Bcbax/nLyMSo1W5GiI6FaYABGRWfn7bBGKK6rh7SJHZLCX2OE0WK+2SgR6OqKyWosdpzgnEJG5YwJERGbllyOXAAAje/pBJpWIHE3DSSQSjO6ln6zx56OXRI6GiG7FLBKg5cuXIygoCA4ODoiMjMTevXtv2Pabb75Bv3794O7uDmdnZ4SHh2P9+vUmbaZOnQqJRGJyGzlyZEufBhHdpmqtDttO6Of+MSQTluTe2pj/SOcwGJG5Ez0B2rRpE+Li4pCQkICDBw8iLCwMMTExuHy5/i5kT09PvPrqq0hJScGRI0cQGxuL2NhYbNu2zaTdyJEjcenSJePtq6++ao3TIaLbkGKhw18Gvdoq0c5DPwyWzGEwIrMmegL03nvvYfr06YiNjUX37t2xcuVKODk5YfXq1fW2Hz58OB544AF069YNHTt2xPPPP4/evXtj165dJu0UCgX8/PyMNw8Pj9Y4HSK6Db/UDh3F9LCs4S8DiURi7AXiMBiReRM1AdJoNDhw4ACio6ON26RSKaKjo5GSknLLxwuCgKSkJJw6dQp33HGHyb7k5GT4+PigS5cumDFjBoqKipo9fiJqPtVaHbYd1w9/3WuBw18GhqG7P05eRlU1h8GIzJWoE2wUFhZCq9XC19fXZLuvry9Onjx5w8eVlJSgbdu2UKvVkMlk+PjjjzFixAjj/pEjR2L8+PEIDg7G2bNn8corr2DUqFFISUmBTCarczy1Wg21Wm28r1KpmuHsiKgx9pwrwtWKang5yzEg2FPscJqsdzv9MNiFq5VIPnUZI3tabjJHZM3Mf4axeri6uiItLQ1lZWVISkpCXFwcQkJCMHz4cADAo48+amzbq1cv9O7dGx07dkRycjLuvvvuOsdLTEzE66+/3lrhE1E9jMNfPf1gJxN9dL7JDFeDfbrzHH4+mscEiMhMifou4+3tDZlMhvz8fJPt+fn58PPzu+HjpFIpQkNDER4ejhdeeAEPPfQQEhMTb9g+JCQE3t7eyMjIqHd/fHw8SkpKjLecHC5oSNSaarQ6bDuufx+w5OEvA8MwWFJ6PofBiMyUqAmQXC5HREQEkpKSjNt0Oh2SkpIwaNCgBh9Hp9OZDGFd78KFCygqKoK/f/1vrAqFAm5ubiY3Imo9e85dwZVyDTyd5Yi04OEvg7B2SrR1d0SFRovkU1wbjMgcid7PHBcXh1WrVmHdunVIT0/HjBkzUF5ejtjYWADA5MmTER8fb2yfmJiI7du349y5c0hPT8e7776L9evXY9KkSQCAsrIyvPjii9izZw+ysrKQlJSEsWPHIjQ0FDExMaKcIxHd3M9HcwHor/6y5OEvA/0wmL4Xm1eDEZkn0WuAJkyYgIKCAsyfPx95eXkIDw/H1q1bjYXR2dnZkEr/eUMsLy/Hs88+iwsXLsDR0RFdu3bFF198gQkTJgAAZDIZjhw5gnXr1qG4uBgBAQG45557sHDhQigUClHOkYhuzNqGvwxG9/LHqr8yjcNgDvZ1L8AgIvFIBEEQxA7C3KhUKiiVSpSUlHA4jKiF7TpTiEmfpcLTWY69r9xtFT1AgH6ajqGLd+BicSVWTorAyJ43rmskoubRmM9v63inISKLtfW4YfJDX6tJfgD9MNio2qTHML8REZkP63m3ISKLIwgCdpzUFwmP6O57i9aWx3BOyacuQ6tjZzuROWECRESiOZ1fhovFlVDYSTEoxFvscJpdRAcPuDnY4WpFNdJyisUOh4iuwQSIiETzx0n9gqGDO3rBUW59RcJ2Minu6NwGALDjJBdHJTInTICISDSGpOCurj4iR9JyDOf2BxMgIrPCBIiIRFFSUY0D2VcBAMO7WG8CFNW5DSQS4MQlFfJKqsQOh4hqMQEiIlH8eaYAWp2ATj4uCPR0EjucFuPlokBYO3cAwI5T7AUiMhdMgIhIFMk2MPxlwGEwIvPDBIiIWp1WJyD5tP7y9zttKAHanVEIdQ0XRyUyB0yAiKjVHb5QjCvlGrg62CGig4fY4bS4HgFu8HFVoEKjReq5K2KHQ0RgAkREIjBc/XVH5zawt6LZn29EIpHgzi4cBiMyJ9b/zkNEZseQBNxlxVd/Xc8w1Lfj1GVwCUYi8TEBIqJWla+qwvFcFSQSYHiXNmKH02qGdvKGvUyC80UVyCwsFzscIpvHBIiIWpVh+CusnTu8XBQiR9N6XBR2iAz2AsBhMCJzwASIiFqVYS6cO21o+MvA0OPF+YCIxMcEiIhajbpGi11nCgHYxvw/1zOc897MKyhT14gcDZFtYwJERK1mX+ZVlGu0aOOqQI8AN7HDaXUhbVwQ5OWEaq2AXWcKxA6HyKYxASKiVvPP8FcbSKUSkaMRh/FqsJNMgIjExASIiFqNYfgrqrPtDX8ZRHXW1wH9daaAl8MTiYgJEBG1isuqKpzKL4VEAgzu6CV2OKIZEOwJuUyK3JIqXg5PJCImQETUKnaf1ff+9AxQwsNZLnI04nGS26FvB3cA+rXBiEgcTICIqFX8VTv8NbSTt8iRiG9YJ8MwGBMgIrEwASKiFicIgrH+Z2goE6Ahtb+DlLNFqNHqRI6GyDYxASKiFpdxuQyXS9VQ2EltYvX3W+nVVgmloz1K1TU4crFE7HCIbBITICJqcYahngHBnnCwl4kcjfhkUomxEHwXh8GIRMEEiIha3K4MDn9dzzAMxgSISBxMgIioRVVrddhzrggAC6CvNaz2d3Ew+yrKuSwGUatjAkRELepQdjEqNFp4OcvRzc/2lr+4kfaeTmjn4YganYDUzCKxwyGyOUyAiKhFGYa/Bod62+zyF/WRSCTGXqBdZ5gAEbU2JkBE1KIMi34OY/1PHUND9fMB7crgumBErY0JEBG1GFVVNQ5f0F/mPYT1P3UM7ugFiQQ4nV+GfFWV2OEQ2RQmQETUYvacLYJWJyDE2xlt3R3FDsfseDjL0TNACYDLYhC1NrNIgJYvX46goCA4ODggMjISe/fuvWHbb775Bv369YO7uzucnZ0RHh6O9evXm7QRBAHz58+Hv78/HB0dER0djTNnzrT0aRDRdQz1P0M4/HVDvByeSByiJ0CbNm1CXFwcEhIScPDgQYSFhSEmJgaXL1+ut72npydeffVVpKSk4MiRI4iNjUVsbCy2bdtmbPP222/jww8/xMqVK5GamgpnZ2fExMSgqopdzEStaRfX/7olYyF0RiEEQRA5GiLbIRFE/h8XGRmJ/v3746OPPgIA6HQ6BAYGYvbs2Xj55ZcbdIy+ffvi3nvvxcKFCyEIAgICAvDCCy9g7ty5AICSkhL4+vpi7dq1ePTRR295PJVKBaVSiZKSEri58bJdoqa4WFyJIYv+gFQCpCXcAzcHe7FDMktV1VqEvf4b1DU6/PbvO9DZ11XskIgsVmM+v0XtAdJoNDhw4ACio6ON26RSKaKjo5GSknLLxwuCgKSkJJw6dQp33HEHACAzMxN5eXkmx1QqlYiMjLzhMdVqNVQqlcmNiG7P7tren7BAdyY/N+FgL8OAYE8AXB2eqDWJmgAVFhZCq9XC19fXZLuvry/y8vJu+LiSkhK4uLhALpfj3nvvxbJlyzBixAgAMD6uMcdMTEyEUqk03gIDA2/ntIgIwO6zXP6ioQy/o79ZCE3UakSvAWoKV1dXpKWlYd++fXjrrbcQFxeH5OTkJh8vPj4eJSUlxltOTk7zBUtkgwRBQMpZ/eR+g2oX/aQbM/yO9mZeQY1WJ3I0RLbBTswn9/b2hkwmQ35+vsn2/Px8+Pn53fBxUqkUoaGhAIDw8HCkp6cjMTERw4cPNz4uPz8f/v7+JscMDw+v93gKhQIKheI2z4aIDM4VluNyqRpyOyn6tvcQOxyz1yNACVcHO5RW1eB4rgphge5ih0Rk9UTtAZLL5YiIiEBSUpJxm06nQ1JSEgYNGtTg4+h0OqjVagBAcHAw/Pz8TI6pUqmQmpraqGMSUdMZen/6tneHg71M5GjMn0wqQWRtHVDKOS6LQdQaRB8Ci4uLw6pVq7Bu3Tqkp6djxowZKC8vR2xsLABg8uTJiI+PN7ZPTEzE9u3bce7cOaSnp+Pdd9/F+vXrMWnSJAD69XXmzJmDN998Ez/88AOOHj2KyZMnIyAgAOPGjRPjFIlsjuFDfFAI638aamCIfhhsDxMgolYh6hAYAEyYMAEFBQWYP38+8vLyEB4ejq1btxqLmLOzsyGV/pOnlZeX49lnn8WFCxfg6OiIrl274osvvsCECROMbV566SWUl5fjqaeeQnFxMYYOHYqtW7fCwcGh1c+PyNYIgoDU2g/xgSGeIkdjOQwJ0L7MK6jW6mAvE/37KZFVE30eIHPEeYCImu5MfilGvL8TCjspjiy4Bwo7DoE1hE4noM/C7SiprMY3zw5m7RRRE1jMPEBEZH0Mw1/9gjyY/DSC9No6oLMcBiNqaUyAiKhZGS9/D+Hl741luByedUBELY8JEBE1G51OMH54c/6fxjP8zvZnXYWmhvMBEbUkJkBE1GxO5ZfiakU1HO1l6N3OXexwLE5nH1d4OstRWa3FkQvFYodDZNWYABFRszEMf/UL8uBVTE3AOiCi1sN3KCJqNhz+un2G3x0nRCRqWUyAiKhZ6HQCUjOvAGAB9O0w/O4OnL8KdY1W5GiIrBcTICJqFicuqVBSWQ0XhR16tVWKHY7FCvVxgbeLAuoaHdKyi8UOh8hqMQEiomZhGP7qH+QBO9b/NJlEIjHOoM1hMKKWw3cpImoWhqLdgRz+um2G3yELoYlaDhMgIrptNVod9hrqf1gAfdsMv8ND2cWoqmYdEFFLYAJERLftxCUVStU1cHWwQ48A1v/crhBvZ/i4KqDR6nAw+6rY4RBZJSZARHTbDEM1kcGekEklIkdj+SQSyT/LYnAYjKhFMAEiottmKIBm/U/zMfwu95y7InIkRNaJCRAR3RatTsD+LP0wTWQwE6DmYpgROi2HdUBELYEJEBHdlhO5tfU/Cjt0D3ATOxyrEeztjDa1dUCHOB8QUbNjAkREtyU185/1v1j/03wkkn/WBTNcYUdEzYcJEBHdFsPyF5Gs/2l2ht+pIckkoubDBIiImkynE7AvqzYBqu2toOYzsPZ3ejD7KjQ1OpGjIbIuTICIqMlO5ZeiuKIaTnIZenL9r2YX6uMCT2c5qqp1OHKhWOxwiKwKEyAiarLU2svfIzp4wJ7rfzU7iUSCAUH6XqBU1gERNSu+YxFRkxnrfzj81WIiaxdG3cOFUYmaFRMgImoSQRCMVyexALrlGOZWOnD+Kmq0rAMiai5MgIioSc4WlKGoXAOFnRS927H+p6V09XOF0tEeFRotjuWqxA6HyGowASKiJjEs0dCnvTsUdjKRo7FeUqkE/Q11QBwGI2o2TICIqEn+qf/h8FdLGxjCQmii5sYEiIgaTRAEY2+EoUiXWo4hydyXeQVanSByNETWgQkQETVaVlEFLpeqIZdJ0be9h9jhWL1u/q5wUdihVF2D9EusAyJqDkyAiKjR9tYuzRAWqISDPet/WpqdTIp+QfpEk8NgRM2DCRARNVpqbQH0AM7/02oMw2AshCZqHkyAiKjRWADd+gy1VnuzrkDHOiCi22YWCdDy5csRFBQEBwcHREZGYu/evTdsu2rVKgwbNgweHh7w8PBAdHR0nfZTp06FRCIxuY0cObKlT4PIJuRcqcDF4krIpBJEdGD9T2vp1VYJJ7kMxRXVOH25VOxwiCye6AnQpk2bEBcXh4SEBBw8eBBhYWGIiYnB5cuX622fnJyMiRMnYseOHUhJSUFgYCDuueceXLx40aTdyJEjcenSJePtq6++ao3TIbJ6ht6fXm2VcFbYiRyN7bCXSY0Jp2EIkoiaTvQE6L333sP06dMRGxuL7t27Y+XKlXBycsLq1avrbf/ll1/i2WefRXh4OLp27Yr//ve/0Ol0SEpKMmmnUCjg5+dnvHl48JsqUXMwFEBz/a/WZ/idp2ayDojodomaAGk0Ghw4cADR0dHGbVKpFNHR0UhJSWnQMSoqKlBdXQ1PT9M34+TkZPj4+KBLly6YMWMGiopu/IahVquhUqlMbkRUv3/W/2IC1NoG1NZc7c28CkFgHRDR7RA1ASosLIRWq4Wvr6/Jdl9fX+Tl5TXoGP/5z38QEBBgkkSNHDkSn3/+OZKSkrB48WL8+eefGDVqFLRabb3HSExMhFKpNN4CAwObflJEVixfVYWsogpIJEC/ICZArS0sUAm5nRSFZWqcKywXOxwii2bRA/iLFi3Cxo0bkZycDAcHB+P2Rx991Phzr1690Lt3b3Ts2BHJycm4++676xwnPj4ecXFxxvsqlYpJEFE9DPU/3f3d4OZgL3I0tkdhJ0OfQHekZl7B3swr6NjGReyQiCyWqD1A3t7ekMlkyM/PN9men58PPz+/mz52yZIlWLRoEX777Tf07t37pm1DQkLg7e2NjIyMevcrFAq4ubmZ3IioLkP9D+f/EY+hDmgvJ0Qkui2iJkByuRwREREmBcyGguZBgwbd8HFvv/02Fi5ciK1bt6Jfv363fJ4LFy6gqKgI/v7+zRI3ka0y1v8wARLNP3VATICIbofoV4HFxcVh1apVWLduHdLT0zFjxgyUl5cjNjYWADB58mTEx8cb2y9evBivvfYaVq9ejaCgIOTl5SEvLw9lZWUAgLKyMrz44ovYs2cPsrKykJSUhLFjxyI0NBQxMTGinCORNbhSrsHpfP3/s/6s/xFN3w7usJNKcLG4EjlXKsQOh8hiiV4DNGHCBBQUFGD+/PnIy8tDeHg4tm7daiyMzs7OhlT6T562YsUKaDQaPPTQQybHSUhIwIIFCyCTyXDkyBGsW7cOxcXFCAgIwD333IOFCxdCoVC06rkRWRNDj0MnHxd4ufD/klic5Hbo1U6JQ9nF2Jt5BYGeTmKHRGSRRE+AAGDWrFmYNWtWvfuSk5NN7mdlZd30WI6Ojti2bVszRUZEBoYEiPU/4hsQ7GlMgB6MaCd2OEQWSfQhMCKyDHuzaidADOH6X2IzFkJnsQ6IqKmYABHRLamqqnEiVz9B6ADW/4iuX5AnJBIgs7Acl1VVYodDZJGalADt2LGjueMgIjN2IOsqdALQwcsJfkqHWz+AWpSbgz26++un60jl1WBETdKkBGjkyJHo2LEj3nzzTeTk5DR3TERkZlJ5+bvZGcD5gIhuS5MSoIsXL2LWrFnYsmULQkJCEBMTg6+//hoajaa54yMiM/DPBIis/zEXnBCR6PY0KQHy9vbGv//9b6SlpSE1NRWdO3fGs88+i4CAADz33HM4fPhwc8dJRCKp0NTgyIUSAOwBMieGuZhO5ZfiSjm/fBI11m0XQfft2xfx8fGYNWsWysrKsHr1akRERGDYsGE4fvx4c8RIRCI6lF2MGp0Af6UD2nk4ih0O1fJyUaCTj34tsH28Goyo0ZqcAFVXV2PLli0YPXo0OnTogG3btuGjjz5Cfn4+MjIy0KFDBzz88MPNGSsRieDa+h+JRCJyNHQt1gERNV2TJkKcPXs2vvrqKwiCgCeeeAJvv/02evbsadzv7OyMJUuWICAgoNkCJSJxsP7HfA0I9sSXqdlMgIiaoEkJ0IkTJ7Bs2TKMHz/+hstLeHt783J5IgunrtHiUHYxAM4AbY4ia5PS47klUFVVw83BXuSIiCxHk4bAEhIS8PDDD9dJfmpqarBz504AgJ2dHaKiom4/QiISzZELJVDX6ODtIkfHNs5ih0PX8VM6oIOXE3QCcOD8VbHDIbIoTUqA7rzzTly5UrfLtaSkBHfeeedtB0VE5uHa9b9Y/2OeDDNzp57jMBhRYzQpARIEod43w6KiIjg781sikbXYc662/ofLX5itfwqhi0SOhMiyNKoGaPz48QAAiUSCqVOnmgyBabVaHDlyBIMHD27eCIlIFNVanXFYhQugmq+Bta/NkQslqNDUwEnepNJOIpvTqP8pSqUSgL4HyNXVFY6O/8wJIpfLMXDgQEyfPr15IyQiURy7WIIKjRbuTvbo4usqdjh0A+08HBGgdEBuSRUOni/G0E7eYodEZBEalQCtWbMGABAUFIS5c+dyuIvIihnm/+kf5AmplPU/5koikSAyxAvfHrqI1MwiJkBEDdTkq8CY/BBZt9Ta+h8uf2H+DK8RC6GJGq7BPUB9+/ZFUlISPDw80KdPn5teEXLw4MFmCY6IxKHVCdifpa//Gcj6H7NnqNFKyylGVbUWDvYykSMiMn8NToDGjh1rLHoeN25cS8VDRGbgRK4KpeoauDrYoZu/m9jh0C0EeTnBx1WBy6VqHMy+isEdOQxGdCsNToASEhLq/ZmIrE9q7SXV/YM8IWP9j9kz1AH9eDgXqeeuMAEiaoDbXg2eiKzPnnP/LIBKlsFYB8T5gIgapME9QB4eHg2eCba+WaKJyDLodAL2ZdUmQKz/sRgDQ/QJ0KHsYqhrtFDYsQ6I6GYanAAtXbq0BcMgInNxMq8UJZXVcJbL0DOA9T+WomMbF3i7yFFYpsHhnBIuXkt0Cw1OgKZMmdKScRCRmTAMoUQEecJOxlFySyGRSDAg2BO/HM1D6rkiJkBEt9DgdzeVSmXy881uRGS5Uln/Y7Eig/VDloZJLInoxhpVA3Tp0iX4+PjA3d293nogwyKpWq22WYMkotYhCAL21tb/GGpKyHJE1r5mB85fRbVWB3v24BHdUIMToD/++AOenvr/XDt27GixgIhIPGcul+FKuQYO9lL0ausudjjUSJ19XOHuZI/iimocuVCCiA4eYodEZLYanABFRUXV+zMRWQ/D8hcRHTwgt2PvgaWRSiUYEOSJ307kIzWziAkQ0U00ajHUa129ehWfffYZ0tPTAQDdu3dHbGyssZeIiCzPnkxD/Q8vf7dUkSFe+gTo3BU8O1zsaIjMV5O+4u3cuRNBQUH48MMPcfXqVVy9ehUffvghgoODsXPnzuaOkYhagSAILIC2AobXbn/WFdRodSJHQ2S+mtQDNHPmTEyYMAErVqyATKafbEur1eLZZ5/FzJkzcfTo0WYNkoha3rnCchSWqaGwkyIs0F3scKiJuvm7wc3BDqqqGhzPVfG1JLqBJvUAZWRk4IUXXjAmPwAgk8kQFxeHjIyMRh9v+fLlCAoKgoODAyIjI7F3794btl21ahWGDRsGDw8PeHh4IDo6uk57QRAwf/58+Pv7w9HREdHR0Thz5kyj4yKyJYbenz7t3bmauAWTSSXGOYC4LAbRjTUpAerbt6+x9uda6enpCAsLa9SxNm3ahLi4OCQkJODgwYMICwtDTEwMLl++XG/75ORkTJw4ETt27EBKSgoCAwNxzz334OLFi8Y2b7/9Nj788EOsXLkSqampcHZ2RkxMDKqqqhp3okQ2ZE9tATTrfyyf4TU0rOlGRHVJBEEQGtLwyJEjxp/T09Px0ksvYfbs2Rg4cCAAYM+ePVi+fDkWLVqECRMmNDiAyMhI9O/fHx999BEAQKfTITAwELNnz8bLL798y8drtVp4eHjgo48+wuTJkyEIAgICAvDCCy9g7ty5AICSkhL4+vpi7dq1ePTRR295TJVKBaVSiZKSEri5cSkAsn6CIGDA/yWhoFSNr6YPxKCOTIIs2dELJRjz0S64KOyQNn8EZ/Qmm9GYz+8G1wCFh4dDIpHg2nzppZdeqtPusccea3ACpNFocODAAcTHxxu3SaVSREdHIyUlpUHHqKioQHV1tfHqs8zMTOTl5SE6OtrYRqlUIjIyEikpKfUmQGq1Gmq12nifs1mTrTlbUI6CUjXkdlL0ae8udjh0m7oH/FMHdCxXhXDWARHV0eAEKDMzs9mfvLCwEFqtFr6+vibbfX19cfLkyQYd4z//+Q8CAgKMCU9eXp7xGNcf07DveomJiXj99dcbGz6R1UgxzP/T3oP1P1ZAXwfkhd/T85FytogJEFE9GpwAdejQoSXjaJJFixZh48aNSE5OhoODQ5OPEx8fj7i4OON9lUqFwMDA5giRyCLsOatPgDj0ZT0GdaxNgM4VYcbwjmKHQ2R2mjwRIgCcOHEC2dnZ0Gg0Jtvvv//+Bj3e29sbMpkM+fn5Jtvz8/Ph5+d308cuWbIEixYtwu+//47evXsbtxsel5+fD39/f5NjhoeH13sshUIBhULRoJiJrI0gCMYCaCZA1mNQiP613J91heuCEdWjSQnQuXPn8MADD+Do0aMmdUGGBVIbuhiqXC5HREQEkpKSMG7cOAD6IuikpCTMmjXrho97++238dZbb2Hbtm3o16+fyb7g4GD4+fkhKSnJmPCoVCqkpqZixowZjTxTIut35nIZiso1cLSXIaydu9jhUDPp6ucKDyd7XOW6YET1atJXgueffx7BwcG4fPkynJyccPz4cezcuRP9+vVDcnJyo44VFxeHVatWYd26dUhPT8eMGTNQXl6O2NhYAMDkyZNNiqQXL16M1157DatXr0ZQUBDy8vKQl5eHsrIyAPokbM6cOXjzzTfxww8/4OjRo5g8eTICAgKMSRYR/SOldvirXxDX/7ImUqnkmsvhOR8Q0fWa1AOUkpKCP/74A97e3pBKpZBKpRg6dCgSExPx3HPP4dChQw0+1oQJE1BQUID58+cjLy8P4eHh2Lp1q7GIOTs7G1LpP2/KK1asgEajwUMPPWRynISEBCxYsACA/uq08vJyPPXUUyguLsbQoUOxdevW26oTIrJWhgRoYAiHv6zNoI5e2Ho8DylnizDzzlCxwyEyK01KgLRaLVxdXQHo63hyc3PRpUsXdOjQAadOnWr08WbNmnXDIa/re5SysrJueTyJRII33ngDb7zxRqNjIbIlOp2APZms/7FWhtd0//krUNdoobDjFX5EBk1KgHr27InDhw8jODgYkZGRePvttyGXy/Hpp58iJCSkuWMkohZyMq8UxRXVcJLL0KutUuxwqJl18nGBl7McReUaHM4pMS6RQURNrAGaN28edDr9KsNvvPEGMjMzMWzYMPzyyy/48MMPmzVAImo5hvl/+gd58iohKySRSIxDm4ahTiLSa1IPUExMjPHn0NBQnDx5EleuXIGHh4fxSjAiMn8pnP/H6g3s6IWfj15CyrlCPI9OYodDZDZuax4gAMjJyQEAThxIZGG0OsG4WvggFkBbLcNrezC7GFXVWs70TVSrSX3eNTU1eO2116BUKhEUFISgoCAolUrMmzcP1dXVzR0jEbWAE7kqlFbVwFVhhx4BXPTXWnVs44w2rgpoanQ4mH1V7HCIzEaTEqDZs2fj008/xdtvv41Dhw7h0KFDePvtt/HZZ5/hueeea+4YiagFpJwrBAAMCPbkauFWTCKRGHuB9rAOiMioSUNgGzZswMaNGzFq1Cjjtt69eyMwMBATJ07EihUrmi1AImoZrP+xHYM6euGHw7nGonciamIPkEKhQFBQUJ3twcHBkMvltxsTEbWwGq0O+7L0wyGcANH6GXqA0nKKUalp2FJFRNauSQnQrFmzsHDhQqjVauM2tVqNt95666ZreBGReTh6sQRl6hooHe3R3Z/1P9aug5cT/JUOqNYK2H/+itjhEJmFBg+BjR8/3uT+77//jnbt2iEsLAwAcPjwYWg0Gtx9993NGyERNTvDUEhksCekUk5dYe0MdUDfHLqIlLNFGNapjdghEYmuwQmQUmk6S+yDDz5ocp+XwRNZDtb/2J6BHfUJ0G4WQhMBaEQCtGbNmpaMg4haSVW1Fnsz9cMgQ0O9RY6GWsuQ2tf66IVilFRWQ+loL3JEROK6rWtfCwoKsGvXLuzatQsFBQXNFRMRtaCD569CXaODj6sCoT4uYodDraStuyNCvJ2hE4A9vBqMqGkJUHl5OZ588kn4+/vjjjvuwB133IGAgABMmzYNFRUVzR0jETWjXRn6+X+Ghnpz6RobY+gF2l37N0Bky5qUAMXFxeHPP//Ejz/+iOLiYhQXF+P777/Hn3/+iRdeeKG5YySiZmT48BvaicNftsbwmu9iAkTUtIkQ//e//2HLli0YPny4cdvo0aPh6OiIRx55hBMhEpmpkopqHLlYAuCf3gCyHQNDvCCVAOcKypFbXIkAd0exQyISTZN6gCoqKuDr61tnu4+PD4fAiMzY32cLIQhAJx8X+Lo5iB0OtTKloz16t3MHwGEwoiYlQIMGDUJCQgKqqqqM2yorK/H6669j0KBBzRYcETUvw9AHe39s11DWAREBaOIQ2NKlSzFy5Mg6EyE6ODhg27ZtzRogETWf3dcUQJNtGhLqjY92ZGBXRhEEQWAhPNmsJiVAvXr1wpkzZ/Dll1/i5MmTAICJEyfi8ccfh6Mjx5SJzFHOlQpkFVVAJpUgMsRT7HBIJH07uMPBXorCMjVO55ehi5+r2CERiaLRCVB1dTW6du2Kn376CdOnT2+JmIioBfx9Vt/7Ex7oDlcHToJnqxR2MgwI9sLO0wXYlVHIBIhsVqNrgOzt7U1qf4jIMuzK0E9+x/ofGhqqXwJl1xlOYEu2q0lF0DNnzsTixYtRU1PT3PEQUQvQ6QT8XVv/M4zz/9i8oaH6xVBTM69AU6MTORoicTSpBmjfvn1ISkrCb7/9hl69esHZ2dlk/zfffNMswRFR8ziZV4qicg2c5TKEB7qLHQ6JrKufK7yc5Sgq1yAtpxgDglkTRranSQmQu7t7ndXgich87crQD3VEhnjBXnZbSwCSFZBKJRgc6o0fD+diV0YhEyCySY1KgHQ6Hd555x2cPn0aGo0Gd911FxYsWMArv4jMHOt/6HpDQ73w4+Fc7M4oRNyIzmKHQ9TqGvVV8K233sIrr7wCFxcXtG3bFh9++CFmzpzZUrERUTNQ12ixN1OfAHH+HzIwJMNpOcUoraoWORqi1teoBOjzzz/Hxx9/jG3btuG7777Djz/+iC+//BI6HYvoiMzVwfPFqKrWwdtFgc6+LmKHQ2ainYcTgrycoNUJSD13RexwiFpdoxKg7OxsjB492ng/OjoaEokEubm5zR4YETWPf2Z/9uKsv2TC0AvE1eHJFjUqAaqpqYGDg+kCivb29qiuZvcpkbnaWTvXC+t/6HqGKRF2cj4gskGNKoIWBAFTp06FQqEwbquqqsIzzzxjcik8L4MnMg+FZWocuVACAIjq0kbkaMjcDA71hp1UgnMF5ci5UoFATyexQyJqNY3qAZoyZQp8fHygVCqNt0mTJiEgIMBkW2MsX74cQUFBcHBwQGRkJPbu3XvDtsePH8eDDz6IoKAgSCQSLF26tE6bBQsWQCKRmNy6du3aqJiIrMVftd/sewS4wcfV4Ratyda4OdijbwcPAEDyafYCkW1pVA/QmjVrmvXJN23ahLi4OKxcuRKRkZFYunQpYmJicOrUKfj4+NRpX1FRgZCQEDz88MP497//fcPj9ujRA7///rvxvp1dk6Y7IrJ4yaf0H2pRndn7Q/WL6twGezOv4M9Tl/HEwA5ih0PUakSdEe29997D9OnTERsbi+7du2PlypVwcnLC6tWr623fv39/vPPOO3j00UdNhuGuZ2dnBz8/P+PN25u1D2R7tDoBO2u/1Q/vUvcLBREADK8dGv37bBHUNVqRoyFqPaIlQBqNBgcOHEB0dPQ/wUiliI6ORkpKym0d+8yZMwgICEBISAgef/xxZGdn37S9Wq2GSqUyuRFZuqMXS3C1ohquCjv0ae8udjhkprr7u6GNqwIVGi32Z10VOxyiViNaAlRYWAitVgtfX1+T7b6+vsjLy2vycSMjI7F27Vps3boVK1asQGZmJoYNG4bS0tIbPiYxMdGkhikwMLDJz09kLpJPXQYADO3kzeUv6IYkEolxiNTwN0NkC6zuXXHUqFF4+OGH0bt3b8TExOCXX35BcXExvv766xs+Jj4+HiUlJcZbTk5OK0ZM1DL+NA5/sf6Hbs7wN/InC6HJhohWHezt7Q2ZTIb8/HyT7fn5+fDz82u253F3d0fnzp2RkZFxwzYKheKmNUVEluZq7SrfAHAHC6DpFoaGekMqAU7nlyG3uBIB7lzfkayfaD1AcrkcERERSEpKMm7T6XRISkrCoEGDmu15ysrKcPbsWfj7+zfbMYnM3V8ZhRAEoKufK/yV/DCjm3N3kqNPe/3l8OwFIlsh6hBYXFwcVq1ahXXr1iE9PR0zZsxAeXk5YmNjAQCTJ09GfHy8sb1Go0FaWhrS0tKg0Whw8eJFpKWlmfTuzJ07F3/++SeysrLw999/44EHHoBMJsPEiRNb/fyIxGKo5eDl79RQrAMiWyPqBDkTJkxAQUEB5s+fj7y8PISHh2Pr1q3Gwujs7GxIpf/kaLm5uejTp4/x/pIlS7BkyRJERUUhOTkZAHDhwgVMnDgRRUVFaNOmDYYOHYo9e/agTRt+EJBt0OkE7DytX9uJsz9TQw3v0gbvbT+N3RlFqNbqWDhPVk8iCIIgdhDmRqVSQalUoqSkBG5ubmKHQ9Qoxy6W4L5lu+Asl+HQ/Hsgt+MHGd2aTieg/1u/o6hcg41PDcTAEC+xQyJqtMZ8fvOdkcjKGIYwBod6M/mhBpNKJcaCecMM4kTWjO+ORFaGl79TU/FyeLIlTICIrEhJZTUOZhcDYAE0Nd6wTm0gkQDpl1TIV1WJHQ5Ri2ICRGRFdmcUQqsTEOrjgnYeTmKHQxbG01mO3u3cAbAXiKwfEyAiK8LL3+l28XJ4shVMgIishFYnICld/6F1V1eu/k5NY/jb2Xm6kKvDk1VjAkRkJdJyrqKoXANXBzsMCPYUOxyyUL3bKuHjqkCZugZ7zl0ROxyiFsMEiMhKbD+h7/25s4sPJ7GjJpNKJbi7m34y2t9P5N+iNZHl4rskkZXYfiIPADCiu6/IkZClu6f2b+j39HxwrlyyVkyAiKzAuYIynC0oh71MwuUv6LYN6ugFJ7kMl0qqcDxXJXY4RC2CCRCRFfg9XT9UMTDEC24O9iJHQ5bOwV6GOzrpE+nfOAxGVooJEJEV+L22/ie6G4e/qHlEd2cdEFk3JkBEFu5KuQb7z+uv1olm/Q81k7u6+kAqAU5cUuHC1QqxwyFqdkyAiCzcHycvQycA3f3d0NbdUexwyEp4OsvRr4N+OgXD/FJE1oQJEJGF49Vf1FIMf1PbOQxGVogJEJEFq6rWYufpQgBMgKj5GYZU95wrgqqqWuRoiJoXEyAiC/b32UJUVmvhr3RAjwA3scMhKxPs7YxQHxfU6AQkn+LiqGRdmAARWbDt11z9JZFIRI6GrFE0Z4UmK8UEiMhC6XSCcf4fDn9RSzH8be04dRnVWp3I0RA1HyZARBbq8IViFJSq4aKwQ2QIFz+llhEe6A5vFzlKq2qQysVRyYowASKyUIben6gubaCwk4kcDVkrmVSCu7sargbLEzkaoubDBIjIAgmCgF+P6j+M7uHwF7UwwzDYtuP50Om4OCpZByZARBYo/VIpzhWWQ24nxd1c/oJa2LDO3nBV2CFPVYUD2VfFDoeoWTABIrJAPx/NBQDc2aUNXBR2IkdD1k5hJ8OIHvpE++cjl0SOhqh5MAEisjCCIBg/hO7tHSByNGQr7uvtDwD45eglaDkMRlaACRCRhTmeq0JWUQUUdlLc3dVH7HDIRgwNbQM3BztcLlVjfxavBiPLxwSIyML8VNv7c1dXHzhz+ItaidxOipgefgD++RsksmRMgIgsiCAIxvqf+zj8Ra3s3tphsF+PcRiMLB8TICILcvRiCXKuVMLRXoY7u7YROxyyMUNCveHuZI/CMg1SM4vEDofotjABIrIghuLnu7r5wEnO4S9qXfYyKUbWDoPxajCydEyAiCyEIAjG2ov7evmLHA3ZKsMw2NZjeajh2mBkwURPgJYvX46goCA4ODggMjISe/fuvWHb48eP48EHH0RQUBAkEgmWLl1628ckshRpOcW4WFwJJ7kMw7vw6i8Sx6AQL3g42aOoXIM9XBuMLJioCdCmTZsQFxeHhIQEHDx4EGFhYYiJicHly5frbV9RUYGQkBAsWrQIfn5+zXJMIkthGHKI7uYLRznX/iJx2MmkGNlT3wtkKMgnskSiJkDvvfcepk+fjtjYWHTv3h0rV66Ek5MTVq9eXW/7/v3745133sGjjz4KhULRLMcksgQ6nYBfjhomP+TwF4lrzDXDYNUcBiMLJVoCpNFocODAAURHR/8TjFSK6OhopKSkmM0xiczBoZyryC2pgrNchqjOvPqLxDUg2BPeLnJcrajG32d5NRhZJtESoMLCQmi1Wvj6mi7k6Ovri7y8vFY9plqthkqlMrkRmZMfD+t7f0Z094WDPYe/SFz6YbDaSREPcxiMLJPoRdDmIDExEUql0ngLDAwUOyQio2qtDj/WfsiMCePkh2QextROxPnrsTxUarQiR0PUeKIlQN7e3pDJZMjPzzfZnp+ff8MC55Y6Znx8PEpKSoy3nJycJj0/UUtIPlWAonINvF0UuIPDX2Qm+gd5or2nE8rUNdh2vGm99kRiEi0BksvliIiIQFJSknGbTqdDUlISBg0a1KrHVCgUcHNzM7kRmYstB/QJ+QN9AmAvY6ctmQepVIIH+7YDAGw5cEHkaIgaT9R307i4OKxatQrr1q1Deno6ZsyYgfLycsTGxgIAJk+ejPj4eGN7jUaDtLQ0pKWlQaPR4OLFi0hLS0NGRkaDj0lkSYrK1EhK10/h8GBEO5GjITI1vm9bAMDus4W4WFwpcjREjSPqXPoTJkxAQUEB5s+fj7y8PISHh2Pr1q3GIubs7GxIpf/kaLm5uejTp4/x/pIlS7BkyRJERUUhOTm5QccksiQ/HM5FjU5Ar7ZKdPVjzySZl0BPJwwK8ULKuSJ8e/ACZt3VSeyQiBpMIggCl/S9jkqlglKpRElJCYfDSFT3fvgXjueq8Pr9PTBlcJDY4RDV8b8DF/DC5sMI8nLCjrnDIZFIxA6JbFhjPr9ZUEBkpk7kqnA8VwV7mQT38+ovMlOjevnBWS5DVlEFDpy/KnY4RA3GBIjITP3voL6wNLqbLzyc5SJHQ1Q/J7kdRtcuzstiaLIkTICIzFC1VofvDl0EADzE4mcyc4a/0Z+OXOKcQGQxmAARmSHO/UOWhHMCkSViAkRkhjj3D1kSzglElojvrERmhnP/kCXinEBkaZgAEZkZzv1DlsgwJ5AgAN8eZC8QmT8mQERmRBAEfLU3GwCLn8nyGP5mN+7LgVbHKebIvDEBIjIjKeeKcDq/DE5yGcb1aSt2OESNcm9vf7g72ePC1Ur8cfKy2OEQ3RQTICIz8vnf5wEAD/RpC6WjvcjREDWOg70ME/oHAgA+T8kSNxiiW2ACRGQmLhZX4rcT+kuIJw8KEjcYoiaaFNkBUgnw15lCZFwuEzscohtiAkRkJr7ccx46ARgU4oUufq5ih0PUJIGeTri7m37x6fXsBSIzxgSIyAxUVWuxcZ9+7p8pgzuIHA3R7ZlS24O55cAFlFZVixsM0Q0wASIyAz8duYQr5RoEKB0QXfvtmchSDQn1Qsc2zijXaPHNwYtih0NULyZARCITBAHr/s4CADw+sAPsOPMzWTiJRIIpg4MAAOtSsiAIvCSezA/faYlEdiinGEcvlkBuJ8WjtVfQEFm68X3bwUVhh3MF5diVUSh2OER1MAEiEtnntb0/Y3oHwMtFIW4wRM3ERWGHB2uXx1hXO70DkTlhAkQkosulVfj56CUALH4m6/NEbTF00sl85FypEDcYouswASIS0ca9OajWCujT3h2927mLHQ5Rswr1ccGwTt4QBGD9HvYCkXlhAkQkkgpNDdbWDn9NrS0YJbI2hkviN6Rmo6SCl8ST+WACRCSSr/bm4Eq5BoGejri3l7/Y4RC1iLu6+qCLryvK1DVYx4kRyYwwASISQVW1Fp/uPAsAeHZ4KC99J6sllUow865QAMDq3ZkoU9eIHBGRHt91iUSw5cAF5KvU8Fc6YHxfrvpO1u3eXv4I9nZGcUU1vmQtEJkJJkBEraxaq8OKZH3vz9N3hEBhJxM5IqKWJZNK8OzwjgCAVX+dQ1W1VuSIiJgAEbW67w5dxMXiSni7yPHogPZih0PUKsb1aYu27o4oLNNg495sscMhYgJE1Jq0OgEf1/b+TB8WAgd79v6QbbCXSTGjthfok53noK5hLxCJiwkQUSv6+eglZBaWw93JHo8P5MSHZFseimgHXzcFLpVUcZFUEh0TIKJWotMJWP5HBgDgySHBcFHYiRwRUetysJfhqTv0vUAfJ2egRqsTOSKyZUyAiFrJ9vR8nMovhavCzrhSNpGtmTggEF7OcuRcqcQPh3PFDodsGBMgolag1Ql4f/tpAMDkwR2gdLQXOSIicTjJ7TBtWDAA4IOkM9DUsBeIxMEEiKgVbN6fg5N5pVA62uNfQ0PEDodIVFMGBaGNqwLniyrwOWeHJpGYRQK0fPlyBAUFwcHBAZGRkdi7d+9N22/evBldu3aFg4MDevXqhV9++cVk/9SpUyGRSExuI0eObMlTILqhMnUNlvym7/157u5O8HCWixwRkbicFXaYe09nAMCHSWdwtVwjckRki0RPgDZt2oS4uDgkJCTg4MGDCAsLQ0xMDC5fvlxv+7///hsTJ07EtGnTcOjQIYwbNw7jxo3DsWPHTNqNHDkSly5dMt6++uqr1jgdojo++fMsCsvUCPJywhO88osIAPBQRCC6+rlCVVWDD/84I3Y4ZINET4Dee+89TJ8+HbGxsejevTtWrlwJJycnrF69ut72H3zwAUaOHIkXX3wR3bp1w8KFC9G3b1989NFHJu0UCgX8/PyMNw8Pj9Y4HSITucWV+HTnOQDAy6O6QW4n+n85IrMgk0ow797uAID1KedxrqBM5IjI1oj6bqzRaHDgwAFER0cbt0mlUkRHRyMlJaXex6SkpJi0B4CYmJg67ZOTk+Hj44MuXbpgxowZKCoqav4TILqFd7adgrpGhwHBnojp4St2OERmZWgnb9zV1Qc1OgGJv54UOxyyMaImQIWFhdBqtfD1Nf1g8PX1RV5eXr2PycvLu2X7kSNH4vPPP0dSUhIWL16MP//8E6NGjYJWW//Mo2q1GiqVyuRGdLuOXCjGt4f0k73Nu7cbJBKJyBERmZ9XRneFTCrB9hP5SDnLL6rUeqyyP/7RRx/F/fffj169emHcuHH46aefsG/fPiQnJ9fbPjExEUql0ngLDAxs3YDJ6giCgDd/SgcAjO/TFr3buYsbEJGZCvVxxWO1a+K9+fMJ6HSCyBGRrRA1AfL29oZMJkN+fr7J9vz8fPj5+dX7GD8/v0a1B4CQkBB4e3sjIyOj3v3x8fEoKSkx3nJychp5JkSmth3Pw96sK3Cwl2JuTBexwyEya3OiO8FVYYfjuSp8c4hLZFDrEDUBksvliIiIQFJSknGbTqdDUlISBg0aVO9jBg0aZNIeALZv337D9gBw4cIFFBUVwd/fv979CoUCbm5uJjeipiqtqsYbP54AoF/wNMDdUeSIiMybl4sCM+8KBQAs+jUdV3hZPLUC0YfA4uLisGrVKqxbtw7p6emYMWMGysvLERsbCwCYPHky4uPjje2ff/55bN26Fe+++y5OnjyJBQsWYP/+/Zg1axYAoKysDC+++CL27NmDrKwsJCUlYezYsQgNDUVMTIwo50i2JfHXk8gtqUJ7Tyfj6tdEdHOxQ4LQ2dcFhWUavP7jcbHDIRsgegI0YcIELFmyBPPnz0d4eDjS0tKwdetWY6FzdnY2Ll26ZGw/ePBgbNiwAZ9++inCwsKwZcsWfPfdd+jZsycAQCaT4ciRI7j//vvRuXNnTJs2DREREfjrr7+gUChEOUeyHX9nFGJDajYAYPGDveEk54KnRA2hsJPhnYfCIJUA36flYvuJ/Fs/iOg2SARBYMXZdVQqFZRKJUpKSjgcRg1Wrq7ByA92IudKJSYNbI83x/USOyQii5P4azo++fMcfFwV2P7vKCiduG4eNVxjPr9F7wEishbvbDuFnCuVaOvuiJdHdRM7HCKL9O/ozghp44zLpWos/PmE2OGQFWMCRNQM9mZewdq/swAAieN7wUXBoS+ipnCwl+Gdh3pDIgG2HLiA5FP1L4tEdLuYABHdpkqNFv/53xEAwIR+gbijcxuRIyKybBEdPBE7OBgAEP/NUZRWVYscEVkjJkBEt+ntbSeRWVgOPzcHvHofh76ImsOLMV3QwcsJl0qqsPAnDoVR82MCRHQbfjqSizW7swAA/ze+J9wcWLBJ1Bwc5TIsflA/FPb1/gv4eh8nqKXmxQSIqInO5JfipS36oa+no0JwV1cudkrUnAaGeCEuujMAYN73x3D0QonIEZE1YQJE1ASlVdV4ev0BVGi0GNzRCy/ew+UuiFrCzDtDEd3NB5oaHZ754gCucpZoaiZMgIgaSRAEzN18GOcKy+GvdMCHE/vATsb/SkQtQSqV4N1HwtHBywkXiyvx3MZD0HLBVGoGfNcmaqSVf57DtuP5kMuk+PjxvvB24QzjRC1J6WiPlZMi4GAvxV9nCvH+9tNih0RWgAkQUSPsOlOId7adBAAk3N8dfdp7iBwRkW3o5u+GReN7AwA+2pGB347niRwRWTomQEQNdPRCCZ754gB0AvBQRDs8NqC92CER2ZRxfdpi6uAgAMBzGw9hX9YVcQMii8YEiKgBzuSXYvLqVJSpaxAZ7Ik3x/WERCIROywim/PK6G4Y3qUNqqp1eHLNPhy7yCvDqGmYABHdQs6VCkz6LBVXK6oR1k6J/07pBwd7mdhhEdkkuZ0UKx6PwIAgT5SqazB59V5kXC4VOyyyQEyAiG4iX1WFx/+binyVGp19XbA2dgBcOdkhkagc5TJ8NrUferVV4kq5BpP+uxc5VyrEDossDBMgohvQv7GmIvtKBdp7OuGLaZHwcJaLHRYRAXB1sMe6Jwegk48L8lRVmPRZKi6rqsQOiywIEyCielwsrsTET/fgzOUy+Lk54Mt/RcLHzUHssIjoGp7Ocnzxr0gEejrifFEFHvkkBVmF5WKHRRaCCRDRdY5dLMEDy3fjVH4p2rgq8MW/BiDQ00nssIioHr5uDtjwr4Fo6+6IrKIKjF/xNw6cvyp2WGQBmAARXWPHqcuY8EkKLpfqa36+mzkEoT6uYodFRDcR6OmEb2cONtYEPbZqD7YeuyR2WGTmmAAR1fpqbzb+tW4/ymvX99r8zGC0dXcUOywiagAfVwdsfGog7u7qA3WNDjO+PIjPdmWKHRaZMSZAZPPUNVos/OkE4r85Cq1OwPi+bbE2dgCUjrzai8iSOCvs8MkTEZg0sD0EAVj40wm88u1RVGq0YodGZogJENm00/mlGLf8b+M3xefv7oR3Hw6D3I7/NYgskZ1MioVje+KV0V0BABtSszHmo12cMJHq4Ls82SRBELB2dybGLNuF9EsqeDrLsWpyP/x7RGfO8Exk4SQSCZ66oyPWTxsAH1cFMi6X4YGPd2Pln2e5kjwZSQRB4F/DdVQqFZRKJUpKSuDm5iZ2ONTMLquq8OKWI/jzdAEAIKpzG7zzcG/4uPIydyJrc6Vcg/hvjmDb8XwAwMAQTyx5OAztPHhlpzVqzOc3E6B6MAGyTlXVWny2KxMf78hAuUYLuZ0Ur47uhsmDOrDXh8iKCYKAr/fnYMEPJ1BZrYWDvRRP3dERz0SFwEluJ3Z41IyYAN0mJkDWRRAE/HTkEhb9ehIXiysBAGHtlHjn4TB09uUl7kS2IrOwHC9tOYx9Wfp5gnzdFHgppise6NMWUim/BFkDJkC3iQmQdRAEAfuyruLtrSexv3ZiND83B/xnVBeMDeMbHpEtEgQBvxzNQ+Kv6bhwVf+FqHc7JV6K6YohoV7sDbZwTIBuExMgy1aj1eHXY3n471/ncPiC/soPR3sZnonqiKfuCIGjnCu5E9m6qmot1uzOwvIdGShT1wAAuvu74V/DgnFf7wBeCWqhmADdJiZAlklVVY3N+y9g9a5M41CX3E6KB/u2xfN3d4afkkXORGSqoFSNj/44g6/3X0BltX6+ID83B0wZHISJAwLh7sQFkC0JE6DbxATIclRVa5F86jK+T8tF0snL0NToAOgXSXxiYAc8MagDvF0UIkdJRObuarkGX6aex7qU8ygoVQMA7GUSDO/ig7HhAbi7qy97jy0AE6DbxATIvJWra7DnXBG2Hc/Dr8fyUFpVY9zXyccFsUOCMb5vWzjY882KiBpHXaPFD2m5WLM7CycuqYzbneUyxPTwQ0xPPwzu6AVXB84Ub46YAN0mJkDmRasTcOxiCXZlFGLn6QIczL6Kau0/f7b+SgfcHxaA+8MD0N3fjUWMdGNHjwLffAMUFwPu7sD48UCvXmJHRWbqVF4pvk+7iO/Tco3D6gAgk0rQJ9Adwzq1wbDO3ujdVgk7GWuGzAEToNvEBEg8giDgwtVKHL5QjMM5xTh8oQTHLpag4rq1fNp5OCKqcxuMCQvAgCBPXtFFN5eRAUyZAvz9NyCTAVIpoNMBWi0wZAiwdi0QGip2lGSmBEHAweyr+PHwJfx5ugCZheUm+x3spegZoETvdu4IC1QirJ07Ong58cuYCCwuAVq+fDneeecd5OXlISwsDMuWLcOAAQNu2H7z5s147bXXkJWVhU6dOmHx4sUYPXq0cb8gCEhISMCqVatQXFyMIUOGYMWKFejUqVOD4mEC1PJUVdXILa7E+aIKZFwuw9mCMpy9XIazBeXGKzKu5aqww6COXhjWuQ2GhXrzzYUaLiMDiIwESkr0Cc/1ZDJAqQRSU5kEUYPkXKnAX2cKsSujALvOFEJVVfc9y0kuQ8c2Lgj1cUHHNs7o2MYFHbyc0dbdEW6Odnz/aiEWlQBt2rQJkydPxsqVKxEZGYmlS5di8+bNOHXqFHx8fOq0//vvv3HHHXcgMTER9913HzZs2IDFixfj4MGD6NmzJwBg8eLFSExMxLp16xAcHIzXXnsNR48exYkTJ+DgcOsrgZgANZ4gCCjXaKGqrEZJZTWuVmhQVKZBUZkahWUaFJWrka9SI7e4EhevVqK0niTHwF4mQTd/N/Rup/8mFRbojo5tXCBjLw81xZAh+uSmvuTHQCYDBg4Edu1qvbjIKuh0As4VluPINb3WJ3JV0Gh1N3yMi8IOAe4OCHB3hK+rA7xd5fByVsDbVQFvZzncneRQOtnDzcEOLgomS41hUQlQZGQk+vfvj48++ggAoNPpEBgYiNmzZ+Pll1+u037ChAkoLy/HTz/9ZNw2cOBAhIeHY+XKlRAEAQEBAXjhhRcwd+5cAEBJSQl8fX2xdu1aPProo7eMyfALPJF1Ca63+AU29LdXXzsBQp19AvTJhOHnf/YLEATDfv1jdTr9v4JwzTYB0AkCBEH/s1YnQKcToK29r9MJ0OoE1Oh0qNEJqNEKtf/qUK3VQaMVUK3VobpGB41WB3WNDlXVWqirdVDXaFFZrUWFRotydY3+X00NyqpqoKqqafQigx5O9mjr4YjQNi7/fFPycUEHLyco7FjATM3g6FGgd++Gtz9yhDVBdNuqtbo6vdsZBWW4cLUSV8o1jTqWVAK4OdrDRWEHZ7kdnBQy/b9yGRzlMijspHCw/+dfuUwKezsp7GVSyGUS2MuksJNJYSeVwE4mgZ1UAplUf18i0dczySQSSKUSyKQSSCX6xWSlktqfoW8nqf1ZKr1mG/TbUXtf/1PtvxIJrk3b/tlfN5lrTH53q7alKhW6B/k3KAESdREUjUaDAwcOID4+3rhNKpUiOjoaKSkp9T4mJSUFcXFxJttiYmLw3XffAQAyMzORl5eH6Oho436lUonIyEikpKTUmwCp1Wqo1WrjfZVKX/kfs/QvSBVcMK8x7GUSKB3t4eZoD28XBbxd5PB2UcDLWYE2rgq09XBEW3cH+Csd4azgGjzUwr75Rt+7c7PeHwOZDPj2WyZAdNvsZVKE+ui/1F2vUqNFbom+Jzy3uBKXS9X6nvJyDQpL1SgsU6OksgaqympotDroBKC4ohrFFdUinInl0akrGtxW1E+gwsJCaLVa+Pr6mmz39fXFyZMn631MXl5eve3z8vKM+w3bbtTmeomJiXj99dfrbFfYSyGzv3Vlf30Zbb3trsuQ9dskdbZBYppFGx4rgWlWLTFk6Ndk6VKJPkOX1mbwEgkgkxgy+9p/pRLY12b7+m8E+m8D8tpvDfYyKeR2+m8ODvYyONhJoaj9hqGwl8FFIYOT3PTbiNLRHkpHezjYS9ldS+ajuFhf8NyQBEgqBa5ebfGQyLY51tYGdWxTNzm6liAIUNfoUFJbVlCmrkGFWt/rXqnRokxdo++dr9FBXftvVbUWGq0ATY2+R79aq4OmRt/br9Xpe/e1OgHVhpEBnQCdoP9XK+hHE3SCfptOZxhN+GekQSeYjlAIgmAclTDEbNiH60Y2rt1vuq3hvzsBt26s1TX8ajx+BQcQHx9v0qukUqkQGBiIA/NGsAaIyJK5u+uv9moInQ7w8GjRcIgaSiKR6L+A2svg68ZZ7BtKpVJB+U7D2oo6cYG3tzdkMhny8/NNtufn58PPz6/ex/j5+d20veHfxhxToVDAzc3N5EZEVmD8+Ib1/gD6duPHt2w8RGQ2RE2A5HI5IiIikJSUZNym0+mQlJSEQYMG1fuYQYMGmbQHgO3btxvbBwcHw8/Pz6SNSqVCamrqDY9JRFaqVy9g8GB9fc/NyGT6q8VqryQlIusn+hBYXFwcpkyZgn79+mHAgAFYunQpysvLERsbCwCYPHky2rZti8TERADA888/j6ioKLz77ru49957sXHjRuzfvx+ffvopAH234Zw5c/Dmm2+iU6dOxsvgAwICMG7cOLFOk4jEsm5dw+YBWru21UMjIvGIngBNmDABBQUFmD9/PvLy8hAeHo6tW7cai5izs7Mhlf7TUTV48GBs2LAB8+bNwyuvvIJOnTrhu+++M84BBAAvvfQSysvL8dRTT6G4uBhDhw7F1q1bGzQHEBFZmdBQ/TxAU6cCu3fXnQl64EDOBE1kg0SfB8gccSJEIit19Kj+UverV/UFz+PHc9iLyIo05vNb9B4gIqJW06sX5/khIgAiF0ETERERiYEJEBEREdkcJkBERERkc5gAERERkc1hAkREREQ2hwkQERER2RwmQERERGRzmAARERGRzeFEiPUwTI6tUqlEjoSIiIgayvC53ZBFLpgA1aO0tBQAEBgYKHIkRERE1FilpaVQKpU3bcO1wOqh0+mQm5sLV1dXSCQSscNpMpVKhcDAQOTk5HBNM5HxtTAffC3MB18L82Etr4UgCCgtLUVAQIDJQur1YQ9QPaRSKdq1ayd2GM3Gzc3Nov+grQlfC/PB18J88LUwH9bwWtyq58eARdBERERkc5gAERERkc1hAmTFFAoFEhISoFAoxA7F5vG1MB98LcwHXwvzYYuvBYugiYiIyOawB4iIiIhsDhMgIiIisjlMgIiIiMjmMAEiIiIim8MEyMao1WqEh4dDIpEgLS1N7HBsTlZWFqZNm4bg4GA4OjqiY8eOSEhIgEajETs0m7F8+XIEBQXBwcEBkZGR2Lt3r9gh2ZzExET0798frq6u8PHxwbhx43Dq1CmxwyIAixYtgkQiwZw5c8QOpcUxAbIxL730EgICAsQOw2adPHkSOp0On3zyCY4fP473338fK1euxCuvvCJ2aDZh06ZNiIuLQ0JCAg4ePIiwsDDExMTg8uXLYodmU/7880/MnDkTe/bswfbt21FdXY177rkH5eXlYodm0/bt24dPPvkEvXv3FjuUVsHL4G3Ir7/+iri4OPzvf/9Djx49cOjQIYSHh4sdls175513sGLFCpw7d07sUKxeZGQk+vfvj48++giAft2/wMBAzJ49Gy+//LLI0dmugoIC+Pj44M8//8Qdd9whdjg2qaysDH379sXHH3+MN998E+Hh4Vi6dKnYYbUo9gDZiPz8fEyfPh3r16+Hk5OT2OHQNUpKSuDp6Sl2GFZPo9HgwIEDiI6ONm6TSqWIjo5GSkqKiJFRSUkJAPD/gYhmzpyJe++91+T/h7XjYqg2QBAETJ06Fc888wz69euHrKwssUOiWhkZGVi2bBmWLFkidihWr7CwEFqtFr6+vibbfX19cfLkSZGiIp1Ohzlz5mDIkCHo2bOn2OHYpI0bN+LgwYPYt2+f2KG0KvYAWbCXX34ZEonkpreTJ09i2bJlKC0tRXx8vNghW62GvhbXunjxIkaOHImHH34Y06dPFylyInHNnDkTx44dw8aNG8UOxSbl5OTg+eefx5dffgkHBwexw2lVrAGyYAUFBSgqKrppm5CQEDzyyCP48ccfIZFIjNu1Wi1kMhkef/xxrFu3rqVDtXoNfS3kcjkAIDc3F8OHD8fAgQOxdu1aSKX8LtLSNBoNnJycsGXLFowbN864fcqUKSguLsb3338vXnA2atasWfj++++xc+dOBAcHix2OTfruu+/wwAMPQCaTGbdptVpIJBJIpVKo1WqTfdaECZANyM7OhkqlMt7Pzc1FTEwMtmzZgsjISLRr107E6GzPxYsXceeddyIiIgJffPGF1b65mKPIyEgMGDAAy5YtA6Affmnfvj1mzZrFIuhWJAgCZs+ejW+//RbJycno1KmT2CHZrNLSUpw/f95kW2xsLLp27Yr//Oc/Vj0syRogG9C+fXuT+y4uLgCAjh07MvlpZRcvXsTw4cPRoUMHLFmyBAUFBcZ9fn5+IkZmG+Li4jBlyhT069cPAwYMwNKlS1FeXo7Y2FixQ7MpM2fOxIYNG/D999/D1dUVeXl5AAClUglHR0eRo7Mtrq6udZIcZ2dneHl5WXXyAzABImpV27dvR0ZGBjIyMuokn+yMbXkTJkxAQUEB5s+fj7y8PISHh2Pr1q11CqOpZa1YsQIAMHz4cJPta9aswdSpU1s/ILJJHAIjIiIim8PKSyIiIrI5TICIiIjI5jABIiIiIpvDBIiIiIhsDhMgIiIisjlMgIiIiMjmMAEiIiIim8MEiMiCDB8+HHPmzBE7jFtasGABfH19IZFI8N1334kdTh0LFixAeHi42GE0q6ac061en6ysLEgkEqSlpd1WbETmiAkQUSsYM2YMRo4cWe++v/76CxKJBEeOHGnlqFpGeno6Xn/9dXzyySe4dOkSRo0aVaeN4YPVcPP09ERUVBT++usvESJuHQcOHIBEIsGePXvq3X/33Xdj/PjxTT7+3LlzkZSU1OTHt5QVK1bA3d0dOTk5Jttnz56Nzp07o6KiQqTIyNYxASJqBdOmTcP27dtx4cKFOvvWrFmDfv36oXfv3iJE1vzOnj0LABg7diz8/PygUChu2Pb333/HpUuXsHPnTgQEBOC+++5Dfn5+a4XaqiIiIhAWFobVq1fX2ZeVlYUdO3Zg2rRpjT6uIAioqamBi4sLvLy8miPUZvXMM89gwIABJueWlJSEFStWYO3atXBychIxOrJlTICIWsF9992HNm3aYO3atSbby8rKsHnzZkybNg1FRUWYOHEi2rZtCycnJ/Tq1QtfffXVTY9b3xCGu7u7yfPk5OTgkUcegbu7Ozw9PTF27FhkZWUZ9ycnJ2PAgAFwdnaGu7s7hgwZUmd16GsdPXoUd911FxwdHeHl5YWnnnoKZWVlAPTDMGPGjAEASKVSSCSSm8bv5eUFPz8/9OzZE6+88gpUKhVSU1ON+9evX49+/frB1dUVfn5+eOyxx3D58mWT2CUSCZKSktCvXz84OTlh8ODBOHXqlMnzLFq0CL6+vnB1dcW0adNQVVVlsl+n0+GNN95Au3btoFAojGuEGRh6rL7++msMGzYMjo6O6N+/P06fPo19+/ahX79+cHFxwahRo0wWuL3etGnTsGnTpjq9HmvXroW/vz9GjhzZ4HP+9ddfERERAYVCgV27dtUZAtu3bx9GjBgBb29vKJVKREVF4eDBg3ViMvTSOTo6IiQkBFu2bLlh/ABw7NgxjBo1Ci4uLvD19cUTTzyBwsLCG7aXSCT47LPPkJqaipUrV0KlUuHJJ59EXFwcBg8efNPnImpJTICIWoGdnR0mT56MtWvXmix6unnzZmi1WkycOBFVVVWIiIjAzz//jGPHjuGpp57CE088gb179zb5eaurqxETEwNXV1f89ddf2L17N1xcXDBy5EhoNBrU1NRg3LhxiIqKwpEjR5CSkoKnnnrqholLeXk5YmJi4OHhgX379mHz5s34/fffMWvWLAD6YZg1a9YA0H+wXrp0qUFxVlZW4vPPPwcAyOVyk/gXLlyIw4cP47vvvkNWVla9i2W++uqrePfdd7F//37Y2dnhySefNO77+uuvsWDBAvzf//0f9u/fD39/f3z88ccmj//ggw/w7rvvYsmSJThy5AhiYmJw//3348yZMybtEhISMG/ePBw8eBB2dnZ47LHH8NJLL+GDDz7AX3/9hYyMDMyfP/+G5/n4449DrVabJBmCIGDdunWYOnUqZDJZg8/55ZdfxqJFi5Cenl5v72FpaSmmTJmCXbt2Yc+ePejUqRNGjx6N0tJSk3avvfYaHnzwQRw+fBiPP/44Hn30UaSnp9cbf3FxMe666y706dMH+/fvx9atW5Gfn49HHnnkhucMAIGBgVi6dClefPFFTJo0CS4uLli4cOFNH0PU4gQiahXp6ekCAGHHjh3GbcOGDRMmTZp0w8fce++9wgsvvGC8HxUVJTz//PPG+wCEb7/91uQxSqVSWLNmjSAIgrB+/XqhS5cugk6nM+5Xq9WCo6OjsG3bNqGoqEgAICQnJzfoHD799FPBw8NDKCsrM277+eefBalUKuTl5QmCIAjffvutcKu3lszMTAGA4OjoKDg7OwsSiUQAIERERAgajeaGj9u3b58AQCgtLRUEQRB27NghABB+//13k3gACJWVlYIgCMKgQYOEZ5991uQ4kZGRQlhYmPF+QECA8NZbb5m06d+/v/Fxhnj/+9//Gvd/9dVXAgAhKSnJuC0xMVHo0qXLTc/90UcfFaKiooz3k5KSBADCmTNnGnXO3333nUm7hIQEk3O6nlarFVxdXYUff/zRuA2A8Mwzz5i0i4yMFGbMmGFy3ocOHRIEQRAWLlwo3HPPPSbtc3JyBADCqVOnbnregiAIAwcOFAAIqampt2xL1NLYA0TUSrp27YrBgwcba0AyMjLw119/GWsjtFotFi5ciF69esHT0xMuLi7Ytm0bsrOzm/ychw8fRkZGBlxdXeHi4gIXFxd4enqiqqoKZ8+ehaenJ6ZOnYqYmBiMGTMGH3zwwU17bdLT0xEWFgZnZ2fjtiFDhkCn09UZdmqITZs24dChQ/jf//6H0NBQrF27Fvb29sb9Bw4cwJgxY9C+fXu4uroiKioKAOr8Tq7tAfH39wcA47BReno6IiMjTdoPGjTI+LNKpUJubi6GDBli0mbIkCF1ekKufR5fX18AQK9evUy2XTtcVZ8nn3wSO3fuNNZKrV69GlFRUQgNDW3UOffr1++mz5Ofn4/p06ejU6dOUCqVcHNzQ1lZWZ3jXPu7MNy/UQ/Q4cOHsWPHDuPfkouLC7p27Qrgn9qvGzl8+DAOHjwIJycnqy52J8thJ3YARLZk2rRpmD17NpYvX441a9agY8eOxg+4d955Bx988AGWLl2KXr16wdnZGXPmzIFGo7nh8SQSicmQGqAfNjIoKytDREQEvvzyyzqPbdOmDQB9EfZzzz2HrVu3YtOmTZg3bx62b9+OgQMHNscp31RgYCA6deqETp06oaamBg888ACOHTsGhUJhHG6LiYnBl19+iTZt2iA7OxsxMTF1fifXJk2G4TudTtfs8db3PNdvu9Xz3n333Wjfvj3Wrl2LF198Ed988w0++eQTAGjUOV+bhNZnypQpKCoqwgcffIAOHTpAoVBg0KBBN/17upWysjKMGTMGixcvrrPPkHjWR6PRYPLkyXj88ccRFRWFZ555Bvfddx+6dOnS5FiIbhd7gIha0SOPPAKpVIoNGzbg888/x5NPPmn8IN29ezfGjh2LSZMmISwsDCEhITh9+vRNj9emTRuTHpszZ86YFNj27dsXZ86cgY+PD0JDQ01uSqXS2K5Pnz6Ij4/H33//jZ49e2LDhg31Pl+3bt1w+PBhlJeXG7ft3r0bUqn0tj/MHnroIdjZ2Rnrc06ePImioiIsWrQIw4YNQ9euXW/Zu3KjmK8trAZgcim6m5sbAgICsHv3bpM2u3fvRvfu3ZtwJjcnlUoRGxuLdevWYcOGDZDL5XjooYcANN85A/r4n3vuOYwePRo9evSAQqGot1j5+svy9+zZg27dutV7zL59++L48eMICgqq8/d0s4TsjTfewJUrV/D+++9jypQpGDFiBGJjY1skSSVqKCZARK3IxcUFEyZMQHx8PC5dumRS3NqpUyds374df//9N9LT0/H000/f8pLwu+66Cx999BEOHTqE/fv345lnnjHpkXj88cfh7e2NsWPH4q+//kJmZiaSk5Px3HPP4cKFC8jMzER8fDxSUlJw/vx5/Pbbbzhz5swNPwAff/xxODg4YMqUKTh27Bh27NiB2bNn44knnjAOCTWVRCLBc889h0WLFqGiogLt27eHXC7HsmXLcO7cOfzwww9NKpx9/vnnsXr1aqxZswanT59GQkICjh8/btLmxRdfxOLFi7Fp0yacOnUKL7/8MtLS0vD888/f1jndSGxsLC5evIhXXnkFEydOhKOjIwA02zkD+r+n9evXIz09HampqXj88ceNz3OtzZs3Y/Xq1cbfzd69e41F7debOXMmrly5gokTJ2Lfvn04e/Ystm3bhtjYWGi12nofs2/fPixevBifffaZMen+5JNPcOrUKbz//vtNOjeiZiF2ERKRrfn7778FAMLo0aNNthcVFQljx44VXFxcBB8fH2HevHnC5MmThbFjxxrbXF8EffHiReGee+4RnJ2dhU6dOgm//PKLSRG0IAjCpUuXhMmTJwve3t6CQqEQQkJChOnTpwslJSVCXl6eMG7cOMHf31+Qy+VChw4dhPnz5wtarfaG8R85ckS48847BQcHB8HT01OYPn26sUBXEBpXBG0orjUoLy8XPDw8hMWLFwuCIAgbNmwQgoKCBIVCIQwaNEj44YcfTB5nKAi+evWq8RiHDh0SAAiZmZnGbW+99Zbg7e0tuLi4CFOmTBFeeuklk4JhrVYrLFiwQGjbtq1gb28vhIWFCb/++utN463vudesWSMolcqbnrvBPffcIwAQ9u7da7K9KecsCHWLoA8ePCj069dPcHBwEDp16iRs3rxZ6NChg/D+++8b2wAQli9fLowYMUJQKBRCUFCQsGnTppue9+nTp4UHHnhAcHd3FxwdHYWuXbsKc+bMMSm0N6iqqhK6desmTJ8+vc6+L7/8UnBwcBBOnjzZoN8XUXOTCMJ1BQREREREVo5DYERERGRzmAARERGRzWECRERERDaHCRARERHZHCZAREREZHOYABEREZHNYQJERERENocJEBEREdkcJkBERERkc5gAERERkc1hAkREREQ2hwkQERER2Zz/B+6l0/Ywi5RMAAAAAElFTkSuQmCC","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["from scipy import stats\n","\n","snd = stats.norm(0, 1)\n","\n","#generates 100 values from -5 to 5, equally spaced\n","x = np.linspace(-5, 5, 100)\n","\n","plt.plot(x, snd.pdf(x))\n","plt.scatter(0, 0, color='red', s=60);\n","\n","plt.xlim(-5, 5)\n","plt.title('Normal Distribution')\n","plt.xlabel('Values of Random Variable X')\n","plt.ylabel('Probability')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"lMLzxQhPDN3x"},"source":["While there is not an easy formula to compute probabilities by hand, the normal curve itself can be written as a function $$f(x)=\\frac{1}{\\sigma \\sqrt{2\\pi}}e^{\\left(-\\frac{1}{2}\\left(\\frac{x-\\mu}{\\sigma}\\right)^{2}\\right)},$$ where probabilites of interest are calculated by finding area under the curve.\n"]},{"cell_type":"markdown","metadata":{"id":"b_amsPoPFuY5"},"source":["## Standard Deviation and the Normal Distribution"]},{"cell_type":"markdown","metadata":{"id":"2Qd1emqtF1IZ"},"source":["Remember how we said standard deviation was a good measure of spread? One reason is because it can be used to bound the data. In particular, a majority of the data that is normally distributed falls within one standard deviation of the mean: about $68.27\\%$ of the data! About $95.45\\%$ of the data falls within two standard deviations, and roughly $99.73\\%$ falls within three standard deviations.\n","\n","![](normal_std_plot.png)"]},{"cell_type":"markdown","metadata":{"id":"OsTsmVkDYAdn"},"source":["The special case of the normal distribution is called the **standard normal** distribution and occurs when $\\mu = 0$ and $\\sigma = 1$.\n","Given a random variable and any values for $\\mu$ and $\\sigma$, that is $X  ∼  N(\\mu, \\sigma)$, we can *transform* to a standard normal, by normalizing it! That is:\n","\n","$$\\frac{X-\\mu}{\\sigma}$$\n","\n","Note this may be useful if you are comparing values from multiple normal distributions."]},{"cell_type":"markdown","metadata":{"id":"aGmmlswknQdG"},"source":["## Central Limit Theorem"]},{"cell_type":"markdown","metadata":{"id":"EvrgC9pP1Rg4"},"source":["Recall the rolling of the die example from [the previous section](../12/2/uniform.html). Now, suppose I am not interested in each roll but in the average value of all the dice rolls of students in my classroom. I have 50 students present and each student rolls 1 die. I then take the mean of all 50 dice rolls. What is the probability distribution for the average dice roll? This is another question can be easily answered through simulation!"]},{"cell_type":"markdown","metadata":{"id":"3-gnT0Dp0qqf"},"source":["Simulations use a computer to mimic real experiments. Returning to our dice-rolling example from the [the previous section](../12/2/uniform.html), instead of asking all of my students to roll dice and take the mean 100s or 1000s of times to plot an empirical distribution (which would be both cumbersome and time consuming), I can have the computer do it for us. First, we can write code that does this experiment one time. We use `sample` to roll our die and `np.mean` to take the average."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hKfpyHL90qqg","outputId":"dd3d4011-2fc9-4a26-dbef-80989804cd1b"},"outputs":[{"data":{"text/plain":["Face    3.6\n","dtype: float64"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","die = pd.DataFrame(\n","    {\n","        'Face': np.arange(1, 7),\n","    }\n",")\n","np.mean(die.sample(50, replace=True))"]},{"cell_type":"markdown","metadata":{"id":"jw3scddc0qqg"},"source":["The next step is to write a function that can repeat the experiment a certain number of times. We can do this using a `for loop` and saving the results of each experiment in a `numpy` array."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vWcmm54V0qqg"},"outputs":[],"source":["def face_mean_sim(nsim):\n","    \"\"\"Simulates nsim repetions of 50 dice rolls and returns their sample mean.\"\"\"\n","    np.random.seed(1234)\n","    means = np.array([])\n","    for i in np.arange(nsim):\n","        means = np.append(means, np.mean(die.sample(50, replace=True)))\n","    return means"]},{"cell_type":"markdown","metadata":{"id":"BLV1ElBdDYRC"},"source":["If we repeat the experiment 10 times we get an idea of what the means look like. Plotting a histogram we see the empirical distribution of these 10 experiments. In particular, we find that 3 of these 10 experiments have a mean of around 3.45, while all experiments have a mean in the range 3.22 to 3.76."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vdGI2hEGDTn-","outputId":"88008e86-b9bb-4869-c128-3242640e9180"},"outputs":[{"data":{"text/plain":["array([3.42, 3.5 , 3.24, 3.44, 3.22, 3.6 , 3.08, 3.76, 3.66, 3.46])"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["ten_runs = face_mean_sim(10)\n","ten_runs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"hWV_0YWlD7C5","outputId":"ffff25a8-2fb5-48ff-d139-89c5f672febb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.hist(ten_runs);\n","plt.xlabel('Mean')\n","plt.ylabel('Frequency')\n","plt.title('Mean Dice Roll Simulation: Ten Runs')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"XFod8VdI0qqg"},"source":["It is difficult to determine what this distribution looks like with only 10 runs, so we experiment by ploting the empirical distributions of results from 100, 1000, and 10000 replications of the dice rolling experiment."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":585},"id":"2OAHXHU80qqg","outputId":"2121369d-e903-4d89-b726-5abf80360976"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 576x576 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig, axs = plt.subplots(3, figsize=(8, 8))\n","fig.suptitle('Mean Dice Roll Simulation: 100, 1000, and 10000', fontsize=16)\n","fig.tight_layout()\n","axs[0].hist(face_mean_sim(100), 30);\n","axs[1].hist(face_mean_sim(1_000), 30);\n","axs[2].hist(face_mean_sim(10_000),30);"]},{"cell_type":"markdown","metadata":{"id":"YXasPZ5g0qqg"},"source":["As we saw with our Uniform Distribution experiment, with larger numbers of experiments/samples our empirical distribution approaches the true probability distribution.\n","\n","Based on our empirical distribution with 10000 experiments, we can see that the average value of the dice rolls is symmetric and approximately bell-shaped with a mean of around 3.5. In fact, these properties of being approximately bell-shaped and symmetric are distinct to the empirical *normal distribution*.\n","\n","As we are plotting the distribution means of samples from a uniform distribution, the resulting probability distribution will always take on this shape, that is it will always be approximately normal, as long as the sample size is sufficiently large. This is due to an important mathematical theorem, the *Central Limit Theorem*[^***]. The Central Limit Theorem (CLT) states that if you take sufficiently large random samples from a population with replacement, the distribution of sample means will be approximately normally distributed. The CLT is a property of sample means and holds true for samples from *any* distribution, not just the uniform distribution of dice rolls. For example, the distribution of average heights, average weights, and average test scores of data science students would all be approximately normally distributed as long as we take large enough random samples from our population with replacement.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"0VW8nyQG4Vot"},"source":["In the [the previous section](../12/1/uniform.html) we calculated the mean and standard deviation of the uniform distribution of our dice rolls. The CLT allows us to use these to learn the mean (μ) and standard deviation (σ) of our distribution of sample means. According to the Central Limit Theorem, if the mean and standard deviation of the population you are sampling from are $\\mu$ and $\\sigma$ respectively, then the mean and standard deviation of the distribution of sample means are $\\mu$ and $\\frac{σ}{\\sqrt{n}}$ respectively where n is the sample size. Therefore, since we know from earlier that the mean of the uniform dice rolling distribution is 3.5 and the standard deviation is 1.71, the mean of the distribution of sample means is also 3.5 and the standard deviation is $\\frac{1.71}{\\sqrt{50}} = 0.24$. Let's investigate this further using our empirical distribution."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LhLQc4oD3zUB","outputId":"1c38e57a-04d1-4d1b-ddd3-9dace39f8bf0"},"outputs":[{"data":{"text/plain":["3.4967740000000007"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["np.mean(face_mean_sim(10_000))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"05URqhM234nV","outputId":"8599cfc7-2f21-4e2c-93e5-a140323e1eba"},"outputs":[{"data":{"text/plain":["0.23814748707471173"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["np.std(face_mean_sim(10_000))"]},{"cell_type":"markdown","metadata":{"id":"cnPp3SB24KBQ"},"source":["Our empirical distribution matches what we expected to see mathematically according to the Central Limit Theorem!"]},{"cell_type":"markdown","metadata":{"id":"wrdpcFZg8BaL"},"source":["[^***]: For more information on the Central Limit Theorem, see the online Statistics Textbook at [OpenStax](https://cnx.org/contents/MBiUQmmY@25.39:MVbL0vFO@10/Introduction)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.9.13 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"tAzM3r78p08k","tags":["remove-cell"]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"id":"HtrRcRR0uFAM"},"source":["# Normal Distribution"]},{"cell_type":"markdown","metadata":{"id":"q0LoZTYHoCuw"},"source":["Let's consider another distribution by considering real data.\n","\n","Below we load in the Galton Height Data. This dataset was collected from families and contains heights of the father, mother, children. We focus on the father heights, and plot the distribution of heights in a histogram."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":235},"executionInfo":{"elapsed":188,"status":"ok","timestamp":1689610841357,"user":{"displayName":"Susanna Lange","userId":"00843628675540967552"},"user_tz":300},"id":"UOdUEyhemMef","outputId":"79220073-def2-4067-bec0-293762a2f8a4"},"outputs":[{"data":{"text/html":["\n","\n","  <div id=\"df-c8b57ff1-3e17-442f-93e2-9b7c00c14e42\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>family</th>\n","      <th>father</th>\n","      <th>mother</th>\n","      <th>midparentHeight</th>\n","      <th>children</th>\n","      <th>childNum</th>\n","      <th>gender</th>\n","      <th>childHeight</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>78.5</td>\n","      <td>67.0</td>\n","      <td>75.43</td>\n","      <td>4</td>\n","      <td>1</td>\n","      <td>male</td>\n","      <td>73.2</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>1</td>\n","      <td>78.5</td>\n","      <td>67.0</td>\n","      <td>75.43</td>\n","      <td>4</td>\n","      <td>2</td>\n","      <td>female</td>\n","      <td>69.2</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>1</td>\n","      <td>78.5</td>\n","      <td>67.0</td>\n","      <td>75.43</td>\n","      <td>4</td>\n","      <td>3</td>\n","      <td>female</td>\n","      <td>69.0</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","    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2    75.5    66.5            73.66         4         1    male   \n","5      2    75.5    66.5            73.66         4         2    male   \n","\n","   childHeight  \n","0         73.2  \n","1         69.2  \n","2         69.0  \n","3         69.0  \n","4         73.5  \n","5         72.5  "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["galton_df = pd.read_csv(\"../../data/galton.csv\")\n","galton_df.head(6)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":1567,"status":"ok","timestamp":1689610867384,"user":{"displayName":"Susanna Lange","userId":"00843628675540967552"},"user_tz":300},"id":"6Mi-17cNm1oG","outputId":"93ce9d81-264e-49aa-c749-3c4867921b15"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["bin_size=np.arange(60, 80.5, 1)\n","plt.hist(galton_df[\"father\"], bins = bin_size, density = True)\n","plt.title('Father heights')\n","plt.xlabel('heights (in inches)')\n","plt.ylabel('Probabilities')\n","plt.scatter(galton_df['father'].mean(), 0, color='red', s=60);\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"MO3r-9W6moSJ"},"source":["This is another empirical distribution, but a special one!\n","An empirical *normal* distribution.\n","\n","The values for the normal distribution are continous variables - as heights can take on any real number in a given range."]},{"attachments":{},"cell_type":"markdown","metadata":{"id":"ja0QmiNunQY0"},"source":["## Normal Probability Distribution\n","\n","Arguably one of the most important continuous distributions, the normal distribution is a common distribution of heights, weights, and SAT scores, to name a few.\n","This distribution is symmetric and bell-shaped, giving it the nickname \"bell-curve\". The three measures of center: mode, median, and mean, are exactly the same for the normal distribution. Further, the distribution itself is defined entirely in terms of its mean and standard deviation. Notationally, given a random variable $X$ that is normally distributed, we can say $X \\sim N(\\mu,\\sigma)$, where $\\mu$ and $\\sigma$ are the mean and standard deviation of the distribution, respectively.\n","\n","\n","Below is a plot of a normal distribution, with the mean in red on the graph. We use the scipy library and the *normal* function by calling\n","\n","```python\n","stats.norm(µ, σ)\n","```\n","where the arguments correspond to the mean and standard deviation of the normal distribution. To get the corresponding $y$ values, we call the pdf method on a given array of x-values:\n","```python\n","stats.norm(µ, σ).pdf(x)\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"executionInfo":{"elapsed":480,"status":"ok","timestamp":1689622686716,"user":{"displayName":"Susanna 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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["from scipy import stats\n","\n","snd = stats.norm(0, 1)\n","\n","#generates 100 values from -5 to 5, equally spaced\n","x = np.linspace(-5, 5, 100)\n","\n","plt.plot(x, snd.pdf(x))\n","plt.scatter(0, 0, color='red', s=60);\n","\n","plt.xlim(-5, 5)\n","plt.title('Normal Distribution')\n","plt.xlabel('Values of Random Variable X')\n","plt.ylabel('Probability')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"lMLzxQhPDN3x"},"source":["While there is not an easy formula to compute probabilities by hand, the normal curve itself can be written as a function $$f(x)=\\frac{1}{\\sigma \\sqrt{2\\pi}}e^{\\left(-\\frac{1}{2}\\left(\\frac{x-\\mu}{\\sigma}\\right)^{2}\\right)},$$ where probabilites of interest are calculated by finding area under the curve.\n"]},{"cell_type":"markdown","metadata":{"id":"b_amsPoPFuY5"},"source":["## Standard Deviation and the Normal Distribution"]},{"cell_type":"markdown","metadata":{"id":"2Qd1emqtF1IZ"},"source":["Remember how we said standard deviation was a good measure of spread? One reason is because it can be used to bound the data. In particular, a majority of the data that is normally distributed falls within one standard deviation of the mean: about $68.27\\%$ of the data! About $95.45\\%$ of the data falls within two standard deviations, and roughly $99.73\\%$ falls within three standard deviations.\n","\n","![](normal_std_plot.png)"]},{"cell_type":"markdown","metadata":{"id":"OsTsmVkDYAdn"},"source":["The special case of the normal distribution is called the **standard normal** distribution and occurs when $\\mu = 0$ and $\\sigma = 1$.\n","Given a random variable and any values for $\\mu$ and $\\sigma$, that is $X  ∼  N(\\mu, \\sigma)$, we can *transform* to a standard normal, by normalizing it! That is:\n","\n","$$\\frac{X-\\mu}{\\sigma}$$\n","\n","Note this may be useful if you are comparing values from multiple normal distributions."]},{"cell_type":"markdown","metadata":{"id":"aGmmlswknQdG"},"source":["## Central Limit Theorem"]},{"cell_type":"markdown","metadata":{"id":"EvrgC9pP1Rg4"},"source":["Recall the rolling of the die example from [the previous section](../2/uniform.html). Now, suppose I am not interested in each roll but in the average value of all the dice rolls of students in my classroom. I have 50 students present and each student rolls 1 die. I then take the mean of all 50 dice rolls. What is the probability distribution for the average dice roll? This is another question can be easily answered through simulation!"]},{"cell_type":"markdown","metadata":{"id":"3-gnT0Dp0qqf"},"source":["Simulations use a computer to mimic real experiments. Returning to our dice-rolling example from the [the previous section](../2/uniform.html), instead of asking all of my students to roll dice and take the mean 100s or 1000s of times to plot an empirical distribution (which would be both cumbersome and time consuming), I can have the computer do it for us. First, we can write code that does this experiment one time. We use `sample` to roll our die and `np.mean` to take the average."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hKfpyHL90qqg","outputId":"dd3d4011-2fc9-4a26-dbef-80989804cd1b"},"outputs":[{"data":{"text/plain":["Face    3.6\n","dtype: float64"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["import numpy as np\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","%matplotlib inline\n","\n","die = pd.DataFrame(\n","    {\n","        'Face': np.arange(1, 7),\n","    }\n",")\n","np.mean(die.sample(50, replace=True))"]},{"cell_type":"markdown","metadata":{"id":"jw3scddc0qqg"},"source":["The next step is to write a function that can repeat the experiment a certain number of times. We can do this using a `for loop` and saving the results of each experiment in a `numpy` array."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vWcmm54V0qqg"},"outputs":[],"source":["def face_mean_sim(nsim):\n","    \"\"\"Simulates nsim repetions of 50 dice rolls and returns their sample mean.\"\"\"\n","    np.random.seed(1234)\n","    means = np.array([])\n","    for i in np.arange(nsim):\n","        means = np.append(means, np.mean(die.sample(50, replace=True)))\n","    return means"]},{"cell_type":"markdown","metadata":{"id":"BLV1ElBdDYRC"},"source":["If we repeat the experiment 10 times we get an idea of what the means look like. Plotting a histogram we see the empirical distribution of these 10 experiments. In particular, we find that 3 of these 10 experiments have a mean of around 3.45, while all experiments have a mean in the range 3.22 to 3.76."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vdGI2hEGDTn-","outputId":"88008e86-b9bb-4869-c128-3242640e9180"},"outputs":[{"data":{"text/plain":["array([3.42, 3.5 , 3.24, 3.44, 3.22, 3.6 , 3.08, 3.76, 3.66, 3.46])"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["ten_runs = face_mean_sim(10)\n","ten_runs"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":295},"id":"hWV_0YWlD7C5","outputId":"ffff25a8-2fb5-48ff-d139-89c5f672febb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["plt.hist(ten_runs);\n","plt.xlabel('Mean')\n","plt.ylabel('Frequency')\n","plt.title('Mean Dice Roll Simulation: Ten Runs')\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"XFod8VdI0qqg"},"source":["It is difficult to determine what this distribution looks like with only 10 runs, so we experiment by ploting the empirical distributions of results from 100, 1000, and 10000 replications of the dice rolling experiment."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":585},"id":"2OAHXHU80qqg","outputId":"2121369d-e903-4d89-b726-5abf80360976"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 576x576 with 3 Axes>"]},"metadata":{"needs_background":"light"},"output_type":"display_data"}],"source":["fig, axs = plt.subplots(3, figsize=(8, 8))\n","fig.suptitle('Mean Dice Roll Simulation: 100, 1000, and 10000', fontsize=16)\n","fig.tight_layout()\n","axs[0].hist(face_mean_sim(100), 30);\n","axs[1].hist(face_mean_sim(1_000), 30);\n","axs[2].hist(face_mean_sim(10_000),30);"]},{"cell_type":"markdown","metadata":{"id":"YXasPZ5g0qqg"},"source":["As we saw with our Uniform Distribution experiment, with larger numbers of experiments/samples our empirical distribution approaches the true probability distribution.\n","\n","Based on our empirical distribution with 10000 experiments, we can see that the average value of the dice rolls is symmetric and approximately bell-shaped with a mean of around 3.5. In fact, these properties of being approximately bell-shaped and symmetric are distinct to the empirical *normal distribution*.\n","\n","As we are plotting the distribution means of samples from a uniform distribution, the resulting probability distribution will always take on this shape, that is it will always be approximately normal, as long as the sample size is sufficiently large. This is due to an important mathematical theorem, the *Central Limit Theorem*[^***]. The Central Limit Theorem (CLT) states that if you take sufficiently large random samples from a population with replacement, the distribution of sample means will be approximately normally distributed. The CLT is a property of sample means and holds true for samples from *any* distribution, not just the uniform distribution of dice rolls. For example, the distribution of average heights, average weights, and average test scores of data science students would all be approximately normally distributed as long as we take large enough random samples from our population with replacement.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"0VW8nyQG4Vot"},"source":["In the [the previous section](../2/uniform.html) we calculated the mean and standard deviation of the uniform distribution of our dice rolls. The CLT allows us to use these to learn the mean (μ) and standard deviation (σ) of our distribution of sample means. According to the Central Limit Theorem, if the mean and standard deviation of the population you are sampling from are $\\mu$ and $\\sigma$ respectively, then the mean and standard deviation of the distribution of sample means are $\\mu$ and $\\frac{σ}{\\sqrt{n}}$ respectively where n is the sample size. Therefore, since we know from earlier that the mean of the uniform dice rolling distribution is 3.5 and the standard deviation is 1.71, the mean of the distribution of sample means is also 3.5 and the standard deviation is $\\frac{1.71}{\\sqrt{50}} = 0.24$. Let's investigate this further using our empirical distribution."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LhLQc4oD3zUB","outputId":"1c38e57a-04d1-4d1b-ddd3-9dace39f8bf0"},"outputs":[{"data":{"text/plain":["3.4967740000000007"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["np.mean(face_mean_sim(10_000))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"05URqhM234nV","outputId":"8599cfc7-2f21-4e2c-93e5-a140323e1eba"},"outputs":[{"data":{"text/plain":["0.23814748707471173"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["np.std(face_mean_sim(10_000))"]},{"cell_type":"markdown","metadata":{"id":"cnPp3SB24KBQ"},"source":["Our empirical distribution matches what we expected to see mathematically according to the Central Limit Theorem!"]},{"cell_type":"markdown","metadata":{"id":"wrdpcFZg8BaL"},"source":["[^***]: For more information on the Central Limit Theorem, see the online Statistics Textbook at [OpenStax](https://cnx.org/contents/MBiUQmmY@25.39:MVbL0vFO@10/Introduction)"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.9.13 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.9.13"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":0}
diff --git a/textbook/12/4/binomial.ipynb b/textbook/12/4/binomial.ipynb
index 2b0cd811..f1e815d7 100644
--- a/textbook/12/4/binomial.ipynb
+++ b/textbook/12/4/binomial.ipynb
@@ -1 +1 @@
-{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"id":"ISsA-8KSIY6e","tags":["remove-cell"]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"id":"APw6oRCNOWHW"},"source":["# Binomial Distribution\n","\n","As the prefix *bi-* implies, the Binomial Probability distribution describes a situation with two possible outcomes - often times described as 'success' or 'failure'.\n","\n","## Bernoulli Trials\n","\n","Statisticians call any random experiment with two possible outcomes as described above a **Bernoulli Trial**. For example, flipping a coin has two outcomes: \"Heads\" or \"Tails\" each with a fixed probability. Each independent coin flip would be a Bernoulli trial. Many other scenarios can be presented as Bernoulli trials as well. Consider a standard, shuffled deck of cards. I am interested in whether the top card in the deck is a 7. The card either is or is not a 7 (two options) so independent events of flipping the top card of a shuffled deck are Bernoulli trials.\n","\n","## What is the Binomial Distribution?\n","\n","The **Binomial distribution** describes the probability of specific types of experiments called a sequence of Bernoulli trials. Such a sequence satisfies\n","\n","*   There are a fixed, $n$, number of trials\n","*   There are exactly two possibile  outcomes - often times described as 'success' or 'failure'.\n","*   Each trial is independent (i.e. does not depend on outcomes of previous trials)\n","*   The probability of success, $p$, is the same for each trial\n","\n","\n","Examples satifiying these requirements include: flipping a coin 5 times to see how many heads occur or having 6 children to see how many girls are born. In each of these cases, we are dealing with counts of successes. This means that the binomial distribution is a *discrete* probability distribution."]},{"cell_type":"markdown","metadata":{"id":"hGQqDSdmzu51"},"source":["## Binomial Empirical Distribution\n","\n","Let's again consider our six-sided die for a different experiment! Suppose we are interested in rolling a die and getting an even number. We want to know what happens if we repeat this trial 10 times, what is the probability that we have 1 success (1 even), 2 successes (2 evens), 3 successes...?\n","\n","This example is indeed a sequence of Bernoulli trials as there are a fixed $n=10$ number of trials, there are exactly two outcomes: evens - 'success' and odds - 'failure', each trail is independent, and the probability of success is $p=0.5$.\n","To determine what this distribution looks like through observation, we would repeat this experiment 100, 1000 or more times to get an empirical distribution.\n","\n","Recall in [Section 5.2: Conditional Statements](../05/2/Control_Statements_conditionals.ipynb) we experimented with a parity function which finds the number of even dice rolls when rolling a six-sided die five times.\n","\n","Below we'll redefine the six-sided die and re-run our simulation from Section 5.2 with experiments of size 10, 100, and 1,000."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":354,"status":"ok","timestamp":1689102186899,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"lCeh_hQxukCu","outputId":"7782de35-f992-442a-fdd5-229f182f77fb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["die = np.arange(1, 7)\n","\n","def parity(input_integer):\n","    if (input_integer % 2) == 0:\n","        return \"even\"\n","    else:\n","        return \"odd\"\n","\n","vec_parity = np.vectorize(parity)\n","\n","np.random.seed(1234)\n","\n","def parity_experiment(num_experiments):\n","  total_evens = np.empty(0)\n","  for i in np.arange(num_experiments):\n","      choices = np.random.choice(die, 5)\n","      labels = vec_parity(choices)\n","\n","      total_evens = np.append(total_evens, sum(labels == 'even'))\n","\n","  legend = f'Even numbers with {num_experiments:,} repetitions'\n","\n","  options, counts = np.unique(total_evens, return_counts=True)\n","  pd.DataFrame({legend:counts}, index=options).plot.bar()\n","\n","  plt.xlabel('Number of Evens')\n","  plt.ylabel(\"Frequency\");\n","\n","parity_experiment(10)"]},{"cell_type":"markdown","metadata":{"id":"jvnkT-_Zz2HZ"},"source":["This histogram records the observed number of evens (out of 5 dice rolls) with this experiment repeated num_experiment=10 times. For example, there were 2 evens recorded from 4 experiments, 3 evens recorded from 3 experiments, and 4 evens recorded from 2 experiments.\n","\n","The empirical probability of 2 evens rolled out of 5 is $4/10=0.4$.\n","\n","We repeat the experiment below by changing the num_experiments to 100 and then 1000 to see the resulting empirical distribution."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":330,"status":"ok","timestamp":1689102191823,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"f2NwgPC-ukMC","outputId":"a8542aa7-ce60-4c6b-bfd5-9515560b7ca0"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["np.random.seed(1234)\n","parity_experiment(100)"]},{"cell_type":"markdown","metadata":{"id":"1SMho1lxJQhg"},"source":["Above the empirical probability of 2 even numbers rolled out of 5 is $33/100 = 0.33$."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":357,"status":"ok","timestamp":1689102217375,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"AGS-xOhoukSq","outputId":"1d5a7343-f280-4c77-ce83-c7815835413f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["parity_experiment(1000)"]},{"cell_type":"markdown","metadata":{"id":"AHt7CUOZJ2lC"},"source":["And above the observed probability that 2 even numbers were rolled in 5 tosses of the die is $304/1000 = 0.304$."]},{"cell_type":"markdown","metadata":{"id":"-0zN7fRpz2cT"},"source":["## Binomial Probability Distribution\n","For the event that we just simulated, we have the ability to calculate the probabilities for each event in the sample space.\n","In particular, we can assign probabilites corresponding to the number of successes. For a trial of $n$ experiments, we are interested in the probability of exactly $j$ successes, where $1 \\leq j \\leq n$.\n","Given a sequence of Bernoulli trials, we define a random variable $X$ that records the number of successes. The discrete distribution function for $X$ is called the Binomial Probability Distribution and its pmf is given by:\n","\n","> $P(X=j) = b(n,p,j) = {n \\choose j} p^j (1-p)^{n-j}.$\n","\n"]},{"cell_type":"markdown","metadata":{"id":"r3KNiOH-LHoy"},"source":["To dissect this formula, we first expand on the notation ${n \\choose j}$, read \"n choose j\". In general, for non-negative numbers $n$ and $j$ with $0 \\leq j \\leq n$, ${n \\choose j}$ counts the number of ways to choose $j$ objects from a set of $n$ while ignoring the order. Mathematically, we have:\n","\n","> ${n \\choose j} = \\frac{n!}{j!(n-j)!} = \\frac{n(n-1)(n-2)\\cdot \\cdot \\cdot (n-(j-1))(n-j)!}{j! (n-j)!} = \\frac{n(n-1)(n-2)\\cdot \\cdot \\cdot (n-(j-1))}{j!},$\n","\n","\n","which says we have $n$ ways to choose the first element from a set of $n$ objects, $n-1$ ways to choose the second element from the remaining $n-1$ elements all the way up to $n-(j+1)$ ways to choose the $j^{th}$ element. But because order does not matter, that is the first element choosen is no different than the third, we have overcounted! There are $j!$ ways of ordering the $j$ chosen elements, so dividing by $j!$ gives us the number of subsets of size $j$ from an $n$ element set. The table below gives examples of how to calculate ${n \\choose j}$. Notice the pattern in results. The number of ways to choose 2 elements from a set of 5 is the same as the number of ways to 3 elements (and therefore not choose 2 elements) from a set of 5."]},{"cell_type":"markdown","metadata":{"id":"v4Cq3ciHkJUV"},"source":["| ${n \\choose j}$  | $\\frac{n!}{j!(n-j)!}$ | Number of ways<br>to choose j elements<br>from a set of n |\n","| ---------------- | ------------------ |------------------ |\n","| ${5 \\choose 0}$     | $\\frac{5*4*3*2*1}{0!(5*4*3*2*1)}$ | $=1$|\n","| ${5 \\choose 1}$     | $\\frac{5*4*3*2*1}{1!(4*3*2*1)}$ |  $=5$|\n","| ${5 \\choose 2}$   |$\\frac{5*4*3*2*1}{(2*1)(3*2*1}$  | $=10$ |\n","| ${5 \\choose 3}$   | $\\frac{5*4*3*2*1}{(3*2*1)(2*1)}$  | $=10$|\n","| ${5 \\choose 4}$     | $\\frac{5*4*3*2*1}{(4*3*2*1)1!}$ |  $=5$|\n","| ${5 \\choose 5}$   |$\\frac{5*4*3*2*1}{(5*4*3*2*1)0!}$  | $=1$|"]},{"cell_type":"markdown","metadata":{"id":"Lu--9AzSLHyV"},"source":["How does ${n \\choose j} p^j (1-p)^{n-j}$ count the probability of exactly $j$ successes in $n$ trials? Since the trials are independent, probabilities are multiplicative, so the probability of a sequence containing $j$ successes and $n-j$ failures is $\\underbrace{p \\cdot p \\cdot \\cdot \\cdot p}_\\text{j times} \\cdot \\underbrace{(1-p) \\cdot \\cdot \\cdot (1-p)}_\\text{n-j times}  = p^j\\, (1-p)^{n-j}$. There are ${n \\choose j}$ such sequences as we are choosing $j$ places in our sequence of length $n$ to be successes. Therefore ${n \\choose j} p^j (1-p)^{n-j}$ gives the probability there are exactly $j$ succeses in $n$ trials."]},{"cell_type":"markdown","metadata":{"id":"k_O92tQz-rve"},"source":["The example above of rolling a die 10 times and considering getting an even number is a sequence of Bernoulli trials and the probability can be determined by the Binomial Probability Distribution. As mentioned above, $n=10$ and $p=0.5$ in this example. Thus $P(X=2) = b(5,0.5,2) = {5 \\choose 2} 0.5^2 (0.5)^{3} = 0.3125$. Recall above our empirical distribution gave a probability of $0.304$ for rolling 2 evens out of 5. Below we graph the bar plot in addition to a table corresponding to the probability distribution of this example.\n","\n","| P(Number of Successes)  | Formula: b(n,p,j) | Probability |\n","| ---------------- | ------------------ |------------------ |\n","| P(X=0)     | $b(5,0.5,0)= {5 \\choose 0} 0.5^0 (0.5)^{10}$ |  $=0.03125$|\n","| P(X=1)     | $b(5,0.5,1)= {5 \\choose 1} 0.5^1 (0.5)^{9}$ |  $=0.15625$|\n","| P(X=2)   |$b(5,0.5,2) = {5 \\choose 2}0.5^2(0.5)^{8}$  | $=0.3125$ |\n","| P(X=3)   | $b(5,0.5,3) = {5 \\choose 3} 0.5^3 (0.5)^{7}$  | $=0.3125$|\n","| P(X=4)     | $b(5,0.5,4)= {5 \\choose 4} 0.5^4 (0.5)^{6}$ |  $=0.15625$|\n","| P(X=5)   |$b(5,0.5,5) = {5 \\choose 5}0.5^5(0.5)^{5}$  | $=0.03125$|\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":549,"status":"ok","timestamp":1689102734096,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"gxzfAzi7N10Z","outputId":"976b959f-5216-4a9c-e7e8-c0ea5c89c605"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["def binomial_prob(n, p, j):\n","  '''A function that calculates probabilities from a binomial distribution with parameters n and p'''\n","  n_choose_j = np.math.factorial(n)/(np.math.factorial(j)*np.math.factorial(n-j))\n","  return  n_choose_j*p**j*(1-p)**(n-j)\n","\n","binomial_list =[]\n","n=5\n","p=0.5\n","for i in range(0,6):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of Evens in 5 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Evens')\n","plt.ylabel(\"Probability\");"]},{"cell_type":"markdown","metadata":{"id":"Kqhf9HTZj4y7"},"source":["Notice that the graph looks symmetric around 2.5. You can see the symmetry even more when we increase the number of trials. Let's look at the number of evens in 10 rolls instead of 5."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":453},"executionInfo":{"elapsed":511,"status":"ok","timestamp":1689102814302,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"c8gaMoy9XUGc","outputId":"d1fb5788-2bbd-416f-8af2-b263618279d1"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_list =[]\n","n=10\n","p=0.5\n","for i in range(0,11):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of Evens in 10 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Evens')\n","plt.ylabel(\"Probability\");"]},{"cell_type":"markdown","metadata":{"id":"03uSxQKaXSeE"},"source":["In fact, the shape of the graph is dependent on $p$, the probability of success. The closer $p$ is to $0.5$ the more symmetric it will be. Suppose we are instead interested in rolling the number 2 on a six-sided die. Here 'success' is rolling a 2, and 'failure' is rolling anything else. Thus, $p=\\frac{1}{6}$. Below we see the probability distribution of this example."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":453},"executionInfo":{"elapsed":998,"status":"ok","timestamp":1689102841520,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"NSURsmnwV6PE","outputId":"849e218b-a65f-4cbf-e39b-5c25cfa0448f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_list =[]\n","n=10\n","p=1/6\n","for i in range(0,11):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of Twos in 10 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Twos')\n","plt.ylabel(\"Probability\");"]},{"cell_type":"markdown","metadata":{"id":"3W5fWUe0ls2Q"},"source":["Here we see the peak of the distribution lies around 1. With a success probability of $\\frac{1}{6}$, on average we should see about 1.6 twos in 10 tosses. Therefore a peak between 1 and 2 makes sense!\n","\n","What we just calculated was the mean of our binomial distribution, 1.6. We calculated it by multiplying the probability of success by the number of trials. In general, we can calculate the mean by drawing a sample and taking the mean empirically, or computing it based on the expected value.\n","For given parameters $n$, and $p$, the formula for the mean of a binomial distribution is $E(X) = \\mu(X) = n*p$. We can also calculate the standard deviation of the binomial distribution using $\\sigma(X) = \\sqrt{n*p*(1-p)}$. Evaluating this equation we see that the standard deviation of the binomial distribution with $n = 5$ and $p = \\frac{1}{6}$ is:\n"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1689102906613,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"Iyn677ONba3F","outputId":"c7dee0ab-2d6c-41e8-997b-efcd5bb3b403"},"outputs":[{"data":{"text/plain":["1.1785113019775793"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["np.sqrt(10*(1/6)*(1-(1/6)))"]},{"cell_type":"markdown","metadata":{"id":"_E-QDNmch2-_"},"source":["## Normal Approximation to the Binomial Distribution\n","\n","We learned in the [last section](../3/normal.ipynb) that the normal distribution has a number of special properties that make it easy to use. You will learn later in this book that many statistical methods are based on the normal distribution. For this reason, it is convenient to be able to model data using the normal distribution (even when the data is not actually normally distributed!). Sometimes, we can approximate other distributions using a version of the normal distribution. Specifically, we can use a **normal approximation to the binomial distribution**.\n","\n","We can approximate a binomial distribution using a normal distribution when the number of trials, n, is 'sufficiently large'. The rule-of-thumb for deciding when n is large enough to do the approximation is to check the following:\n","- Is $np \\geq 5$?\n","- Is $n(1-p) \\geq 5$?\n","\n","If both conditions are met, you can approximate the binomial distribution with a normal distribution with mean:\n","\n","> $\\mu = np$\n","\n","and standard deviation:\n","\n","> $\\sigma = \\sqrt{np(1-p)}$\n","\n","Recall, we've been counting the number of occurrence of certain dice rolls. Let's now call a 'success' in our binomial experiment rolling either a 1 or a 6. There is a 1/3 probability of rolling a 1 or a 6 so this is our $p$. Now, let's count the number of successes in 50 dice rolls.\n","\n","First, we check if n is large enough. We have $np = 50*(1/3) \\approx 16.66 \\geq 5$ and $np = 100*(1 - 1/3) \\approx 33.33 \\geq 5$. So, we can use the approximation.\n","\n","Now, let's characterize our normal distribution. The mean is $np$ which we already calculated is approximately 16.66. The standard deviation is $\\sqrt{np(1-p)} = \\sqrt{100*(1/3)(2/3)} \\approx 3.33$.\n","\n","Below we compare the graph of the binomial distribution with n = 50 an p = 1/3 to the graph of the normal distribution with mean 16.66 and standard deviation 3.33."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"executionInfo":{"elapsed":1464,"status":"ok","timestamp":1689108344229,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"4F_zMNTqriiw","outputId":"495e0c9a-3ef8-462b-9fac-08db009b8d30"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_list =[]\n","n=50\n","p=1/3\n","for i in range(0,51):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of 1s or 6s in 50 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Ones or Sixes');\n","plt.ylabel(\"Probability\");\n","plt.xticks(rotation = 90, fontsize=7);"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"executionInfo":{"elapsed":446,"status":"ok","timestamp":1689111836735,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"JjPsIwU4s_ap","outputId":"c5259d5b-9385-47c4-a3d2-d2b49c97b760"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import scipy\n","import math\n","\n","def normal_prob(mu, sigma, x):\n","    '''A function that calculates probabilities from a normal distribution with mean mu and standard deviation sigma'''\n","    bottom = math.sqrt(2 * math.pi * sigma**2)\n","    top = math.exp(-(x - mu)**2/(2 * sigma**2))\n","    return top/bottom\n","\n","normal_list =[]\n","mu=16.66\n","sigma=3.33\n","for i in range(0,51):\n","  normal_list.append(normal_prob(mu, sigma, i))\n","\n","legend = f'Approximate Probability Distribution'\n","\n","pd.DataFrame({legend: normal_list}).plot();\n","plt.xlabel('Number of Ones or Sixes');\n","plt.ylabel(\"Probability\");\n","#plt.xticks(rotation = 90, fontsize=7);"]},{"cell_type":"markdown","metadata":{"id":"zjaN_6qWuBZO"},"source":["Now, let's view them together:"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":497,"status":"ok","timestamp":1689109464833,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"lvC1Hu-puA64","outputId":"86bf4f58-985a-4d17-cef4-51fd848142a5"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_df = pd.DataFrame({\"Real\": binomial_list})\n","normal_df = pd.DataFrame({\"Approximate\": normal_list})\n","\n","bin = plt.bar(height = binomial_df.Real, x = binomial_df.index)\n","norm, = plt.plot(normal_df, color='red')\n","plt.legend([bin, norm], ['True Probability Distribution','Approximate Probability Distribution'])\n","plt.xlabel('Number of Ones or Sixes');\n","plt.ylabel(\"Probability\");\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Qc0T9apDxS2X"},"source":["It looks like the normal is a pretty good approximation to the binomial in this case!"]},{"cell_type":"markdown","metadata":{"id":"DlLOvh0yYWQu"},"source":["Learning about distributions like the ones presented in this chapter can help us better understand the data that we work with as data scientists. In the next few chapters, we will explore ways that statisticians and data scientists use distributions to test hypotheses about data."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BuZFEc-iYEtr"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.9.13 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.9"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":0}
+{"cells":[{"cell_type":"code","execution_count":2,"metadata":{"id":"ISsA-8KSIY6e","tags":["remove-cell"]},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","%matplotlib inline"]},{"cell_type":"markdown","metadata":{"id":"APw6oRCNOWHW"},"source":["# Binomial Distribution\n","\n","As the prefix *bi-* implies, the Binomial Probability distribution describes a situation with two possible outcomes - often times described as 'success' or 'failure'.\n","\n","## Bernoulli Trials\n","\n","Statisticians call any random experiment with two possible outcomes as described above a **Bernoulli Trial**. For example, flipping a coin has two outcomes: \"Heads\" or \"Tails\" each with a fixed probability. Each independent coin flip would be a Bernoulli trial. Many other scenarios can be presented as Bernoulli trials as well. Consider a standard, shuffled deck of cards. I am interested in whether the top card in the deck is a 7. The card either is or is not a 7 (two options) so independent events of flipping the top card of a shuffled deck are Bernoulli trials.\n","\n","## What is the Binomial Distribution?\n","\n","The **Binomial distribution** describes the probability of specific types of experiments called a sequence of Bernoulli trials. Such a sequence satisfies\n","\n","*   There are a fixed, $n$, number of trials\n","*   There are exactly two possibile  outcomes - often times described as 'success' or 'failure'.\n","*   Each trial is independent (i.e. does not depend on outcomes of previous trials)\n","*   The probability of success, $p$, is the same for each trial\n","\n","\n","Examples satifiying these requirements include: flipping a coin 5 times to see how many heads occur or having 6 children to see how many girls are born. In each of these cases, we are dealing with counts of successes. This means that the binomial distribution is a *discrete* probability distribution."]},{"cell_type":"markdown","metadata":{"id":"hGQqDSdmzu51"},"source":["## Binomial Empirical Distribution\n","\n","Let's again consider our six-sided die for a different experiment! Suppose we are interested in rolling a die and getting an even number. We want to know what happens if we repeat this trial 10 times, what is the probability that we have 1 success (1 even), 2 successes (2 evens), 3 successes...?\n","\n","This example is indeed a sequence of Bernoulli trials as there are a fixed $n=10$ number of trials, there are exactly two outcomes: evens - 'success' and odds - 'failure', each trail is independent, and the probability of success is $p=0.5$.\n","To determine what this distribution looks like through observation, we would repeat this experiment 100, 1000 or more times to get an empirical distribution.\n","\n","Recall in [Section 5.2: Conditional Statements](../../05/2/Control_Statements_conditionals.ipynb) we experimented with a parity function which finds the number of even dice rolls when rolling a six-sided die five times.\n","\n","Below we'll redefine the six-sided die and re-run our simulation from Section 5.2 with experiments of size 10, 100, and 1,000."]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":354,"status":"ok","timestamp":1689102186899,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"lCeh_hQxukCu","outputId":"7782de35-f992-442a-fdd5-229f182f77fb"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["die = np.arange(1, 7)\n","\n","def parity(input_integer):\n","    if (input_integer % 2) == 0:\n","        return \"even\"\n","    else:\n","        return \"odd\"\n","\n","vec_parity = np.vectorize(parity)\n","\n","np.random.seed(1234)\n","\n","def parity_experiment(num_experiments):\n","  total_evens = np.empty(0)\n","  for i in np.arange(num_experiments):\n","      choices = np.random.choice(die, 5)\n","      labels = vec_parity(choices)\n","\n","      total_evens = np.append(total_evens, sum(labels == 'even'))\n","\n","  legend = f'Even numbers with {num_experiments:,} repetitions'\n","\n","  options, counts = np.unique(total_evens, return_counts=True)\n","  pd.DataFrame({legend:counts}, index=options).plot.bar()\n","\n","  plt.xlabel('Number of Evens')\n","  plt.ylabel(\"Frequency\");\n","\n","parity_experiment(10)"]},{"cell_type":"markdown","metadata":{"id":"jvnkT-_Zz2HZ"},"source":["This histogram records the observed number of evens (out of 5 dice rolls) with this experiment repeated num_experiment=10 times. For example, there were 2 evens recorded from 4 experiments, 3 evens recorded from 3 experiments, and 4 evens recorded from 2 experiments.\n","\n","The empirical probability of 2 evens rolled out of 5 is $4/10=0.4$.\n","\n","We repeat the experiment below by changing the num_experiments to 100 and then 1000 to see the resulting empirical distribution."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":330,"status":"ok","timestamp":1689102191823,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"f2NwgPC-ukMC","outputId":"a8542aa7-ce60-4c6b-bfd5-9515560b7ca0"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["np.random.seed(1234)\n","parity_experiment(100)"]},{"cell_type":"markdown","metadata":{"id":"1SMho1lxJQhg"},"source":["Above the empirical probability of 2 even numbers rolled out of 5 is $33/100 = 0.33$."]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":475},"executionInfo":{"elapsed":357,"status":"ok","timestamp":1689102217375,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"AGS-xOhoukSq","outputId":"1d5a7343-f280-4c77-ce83-c7815835413f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["parity_experiment(1000)"]},{"cell_type":"markdown","metadata":{"id":"AHt7CUOZJ2lC"},"source":["And above the observed probability that 2 even numbers were rolled in 5 tosses of the die is $304/1000 = 0.304$."]},{"cell_type":"markdown","metadata":{"id":"-0zN7fRpz2cT"},"source":["## Binomial Probability Distribution\n","For the event that we just simulated, we have the ability to calculate the probabilities for each event in the sample space.\n","In particular, we can assign probabilites corresponding to the number of successes. For a trial of $n$ experiments, we are interested in the probability of exactly $j$ successes, where $1 \\leq j \\leq n$.\n","Given a sequence of Bernoulli trials, we define a random variable $X$ that records the number of successes. The discrete distribution function for $X$ is called the Binomial Probability Distribution and its pmf is given by:\n","\n","> $P(X=j) = b(n,p,j) = {n \\choose j} p^j (1-p)^{n-j}.$\n","\n"]},{"cell_type":"markdown","metadata":{"id":"r3KNiOH-LHoy"},"source":["To dissect this formula, we first expand on the notation ${n \\choose j}$, read \"n choose j\". In general, for non-negative numbers $n$ and $j$ with $0 \\leq j \\leq n$, ${n \\choose j}$ counts the number of ways to choose $j$ objects from a set of $n$ while ignoring the order. Mathematically, we have:\n","\n","> ${n \\choose j} = \\frac{n!}{j!(n-j)!} = \\frac{n(n-1)(n-2)\\cdot \\cdot \\cdot (n-(j-1))(n-j)!}{j! (n-j)!} = \\frac{n(n-1)(n-2)\\cdot \\cdot \\cdot (n-(j-1))}{j!},$\n","\n","\n","which says we have $n$ ways to choose the first element from a set of $n$ objects, $n-1$ ways to choose the second element from the remaining $n-1$ elements all the way up to $n-(j+1)$ ways to choose the $j^{th}$ element. But because order does not matter, that is the first element choosen is no different than the third, we have overcounted! There are $j!$ ways of ordering the $j$ chosen elements, so dividing by $j!$ gives us the number of subsets of size $j$ from an $n$ element set. The table below gives examples of how to calculate ${n \\choose j}$. Notice the pattern in results. The number of ways to choose 2 elements from a set of 5 is the same as the number of ways to 3 elements (and therefore not choose 2 elements) from a set of 5."]},{"cell_type":"markdown","metadata":{"id":"v4Cq3ciHkJUV"},"source":["| ${n \\choose j}$  | $\\frac{n!}{j!(n-j)!}$ | Number of ways<br>to choose j elements<br>from a set of n |\n","| ---------------- | ------------------ |------------------ |\n","| ${5 \\choose 0}$     | $\\frac{5*4*3*2*1}{0!(5*4*3*2*1)}$ | $=1$|\n","| ${5 \\choose 1}$     | $\\frac{5*4*3*2*1}{1!(4*3*2*1)}$ |  $=5$|\n","| ${5 \\choose 2}$   |$\\frac{5*4*3*2*1}{(2*1)(3*2*1}$  | $=10$ |\n","| ${5 \\choose 3}$   | $\\frac{5*4*3*2*1}{(3*2*1)(2*1)}$  | $=10$|\n","| ${5 \\choose 4}$     | $\\frac{5*4*3*2*1}{(4*3*2*1)1!}$ |  $=5$|\n","| ${5 \\choose 5}$   |$\\frac{5*4*3*2*1}{(5*4*3*2*1)0!}$  | $=1$|"]},{"cell_type":"markdown","metadata":{"id":"Lu--9AzSLHyV"},"source":["How does ${n \\choose j} p^j (1-p)^{n-j}$ count the probability of exactly $j$ successes in $n$ trials? Since the trials are independent, probabilities are multiplicative, so the probability of a sequence containing $j$ successes and $n-j$ failures is $\\underbrace{p \\cdot p \\cdot \\cdot \\cdot p}_\\text{j times} \\cdot \\underbrace{(1-p) \\cdot \\cdot \\cdot (1-p)}_\\text{n-j times}  = p^j\\, (1-p)^{n-j}$. There are ${n \\choose j}$ such sequences as we are choosing $j$ places in our sequence of length $n$ to be successes. Therefore ${n \\choose j} p^j (1-p)^{n-j}$ gives the probability there are exactly $j$ succeses in $n$ trials."]},{"cell_type":"markdown","metadata":{"id":"k_O92tQz-rve"},"source":["The example above of rolling a die 10 times and considering getting an even number is a sequence of Bernoulli trials and the probability can be determined by the Binomial Probability Distribution. As mentioned above, $n=10$ and $p=0.5$ in this example. Thus $P(X=2) = b(5,0.5,2) = {5 \\choose 2} 0.5^2 (0.5)^{3} = 0.3125$. Recall above our empirical distribution gave a probability of $0.304$ for rolling 2 evens out of 5. Below we graph the bar plot in addition to a table corresponding to the probability distribution of this example.\n","\n","| P(Number of Successes)  | Formula: b(n,p,j) | Probability |\n","| ---------------- | ------------------ |------------------ |\n","| P(X=0)     | $b(5,0.5,0)= {5 \\choose 0} 0.5^0 (0.5)^{10}$ |  $=0.03125$|\n","| P(X=1)     | $b(5,0.5,1)= {5 \\choose 1} 0.5^1 (0.5)^{9}$ |  $=0.15625$|\n","| P(X=2)   |$b(5,0.5,2) = {5 \\choose 2}0.5^2(0.5)^{8}$  | $=0.3125$ |\n","| P(X=3)   | $b(5,0.5,3) = {5 \\choose 3} 0.5^3 (0.5)^{7}$  | $=0.3125$|\n","| P(X=4)     | $b(5,0.5,4)= {5 \\choose 4} 0.5^4 (0.5)^{6}$ |  $=0.15625$|\n","| P(X=5)   |$b(5,0.5,5) = {5 \\choose 5}0.5^5(0.5)^{5}$  | $=0.03125$|\n","\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":444},"executionInfo":{"elapsed":549,"status":"ok","timestamp":1689102734096,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"gxzfAzi7N10Z","outputId":"976b959f-5216-4a9c-e7e8-c0ea5c89c605"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["def binomial_prob(n, p, j):\n","  '''A function that calculates probabilities from a binomial distribution with parameters n and p'''\n","  n_choose_j = np.math.factorial(n)/(np.math.factorial(j)*np.math.factorial(n-j))\n","  return  n_choose_j*p**j*(1-p)**(n-j)\n","\n","binomial_list =[]\n","n=5\n","p=0.5\n","for i in range(0,6):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of Evens in 5 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Evens')\n","plt.ylabel(\"Probability\");"]},{"cell_type":"markdown","metadata":{"id":"Kqhf9HTZj4y7"},"source":["Notice that the graph looks symmetric around 2.5. You can see the symmetry even more when we increase the number of trials. Let's look at the number of evens in 10 rolls instead of 5."]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":453},"executionInfo":{"elapsed":511,"status":"ok","timestamp":1689102814302,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"c8gaMoy9XUGc","outputId":"d1fb5788-2bbd-416f-8af2-b263618279d1"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAkAAAAG0CAYAAADacZikAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAABJM0lEQVR4nO3deVwV9f7H8fcBZZNNRVmMAPcd3ELM1IxCK8tuuZDlkllZuIRa2VUhrUAzU9P0ZlfTrlu3zLQSS67YzXBPLberpT/cQDOX3EBhfn/44OQRUEDgoPN6Ph7zkDPzPd/5zBEOb77znTkWwzAMAQAAmIiDvQsAAAAoawQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOgQgAABgOhXsXUB5lJOToyNHjsjDw0MWi8Xe5QAAgEIwDEN//vmnAgIC5OBw/TEeAlA+jhw5osDAQHuXAQAAiuHgwYO64447rtuGAJQPDw8PSVdeQE9PTztXAwAACuPMmTMKDAy0/h6/HgJQPnJPe3l6ehKAAAC4xRRm+gqToAEAgOkQgAAAgOkQgAAAgOkwBwgFys7O1qVLl+xdBgAAkqSKFSvK0dGxRPoiACEPwzCUnp6uU6dO2bsUAABseHt7y8/P76bv00cAQh654ad69epyc3PjZpAAALszDEPnz5/XsWPHJEn+/v431V+5CEDTp0/XO++8o/T0dIWGhur999/XXXfdlW/bWbNmad68efrll18kSS1atNDbb79t075v376aO3euzfOioqKUlJRUegdxm8jOzraGn6pVq9q7HAAArFxdXSVJx44dU/Xq1W/qdJjdJ0EvXrxYsbGxiouL05YtWxQaGqqoqChrwrtWSkqKoqOjtXr1aqWmpiowMFAPPPCADh8+bNOuU6dOOnr0qHVZuHBhWRzOLS93zo+bm5udKwEAIK/c3083O0fV7gFo0qRJGjBggPr166eGDRtq5syZcnNz0+zZs/NtP3/+fL344osKCwtT/fr19dFHHyknJ0fJyck27ZydneXn52ddKleuXBaHc9vgtBcAoDwqqd9Pdg1AWVlZ2rx5syIjI63rHBwcFBkZqdTU1EL1cf78eV26dElVqlSxWZ+SkqLq1aurXr16GjhwoE6cOFFgH5mZmTpz5ozNAgAAbl92DUC///67srOz5evra7Pe19dX6enpherj1VdfVUBAgE2I6tSpk+bNm6fk5GSNHz9ea9asUefOnZWdnZ1vHwkJCfLy8rIufBAqAAC3t3IxCbq4EhMTtWjRIqWkpMjFxcW6vmfPntavmzRpoqZNm6pWrVpKSUnRfffdl6efkSNHKjY21vo498PUYCv4ta/LdH8HEh8q0/0VpG/fvjp16pSWLl1a7D4OHDigkJAQ/fTTTwoLC8u3TUpKiu69916dPHlS3t7e+vjjjzV06FDr7Qji4+O1dOlSbd26tdh13KwOHTooLCxMkydPLtF+rz22knjNC7svMymt/7/iMgxDzz//vD777DOdPHnyuj8ft5rS/B5GybDrCJCPj48cHR2VkZFhsz4jI0N+fn7Xfe7EiROVmJiob7/9Vk2bNr1u25o1a8rHx0f79u3Ld7uzs7P1g0/5ANRbV9++fWWxWGSxWOTk5KTatWtr7Nixunz5sr1LK5Q2bdro6NGj8vLyynf78OHDbea69e3bV127dr3p/X788cfW183R0VGVK1dWeHi4xo4dq9OnT9u0XbJkicaNG1eofjt06KChQ4cWqu21x1ZSLBZLnl9ApbWvG0lJSZHFYlGjRo3yjEbnBl6zSUpK0scff6yvvvpKR48eVePGjfO0yX3d8lsKe6bAHqZMmXLT/6c7duzQ448/ruDgYFkslgKD6/Tp0xUcHCwXFxeFh4drw4YNBfaZ21dBS9++fW+q5luJXQOQk5OTWrRoYfNmlDuhOSIiosDnTZgwQePGjVNSUpJatmx5w/0cOnRIJ06cuOl7BqD8y736b+/evRo2bJji4+P1zjvv5Ns2KyurjKu7Picnp+ve3Mvd3b3Ubk3g6empo0eP6tChQ/rxxx/13HPPad68eQoLC9ORI0es7apUqSIPD48S269hGLp8+XKpHtu1ynJf+fntt980b948u+2/pGVnZysnJ6dYz/3111/l7++vNm3ayM/PTxUqFHxSYs+ePTZX9h49elTVq1cvbtmlzsvLS97e3jfVx/nz51WzZk0lJiYWOChQ1CupN27caH39Pv/8c0m2r+2UKVNuquZbid2vAouNjdWsWbM0d+5c7dq1SwMHDtS5c+fUr18/SVLv3r01cuRIa/vx48dr9OjRmj17toKDg5Wenq709HSdPXtWknT27FmNGDFC69at04EDB5ScnKxHH31UtWvXVlRUlF2OEWUn9+q/oKAgDRw4UJGRkVq2bJmkv0ZM3nrrLQUEBKhevXqSpJ9//lkdO3aUq6urqlatqueee876/XS1N954Q9WqVZOnp6deeOEFmwCVlJSktm3bytvbW1WrVtXDDz+sX3/9NU8fu3fvVps2beTi4qLGjRtrzZo11m25f+kWdAfu+Ph46+mB+Ph4zZ07V19++aX1L7eUlBR17NhRMTExNs87fvy4nJycrjvqYbFY5OfnJ39/fzVo0ED9+/fXjz/+qLNnz+qVV16xtrt2VOeDDz5QnTp15OLiIl9fXz3xxBPW13rNmjWaMmWKtb4DBw5Yj3HFihVq0aKFnJ2d9cMPP9gcW2Ff8+Dg4Dx/EYeFhSk+Pt66XZIee+wxWSwW6+Nr95WTk6OxY8fqjjvukLOzs8LCwmzuGXbgwAFZLBYtWbJE9957r9zc3BQaGlroCzWuNWjQIMXFxSkzMzPf7bn7u/oU3alTp6z/x9Jf3ysrV65Us2bN5Orqqo4dO+rYsWNasWKFGjRoIE9PTz355JM6f/68Tf+XL19WTEyMvLy85OPjo9GjR8swDOv2zMxMDR8+XDVq1FClSpUUHh5u3a90ZcTQ29tby5YtU8OGDeXs7Ky0tLR8j2XNmjW666675OzsLH9/f7322mvWEdm+fftq0KBBSktLs/n/KUj16tVtruz18/OTg4ODvv32W7m4uOT5uRkyZIg6duxoffzDDz/onnvukaurqwIDAzV48GCdO3fOuj04OFhvv/22nnnmGXl4eOjOO+/Uhx9+aN2elZWlmJgY+fv7y8XFRUFBQUpISCiw3mtHaDt06KDBgwfrlVdeUZUqVeTn52f9Xi1Iq1at9M4776hnz55ydnbOt01Rr6SuVq2a9fXLvXjo6td2wYIFqlWrlpycnFSvXj198skn1ucahqH4+HjdeeedcnZ2VkBAgAYPHmzdXtD7gXTl5ywhIUEhISFydXVVaGioPvvsM+v2kydPqlevXqpWrZpcXV1Vp04dzZkz57qvz82yewDq0aOHJk6cqDFjxigsLExbt25VUlKSdWJ0Wlqajh49am0/Y8YMZWVl6YknnpC/v791mThxoiTJ0dFR27dv1yOPPKK6deuqf//+atGihf773/8W+A2E25erq6vNL83k5GTt2bNH3333nb766iudO3dOUVFRqly5sjZu3Kh///vfWrVqVZ4QkZycrF27diklJUULFy7UkiVL9MYbb1i3nzt3TrGxsdq0aZOSk5Pl4OCgxx57LM9fxiNGjNCwYcP0008/KSIiQl26dLnuFYoFGT58uLp3725zv6s2bdro2Wef1YIFC2x+uf7rX/9SjRo1bH4ZFEb16tXVq1cvLVu2LN8LCDZt2qTBgwdr7Nix2rNnj5KSktSuXTtJV4b/IyIiNGDAAGt9V8+re+2115SYmKhdu3YVeAr7Rq/5tbYfOqWLl7KVceaith86pY+/XCVJGvvudCVv3q2Pv1yl7YdOKePMRV28lK3th05p+6FTevWNRL0zcaIGjXxD//72BzVr015dHnlEy/+7WdsPnbL2//e//13Dhw/X1q1bVbduXUVHR9ucXrVYLIU65TF06FBdvnxZ77///g3b3kh8fLymTZumH3/8UQcPHlT37t01efJkLViwQF9//bW+/fbbPPuZO3euKlSooA0bNmjKlCmaNGmSPvroI+v2mJgYpaamatGiRdq+fbu6deumTp06ae/evdY258+f1/jx4/XRRx9px44d+Y7EHD58WA8++KBatWqlbdu2acaMGfrnP/+pN998U9KV75Hc4Hn06FFt3LixWK/BfffdJ29vb+tohnRlVGrx4sXq1auXpCsjTZ06ddLjjz+u7du3a/Hixfrhhx/y/Jy/++67atmypX766Se9+OKLGjhwoPbs2SNJmjp1qpYtW6ZPP/1Ue/bs0fz5828Y2q41d+5cVapUSevXr9eECRM0duxYfffdd8U6bqlkrqS+2hdffKEhQ4Zo2LBh+uWXX/T888+rX79+Wr16tSTp888/13vvvad//OMf2rt3r5YuXaomTZpIuv77gXTlYqN58+Zp5syZ2rFjh15++WU99dRT1j8CR48erZ07d2rFihXatWuXZsyYIR8fn2K/NoVRLiZBx8TE5PlGzHX1Xx7Slb+OrsfV1VUrV64socpwqzIMQ8nJyVq5cqUGDRpkXV+pUiV99NFHcnJyknTlzuIXL17UvHnzVKlSJUnStGnT1KVLF40fP94axJ2cnDR79my5ubmpUaNGGjt2rEaMGKFx48bJwcFBjz/+uM3+Z8+erWrVqmnnzp028xpiYmKsbWfMmKGkpCT985//tBllKQx3d3e5uroqMzPTZmj8b3/7m2JiYvTll1+qe/fukq78xZ47P6qo6tevrz///FMnTpzI80suLS1NlSpV0sMPPywPDw8FBQWpWbNmkq4M/zs5OcnNzS3fofuxY8fq/vvvv+6+b/Sa30iVqlfePD08veRT3bfAdnP/MU39Bg5R50ev/L+8/Pob2vjjD5r/0Qy9/tZEa7vhw4froYeuTMx/44031KhRI+3bt0/169eXJNWrV6/A+VtXc3NzU1xcnF5//XUNGDCgUM8pyJtvvqm7775bktS/f3+NHDlSv/76q2rWrClJeuKJJ7R69Wq9+uqr1ucEBgbqvffek8ViUb169fTzzz/rvffe04ABA5SWlqY5c+YoLS1NAQEB1uNOSkrSnDlz9Pbbb0u6cgO6Dz74QKGhoQXW9sEHHygwMFDTpk2TxWJR/fr1deTIEb366qsaM2aMvLy85OHhIUdHxxvO+ZSkO+64w+ZxUFCQduzYIUdHR/Xs2VMLFixQ//79JV0Jz6dOnbL+rCUkJKhXr17W0cs6depo6tSpat++vWbMmGG9iObBBx/Uiy++KOnKFcbvvfeeVq9erXr16iktLU116tRR27ZtZbFYFBQUdMOar9W0aVPFxcVZa5g2bZqSk5Nv+LNQkOtdSb179+4i9zdx4kT17dvX+hrExsZq3bp1mjhxou69916lpaXJz89PkZGRqlixou68807rpzBc7/0gMzNTb7/9tlatWmWd3lKzZk398MMP+sc//qH27dsrLS1NzZo1s05rKWq4LA67jwABJemrr76Su7u7XFxc1LlzZ/Xo0cNmmLlJkybW8CNJu3btUmhoqDX8SNLdd9+tnJwc619+khQaGmpzd+yIiAidPXtWBw8elCTt3btX0dHRqlmzpjw9Pa0/vNeeGrh6bluFChXUsmVL7dq1q0SOXZJcXFz09NNPW4e/t2zZol9++aXYExtzT43kF57uv/9+BQUFqWbNmnr66ac1f/78PKdbClKYuXs3es1Lwtk/z+h4xlGFtWxts75Zy3D9tu9/NuuuHqnKnU949TyL3bt367HHHivUfvv376+qVatq/PjxxS09T02+vr5yc3Ozhp/cddfOBWndurXN/2dERIT27t2r7Oxs/fzzz8rOzlbdunXl7u5uXdasWWNzStfJyemGF5/s2rVLERERNvu6++67dfbsWR06dKjIx/rf//5XW7dutS7ffPONdVuvXr2UkpJina82f/58PfTQQ9Y5ONu2bdPHH39sc0xRUVHKycnR/v37rf1cfUy5p4VzX7++fftq69atqlevngYPHqxvv/22yMdw7Wvm7+9f4Fwde9i1a5c1UOe6++67re9R3bp104ULF1SzZk0NGDBAX3zxhXUU9HrvB/v27dP58+d1//332/wfzJs3z/p9NXDgQC1atEhhYWF65ZVX9OOPP5b68ZaLESCgpNx7772aMWOGnJycFBAQkGdS5dVBpyR16dJFQUFBmjVrlgICApSTk6PGjRvbZaL1s88+q7CwMB06dEhz5sxRx44di/XXqnTlDdHT0zPfScMeHh7asmWLUlJS9O2332rMmDGKj4/Xxo0bbzj5syT+HxwcHGzmrkjSpVK84q9ixYrWr3N/qRd38m+FChX01ltvqW/fvnlGv3NHt64+toJu+X9tTVc/zl1XlBrPnj0rR0dHbd68Oc9nLLm7u1u/dnV1LfO7xYeEhBT4fdWqVSvVqlVLixYt0sCBA/XFF1/YnI48e/asnn/+eZv5KrnuvPNO69fXe/2aN2+u/fv3a8WKFVq1apW6d++uyMhIm3ksN3Kz/z/XupkrqYsjMDBQe/bs0apVq/Tdd9/pxRdf1DvvvKM1a9Zc9/0gd07l119/rRo1atj0mTs1pXPnzvq///s/ffPNN/ruu+9033336aWXXrJObykNjADhtlKpUiXVrl1bd95553WvKMnVoEEDbdu2zWYy5Nq1a+Xg4GCdJC1d+QvywoUL1sfr1q2Tu7u7AgMDdeLECe3Zs0ejRo3SfffdpwYNGujkyZP57m/dunXWry9fvqzNmzerQYMGxTlUOTk55Ts3p0mTJmrZsqVmzZqlBQsW6JlnnilW/8eOHdOCBQvUtWvXAk85VahQQZGRkZowYYK2b9+uAwcO6D//+c916yus673m0pXJnFfPDzz75xkdSfs/2/oqVlROTsE1uHt4qpqvv7ZuWmez/qdN61WzTr0CnlUyunXrpkaNGuWZ11StWjVJsjm2krxn0fr1620er1u3TnXq1JGjo6OaNWum7OxsHTt2TLVr17ZZivoLtUGDBkpNTbUJcmvXrpWHh0ee01kloVevXpo/f76WL18uBwcH6+lK6Up42blzZ55jql27ts2I8I14enqqR48emjVrlhYvXqzPP/9cf/zxR4kfS2EV90rqgjRo0EBr1661Wbd27Vo1bNjQ+tjV1VVdunTR1KlTlZKSotTUVP3888+SCn4/uHqy/LWv/9VzA6tVq6Y+ffroX//6lyZPnmwzCb00MAIEU+vVq5fi4uLUp08fxcfH6/jx4xo0aJCefvppm/PqWVlZ6t+/v0aNGqUDBw4oLi5OMTExcnBwUOXKlVW1alV9+OGH8vf3V1paml577bV89zd9+nTVqVNHDRo00HvvvaeTJ08WO6AEBwdr5cqV2rNnj6pWrSovLy/rX5jPPvusYmJiVKlSpUKdljEMQ+np6TIMQ6dOnVJqaqrefvtteXl5KTExMd/nfPXVV/rtt9/Url07Va5cWd98841ycnKswTE4OFjr16/XgQMH5O7unufjam7keq+5JHXs2FEff/yxunTpIm9vb40aMVIO14xaBNxxp9b/sEZhLcPl5OQsz3xGEPq+MEgzJiXojqAQ1W/UREs/na89O39WwvtFe/OtX7++EhISCn0aTLpyM9drr051dXVV69atlZiYqJCQEB07dkyjRo0qUi3Xk5aWptjYWD3//PPasmWL3n//fb377ruSpLp166pXr17q3bu33n33XTVr1kzHjx9XcnKymjZtahMqbuTFF1/U5MmTNWjQIMXExGjPnj2Ki4tTbGxsoeZwXevYsWO6ePGizbqqVatav+d79eql+Ph4vfXWW3riiSdsLnp59dVX1bp1a8XExOjZZ59VpUqVtHPnTn333XeaNm1aofY/adIk+fv7q1mzZnJwcNC///1v+fn53fSl7teTlZWlnTt3Wr8+fPiwtm7dKnd3d9WuXVvSlXk6ffr0UcuWLXXXXXdp8uTJNldSF8WIESPUvXt3NWvWTJGRkVq+fLmWLFmiVauuXFDw8ccfKzs7W+Hh4XJzc9O//vUvubq6Kigo6LrvBx4eHho+fLhefvll5eTkqG3btjp9+rTWrl0rT09P9enTR2PGjFGLFi3UqFEjZWZm6quvvir2H4eFRQBCoZWXOzOXJDc3N61cuVJDhgxRq1at5Obmpscff1yTJk2yaXffffepTp06ateunTIzMxUdHW2dW+Tg4KBFixZp8ODBaty4serVq6epU6eqQ4cOefaXmJioxMREbd26VbVr19ayZcuKfaXDgAEDlJKSopYtW+rs2bNavXq1dZ/R0dEaOnSooqOjbe6SXpAzZ87I399fFotFnp6eqlevnvr06aMhQ4YUeGNQb29vLVmyRPHx8bp48aLq1KmjhQsXqlGjRpKuTJ7t06ePGjZsqAsXLtjMtSiM673m0pU7uO/fv18PP/ywvLy89NzLI3XkoO0I0LDR4/Tu2FFasnCeqvv5a0Xq9jz7efKZ53X2zzN6d9xo/XHiuGrVqaep/1ygoJBaRap3z549eW4ceSMdO3ZUx44d88wnmT17tvUK1nr16mnChAl64IEHitR3QXr37q0LFy7orrvukqOjo4YMGaLnnnvOun3OnDl68803NWzYMB0+fFg+Pj5q3bq1Hn744SLtp0aNGvrmm280YsQIhYaGqkqVKtZAWxxXj8jmSk1NVevWV+Zv1a5dW3fddZc2bNiQ5/YITZs21Zo1a/T3v/9d99xzjwzDUK1atdSjR49C79/Dw0MTJkzQ3r175ejoqFatWumbb74pVpgrrCNHjlgnEktXJilPnDhR7du3t14g1KNHDx0/flxjxoxRenq69TYO106MLoyuXbtqypQpmjhxooYMGaKQkBDNmTPH+r7i7e2txMRExcbGKjs7W02aNNHy5ctVtWrVG74fjBs3TtWqVVNCQoJ+++03eXt7q3nz5nr99dclXRnNGjlypA4cOCBXV1fdc889WrRo0c29gDdgMa49iQ6dOXNGXl5eOn36tOnuCn3x4kXt379fISEhhfrFifLpwIEDqlWrljZu3KjmzZvbu5wycfUl6yWp6R3epdIvgOK53u+povz+ZgQIuI1cunRJJ06c0KhRo9S6dWvThB8AKComQQO3kbVr18rf318bN27UzJkz7V0OAJRbjAABt5EOHTrkuTQcAJAXI0AAAMB0CEDIF6MIAIDyqKR+PxGAYCP3nhqF/UgDAADKUu7vp2vvrF1UzAGCDUdHR3l7e1s/n8bNza3Mb3kPFIdxuXQ+duTam+8BsA/DMHT+/HkdO3ZM3t7eeT6upagIQMgj95b35elD+oAbOXbywo0bFYPTBddS6RdA8Xh7e5fIZ50RgJCHxWKRv7+/qlevXuCHMALlzbNLUkql3+RhHUqlXwBFV7FixZse+clFAEKBHB0dS+wbDShth/8s/gevXg93RAduT0yCBgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAApkMAAgAAplPB3gUAKH+CX/u6VPo9kPhQqfR7q+J1BuyHESAAAGA6BCAAAGA65SIATZ8+XcHBwXJxcVF4eLg2bNhQYNtZs2bpnnvuUeXKlVW5cmVFRkbmaW8YhsaMGSN/f3+5uroqMjJSe/fuLe3DAAAAtwi7B6DFixcrNjZWcXFx2rJli0JDQxUVFaVjx47l2z4lJUXR0dFavXq1UlNTFRgYqAceeECHDx+2tpkwYYKmTp2qmTNnav369apUqZKioqJ08eLFsjosAABQjtk9AE2aNEkDBgxQv3791LBhQ82cOVNubm6aPXt2vu3nz5+vF198UWFhYapfv74++ugj5eTkKDk5WdKV0Z/Jkydr1KhRevTRR9W0aVPNmzdPR44c0dKlS8vwyAAAQHll1wCUlZWlzZs3KzIy0rrOwcFBkZGRSk1NLVQf58+f16VLl1SlShVJ0v79+5Wenm7Tp5eXl8LDwwvsMzMzU2fOnLFZAADA7cuuAej3339Xdna2fH19bdb7+voqPT29UH28+uqrCggIsAae3OcVpc+EhAR5eXlZl8DAwKIeCgAAuIXY/RTYzUhMTNSiRYv0xRdfyMXFpdj9jBw5UqdPn7YuBw8eLMEqAQBAeWPXGyH6+PjI0dFRGRkZNuszMjLk5+d33edOnDhRiYmJWrVqlZo2bWpdn/u8jIwM+fv72/QZFhaWb1/Ozs5ydnYu5lEAAIBbjV1HgJycnNSiRQvrBGZJ1gnNERERBT5vwoQJGjdunJKSktSyZUubbSEhIfLz87Pp88yZM1q/fv11+wQAAOZh94/CiI2NVZ8+fdSyZUvdddddmjx5ss6dO6d+/fpJknr37q0aNWooISFBkjR+/HiNGTNGCxYsUHBwsHVej7u7u9zd3WWxWDR06FC9+eabqlOnjkJCQjR69GgFBASoa9eu9jpMAABQjtg9APXo0UPHjx/XmDFjlJ6errCwMCUlJVknMaelpcnB4a+BqhkzZigrK0tPPPGETT9xcXGKj4+XJL3yyis6d+6cnnvuOZ06dUpt27ZVUlLSTc0TAgAAtw+7ByBJiomJUUxMTL7bUlJSbB4fOHDghv1ZLBaNHTtWY8eOLYHqAADA7eaWvgoMAACgOAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdAhAAADAdIoVgFavXl1iBUyfPl3BwcFycXFReHi4NmzYUGDbHTt26PHHH1dwcLAsFosmT56cp018fLwsFovNUr9+/RKrFwAA3PqKFYA6deqkWrVq6c0339TBgweLvfPFixcrNjZWcXFx2rJli0JDQxUVFaVjx47l2/78+fOqWbOmEhMT5efnV2C/jRo10tGjR63LDz/8UOwaAQDA7adYAejw4cOKiYnRZ599ppo1ayoqKkqffvqpsrKyitTPpEmTNGDAAPXr108NGzbUzJkz5ebmptmzZ+fbvlWrVnrnnXfUs2dPOTs7F9hvhQoV5OfnZ118fHyKVBcAALi9FSsA+fj46OWXX9bWrVu1fv161a1bVy+++KICAgI0ePBgbdu27YZ9ZGVlafPmzYqMjPyrGAcHRUZGKjU1tThlWe3du1cBAQGqWbOmevXqpbS0tOu2z8zM1JkzZ2wWAABw+7rpSdDNmzfXyJEjFRMTo7Nnz2r27Nlq0aKF7rnnHu3YsaPA5/3+++/Kzs6Wr6+vzXpfX1+lp6cXu57w8HB9/PHHSkpK0owZM7R//37dc889+vPPPwt8TkJCgry8vKxLYGBgsfcPAADKv2IHoEuXLumzzz7Tgw8+qKCgIK1cuVLTpk1TRkaG9u3bp6CgIHXr1q0kay2Uzp07q1u3bmratKmioqL0zTff6NSpU/r0008LfM7IkSN1+vRp63Iz85oAAED5V6E4Txo0aJAWLlwowzD09NNPa8KECWrcuLF1e6VKlTRx4kQFBAQU2IePj48cHR2VkZFhsz4jI+O6E5yLytvbW3Xr1tW+ffsKbOPs7HzdOUUAAOD2UqwRoJ07d+r999/XkSNHNHnyZJvwk8vHx+e6l8s7OTmpRYsWSk5Otq7LyclRcnKyIiIiilNWvs6ePatff/1V/v7+JdYnAAC4tRUrAMXFxalbt255Rk0uX76s77//XtKVK7Hat29/3X5iY2M1a9YszZ07V7t27dLAgQN17tw59evXT5LUu3dvjRw50to+KytLW7du1datW5WVlaXDhw9r69atNqM7w4cP15o1a3TgwAH9+OOPeuyxx+To6Kjo6OjiHCoAALgNFesU2L333qujR4+qevXqNutPnz6te++9V9nZ2YXqp0ePHjp+/LjGjBmj9PR0hYWFKSkpyToxOi0tTQ4Of2W0I0eOqFmzZtbHEydO1MSJE9W+fXulpKRIkg4dOqTo6GidOHFC1apVU9u2bbVu3TpVq1atOIcKAABuQ8UKQIZhyGKx5Fl/4sQJVapUqUh9xcTEKCYmJt9tuaEmV3BwsAzDuG5/ixYtKtL+AQCA+RQpAP3tb3+TJFksFvXt29fmFFh2dra2b9+uNm3alGyFAAAAJaxIAcjLy0vSlREgDw8Pubq6Wrc5OTmpdevWGjBgQMlWCAAAUMKKFIDmzJkj6cqpqOHDhxf5dBcAAEB5UKw5QHFxcSVdBwAAQJkpdABq3ry5kpOTVblyZTVr1izfSdC5tmzZUiLFAQAAlIZCB6BHH33UOum5a9eupVUPAABAqSt0ALr6tBenwAAAwK3spj8NHgAA4FZT6BGgypUrX3fez9X++OOPYhcEAABQ2godgCZPnlyKZQAAAJSdQgegPn36lGYdAAAAZabQAejMmTPy9PS0fn09ue0AAADKoyLNAcr9BHhvb+985wPlfkhqYT8NHgAAwB4KHYD+85//qEqVKpKk1atXl1pBAAAApa3QAah9+/b5fg0AAHCrKdZngUnSyZMn9c9//lO7du2SJDVs2FD9+vWzjhIBAACUV8W6EeL333+v4OBgTZ06VSdPntTJkyc1depUhYSE6Pvvvy/pGgEAAEpUsUaAXnrpJfXo0UMzZsyQo6OjJCk7O1svvviiXnrpJf38888lWiQAAEBJKtYI0L59+zRs2DBr+JEkR0dHxcbGat++fSVWHAAAQGkoVgBq3ry5de7P1Xbt2qXQ0NCbLgoAAKA0FfoU2Pbt261fDx48WEOGDNG+ffvUunVrSdK6des0ffp0JSYmlnyVAAAAJajQASgsLEwWi0WGYVjXvfLKK3naPfnkk+rRo0fJVAcAAFAKCh2A9u/fX5p1AAAAlJlCB6CgoKDSrAMAAKDMFPtGiJK0c+dOpaWlKSsry2b9I488clNFAQAAlKZiBaDffvtNjz32mH7++WebeUG5H5DKh6ECAIDyrFiXwQ8ZMkQhISE6duyY3NzctGPHDn3//fdq2bKlUlJSSrhEAACAklWsEaDU1FT95z//kY+PjxwcHOTg4KC2bdsqISFBgwcP1k8//VTSdQIAAJSYYo0AZWdny8PDQ5Lk4+OjI0eOSLoyUXrPnj0lVx0AAEApKNYIUOPGjbVt2zaFhIQoPDxcEyZMkJOTkz788EPVrFmzpGsEAAAoUcUKQKNGjdK5c+ckSWPHjtXDDz+se+65R1WrVtXixYtLtEAAAICSVqwAFBUVZf26du3a2r17t/744w9VrlzZeiUYAABAeXVT9wGSpIMHD0qSAgMDb7oYAACAslCsSdCXL1/W6NGj5eXlpeDgYAUHB8vLy0ujRo3SpUuXSrpGAACAElWsEaBBgwZpyZIlmjBhgiIiIiRduTQ+Pj5eJ06c0IwZM0q0SAAAgJJUrAC0YMECLVq0SJ07d7aua9q0qQIDAxUdHU0AAgAA5VqxToE5OzsrODg4z/qQkBA5OTndbE0AAAClqlgBKCYmRuPGjVNmZqZ1XWZmpt566y3FxMSUWHEAAAClodCnwP72t7/ZPF61apXuuOMOhYaGSpK2bdumrKws3XfffSVbIQAAQAkrdADy8vKyefz444/bPOYyeCB/wa99XSr9Hkh8qFT6Ba6H72fcLgodgObMmVOadQAAAJSZm7oR4vHjx60fflqvXj1Vq1atRIoCAAAoTcWaBH3u3Dk988wz8vf3V7t27dSuXTsFBASof//+On/+fEnXCAAAUKKKFYBiY2O1Zs0aLV++XKdOndKpU6f05Zdfas2aNRo2bFhJ1wgAAFCiinUK7PPPP9dnn32mDh06WNc9+OCDcnV1Vffu3bkRIgAAKNeKNQJ0/vx5+fr65llfvXp1ToEBAIByr1gBKCIiQnFxcbp48aJ13YULF/TGG29YPxsMAACgvCrWKbDJkyerU6dOeW6E6OLiopUrV5ZogQAAACWtWAGoSZMm2rt3r+bPn6/du3dLkqKjo9WrVy+5urqWaIEAAAAlrcgB6NKlS6pfv76++uorDRgwoDRqAgAAKFVFngNUsWJFm7k/AAAAt5piTYJ+6aWXNH78eF2+fLmk6wEAACh1xZoDtHHjRiUnJ+vbb79VkyZNVKlSJZvtS5YsKZHiAAAASkOxApC3t3eeT4MHAAC4VRQpAOXk5Oidd97R//73P2VlZaljx46Kj4/nyi8AAHBLKdIcoLfeekuvv/663N3dVaNGDU2dOlUvvfRSadUGAABQKooUgObNm6cPPvhAK1eu1NKlS7V8+XLNnz9fOTk5pVUfAABAiStSAEpLS9ODDz5ofRwZGSmLxaIjR46UeGEAAAClpUgB6PLly3JxcbFZV7FiRV26dKlEiwIAAChNRZoEbRiG+vbtK2dnZ+u6ixcv6oUXXrC5FJ7L4AEAQHlWpBGgPn36qHr16vLy8rIuTz31lAICAmzWFcX06dMVHBwsFxcXhYeHa8OGDQW23bFjhx5//HEFBwfLYrFo8uTJN90nAAAwnyKNAM2ZM6dEd7548WLFxsZq5syZCg8P1+TJkxUVFaU9e/aoevXqedqfP39eNWvWVLdu3fTyyy+XSJ8AAMB8ivVRGCVl0qRJGjBggPr166eGDRtq5syZcnNz0+zZs/Nt36pVK73zzjvq2bOnzWm4m+kTAACYj90CUFZWljZv3qzIyMi/inFwUGRkpFJTU8u0z8zMTJ05c8ZmAQAAty+7BaDff/9d2dnZ8vX1tVnv6+ur9PT0Mu0zISHBZg5TYGBgsfYPAABuDXY9BVZejBw5UqdPn7YuBw8etHdJAACgFBXrw1BLgo+PjxwdHZWRkWGzPiMjQ35+fmXap7Ozc4FzigAAwO3HbiNATk5OatGihZKTk63rcnJylJycrIiIiHLTJwAAuP3YbQRIkmJjY9WnTx+1bNlSd911lyZPnqxz586pX79+kqTevXurRo0aSkhIkHRlkvPOnTutXx8+fFhbt26Vu7u7ateuXag+AQAA7BqAevTooePHj2vMmDFKT09XWFiYkpKSrJOY09LS5ODw1yDVkSNH1KxZM+vjiRMnauLEiWrfvr1SUlIK1ScAAIBdA5AkxcTEKCYmJt9tuaEmV3BwsAzDuKk+AQAAuAoMAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYDgEIAACYTrkIQNOnT1dwcLBcXFwUHh6uDRs2XLf9v//9b9WvX18uLi5q0qSJvvnmG5vtffv2lcVisVk6depUmocAAABuIXYPQIsXL1ZsbKzi4uK0ZcsWhYaGKioqSseOHcu3/Y8//qjo6Gj1799fP/30k7p27aquXbvql19+sWnXqVMnHT161LosXLiwLA4HAADcAuwegCZNmqQBAwaoX79+atiwoWbOnCk3NzfNnj073/ZTpkxRp06dNGLECDVo0EDjxo1T8+bNNW3aNJt2zs7O8vPzsy6VK1cui8MBAAC3ALsGoKysLG3evFmRkZHWdQ4ODoqMjFRqamq+z0lNTbVpL0lRUVF52qekpKh69eqqV6+eBg4cqBMnThRYR2Zmps6cOWOzAACA25ddA9Dvv/+u7Oxs+fr62qz39fVVenp6vs9JT0+/YftOnTpp3rx5Sk5O1vjx47VmzRp17txZ2dnZ+faZkJAgLy8v6xIYGHiTRwYAAMqzCvYuoDT07NnT+nWTJk3UtGlT1apVSykpKbrvvvvytB85cqRiY2Otj8+cOUMIAgDgNmbXESAfHx85OjoqIyPDZn1GRob8/PzyfY6fn1+R2ktSzZo15ePjo3379uW73dnZWZ6enjYLAAC4fdk1ADk5OalFixZKTk62rsvJyVFycrIiIiLyfU5ERIRNe0n67rvvCmwvSYcOHdKJEyfk7+9fMoUDAIBbmt2vAouNjdWsWbM0d+5c7dq1SwMHDtS5c+fUr18/SVLv3r01cuRIa/shQ4YoKSlJ7777rnbv3q34+Hht2rRJMTExkqSzZ89qxIgRWrdunQ4cOKDk5GQ9+uijql27tqKiouxyjAAAoHyx+xygHj166Pjx4xozZozS09MVFhampKQk60TntLQ0OTj8ldPatGmjBQsWaNSoUXr99ddVp04dLV26VI0bN5YkOTo6avv27Zo7d65OnTqlgIAAPfDAAxo3bpycnZ3tcowAAKB8sXsAkqSYmBjrCM61UlJS8qzr1q2bunXrlm97V1dXrVy5siTLAwAAtxm7nwIDAAAoa+ViBAgojODXvi61vg8kPlRqfQOwL947kB9GgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOkQgAAAgOlUsHcBsI/g174utb4PJD5Uan0DgBnwHl36GAECAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmQwACAACmUy4C0PTp0xUcHCwXFxeFh4drw4YN123/73//W/Xr15eLi4uaNGmib775xma7YRgaM2aM/P395erqqsjISO3du7c0DwEAANxC7B6AFi9erNjYWMXFxWnLli0KDQ1VVFSUjh07lm/7H3/8UdHR0erfv79++uknde3aVV27dtUvv/xibTNhwgRNnTpVM2fO1Pr161WpUiVFRUXp4sWLZXVYAACgHLN7AJo0aZIGDBigfv36qWHDhpo5c6bc3Nw0e/bsfNtPmTJFnTp10ogRI9SgQQONGzdOzZs317Rp0yRdGf2ZPHmyRo0apUcffVRNmzbVvHnzdOTIES1durQMjwwAAJRXdv0w1KysLG3evFkjR460rnNwcFBkZKRSU1PzfU5qaqpiY2Nt1kVFRVnDzf79+5Wenq7IyEjrdi8vL4WHhys1NVU9e/bM02dmZqYyMzOtj0+fPi1JOnPmTKGOo3HcykK1K45f3ogqlX5zMs+XSr9S4V+3oroVa5ZKr25qtkXNf6FmW7fie8etWHN5+F2Ye2yGYdywrV0D0O+//67s7Gz5+vrarPf19dXu3bvzfU56enq+7dPT063bc9cV1OZaCQkJeuONN/KsDwwMLNyBlCKvyfauoOiouWxQc9mg5rJxK9Ys3Zp1m6HmP//8U15eXtdtY9cAVF6MHDnSZlQpJydHf/zxh6pWrSqLxVKi+zpz5owCAwN18OBBeXp6lmjfpYWaywY1lw1qLhvUXDao2ZZhGPrzzz8VEBBww7Z2DUA+Pj5ydHRURkaGzfqMjAz5+fnl+xw/P7/rts/9NyMjQ/7+/jZtwsLC8u3T2dlZzs7ONuu8vb2LcihF5unpect8s+ai5rJBzWWDmssGNZcNav7LjUZ+ctl1ErSTk5NatGih5ORk67qcnBwlJycrIiIi3+dERETYtJek7777zto+JCREfn5+Nm3OnDmj9evXF9gnAAAwF7ufAouNjVWfPn3UsmVL3XXXXZo8ebLOnTunfv36SZJ69+6tGjVqKCEhQZI0ZMgQtW/fXu+++64eeughLVq0SJs2bdKHH34oSbJYLBo6dKjefPNN1alTRyEhIRo9erQCAgLUtWtXex0mAAAoR+wegHr06KHjx49rzJgxSk9PV1hYmJKSkqyTmNPS0uTg8NdAVZs2bbRgwQKNGjVKr7/+uurUqaOlS5eqcePG1javvPKKzp07p+eee06nTp1S27ZtlZSUJBcXlzI/vms5OzsrLi4uzym38oyaywY1lw1qLhvUXDaoufgsRmGuFQMAALiN2P1GiAAAAGWNAAQAAEyHAAQAAEyHAAQAAEyHAATgpnAdBYBbkd0vg7+d/f7775o9e7ZSU1Otn0Pm5+enNm3aqG/fvqpWrZqdKwRunrOzs7Zt26YGDRrYuxTgtnf06FHNmDFDP/zwg44ePSoHBwfVrFlTXbt2Vd++feXo6GjvEm8ZXAZfSjZu3KioqCi5ubkpMjLSel+jjIwMJScn6/z581q5cqVatmxp50qL5uDBg4qLi9Ps2bPtXYqNCxcuaPPmzapSpYoaNmxos+3ixYv69NNP1bt3bztVl79du3Zp3bp1ioiIUP369bV7925NmTJFmZmZeuqpp9SxY0d7l2jj6s/Lu9qUKVP01FNPqWrVqpKkSZMmlWVZRXLu3Dl9+umn2rdvn/z9/RUdHW2tu7zYsmWLKleurJCQEEnSJ598opkzZyotLU1BQUGKiYlRz5497VylrUGDBql79+6655577F1KkUybNk0bNmzQgw8+qJ49e+qTTz5RQkKCcnJy9Le//U1jx45VhQrlZ5xg06ZNioyMVO3ateXq6qrU1FQ9+eSTysrK0sqVK9WwYUMlJSXJw8PD3qXeGgyUivDwcOO5554zcnJy8mzLyckxnnvuOaN169Z2qOzmbN261XBwcLB3GTb27NljBAUFGRaLxXBwcDDatWtnHDlyxLo9PT293NW8YsUKw8nJyahSpYrh4uJirFixwqhWrZoRGRlpdOzY0XB0dDSSk5PtXaYNi8VihIWFGR06dLBZLBaL0apVK6NDhw7Gvffea+8ybTRo0MA4ceKEYRiGkZaWZgQHBxteXl5Gq1atjCpVqhjVq1c3fvvtNztXaatp06bGd999ZxiGYcyaNctwdXU1Bg8ebMyYMcMYOnSo4e7ubvzzn/+0c5W2cn/26tSpYyQmJhpHjx61d0k3NG7cOMPDw8N4/PHHDT8/PyMxMdGoWrWq8eabbxpvv/22Ua1aNWPMmDH2LtPG3XffbcTHx1sff/LJJ0Z4eLhhGIbxxx9/GGFhYcbgwYPtVd51ZWZmGosXLzaGDh1q9OzZ0+jZs6cxdOhQ49NPPzUyMzPtUhMBqJS4uLgYu3btKnD7rl27DBcXlzKsqHC+/PLL6y7vvfdeuQsTXbt2NR566CHj+PHjxt69e42HHnrICAkJMf7v//7PMIzyGYAiIiKMv//974ZhGMbChQuNypUrG6+//rp1+2uvvWbcf//99iovXwkJCUZISEieYFahQgVjx44ddqrq+iwWi5GRkWEYhmH06tXLaNOmjXHq1CnDMAzjzz//NCIjI43o6Gh7lpiHq6urceDAAcMwDKNZs2bGhx9+aLN9/vz5RsOGDe1RWoEsFouxatUqY8iQIYaPj49RsWJF45FHHjGWL19uZGdn27u8fNWqVcv4/PPPDcO48oedo6Oj8a9//cu6fcmSJUbt2rXtVV6+XF1djV9//dX6ODs726hYsaKRnp5uGIZhfPvtt0ZAQIC9yivQ3r17jZo1axouLi5G+/btje7duxvdu3c32rdvb7i4uBi1a9c29u7dW+Z1EYBKSXBwsDF37twCt8+dO9cICgoqu4IKKfcvOYvFUuBS3sJE9erVje3bt1sf5+TkGC+88IJx5513Gr/++mu5DECenp7WH/js7GyjQoUKxpYtW6zbf/75Z8PX19de5RVow4YNRt26dY1hw4YZWVlZhmHcOgGoZs2axrfffmuzfe3atUZgYKA9SitQ1apVjU2bNhmGceV7e+vWrTbb9+3bZ7i6utqjtAJd/TpnZWUZixcvNqKiogxHR0cjICDAeP311+3yC+56XF1drX8kGYZhVKxY0fjll1+sjw8cOGC4ubnZo7QCBQUFGT/88IP18ZEjRwyLxWKcP3/eMAzD2L9/f7n8wzoyMtJ49NFHjdOnT+fZdvr0aePRRx81HnjggTKvi6vASsnw4cP13HPPaciQIVq2bJnWr1+v9evXa9myZRoyZIheeOEFvfLKK/YuMw9/f38tWbJEOTk5+S5btmyxd4l5XLhwweY8vcVi0YwZM9SlSxe1b99e//vf/+xYXcEsFoskycHBQS4uLvLy8rJu8/Dw0OnTp+1VWoFatWqlzZs36/jx42rZsqV++eUX63GUV7n1Xbx4Uf7+/jbbatSooePHj9ujrAJ17txZM2bMkCS1b99en332mc32Tz/9VLVr17ZHaYVSsWJFde/eXUlJSfrtt980YMAAzZ8/X/Xq1bN3aTb8/Py0c+dOSdLevXuVnZ1tfSxJO3bsUPXq1e1VXr66du2qF154QUlJSVq9erV69eql9u3by9XVVZK0Z88e1ahRw85V5rV27Vq9+eab8vT0zLPN09NT48aN03//+9+yL6zMI5eJLFq0yAgPDzcqVKhgHT2pUKGCER4ebixevNje5eWrS5cuxujRowvcvnXrVsNisZRhRTfWqlUrY968eflue+mllwxvb+9yNwLUtGlTY8WKFdbHP//8s3Hp0iXr4++//94ICQmxR2mFtnDhQsPX19dwcHAo1yNATZo0MZo1a2a4u7sbn332mc32NWvWGDVq1LBTdfk7fPiwERwcbLRr186IjY01XF1djbZt2xoDBgww2rVrZzg5ORlff/21vcu0cfUIUH5ycnLyjL7Z26hRo4xq1aoZzz77rBESEmK89tprxp133mnMmDHDmDlzphEYGGi8/PLL9i7Txp9//ml0797d+julTZs2NnPYVq5caXz66ad2rDB//v7+xvLlywvcvmzZMsPf378MK7qi/Exvvw316NFDPXr00KVLl/T7779Lknx8fFSxYkU7V1awESNG6Ny5cwVur127tlavXl2GFd3YY489poULF+rpp5/Os23atGnKycnRzJkz7VBZwQYOHKjs7Gzr48aNG9tsX7FiRbm7CuxaPXv2VNu2bbV582YFBQXZu5x8xcXF2Tx2d3e3ebx8+fJyd+VSQECAfvrpJyUmJmr58uUyDEMbNmzQwYMHdffdd2vt2rXl7urRoKCg615+bbFYdP/995dhRTf2xhtvWK+kGjBggF577TWFhobqlVde0fnz59WlSxeNGzfO3mXacHd31+LFi3Xx4kVdvnw5z/fzAw88YKfKru/ZZ59V7969NXr0aN133315rop+8803NWjQoDKvi8vgAQBAqRo/frymTJmi9PR062lpwzDk5+enoUOH2mVKCAEIAACUif3799vcGDj3flf2QAACAAB2Y68b7BKAAACA3Wzbtk3Nmze3mRdZFpgEDQAASs2yZcuuu/23334ro0psMQIEAABKjYODgywWi64XNywWS5mPAHEjRAAAUGrK6w12CUAAAKDUtGjRQps3by5w+41Gh0oLc4AAAECpKa832GUOEAAAMB1OgQEAANMhAAEAANMhAAEAANMhAAEAANMhAAEoFw4cOCCLxaKtW7fauxSr3bt3q3Xr1nJxcVFYWJi9ywFQgghAACRJffv2lcViUWJios36pUuXymKx2Kkq+4qLi1OlSpW0Z88eJScn59sm93W7dunUqVMZVwugKAhAAKxcXFw0fvx4nTx50t6llJisrKxiP/fXX39V27ZtFRQUpKpVqxbYrlOnTjp69KjNsnDhwmLvF0DpIwABsIqMjJSfn58SEhIKbBMfH5/ndNDkyZMVHBxsfdy3b1917dpVb7/9tnx9feXt7a2xY8fq8uXLGjFihKpUqaI77rhDc+bMydP/7t271aZNG7m4uKhx48Zas2aNzfZffvlFnTt3lru7u3x9ffX000/r999/t27v0KGDYmJiNHToUPn4+CgqKirf48jJydHYsWN1xx13yNnZWWFhYUpKSrJut1gs2rx5s8aOHSuLxaL4+PgCXxNnZ2f5+fnZLJUrV5YkPfnkk+rRo4dN+0uXLsnHx0fz5s2z1pKQkKCQkBC5uroqNDRUn332mbV9SkqKLBaLkpOT1bJlS7m5ualNmzbas2ePtc22bdt07733ysPDQ56enmrRooU2bdpUYM2A2RGAAFg5Ojrq7bff1vvvv69Dhw7dVF//+c9/dOTIEX3//feaNGmS4uLi9PDDD6ty5cpav369XnjhBT3//PN59jNixAgNGzZMP/30kyIiItSlSxedOHFCknTq1Cl17NhRzZo106ZNm5SUlKSMjAx1797dpo+5c+fKyclJa9eu1cyZM/Otb8qUKXr33Xc1ceJEbd++XVFRUXrkkUe0d+9eSdLRo0fVqFEjDRs2TEePHtXw4cOL9Tr06tVLy5cv19mzZ63rVq5cqfPnz+uxxx6TJCUkJGjevHmaOXOmduzYoZdffllPPfVUnvD397//Xe+++642bdqkChUq6JlnnrHZzx133KGNGzdq8+bNeu2111SxYsVi1QyYggEAhmH06dPHePTRRw3DMIzWrVsbzzzzjGEYhvHFF18YV79VxMXFGaGhoTbPfe+994ygoCCbvoKCgozs7Gzrunr16hn33HOP9fHly5eNSpUqGQsXLjQMwzD2799vSDISExOtbS5dumTccccdxvjx4w3DMIxx48YZDzzwgM2+Dx48aEgy9uzZYxiGYbRv395o1qzZDY83ICDAeOutt2zWtWrVynjxxRetj0NDQ424uLjr9tOnTx/D0dHRqFSpks2S2/elS5cMHx8fY968edbnREdHGz169DAMwzAuXrxouLm5GT/++KNNv/379zeio6MNwzCM1atXG5KMVatWWbd//fXXhiTjwoULhmEYhoeHh/Hxxx/f8LgBXMFngQHIY/z48erYsWOxRz0kqVGjRnJw+GuQ2dfXV40bN7Y+dnR0VNWqVXXs2DGb50VERFi/rlChglq2bKldu3ZJunKaZ/Xq1XJ3d8+zv19//VV169aVdOXDF6/nzJkzOnLkiO6++26b9Xfffbe2bdtWyCP8y7333qsZM2bYrKtSpYr1GLp376758+fr6aef1rlz5/Tll19q0aJFkqR9+/bp/Pnzuv/++22en5WVpWbNmtmsa9q0qfVrf39/SdKxY8d05513KjY2Vs8++6w++eQTRUZGqlu3bqpVq1aRjwUwCwIQgDzatWunqKgojRw5Un379rXZ5uDgkOeTmy9dupSnj2tPv1gslnzX5eTkFLqus2fPqkuXLho/fnyebbmBQJIqVapU6D5LQqVKlVS7du0Ct/fq1Uvt27fXsWPH9N1338nV1dV6lVjuqbGvv/5aNWrUsHmes7OzzeOrX7/cK/NyX7/4+Hg9+eST+vrrr7VixQrFxcVp0aJF1tNsAGwRgADkKzExUWFhYapXr57N+mrVqik9PV2GYVh/CZfkvXvWrVundu3aSZIuX76szZs3KyYmRpLUvHlzff755woODlaFCsV/+/L09FRAQIDWrl2r9u3bW9evXbtWd911180dQD7atGmjwMBALV68WCtWrFC3bt2sYaZhw4ZydnZWWlqaTS3FUbduXdWtW1cvv/yyoqOjNWfOHAIQUAACEIB8NWnSRL169dLUqVNt1nfo0EHHjx/XhAkT9MQTTygpKUkrVqyQp6dniex3+vTpqlOnjho0aKD33ntPJ0+etE72femllzRr1ixFR0frlVdeUZUqVbRv3z4tWrRIH330kRwdHQu9nxEjRiguLk61atVSWFiY5syZo61bt2r+/PlFrjkzM1Pp6ek26ypUqCAfHx/r4yeffFIzZ87U//73P61evdq63sPDQ8OHD9fLL7+snJwctW3bVqdPn9batWvl6empPn363HD/Fy5c0IgRI/TEE08oJCREhw4d0saNG/X4448X+VgAs+AqMAAFGjt2bJ5TVA0aNNAHH3yg6dOnKzQ0VBs2bLipuULXSkxMVGJiokJDQ/XDDz9o2bJl1iCRO2qTnZ2tBx54QE2aNNHQoUPl7e1tM9+oMAYPHqzY2FgNGzZMTZo0UVJSkpYtW6Y6deoUueakpCT5+/vbLG3btrVp06tXL+3cuVM1atTIM/do3LhxGj16tBISEtSgQQN16tRJX3/9tUJCQgq1f0dHR504cUK9e/dW3bp11b17d3Xu3FlvvPFGkY8FMAuLce3JfAAAgNscI0AAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0CEAAAMB0/h8LGT7TevLdrgAAAABJRU5ErkJggg==","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_list =[]\n","n=10\n","p=0.5\n","for i in range(0,11):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of Evens in 10 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Evens')\n","plt.ylabel(\"Probability\");"]},{"cell_type":"markdown","metadata":{"id":"03uSxQKaXSeE"},"source":["In fact, the shape of the graph is dependent on $p$, the probability of success. The closer $p$ is to $0.5$ the more symmetric it will be. Suppose we are instead interested in rolling the number 2 on a six-sided die. Here 'success' is rolling a 2, and 'failure' is rolling anything else. Thus, $p=\\frac{1}{6}$. Below we see the probability distribution of this example."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":453},"executionInfo":{"elapsed":998,"status":"ok","timestamp":1689102841520,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"NSURsmnwV6PE","outputId":"849e218b-a65f-4cbf-e39b-5c25cfa0448f"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_list =[]\n","n=10\n","p=1/6\n","for i in range(0,11):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of Twos in 10 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Twos')\n","plt.ylabel(\"Probability\");"]},{"cell_type":"markdown","metadata":{"id":"3W5fWUe0ls2Q"},"source":["Here we see the peak of the distribution lies around 1. With a success probability of $\\frac{1}{6}$, on average we should see about 1.6 twos in 10 tosses. Therefore a peak between 1 and 2 makes sense!\n","\n","What we just calculated was the mean of our binomial distribution, 1.6. We calculated it by multiplying the probability of success by the number of trials. In general, we can calculate the mean by drawing a sample and taking the mean empirically, or computing it based on the expected value.\n","For given parameters $n$, and $p$, the formula for the mean of a binomial distribution is $E(X) = \\mu(X) = n*p$. We can also calculate the standard deviation of the binomial distribution using $\\sigma(X) = \\sqrt{n*p*(1-p)}$. Evaluating this equation we see that the standard deviation of the binomial distribution with $n = 5$ and $p = \\frac{1}{6}$ is:\n"]},{"cell_type":"code","execution_count":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1689102906613,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"Iyn677ONba3F","outputId":"c7dee0ab-2d6c-41e8-997b-efcd5bb3b403"},"outputs":[{"data":{"text/plain":["1.1785113019775793"]},"execution_count":9,"metadata":{},"output_type":"execute_result"}],"source":["np.sqrt(10*(1/6)*(1-(1/6)))"]},{"cell_type":"markdown","metadata":{"id":"_E-QDNmch2-_"},"source":["## Normal Approximation to the Binomial Distribution\n","\n","We learned in the [last section](../3/normal.ipynb) that the normal distribution has a number of special properties that make it easy to use. You will learn later in this book that many statistical methods are based on the normal distribution. For this reason, it is convenient to be able to model data using the normal distribution (even when the data is not actually normally distributed!). Sometimes, we can approximate other distributions using a version of the normal distribution. Specifically, we can use a **normal approximation to the binomial distribution**.\n","\n","We can approximate a binomial distribution using a normal distribution when the number of trials, n, is 'sufficiently large'. The rule-of-thumb for deciding when n is large enough to do the approximation is to check the following:\n","- Is $np \\geq 5$?\n","- Is $n(1-p) \\geq 5$?\n","\n","If both conditions are met, you can approximate the binomial distribution with a normal distribution with mean:\n","\n","> $\\mu = np$\n","\n","and standard deviation:\n","\n","> $\\sigma = \\sqrt{np(1-p)}$\n","\n","Recall, we've been counting the number of occurrence of certain dice rolls. Let's now call a 'success' in our binomial experiment rolling either a 1 or a 6. There is a 1/3 probability of rolling a 1 or a 6 so this is our $p$. Now, let's count the number of successes in 50 dice rolls.\n","\n","First, we check if n is large enough. We have $np = 50*(1/3) \\approx 16.66 \\geq 5$ and $np = 100*(1 - 1/3) \\approx 33.33 \\geq 5$. So, we can use the approximation.\n","\n","Now, let's characterize our normal distribution. The mean is $np$ which we already calculated is approximately 16.66. The standard deviation is $\\sqrt{np(1-p)} = \\sqrt{100*(1/3)(2/3)} \\approx 3.33$.\n","\n","Below we compare the graph of the binomial distribution with n = 50 an p = 1/3 to the graph of the normal distribution with mean 16.66 and standard deviation 3.33."]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"executionInfo":{"elapsed":1464,"status":"ok","timestamp":1689108344229,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"4F_zMNTqriiw","outputId":"495e0c9a-3ef8-462b-9fac-08db009b8d30"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_list =[]\n","n=50\n","p=1/3\n","for i in range(0,51):\n","  binomial_list.append(binomial_prob(n, p, i))\n","\n","legend = f'Probability Distribution: Number of 1s or 6s in 50 Tosses'\n","\n","pd.DataFrame({legend: binomial_list}).plot.bar();\n","plt.xlabel('Number of Ones or Sixes');\n","plt.ylabel(\"Probability\");\n","plt.xticks(rotation = 90, fontsize=7);"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":448},"executionInfo":{"elapsed":446,"status":"ok","timestamp":1689111836735,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"JjPsIwU4s_ap","outputId":"c5259d5b-9385-47c4-a3d2-d2b49c97b760"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import scipy\n","import math\n","\n","def normal_prob(mu, sigma, x):\n","    '''A function that calculates probabilities from a normal distribution with mean mu and standard deviation sigma'''\n","    bottom = math.sqrt(2 * math.pi * sigma**2)\n","    top = math.exp(-(x - mu)**2/(2 * sigma**2))\n","    return top/bottom\n","\n","normal_list =[]\n","mu=16.66\n","sigma=3.33\n","for i in range(0,51):\n","  normal_list.append(normal_prob(mu, sigma, i))\n","\n","legend = f'Approximate Probability Distribution'\n","\n","pd.DataFrame({legend: normal_list}).plot();\n","plt.xlabel('Number of Ones or Sixes');\n","plt.ylabel(\"Probability\");\n","#plt.xticks(rotation = 90, fontsize=7);"]},{"cell_type":"markdown","metadata":{"id":"zjaN_6qWuBZO"},"source":["Now, let's view them together:"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":430},"executionInfo":{"elapsed":497,"status":"ok","timestamp":1689109464833,"user":{"displayName":"Amanda Jotte","userId":"14869546754145298709"},"user_tz":300},"id":"lvC1Hu-puA64","outputId":"86bf4f58-985a-4d17-cef4-51fd848142a5"},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["binomial_df = pd.DataFrame({\"Real\": binomial_list})\n","normal_df = pd.DataFrame({\"Approximate\": normal_list})\n","\n","bin = plt.bar(height = binomial_df.Real, x = binomial_df.index)\n","norm, = plt.plot(normal_df, color='red')\n","plt.legend([bin, norm], ['True Probability Distribution','Approximate Probability Distribution'])\n","plt.xlabel('Number of Ones or Sixes');\n","plt.ylabel(\"Probability\");\n","plt.show()"]},{"cell_type":"markdown","metadata":{"id":"Qc0T9apDxS2X"},"source":["It looks like the normal is a pretty good approximation to the binomial in this case!"]},{"cell_type":"markdown","metadata":{"id":"DlLOvh0yYWQu"},"source":["Learning about distributions like the ones presented in this chapter can help us better understand the data that we work with as data scientists. In the next few chapters, we will explore ways that statisticians and data scientists use distributions to test hypotheses about data."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BuZFEc-iYEtr"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3.9.13 64-bit","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.9"},"vscode":{"interpreter":{"hash":"aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"}}},"nbformat":4,"nbformat_minor":0}
diff --git a/textbook/13/1/HypothesisTesting_1_Consistency.ipynb b/textbook/13/1/HypothesisTesting_1_Consistency.ipynb
index 3c0aceac..947dec36 100644
--- a/textbook/13/1/HypothesisTesting_1_Consistency.ipynb
+++ b/textbook/13/1/HypothesisTesting_1_Consistency.ipynb
@@ -253,7 +253,7 @@
    ],
    "source": [
     "#data from CDC\n",
-    "natality2016=pd.read_csv(\"Natality2016.csv\")\n",
+    "natality2016=pd.read_csv(\"../../data/Natality2016.csv\")\n",
     "natality2016.head(3)"
    ]
   },
diff --git a/textbook/13/2/HypothesisTesting_2_Test.ipynb b/textbook/13/2/HypothesisTesting_2_Test.ipynb
index fb051a18..afc61960 100644
--- a/textbook/13/2/HypothesisTesting_2_Test.ipynb
+++ b/textbook/13/2/HypothesisTesting_2_Test.ipynb
@@ -18,7 +18,7 @@
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('fivethirtyeight')\n",
     "\n",
-    "natality2016=pd.read_csv(\"Natality2016.csv\")"
+    "natality2016=pd.read_csv(\"../../data/Natality2016.csv\")"
    ]
   },
   {
diff --git a/textbook/13/3/HypothesisTesting_3_TwoSample.ipynb b/textbook/13/3/HypothesisTesting_3_TwoSample.ipynb
index 519c396b..47ac7eaf 100644
--- a/textbook/13/3/HypothesisTesting_3_TwoSample.ipynb
+++ b/textbook/13/3/HypothesisTesting_3_TwoSample.ipynb
@@ -166,7 +166,7 @@
     }
    ],
    "source": [
-    "diabetes=pd.read_csv(\"diabetes.csv\")\n",
+    "diabetes=pd.read_csv(\"../../data/diabetes.csv\")\n",
     "diabetes.head(5)"
    ]
   },
diff --git a/textbook/13/4/HypothesisTesting_4_Categorical.ipynb b/textbook/13/4/HypothesisTesting_4_Categorical.ipynb
index 7ddd0c83..bb79c68f 100644
--- a/textbook/13/4/HypothesisTesting_4_Categorical.ipynb
+++ b/textbook/13/4/HypothesisTesting_4_Categorical.ipynb
@@ -123,7 +123,7 @@
     }
    ],
    "source": [
-    "gss=pd.read_csv(\"gss.csv\")\n",
+    "gss=pd.read_csv(\"../../data/gss.csv\")\n",
     "gss"
    ]
   },
diff --git a/textbook/14/2/ConfidenceIntervals_2_Bootstrap.ipynb b/textbook/14/2/ConfidenceIntervals_2_Bootstrap.ipynb
index a1b6ccd9..94467561 100644
--- a/textbook/14/2/ConfidenceIntervals_2_Bootstrap.ipynb
+++ b/textbook/14/2/ConfidenceIntervals_2_Bootstrap.ipynb
@@ -18,7 +18,7 @@
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('fivethirtyeight')\n",
     "\n",
-    "population_salary=pd.read_csv(\"Salaries.csv\")"
+    "population_salary=pd.read_csv(\"../../data/Salaries.csv\")"
    ]
   },
   {
diff --git a/textbook/14/3/ConfidenceIntervals_3_PercentileBootstrap.ipynb b/textbook/14/3/ConfidenceIntervals_3_PercentileBootstrap.ipynb
index 84cf28c7..86ea881d 100644
--- a/textbook/14/3/ConfidenceIntervals_3_PercentileBootstrap.ipynb
+++ b/textbook/14/3/ConfidenceIntervals_3_PercentileBootstrap.ipynb
@@ -18,7 +18,7 @@
     "import matplotlib.pyplot as plt\n",
     "plt.style.use('fivethirtyeight')\n",
     "\n",
-    "population_salary=pd.read_csv(\"Salaries.csv\")"
+    "population_salary=pd.read_csv(\"../../data/Salaries.csv\")"
    ]
   },
   {
diff --git a/textbook/14/ConfidenceIntervals_Intro.ipynb b/textbook/14/ConfidenceIntervals_Intro.ipynb
index 9cb60b06..29d313a6 100644
--- a/textbook/14/ConfidenceIntervals_Intro.ipynb
+++ b/textbook/14/ConfidenceIntervals_Intro.ipynb
@@ -58,7 +58,7 @@
     }
    ],
    "source": [
-    "population_salary=pd.read_csv(\"Salaries.csv\")\n",
+    "population_salary=pd.read_csv(\"../../data/Salaries.csv\")\n",
     "population_salary.shape"
    ]
   },
diff --git a/textbook/15/1/ethics-and-law.ipynb b/textbook/15/1/ethics-and-law.ipynb
index 99231100..a3a17132 100644
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@@ -28,10 +28,10 @@
     "Many legal and ethical concerns surrounding data-centered conduct and decision-making involves maximizing human benefits while minimizing unintended damage. Using a synopsis of the major emphasized topics outlined in state-level, federal, and international guides (see references below and throughout the chapter), this approach can be summarized into 4 major pillars that may serve as the basis of decisions, research, and usage of data involving human subjects:\n",
     "\n",
     "\n",
-    "1. Communication, interpretation, and application of human data should be accurate and consider social, political, and economic contexts and ramifications, especially when involving vulnerable populations. \n",
+    "1. The attainment, usage, storage, analysis, and maintenance of human data should be as transparent, accountable, and honest as possible and intended for some sort of human benefit.\n",
     "2. Collected human data should be shared and maintained in a way that protects the privacy of subjects.\n",
     "3. Acquisition and collection of data should involve volition and informed consent from human subjects. \n",
-    "4. The attainment, usage, storage, analysis, and maintenance of human data should be as transparent, accountable, and honest as possible and intended for some sort of human benefit.\n",
+    "4. Communication, interpretation, and application of human data should be accurate and consider social, political, and economic contexts and ramifications, especially when involving vulnerable populations. \n",
     "\n",
     "While these pillars have not been adopted by government agencies verbatim, they serve as a basis for data science students to consider as they explore and move throughout data science courses and professional opportunities."
    ]
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    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Pillar 1: Mitigating Unintended Consequences\n",
+    "# Pillar 1:  Data Transparency & Accountability\n",
     "\n",
-    "**Communication, interpretation, and application of human data should be accurate and consider social, political, and economic contexts and ramifications, especially when involving vulnerable populations**\n",
+    "**The attainment, usage, storage, analysis, and maintenance of human data should be as transparent, accountable, and honest as possible and intended for some sort of human benefit.**\n",
     "\n",
+    "In order to facilitate reproducibility, trusted participation, and complete comprehension of terminal goals and results, a policy of transparency and honesty should be embedded into the protocol of handling human data. Transparency and honesty as it pertains to acquiring, using, storing, and analyzing data can include sharing information about these methods not only with stakeholders, but relevant government and regulatory agencies and the people that the data is collected from, when possible. Use of transparent and honest practices in each of these areas may increase the likelihood that the use of the data will in fact be beneficial to humans and may include, but are not limited to, the following practices:\n",
     "\n",
+    "- Attainment - data was gathered through ethical and honest means, with the consent of subjects (See Pillar 2)\n",
+    "- Usage - the use of data coincides with the intended use communicated to the subjects and does not include  practices that maliciously targets groups of people or spreads misinformation (See Pillar 4).\n",
+    "- Analysis - proper analytical methods are applied to garner understanding of data and does not include biased or inappropriate statistical applications, such as p-hacking, cherry picking, selective omission, and other problematic research practices[^*]. This also includes conducting a meaningful cost-benefit analysis pertaining to accuracy vs. transparency.\n",
+    "- Storage & Maintenance - secure means are used to preserve and house data physically or on an online server to ensure the privacy of subjects. Prior to collection, subjects are informed about what these privacy and security measures are (See Pillar 2).\n",
     "\n",
     "\n",
+    "## Trust & Accountability\n",
     "\n",
-    "Science emphasizes the use of quantitative methods and objective investigation as a means of determining accurate, unbiased conclusions about the observable world. As such, many place science into a vacuum lacking social, political, and economic influence and consider it the “closest” form of truth that we as humans can attain. As many have witnessed during the COVID-19 pandemic, science and society are very much interconnected and coevolving, for better and for worse. At certain points in history, science has been used as a tool for justification of oppression and violence against groups of people. For instance, public sentiments and “scientific” theories expressed by Sir Francis Galton, Karl Pearson, Sir Ronald Fisher, and other scientists were used to justify eugenic laws and practices across the world; some of these include the Racial Integrity Act of 1924/Buck v. Bell[^*][^**], the 1907 Indiana Eugenics Law[^***], and the sweep of many other legislations across North America and Europe[^****] allowing for involuntary sterilization of groups of people. This fueled several other unethical and moral crimes against humanity, including the Holocaust, forced sterilization of over 60,000 Americans (many of which were African-American and Latina women), and other forms of medical genocide. More recently, these sentiments have been cited in motivating modern racially-directed violence, as seen in the Buffalo Shooting in May 2022[^*****]. In order for science to continue facilitating advancements for the benefit of humanity, knowing this history and understanding how not to repeat it is crucial, as the effects can be detrimental and generationally traumatizing. \n",
     "\n",
-    "<img align=\"center\" src=\"./img/virginia-eugenics.jpeg\" width=\"33%\"/>\n",
+    "The aforementioned practices may help to facilitate trust between human subjects/users and technologies that rely on data acquired from them [^**], [^***]. The establishment of trust within communities can have several advantages, including increased use of a product or technology, expanded collection of data, improved productivity and efficiency in tasks, and other benefits [^****]. However, with the occurence of numerous malevolent incidences, including massive data breaches that revealed sensitive financial information [^*****] or the use of \"synthetic\" media that has contributed to social and political confusion and misinformation [^******], it is unsurprising that some of the population has a level of distrust and concern when it comes to collection of personal data \n",
+    "[^*******] and emergent technologies [^********] such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), in the public and private sectors.\n",
     "\n",
-    "<figcaption>Pamphlet of the Virginia Health Bulletin reporting passing of the Racial Integrity Act of 1924. “Virginia Health Bulletin: The New Virginia Law To Preserve Racial Integrity, March 1924,” Document Bank of Virginia, accessed August 1, 2023, https://edu.lva.virginia.gov/dbva/items/show/226.</figcaption>\n",
     "\n",
-    "</br>\n",
-    "</br>\n",
-    "While we as a society have evolved our moral compass, remnants of previous ethical shortcomings creep into scientific and technological practices in the digital age. As the rapid utilization of rich datasets drive discoveries in the fields of medicine, physics, engineering, artificial intelligence, and other sciences, few are slowing down to ask the questions that weigh innovation against morality. Most of the time, such ethical considerations are not acknowledged until cumulative damage is done. A well-known example is the report published by ProPublica[^******] in 2016, which described how a recidivism algorithm called COMPAS exhibited racial biases toward offenders, rating Black offenders as more likely to re-offend than White offenders. This algorithm has been used in several states in judicial decision-making processes.\n",
     "\n",
-    "Surprisingly, the reported analysis showed that of those deemed “high risk” to reoffend by COMPAS, 28.0% of Black offenders and 47.7% of White offenders did in fact reoffend. On the other hand, 44.9% of Black offenders and 23.5% of White offenders actually did not reoffend. How may an algorithm exhibit such suboptimal predictions and biases in its outputs? This would depend on the lens of what is considered \"suboptimal\" or even \"fair.\" According to a report published by Northpointe Incorporated [^*******], the creator of COMPAS, the algorithm does not confer biases with the statistical measures they used to assess accuracy and fairness. Affiliates of the United States Courts have also criticized the Propublica report [^********], stating that the study did not apply appropriate calculations in determining recidivism rates. Since the publishing of the original Propublica report, representatives of the academic, government, and private sectors have been in debate about what the appropriate measures of fairness are and the importance of these measures for crucial decisions such as recidivism[^*********][^**********].\n",
+    "In 2015, the Harvard Business Review published an article surveying consumers across several countries on sentiments around data privacy and security[^**********]. Interestingly, the entities people trusted most with their data were healthcare providers and financial institutions, while the least trusted were entertainment companies and social media firms. Arguably, the level of trust may coincide with mechanisms of accountability, such as HIPPA legislations and financial privacy laws like the Gramm–Leach–Bliley Act, which may not apply to entertainment and social media platforms in the same way. While legal obligation is a strong way to enforce accountability, providing open and accessible information on conduct around data security, privacy, and collection and protocols for when someone has questions or ailments about them shows an organization’s intrinsic commitment to transparent practices, which can enhance trust between the organization and customers, users, and other subjects from which data may be collected. \n",
     "\n",
-    "Following up on this report, Dressel and Farid (2018)[^***********] compared the accuracy of COMPAS’s prediction of recidivism to predictions made by human participants using an online survey. They found that human prediction was slightly more accurate than COMPAS (67% vs 65.2%) but was not significantly different. Using a developed classifier, they determined that age and total number of previous convictions yielded a similar racial bias as was reported in the Propublica report. Out of context, this could be viewed at most as a major flaw of the data inputs and outputs for this algorithm (garbage in, garbage out). A more contextual interpretation of how either of these factors lead to a racial bias in recidivism ratings could be supported by the numerous studies documenting racial bias arrests and confrontations with law enforcement[^************], as well as the track record of controversial police encounters in America. Thus, it is important to understand the greater macrocosm from which this data is derived to garner understanding of what the data may mean. Furthermore, it is imperative to acknowledge the strengths and limitations of statistical models and measures in relevant applications involving human behavior.\n",
+    "<img align=\"center\" src=\"./img/trust.png\" width=\"75%\"/>\n",
     "\n",
+    "As laws relevant to companies outside of the healthcare and financial sectors are still developing, accountability for such entities is currently nebulous. The <u>data lifecycle</u> is a pipeline that considers the past, present, and future of data and its application in technologies by said companies. This pipeline involves numerous steps, such as data collection, processessing, analysis, dissemination, and maintenance, which can be lead by numerous teams or organizations of people. Thus, for terminal applications of data, how do we determine accountability when things go wrong? While assignment of accountability is complex and multifaceted, Virginia Dignum, an AI ethicist and researcher, suggests that when thinking about accountability in the data lifecycle, the larger sociotechnical ecosystem must be considered within a *Accountability-Responsibility-Transparency* (ART) framework [^***********]. Those involved throughout various phases of the data lifecycle should be able to explain and justify their decisions around data (*accountability*), acknowledge the role that they play in the data lifecyle (*responsibility*), and describe, inspect and reproduce mechanisms contributing to end products of the life cycle (*transparency*). Utilizing this framework requires a level of open discussion with stakeholders, whether they be clients, users, or society. Such discussions facilitate iterative modification and reworking to improve products and services from an ethical and technological standpoint.\n",
     "\n",
-    "<img align=\"center\" src=\"./img/Dresseletal.png\" width=\"50%\"/>\n",
+    "<img align=\"center\" src=\"./img/lifecycleofdata.png\" width=\"50%\"/>\n",
     "\n",
-    "<figcaption> Human (no-race condition) versus COMPAS algorithmic predictions. Adapted from Dressel and Farrid, 2018.</figcaption>\n",
+    "<figcaption>The data lifecycle. Adapted from Communications of the ACM, July 2020, Vol. 63 No. 7, Pages 58-66\n",
+    "10.1145/3360646</figcaption>\n",
     "\n",
-    "<br/>\n",
     "\n",
-    "While the use of tools like COMPAS is still an ongoing debate, important questions and considerations have emerged from this discussion. Particularly, how do tools like COMPAS affect decisions made by judges presented with recidivism predictions? Do these tools significantly impact one's right to due process? Does automation bias work in concert with implicit biases that are already prominent in the justice system[^*************]? Should we be using algorithmic predictions about human behavior in these circumstances at all?\n",
     "\n",
-    "Past ethical faults within the science and technology sectors may not have fully considered the damaging ramifications of certain studies, methods, and drawn conclusions on society. Moreover, it is impossible to anticipate the myriad of ways scientific findings may positively or negatively influence society. However, when it comes to working with human-derived data, or even data that can greatly affect human lives, these damages can be mitigated by staying well informed about the populations from which the data are collected and using ethical and contextual discernment when interpreting, communicating, and utilizing these data. As data scientists, it is our job to have ethics and morality as a basis by which we make these decisions around data."
+    "## The Trade-Off Between Transparency and Accuracy\n",
+    "\n",
+    "The development of artificial intelligence algorithms has aided, informed, and/or influenced human decision-making processes in a number of low-stakes and high-stakes scenarios. Because of this, conversation around the transparency of these algorithms has highlighted important perspectives and viewpoints regarding the implications of these technologies. \n",
+    "\n",
+    "Some algorithms disclose the use of parameters and models applied (i.e., \"white-box\" algorithms), while others may not readily explain the parameters and models used or may utilize another model that approximates the original model(s) (i.e., \"black-box algorithms). Some black-box algorithms also may utilize more sophistocated and complex methods, such as random forests and neural networks, which may not be readily explainable. Depending on the context of use, the methods backing white-box and black-box algorithms can influence the accuracy, and thus impact terminal services, decisions, products, or actions. \n",
+    "\n",
+    "Explainable AI (XAI) and ML (XML) explores the intersection of transparency and technical applicability in AI and ML. While transparency can help promote accountability and trust, limitations to prioritizing transparency may present in the use of AI/ML. Some of these include malicous and unintended misuse of developed AI/ML tools [^***********], exposure of trade secrets [^************], domain and technical knowledge requirements for full comprehension, and several others. In high-stakes situations, such as the use of AI/ML in healthcare decisions, some argue that accuracy should be more important than transperancy [^*************], while others suggest the use of *interpretable* models instead of black-box algorithms where possible [^**************]. Sometimes, black-box algorithms can be replaced with more simpler and transparent ones with little compromise of accuracy [^***************], [^****************]. In cases where black-box methods must be used, attempts to provide transparency through justification as opposed to explanation may be the best case scenario [^*****************]. An evalulation of the situation and associated risks and rewards, as well as testing of multiple black-box, white-box, and interpretable options, is important in determining the the best way to balance transparency and accuracy. \n",
+    "\n",
+    "\n",
+    "## Honest Statistical Practices\n",
+    "\n",
+    "A part of getting accurate insights from data includes using honest and appropriate statistical methods during data analysis. Doing such can enhance reproducibility, allowing for economical allocation of time and resources toward follow up studies. Some common pitfalls in research and analysis include <u>h</u>ypothesizing <u>a</u>fter <u>r</u>esults are <u>k</u>nown (HARKing), p-hacking, and cherry-picking. Below is a discussion of each and how they impact research and knowledge generation.\n",
+    "\n",
+    "#### HARKing\n",
+    "As the acroynm states, HARKing is developing a hypothesis about data after knowing the results that the data depict and then reporting conclusions as if they were hypothesized *a priori*. HARKing does not fully disclose the process leading to the hypothesis and conclusions from data and thus can be seen as dishonest in nature. HARKing may or may not include performing statistical analysis and determining significant variables in a dataset; plotting and cross-examining variables can also be a part of HARKing. When performing exploratory studies, examining the relationships between multiple variables from a dataset can be a useful process to generate new hypotheses, but these hypotheses should be tested with a new dataset to confirm previous observations.\n",
+    "\n",
+    "#### P-hacking\n",
+    "\n",
+    "P-hacking can involve the use of multiple testing, various kinds of statistical tests, and/or specific subsetting of data in order to generate a significant p-value. Like HARKing, p-hacking does not fully account for the process leading up to a significant result. Because hypothesis testing reports the probability of an observed outcome occuring given that the null hypothesis is true, testing multiple hypothesis on the same data will give a false positive at some point. To address this, multiple testing corrections can be used [^******************].\n",
+    "\n",
+    "#### Cherry-picking\n",
+    "Cherry-picking involves biased selection of data for analysis or reporting conclusions. Cherry-picking can be used to fuel p-hacking or paint an incomplete picture of a research process. Reporting only data that aligns with a hypothesis can be an impediment to those trying to repeat a reported experiment because it can lead researchers down an avoidable rabbit hole. Furthermore, not reporting null data that is not in alignment with a hypothesis can similarly hinder the scientific process. When outliers are found within data, it is only appropriate to exclude it if there is sound justification based on the data collection process or statistical backing.\n",
+    "\n",
+    "All-in-all, when it comes to data, especially when derived from humans, centering honesty as much as possible is the best policy.\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[^*]: General Assembly. \"Preservation of Racial Integrity (1924)\" Encyclopedia Virginia. Virginia Humanities, (07 Dec. 2020). Web. 01 Dec. 2022\n",
+    "[^*]: Andrade, Chittaranjan. “HARKing, Cherry-Picking, P-Hacking, Fishing Expeditions, and Data Dredging and Mining as Questionable Research Practices.” The Journal of Clinical Psychiatry, vol. 82, no. 1, Feb. 2021, p. 20f13804, https://doi.org/10.4088/JCP.20f13804.\n",
+    "\n",
+    "[^**]: Lin, D., Crabtree, J., Dillo, I. et al. The TRUST Principles for digital repositories. Sci Data 7, 144 (2020). https://doi.org/10.1038/s41597-020-0486-7\n",
+    "\n",
+    "[^***]: Atske, Sara. “2. Americans Concerned, Feel Lack of Control over Personal Data Collected by Both Companies and the Government.” Pew Research Center: Internet, Science & Tech, 15 Nov. 2019, https://www.pewresearch.org/internet/2019/11/15/americans-concerned-feel-lack-of-control-over-personal-data-collected-by-both-companies-and-the-government/.\n",
+    "\n",
+    "[^****]: Robles, Pedro, and Daniel J. Mallinson. “Artificial Intelligence Technology, Public Trust, and Effective Governance.” Review of Policy Research, May 2023, p. ropr.12555, https://doi.org/10.1111/ropr.12555.\n",
+    "\n",
+    "[^*****]: Mathews, Lee. “Equifax Data Breach Impacts 143 Million Americans.” Forbes, https://www.forbes.com/sites/leemathews/2017/09/07/equifax-data-breach-impacts-143-million-americans/. Accessed 31 July 2023.\n",
+    "\n",
+    "[^******]: U.S. Department of Homeland Security. Increasing Threat of DEEPFAKE Identities. Accessed 31 July 2023. https://www.dhs.gov/sites/default/files/publications/increasing_threats_of_deepfake_identities_0.pdf\n",
     "\n",
-    "[^**]: BUCK v. BELL. 2 May 1927, https://www.loc.gov/item/usrep274200/.\n",
+    "[^*******]: “Americans Widely Distrust Facebook, TikTok and Instagram with Their Data, Poll Finds.” Washington Post, 22 Dec. 2021, https://www.washingtonpost.com/technology/2021/12/22/tech-trust-survey/.\n",
     "\n",
-    "[^***]: Laws of Indiana, 1907, pp. 377-78 (B050823). https://www.in.gov/history/state-historical-markers/find-a-marker/1907-indiana-eugenics-law/\n",
+    "[^********]: Nadeem, Reem. “Public Awareness of Artificial Intelligence in Everyday Activities.” Pew Research Center Science & Society, 15 Feb. 2023, https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/.\n",
     "\n",
-    "[^****]: Reilly, Philip R. “Eugenics and Involuntary Sterilization: 1907-2015.” Annual Review of Genomics and Human Genetics, vol. 16, 2015, pp. 351–68, https://doi.org/10.1146/annurev-genom-090314-024930.\n",
+    "[^**********]: Morey, Timothy, et al. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review, 1 May 2015, https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust.\n",
     "\n",
-    "[^*****]: Office of the New York State Attorney General. Investigative Report on the role of online platforms in the tragic mass shooting in Buffalo on May 14, 2022. Published OCTOBER 18, 2022. https://ag.ny.gov/sites/default/files/buffaloshooting-onlineplatformsreport.pdf\n",
+    "[^***********]: Virginia Dignum. The role and challenges of education for responsible AI. London Review of Education. 2021. Vol. 19(1). DOI: 10.14324/LRE.19.1.01\n",
     "\n",
-    "[^******]: Angwin, Julia, et al. “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks,” ProPublica, 23 May 2016, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.\n",
+    "[^***********]: Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020. Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 648–657. https://doi.org/10.1145/3351095.3375624\n",
     "\n",
-    "[^*******]: Dieterich, William, et al. \"COMPAS Risk Scales: Demonstrating\n",
-    "Accuracy Equity and Predictive Parity,\" 8 July 2016, https://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf.\n",
+    "[^************]: Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512\n",
     "\n",
-    "[^********]: Flores, Anthony, et al. \"False Positives, False Negatives, and False Analyses: A Rejoinder to 'Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And It's Biased Against Blacks.'\" Federal Probation, Vol. 80 Number 2, September 2016, https://www.uscourts.gov/federal-probation-journal/2016/09/false-positives-false-negatives-and-false-analyses-rejoinder.\n",
+    "[^*************]: Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021 Nov;3(11):e745-e750. doi: 10.1016/S2589-7500(21)00208-9. PMID: 34711379.\n",
     "\n",
-    "[^*********]: Corbett-Davies, Sam, and Sharad Goel. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. arXiv, 14 Aug. 2018, https://arxiv.org/abs/1808.00023.\n",
+    "[^**************]: Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13. PMID: 35603010; PMCID: PMC9122117.\n",
     "\n",
-    "[^**********]: Pleiss, Geoff, et al. “On Fairness and Calibration.” Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017, https://papers.nips.cc/paper/2017/hash/b8b9c74ac526fffbeb2d39ab038d1cd7-Abstract.html.\n",
+    "[^***************]: Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. 2019. This looks like that: deep learning for interpretable image recognition. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 801, 8930–8941.\n",
     "\n",
-    "[^***********]: Dressel, Julia and Farid, Hany. “The Accuracy, Fairness, and Limits of Predicting Recidivism.” Science Advances, vol. 4, no. 1, Jan. 2018, p. eaao5580, https://doi.org/10.1126/sciadv.aao5580.\n",
+    "[^****************]: Barnett, A.J., Schwartz, F.R., Tao, C. et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat Mach Intell 3, 1061–1070 (2021). https://doi.org/10.1038/s42256-021-00423-x\n",
     "\n",
-    "[^************]: Eberhardt, Jennifer. L. “Strategies for change: Research initiatives and recommendations to improve police- community relations in Oakland, Calif.” 2016. Stanford University, SPARQ: Social Psychological Answers to Real-world Questions. \n",
+    "[^*****************]: Biran, Or and Courtenay V. Cotton. “Explanation and Justification in Machine Learning : A Survey Or.” (2017).\n",
     "\n",
-    "[^*************]: Jeffrey J. Rachlinski & Sheri L. Johnson, Does Unconscious Racial Bias Affect Trial Judges, 84 Notre Dame L. Rev. 1195 (2009). Available at: http://scholarship.law.nd.edu/ndlr/vol84/iss3/4.\n"
+    "[^******************]: Andrade C. Multiple Testing and Protection Against a Type 1 (False Positive) Error Using the Bonferroni and Hochberg Corrections. Indian Journal of Psychological Medicine. 2019;41(1):99-100. doi:10.4103/IJPSYM.IJPSYM_499_18\n"
    ]
   },
   {
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+++ b/textbook/15/5/pillar4.ipynb
@@ -4,121 +4,70 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Pillar 4:  Data Transparency & Accountability\n",
+    "# Pillar 4: Mitigating Unintended Consequences\n",
     "\n",
-    "**The attainment, usage, storage, analysis, and maintenance of human data should be as transparent, accountable, and honest as possible and intended for some sort of human benefit.**\n",
+    "**Communication, interpretation, and application of human data should be accurate and consider social, political, and economic contexts and ramifications, especially when involving vulnerable populations**\n",
     "\n",
-    "In order to facilitate reproducibility, trusted participation, and complete comprehension of terminal goals and results, a policy of transparency and honesty should be embedded into the protocol of handling human data. Transparency and honesty as it pertains to acquiring, using, storing, and analyzing data can include sharing information about these methods not only with stakeholders, but relevant government and regulatory agencies and the people that the data is collected from, when possible. Use of transparent and honest practices in each of these areas may increase the likelihood that the use of the data will in fact be beneficial to humans and may include, but are not limited to, the following practices:\n",
     "\n",
-    "- Attainment - data was gathered through ethical and honest means, with the consent of subjects [(See Pillar 2)](../3/pillar2.ipynb).\n",
-    "- Usage - the use of data coincides with the intended use communicated to the subjects and does not include  practices that maliciously targets groups of people or spreads misinformation [(See Pillar 1)](../2/pillar1.ipynb).\n",
-    "- Analysis - proper analytical methods are applied to garner understanding of data and does not include biased or inappropriate statistical applications, such as p-hacking, cherry picking, selective omission, and other problematic research practices[^*]. This also includes conducting a meaningful cost-benefit analysis pertaining to accuracy vs. transparency.\n",
-    "- Storage & Maintenance - secure means are used to preserve and house data physically or on an online server to ensure the privacy of subjects. Prior to collection, subjects are informed about what these privacy and security measures are [(See Pillar 2)](../3/pillar2.ipynb).\n",
     "\n",
     "\n",
-    "## Trust & Accountability\n",
     "\n",
+    "Science emphasizes the use of quantitative methods and objective investigation as a means of determining accurate, unbiased conclusions about the observable world. As such, many place science into a vacuum lacking social, political, and economic influence and consider it the “closest” form of truth that we as humans can attain. As many have witnessed during the COVID-19 pandemic, science and society are very much interconnected and coevolving, for better and for worse. At certain points in history, science has been used as a tool for justification of oppression and violence against groups of people. For instance, public sentiments and “scientific” theories expressed by Sir Francis Galton, Karl Pearson, Sir Ronald Fisher, and other scientists were used to justify eugenic laws and practices across the world; some of these include the Racial Integrity Act of 1924/Buck v. Bell[^*][^**], the 1907 Indiana Eugenics Law[^***], and the sweep of many other legislations across North America and Europe[^****] allowing for involuntary sterilization of groups of people. This fueled several other unethical and moral crimes against humanity, including the Holocaust, forced sterilization of over 60,000 Americans (many of which were African-American and Latina women), and other forms of medical genocide. More recently, these sentiments have been cited in motivating modern racially-directed violence, as seen in the Buffalo Shooting in May 2022[^*****]. In order for science to continue facilitating advancements for the benefit of humanity, knowing this history and understanding how not to repeat it is crucial, as the effects can be detrimental and generationally traumatizing. \n",
     "\n",
-    "The aforementioned practices may help to facilitate trust between human subjects/users and technologies that rely on data acquired from them [^**], [^***]. The establishment of trust within communities can have several advantages, including increased use of a product or technology, expanded collection of data, improved productivity and efficiency in tasks, and other benefits [^****]. However, with the occurence of numerous malevolent incidences, including massive data breaches that revealed sensitive financial information [^*****] or the use of \"synthetic\" media that has contributed to social and political confusion and misinformation [^******], it is unsurprising that some of the population has a level of distrust and concern when it comes to collection of personal data \n",
-    "[^*******] and emergent technologies [^********] such as artificial intelligence (AI), machine learning (ML), and natural language processing (NLP), in the public and private sectors.\n",
+    "<img align=\"center\" src=\"./img/virginia-eugenics.jpeg\" width=\"33%\"/>\n",
     "\n",
+    "<figcaption>Pamphlet of the Virginia Health Bulletin reporting passing of the Racial Integrity Act of 1924. “Virginia Health Bulletin: The New Virginia Law To Preserve Racial Integrity, March 1924,” Document Bank of Virginia, accessed August 1, 2023, https://edu.lva.virginia.gov/dbva/items/show/226.</figcaption>\n",
     "\n",
+    "</br>\n",
+    "</br>\n",
+    "While we as a society have evolved our moral compass, remnants of previous ethical shortcomings creep into scientific and technological practices in the digital age. As the rapid utilization of rich datasets drive discoveries in the fields of medicine, physics, engineering, artificial intelligence, and other sciences, few are slowing down to ask the questions that weigh innovation against morality. Most of the time, such ethical considerations are not acknowledged until cumulative damage is done. A well-known example is the report published by ProPublica[^******] in 2016, which described how a recidivism algorithm called COMPAS exhibited racial biases toward offenders, rating Black offenders as more likely to re-offend than White offenders. This algorithm has been used in several states in judicial decision-making processes.\n",
     "\n",
-    "In 2015, the Harvard Business Review published an article surveying consumers across several countries on sentiments around data privacy and security[^**********]. Interestingly, the entities people trusted most with their data were healthcare providers and financial institutions, while the least trusted were entertainment companies and social media firms. Arguably, the level of trust may coincide with mechanisms of accountability, such as HIPPA legislations and financial privacy laws like the Gramm–Leach–Bliley Act, which may not apply to entertainment and social media platforms in the same way. While legal obligation is a strong way to enforce accountability, providing open and accessible information on conduct around data security, privacy, and collection and protocols for when someone has questions or ailments about them shows an organization’s intrinsic commitment to transparent practices, which can enhance trust between the organization and customers, users, and other subjects from which data may be collected. \n",
+    "Surprisingly, the reported analysis showed that of those deemed “high risk” to reoffend by COMPAS, 28.0% of Black offenders and 47.7% of White offenders did in fact reoffend. On the other hand, 44.9% of Black offenders and 23.5% of White offenders actually did not reoffend. How may an algorithm exhibit such suboptimal predictions and biases in its outputs? This would depend on the lens of what is considered \"suboptimal\" or even \"fair.\" According to a report published by Northpointe Incorporated [^*******], the creator of COMPAS, the algorithm does not confer biases with the statistical measures they used to assess accuracy and fairness. Affiliates of the United States Courts have also criticized the Propublica report [^********], stating that the study did not apply appropriate calculations in determining recidivism rates. Since the publishing of the original Propublica report, representatives of the academic, government, and private sectors have been in debate about what the appropriate measures of fairness are and the importance of these measures for crucial decisions such as recidivism[^*********][^**********].\n",
     "\n",
-    "<img align=\"center\" src=\"./img/trust.png\" width=\"75%\"/>\n",
+    "Following up on this report, Dressel and Farid (2018)[^***********] compared the accuracy of COMPAS’s prediction of recidivism to predictions made by human participants using an online survey. They found that human prediction was slightly more accurate than COMPAS (67% vs 65.2%) but was not significantly different. Using a developed classifier, they determined that age and total number of previous convictions yielded a similar racial bias as was reported in the Propublica report. Out of context, this could be viewed at most as a major flaw of the data inputs and outputs for this algorithm (garbage in, garbage out). A more contextual interpretation of how either of these factors lead to a racial bias in recidivism ratings could be supported by the numerous studies documenting racial bias arrests and confrontations with law enforcement[^************], as well as the track record of controversial police encounters in America. Thus, it is important to understand the greater macrocosm from which this data is derived to garner understanding of what the data may mean. Furthermore, it is imperative to acknowledge the strengths and limitations of statistical models and measures in relevant applications involving human behavior.\n",
     "\n",
-    "As laws relevant to companies outside of the healthcare and financial sectors are still developing, accountability for such entities is currently nebulous. The <u>data lifecycle</u> is a pipeline that considers the past, present, and future of data and its application in technologies by said companies. This pipeline involves numerous steps, such as data collection, processessing, analysis, dissemination, and maintenance, which can be lead by numerous teams or organizations of people. Thus, for terminal applications of data, how do we determine accountability when things go wrong? While assignment of accountability is complex and multifaceted, Virginia Dignum, an AI ethicist and researcher, suggests that when thinking about accountability in the data lifecycle, the larger sociotechnical ecosystem must be considered within a *Accountability-Responsibility-Transparency* (ART) framework [^***********]. Those involved throughout various phases of the data lifecycle should be able to explain and justify their decisions around data (*accountability*), acknowledge the role that they play in the data lifecyle (*responsibility*), and describe, inspect and reproduce mechanisms contributing to end products of the life cycle (*transparency*). Utilizing this framework requires a level of open discussion with stakeholders, whether they be clients, users, or society. Such discussions facilitate iterative modification and reworking to improve products and services from an ethical and technological standpoint.\n",
     "\n",
-    "<img align=\"center\" src=\"./img/lifecycleofdata.png\" width=\"75%\"/>\n",
+    "<img align=\"center\" src=\"./img/Dresseletal.png\" width=\"50%\"/>\n",
     "\n",
-    "<figcaption>The data lifecycle. Adapted from Communications of the ACM, July 2020, Vol. 63 No. 7, Pages 58-66\n",
-    "10.1145/3360646</figcaption>\n",
+    "<figcaption> Human (no-race condition) versus COMPAS algorithmic predictions. Adapted from Dressel and Farrid, 2018.</figcaption>\n",
     "\n",
+    "<br/>\n",
     "\n",
+    "While the use of tools like COMPAS is still an ongoing debate, important questions and considerations have emerged from this discussion. Particularly, how do tools like COMPAS affect decisions made by judges presented with recidivism predictions? Do these tools significantly impact one's right to due process? Does automation bias work in concert with implicit biases that are already prominent in the justice system[^*************]? Should we be using algorithmic predictions about human behavior in these circumstances at all?\n",
     "\n",
-    "## The Trade-Off Between Transparency and Accuracy\n",
-    "\n",
-    "The development of artificial intelligence algorithms has aided, informed, and/or influenced human decision-making processes in a number of low-stakes and high-stakes scenarios. Because of this, conversation around the transparency of these algorithms has highlighted important perspectives and viewpoints regarding the implications of these technologies. \n",
-    "\n",
-    "Some algorithms disclose the use of parameters and models applied (i.e., \"white-box\" algorithms), while others may not readily explain the parameters and models used or may utilize another model that approximates the original model(s) (i.e., \"black-box\" algorithms). Some black-box algorithms also may utilize more sophistocated and complex methods, such as random forests and neural networks, which may not be readily explainable. Depending on the context of use, the methods backing white-box and black-box algorithms can influence the accuracy, and thus impact terminal services, decisions, products, or actions. \n",
-    "\n",
-    "Explainable AI (XAI) and ML (XML) explores the intersection of transparency and technical applicability in AI and ML. While transparency can help promote accountability and trust, limitations to prioritizing transparency may present in the use of AI/ML. Some of these include malicous and unintended misuse of developed AI/ML tools [^***********], exposure of trade secrets [^************], domain and technical knowledge requirements for full comprehension, and several others. In high-stakes situations, such as the use of AI/ML in healthcare decisions, some argue that accuracy should be more important than transperancy [^*************], while others suggest the use of *interpretable* models instead of black-box algorithms where possible [^**************]. Sometimes, black-box algorithms can be replaced with more simpler and transparent ones with little compromise of accuracy [^***************], [^****************]. In cases where black-box methods must be used, attempts to provide transparency through justification as opposed to explanation may be the best case scenario [^*****************]. An evalulation of the situation and associated risks and rewards, as well as testing of multiple black-box, white-box, and interpretable options, is important in determining the the best way to balance transparency and accuracy. \n",
-    "\n",
-    "\n",
-    "## Dishonest Statistical Practices\n",
-    "\n",
-    "A part of getting accurate insights from data includes using honest and appropriate statistical methods during data analysis. Doing such can enhance reproducibility, allowing for economical allocation of time and resources toward follow up studies. Some common pitfalls in research and analysis include <u>h</u>ypothesizing <u>a</u>fter <u>r</u>esults are <u>k</u>nown (HARKing), p-hacking, and cherry-picking. Below is a discussion of each and how they impact research and knowledge generation.\n",
-    "\n",
-    "### HARKing\n",
-    "As the acroynm states, HARKing is developing a hypothesis about data after knowing the results that the data depict and then reporting conclusions as if they were hypothesized *a priori*. HARKing does not fully disclose the process leading to the hypothesis and conclusions from data and thus can be seen as dishonest in nature. HARKing may or may not include performing statistical analysis and determining significant variables in a dataset; plotting and cross-examining variables can also be a part of HARKing. When performing exploratory studies, examining the relationships between multiple variables from a dataset can be a useful process to generate new hypotheses, but these hypotheses should be tested with a new dataset to confirm previous observations.\n",
-    "\n",
-    "### P-hacking\n",
-    "\n",
-    "P-hacking can involve the use of multiple testing, various kinds of statistical tests, and/or specific subsetting of data in order to generate a significant p-value. Like HARKing, p-hacking does not fully account for the process leading up to a significant result. Analyzing data in various ways with the specific intent to show a significant p-value, rather than analyzing in an objective fashion, can lead to erroneous conclusions, misguided research directions, and retraction of scientific papers, as seen in the research of Dr. Brian Wansink [^******************]. Because hypothesis testing reports the probability of an observed outcome occuring given that the null hypothesis is true, testing multiple hypothesis on the same data will give a false positive at some point. To address this, multiple testing corrections and adjustments should be used [^*******************], [^********************].\n",
-    "\n",
-    "### Cherry-picking\n",
-    "Cherry-picking involves biased selection of data for analysis or reporting conclusions. Cherry-picking can be used to fuel p-hacking or paint an incomplete picture of a research process. Reporting only data that aligns with a hypothesis can be an impediment to those trying to repeat a reported experiment because it can lead researchers down an avoidable rabbit hole. Furthermore, not reporting null data that is not in alignment with a hypothesis can similarly hinder the scientific process. \n",
-    "\n",
-    "### Data manipulation\n",
-    "Data manipulation includes practices of fabrication and falsification that can be fueled by the omission, addition, and/or alteration of raw data. According to the National Science Foundation's policy (45 CFR 689)[^*********************], fabrication means “making up data or results and recording or reporting them”, while falsification means “manipulating research materials, equipment, or processes, or changing or omitting data or results such that the research is not accurately represented in the research record.” \n",
-    "\n",
-    "Data manipulation is one of the most blatantly dishonest research and statistical practices an investigator can do. It is greatly frowned upon within the scientific community and can reduce one's credibility as a researcher, as seen with Dr. Francesca Gino[^**********************] and Dr. Dan Ariely.[^***********************] During the research process, it may be tempting to exclude perceived outliers within a dataset, but this should be avoided unless there is sound justification based on the data collection process or statistical backing; otherwise, keeping data points that deviate highly from others within the dataset is the most honest thing to do.\n",
-    "\n",
-    "\n",
-    "All-in-all, when it comes to data, especially when derived from humans, centering honesty as much as possible is the best policy.\n"
+    "Past ethical faults within the science and technology sectors may not have fully considered the damaging ramifications of certain studies, methods, and drawn conclusions on society. Moreover, it is impossible to anticipate the myriad of ways scientific findings may positively or negatively influence society. However, when it comes to working with human-derived data, or even data that can greatly affect human lives, these damages can be mitigated by staying well informed about the populations from which the data are collected and using ethical and contextual discernment when interpreting, communicating, and utilizing these data. As data scientists, it is our job to have ethics and morality as a basis by which we make these decisions around data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[^*]: Andrade, Chittaranjan. “HARKing, Cherry-Picking, P-Hacking, Fishing Expeditions, and Data Dredging and Mining as Questionable Research Practices.” The Journal of Clinical Psychiatry, vol. 82, no. 1, Feb. 2021, p. 20f13804, https://doi.org/10.4088/JCP.20f13804.\n",
-    "\n",
-    "[^**]: Lin, D., Crabtree, J., Dillo, I. et al. The TRUST Principles for digital repositories. Sci Data 7, 144 (2020). https://doi.org/10.1038/s41597-020-0486-7\n",
-    "\n",
-    "[^***]: Atske, Sara. “2. Americans Concerned, Feel Lack of Control over Personal Data Collected by Both Companies and the Government.” Pew Research Center: Internet, Science & Tech, 15 Nov. 2019, https://www.pewresearch.org/internet/2019/11/15/americans-concerned-feel-lack-of-control-over-personal-data-collected-by-both-companies-and-the-government/.\n",
-    "\n",
-    "[^****]: Robles, Pedro, and Daniel J. Mallinson. “Artificial Intelligence Technology, Public Trust, and Effective Governance.” Review of Policy Research, May 2023, p. ropr.12555, https://doi.org/10.1111/ropr.12555.\n",
-    "\n",
-    "[^*****]: Mathews, Lee. “Equifax Data Breach Impacts 143 Million Americans.” Forbes, https://www.forbes.com/sites/leemathews/2017/09/07/equifax-data-breach-impacts-143-million-americans/. Accessed 31 July 2023.\n",
-    "\n",
-    "[^******]: U.S. Department of Homeland Security. Increasing Threat of DEEPFAKE Identities. Accessed 31 July 2023. https://www.dhs.gov/sites/default/files/publications/increasing_threats_of_deepfake_identities_0.pdf\n",
-    "\n",
-    "[^*******]: “Americans Widely Distrust Facebook, TikTok and Instagram with Their Data, Poll Finds.” Washington Post, 22 Dec. 2021, https://www.washingtonpost.com/technology/2021/12/22/tech-trust-survey/.\n",
-    "\n",
-    "[^********]: Nadeem, Reem. “Public Awareness of Artificial Intelligence in Everyday Activities.” Pew Research Center Science & Society, 15 Feb. 2023, https://www.pewresearch.org/science/2023/02/15/public-awareness-of-artificial-intelligence-in-everyday-activities/.\n",
-    "\n",
-    "[^**********]: Morey, Timothy, et al. “Customer Data: Designing for Transparency and Trust.” Harvard Business Review, 1 May 2015, https://hbr.org/2015/05/customer-data-designing-for-transparency-and-trust.\n",
-    "\n",
-    "[^***********]: Virginia Dignum. The role and challenges of education for responsible AI. London Review of Education. 2021. Vol. 19(1). DOI: 10.14324/LRE.19.1.01\n",
-    "\n",
-    "[^***********]: Umang Bhatt, Alice Xiang, Shubham Sharma, Adrian Weller, Ankur Taly, Yunhan Jia, Joydeep Ghosh, Ruchir Puri, José M. F. Moura, and Peter Eckersley. 2020. Explainable machine learning in deployment. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). Association for Computing Machinery, New York, NY, USA, 648–657. https://doi.org/10.1145/3351095.3375624\n",
-    "\n",
-    "[^************]: Burrell, J. (2016). How the machine ‘thinks’: Understanding opacity in machine learning algorithms. Big Data & Society, 3(1). https://doi.org/10.1177/2053951715622512\n",
+    "[^*]: General Assembly. \"Preservation of Racial Integrity (1924)\" Encyclopedia Virginia. Virginia Humanities, (07 Dec. 2020). Web. 01 Dec. 2022\n",
     "\n",
-    "[^*************]: Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021 Nov;3(11):e745-e750. doi: 10.1016/S2589-7500(21)00208-9. PMID: 34711379.\n",
+    "[^**]: BUCK v. BELL. 2 May 1927, https://www.loc.gov/item/usrep274200/.\n",
     "\n",
-    "[^**************]: Rudin C. Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead. Nat Mach Intell. 2019 May;1(5):206-215. doi: 10.1038/s42256-019-0048-x. Epub 2019 May 13. PMID: 35603010; PMCID: PMC9122117.\n",
+    "[^***]: Laws of Indiana, 1907, pp. 377-78 (B050823). https://www.in.gov/history/state-historical-markers/find-a-marker/1907-indiana-eugenics-law/\n",
     "\n",
-    "[^***************]: Chaofan Chen, Oscar Li, Chaofan Tao, Alina Jade Barnett, Jonathan Su, and Cynthia Rudin. 2019. This looks like that: deep learning for interpretable image recognition. Proceedings of the 33rd International Conference on Neural Information Processing Systems. Curran Associates Inc., Red Hook, NY, USA, Article 801, 8930–8941.\n",
+    "[^****]: Reilly, Philip R. “Eugenics and Involuntary Sterilization: 1907-2015.” Annual Review of Genomics and Human Genetics, vol. 16, 2015, pp. 351–68, https://doi.org/10.1146/annurev-genom-090314-024930.\n",
     "\n",
-    "[^****************]: Barnett, A.J., Schwartz, F.R., Tao, C. et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat Mach Intell 3, 1061–1070 (2021). https://doi.org/10.1038/s42256-021-00423-x\n",
+    "[^*****]: Office of the New York State Attorney General. Investigative Report on the role of online platforms in the tragic mass shooting in Buffalo on May 14, 2022. Published OCTOBER 18, 2022. https://ag.ny.gov/sites/default/files/buffaloshooting-onlineplatformsreport.pdf\n",
     "\n",
-    "[^*****************]: Biran, Or and Courtenay V. Cotton. “Explanation and Justification in Machine Learning : A Survey Or.” (2017).\n",
+    "[^******]: Angwin, Julia, et al. “Machine bias: There’s software used across the country to predict future criminals. And it’s biased against blacks,” ProPublica, 23 May 2016, www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.\n",
     "\n",
-    "[^******************]: “More Evidence That Nutrition Studies Don’t Always Add Up.” The New York Times, 29 Sept. 2018, https://www.nytimes.com/2018/09/29/sunday-review/cornell-food-scientist-wansink-misconduct.html.\n",
+    "[^*******]: Dieterich, William, et al. \"COMPAS Risk Scales: Demonstrating\n",
+    "Accuracy Equity and Predictive Parity,\" 8 July 2016, https://go.volarisgroup.com/rs/430-MBX-989/images/ProPublica_Commentary_Final_070616.pdf.\n",
     "\n",
+    "[^********]: Flores, Anthony, et al. \"False Positives, False Negatives, and False Analyses: A Rejoinder to 'Machine Bias: There's Software Used Across the Country to Predict Future Criminals. And It's Biased Against Blacks.'\" Federal Probation, Vol. 80 Number 2, September 2016, https://www.uscourts.gov/federal-probation-journal/2016/09/false-positives-false-negatives-and-false-analyses-rejoinder.\n",
     "\n",
-    "[^*******************]: Benjamini, Yoav, and Yosef Hochberg. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” Journal of the Royal Statistical Society. Series B (Methodological), vol. 57, no. 1, 1995, pp. 289–300. JSTOR, http://www.jstor.org/stable/2346101. Accessed 22 Aug. 2023.\n",
+    "[^*********]: Corbett-Davies, Sam, and Sharad Goel. The Measure and Mismeasure of Fairness: A Critical Review of Fair Machine Learning. arXiv, 14 Aug. 2018, https://arxiv.org/abs/1808.00023.\n",
     "\n",
-    "[^********************]: Zbyněk Šidák (1967) Rectangular Confidence Regions for the Means of Multivariate Normal Distributions, Journal of the American Statistical Association, 62:318, 626-633, DOI: 10.1080/01621459.1967.10482935\n",
+    "[^**********]: Pleiss, Geoff, et al. “On Fairness and Calibration.” Advances in Neural Information Processing Systems, vol. 30, Curran Associates, Inc., 2017, https://papers.nips.cc/paper/2017/hash/b8b9c74ac526fffbeb2d39ab038d1cd7-Abstract.html.\n",
     "\n",
-    "[^*********************]: Code of Federal Regulations. Title 45, Part 689 - Research Misconduct. 18 Mar. 2002. https://www.ecfr.gov/current/title-45/subtitle-B/chapter-VI/part-689#.\n",
+    "[^***********]: Dressel, Julia and Farid, Hany. “The Accuracy, Fairness, and Limits of Predicting Recidivism.” Science Advances, vol. 4, no. 1, Jan. 2018, p. eaao5580, https://doi.org/10.1126/sciadv.aao5580.\n",
     "\n",
-    "[^**********************]: \"Professor accused of faking data in studies on dishonesty sues Harvard.\" Washington Post, 3 Aug. 2023, https://www.washingtonpost.com/education/2023/08/03/harvard-honesty-lawsuit-research-misconduct/.\n",
+    "[^************]: Eberhardt, Jennifer. L. “Strategies for change: Research initiatives and recommendations to improve police- community relations in Oakland, Calif.” 2016. Stanford University, SPARQ: Social Psychological Answers to Real-world Questions. \n",
     "\n",
-    "[^***********************]: \"An Influential Study Of Dishonesty Was Dishonest.\" Forbes, 30 Aug. 2021. https://www.forbes.com/sites/christianmiller/2021/08/30/an-influential-study-of-dishonesty-was-dishonest/?sh=6e9e992c2c72."
+    "[^*************]: Jeffrey J. Rachlinski & Sheri L. Johnson, Does Unconscious Racial Bias Affect Trial Judges, 84 Notre Dame L. Rev. 1195 (2009). Available at: http://scholarship.law.nd.edu/ndlr/vol84/iss3/4.\n"
    ]
   },
   {
diff --git a/textbook/16/1/background.ipynb b/textbook/16/1/background.ipynb
index 7c7a0ead..9455b2c6 100644
--- a/textbook/16/1/background.ipynb
+++ b/textbook/16/1/background.ipynb
@@ -8,7 +8,9 @@
     "\n",
     "Beginning in 2004, the Illlinois Department of Transportation (IDOT) began a statistical study of traffic and pedestrian stops throughout Illinois [^*]. This study has been extended multiple times and continues as of the writing of this chapter. After the institution of the Racial Profiling Prevention and Data Oversight Board in 2008, the collection of racial profiling information as a part of this study was improved and became an additional focus of the study [^**].\n",
     "\n",
-    "In our investigation, we will use data for traffic stops in 2020 in the City of Chicago collected by IDOT and used for Illinois Traffic and Pedestrian Stop Study (made available by request [^***]). We will conduct an analysis similar to that done in the IDOT statistical study called **benchmarking**. Benchmarking involves comparing proportions of the population who are stopped by police to the proportions of the total population those groups make up. The idea is that if 30% of those who are stopped by police are Hispanic or Latino and the population of the area is also 30% Hispanic or Latino, there is no bias present. However, if the population of the area is only 10% Hispanic or Latino, this group is being stopped as a rate disproportionate to its population proportion indicating potential bias.\n",
+    "In our investigation, we will use data for traffic stops in 2020 in the City of Chicago collected by IDOT and used for Illinois Traffic and Pedestrian Stop Study (made available by request [^***]). The subset of data used here includes 7 variables collected by the officer at the time of the traffic stop including: when and where the stop was made, the reason for the stop, the perceived race of the driver, whether the officer searched the car or its passengers, and, if so, whether the officer found any contraband during that search.\n",
+    "\n",
+    "We will conduct an analysis similar to that done in the IDOT statistical study called **benchmarking**. Benchmarking involves comparing proportions of the population who are stopped by police to the proportions of the local population those groups make up. The idea is that if 30% of those who are stopped by police are Hispanic or Latino and the population of the area is also 30% Hispanic or Latino, there is no bias present. However, if the population of the area is only 10% Hispanic or Latino, this group is being stopped as a rate disproportionate to its population proportion indicating potential bias.\n",
     "\n",
     "Benchmarking works well only if the benchmark used for comparison is accurate. In this case, we are considering drivers who are stopped by police. The best comparison to use would be the total population of *drivers*. However, this information is not freely available. It can, however, be estimated using additional data and knowledge of probability. IDOT did exactly this in their recent 2019 and 2020 traffic stop report which can be found [here](https://idot.illinois.gov/transportation-system/local-transportation-partners/law-enforcement/reporting/illinois-traffic-and-pedestrian-stop-study/studies.html). We have already done this estimation and will be using the estimated population of drivers by race for our investigation."
    ]
diff --git a/textbook/16/2/investigation.ipynb b/textbook/16/2/investigation.ipynb
index e4dbc829..f8a4c3db 100644
--- a/textbook/16/2/investigation.ipynb
+++ b/textbook/16/2/investigation.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 28,
    "metadata": {
     "tags": [
      "remove-cell"
@@ -10,7 +10,12 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt\n",
+    "plt.style.use('fivethirtyeight')"
    ]
   },
   {
@@ -24,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -121,19 +126,19 @@
        "4  2019     moving           0           0    hispanic  2423        24"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "idot = pd.read_csv('./idot_abbrv.csv')\n",
+    "idot = pd.read_csv('../../data/idot_abbrv.csv')\n",
     "idot.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +147,7 @@
        "(925805, 7)"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -155,12 +160,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Our dataset has 7 columns and 925,805 rows where each row is a traffic stop that occurred in Chicago."
+    "Our dataset has 7 columns and 925,805 rows where each row is a traffic stop that occurred in Chicago. \n",
+    "\n",
+    "The 7 variables can be described as follows:\n",
+    "- `year` - the year the traffic stop happened\n",
+    "- `reason` - the reason for the stop, one of: moving, equipment, liscence, or none\n",
+    "- `any_search` - a boolean variable indicating whether (1) or not (0) the car or any of its passengers were searched during the traffic stop\n",
+    "- `search_hit` - a boolean variable indicating whether (1) or not (0) anything illegal was found during a search (value is 0 if no search was conducted)\n",
+    "- `driver_race` - perceived race of the driver as reported by the officer, one of: black, white, hispanic, asian, am_indian, or other\n",
+    "- `beat` - the police beat in which the traffic stop occurred (first 2 digits indicate the surrounding police district)\n",
+    "- `district` - the police district in which the traffic stop occurred\n",
+    "\n",
+    "Let's look at these variables more closely."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -169,7 +185,7 @@
        "array([2019, 2020])"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,7 +203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -284,7 +300,7 @@
        "4  2020     moving           0           0       black  2011        20"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -296,7 +312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -305,7 +321,7 @@
        "(327290, 7)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -323,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +372,7 @@
        "Name: reason, dtype: int64"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,12 +385,57 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "So, there were 15,905 traffic stops made in the district surrounding Hyde Park in 2020. We can also investigate the reason for the traffic stops."
+    "So, there were 15,905 traffic stops made in the district surrounding Hyde Park in 2020. \n",
+    "\n",
+    "We can create a histogram to see the distribution of numbers of stops per police beat."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Distribution of Traffice Stops By Beat')"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hist(idot_20.groupby(\"beat\")[\"reason\"].count());\n",
+    "plt.xlabel(\"Number of Traffic Stops\")\n",
+    "plt.ylabel(\"Frequency\")\n",
+    "plt.title(\"Distribution of Traffice Stops By Beat\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This shows that there is a wide range of numbers of stops in each police beat, with one beat having around 6,000 stops in 2020.\n",
+    "\n",
+    "\n",
+    "We can also investigate the reason for the traffic stops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -388,7 +449,7 @@
        "Name: district, dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -415,7 +476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -545,7 +606,7 @@
        "[327290 rows x 4 columns]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -561,12 +622,12 @@
    "source": [
     "Recall, we also could have selected only the columns we wanted instead of dropping the columns we didn't need (see [Chapter 6](../../06/DataFrames.ipynb)).\n",
     "\n",
-    "Now, let's group the data by `driver_race` and count how many drivers of each race were stopped."
+    "Now, let's group the data by `driver_race` and count how many drivers of each race were stopped in 2020."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -639,7 +700,7 @@
        "5       white        35053"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -653,12 +714,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "From this table, it certainly looks like more Black and Hispanic drivers were stopped in 2020 than drivers of other races. However, we are only looking at raw counts. It will be easier to see a pattern if we convert these counts to a proportion of total stops."
+    "From this table, it certainly looks like more Black and Hispanic drivers were stopped in 2020 than drivers of other races. However, we are only looking at raw counts. It will be easier to see a pattern if we convert these counts to a proportion of total stops and then visualize the data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -738,7 +799,7 @@
        "5       white        35053      0.107101"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,11 +809,34 @@
     "stopped"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(stopped['driver_race'], stopped[\"prop_stopped\"])\n",
+    "plt.xlabel(\"Driver's Race\")\n",
+    "plt.ylabel(\"Proportion of Stops\")\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now, we can see that 62% of drivers stopped were Black, 24% were Hispanic, and 11% were White."
+    "From this figure, we can see that approximately 62% of drivers stopped were Black, 24% were Hispanic, and 11% were White."
    ]
   },
   {
@@ -766,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -863,13 +947,13 @@
        "4  1913   739.593202  2429.896179   535.721544  121.083960  159.015646    0.0"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pop = pd.read_csv('./adjusted_population_beat.csv')\n",
+    "pop = pd.read_csv('../../data/adjusted_population_beat.csv')\n",
     "pop.head()"
    ]
   },
@@ -882,7 +966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -961,7 +1045,7 @@
        "6       Other     212.537290"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -980,7 +1064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -1006,7 +1090,6 @@
        "      <th></th>\n",
        "      <th>driver_race</th>\n",
        "      <th>est_pop</th>\n",
-       "      <th>prop_pop</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1014,53 +1097,47 @@
        "      <th>1</th>\n",
        "      <td>White</td>\n",
        "      <td>298755.197477</td>\n",
-       "      <td>0.255627</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>Black</td>\n",
        "      <td>248709.530847</td>\n",
-       "      <td>0.212806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>Hispanic</td>\n",
        "      <td>222341.224432</td>\n",
-       "      <td>0.190244</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>Asian</td>\n",
        "      <td>60069.768541</td>\n",
-       "      <td>0.051398</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>Native</td>\n",
        "      <td>2664.876752</td>\n",
-       "      <td>0.002280</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>Other</td>\n",
        "      <td>212.537290</td>\n",
-       "      <td>0.000182</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  driver_race        est_pop  prop_pop\n",
-       "1       White  298755.197477  0.255627\n",
-       "2       Black  248709.530847  0.212806\n",
-       "3    Hispanic  222341.224432  0.190244\n",
-       "4       Asian   60069.768541  0.051398\n",
-       "5      Native    2664.876752  0.002280\n",
-       "6       Other     212.537290  0.000182"
+       "  driver_race        est_pop\n",
+       "1       White  298755.197477\n",
+       "2       Black  248709.530847\n",
+       "3    Hispanic  222341.224432\n",
+       "4       Asian   60069.768541\n",
+       "5      Native    2664.876752\n",
+       "6       Other     212.537290"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1079,7 +1156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1159,7 +1236,7 @@
        "6       Other     212.537290  0.000255"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1173,12 +1250,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We want to compare these proportions with the proportion of stops we calculated above. This is easier to do if they are in the same DataFrame. We can combine them using `merge`, but first, we need to make sure each race has the same name in both DataFrames. One data frame has races capitalized, while the other is all lowercase. Also, one data frame uses the shortened term 'native' while the other uses 'am_indian'. Let's use `.apply` and `.replace` to fix this."
+    "We want to compare these proportions with the proportion of stops we calculated above. This is easier to do if they are in the same DataFrame. We can combine them using `merge`, but first, we need to make sure each race has the same name in both DataFrames. One DataFrame has races capitalized, while the other is all lowercase. Also, one DataFrame uses the shortened term 'native' while the other uses 'am_indian'. Let's use `.apply` and `.replace` to fix this."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1258,19 +1335,30 @@
        "6       other     212.537290  0.000255"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "pop_total['driver_race'] = pop_total['driver_race'].apply(lambda x: x.lower())\n",
+    "def to_lowercase(x):\n",
+    "    '''A function that takes in a string and returns that string converted to all lowercase letters'''\n",
+    "    return x.lower()\n",
+    "\n",
+    "pop_total['driver_race'] = pop_total['driver_race'].apply(to_lowercase)\n",
     "pop_total"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `.replace` method can be used along with a dictionary to map values from a series onto different values. In this case,  we are mapping all occurrences of 'native' to 'am_indian'."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
@@ -1350,7 +1438,7 @@
        "6       other     212.537290  0.000255"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1364,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1458,7 +1546,7 @@
        "5       white        35053      0.107101  298755.197477  0.358756"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1468,11 +1556,49 @@
     "stops_vs_pop"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "x = np.arange(len(stops_vs_pop.driver_race))  # the label locations\n",
+    "width = 0.44  # the width of the bars\n",
+    "\n",
+    "fig, ax = plt.subplots()\n",
+    "\n",
+    "rects1 = ax.bar(x, round(stops_vs_pop.prop_stopped,2), width, label=\"Proportion of Stops\")\n",
+    "ax.bar_label(rects1, padding=4)\n",
+    "\n",
+    "rects2 = ax.bar(x + width, round(stops_vs_pop.prop_pop,2), width, label=\"Proportion of Population\")\n",
+    "ax.bar_label(rects2, padding=4)\n",
+    "\n",
+    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
+    "ax.set_ylabel('Proportion of Stops/Population')\n",
+    "ax.set_xlabel(\"Driver's Race\")\n",
+    "ax.set_title('Traffic Stops by Race')\n",
+    "ax.set_xticks(x + width/2, stops_vs_pop.driver_race)\n",
+    "ax.legend(loc=4, bbox_to_anchor=(1.3, 0.7))\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Using this new table, we can see that American Indian, Hispanic, and Other drivers are all stopped at a proportion that is similar to the proportion of the population they make up. However, Black drivers are stopped twice as often as they should be according to their population, and Asian and White drivers are stopped at rates less than their proportion of the population."
+    "Using this figure, we can see that American Indian, Hispanic, and Other drivers are all stopped at a proportion that is similar to the proportion of the population they make up. However, Black drivers are stopped twice as often as they should be according to their population, and Asian and White drivers are stopped at rates less than their proportion of the population."
    ]
   },
   {
@@ -1481,12 +1607,12 @@
    "source": [
     "## Searches\n",
     "\n",
-    "Our data doesn't just have information on stops. It also has information on whether the driver or car were searched during the traffic stop. Let's investigate this by race as well."
+    "Our data doesn't just have information on stops. It also has information on whether the driver or car were searched during the traffic stop. Let's investigate searches and hits by race as well. This type of investigation is called an **outcomes analysis** since we are comparing the outcomes (hits) of searches. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -1566,7 +1692,7 @@
        "5       white           190       66"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1585,7 +1711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1612,7 +1738,6 @@
        "      <th>driver_race</th>\n",
        "      <th>num_searched</th>\n",
        "      <th>num_hit</th>\n",
-       "      <th>prop_hit</th>\n",
        "      <th>prop_searched</th>\n",
        "    </tr>\n",
        "  </thead>\n",
@@ -1622,7 +1747,6 @@
        "      <td>am_indian</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.333333</td>\n",
        "      <td>0.000590</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1630,7 +1754,6 @@
        "      <td>asian</td>\n",
        "      <td>31</td>\n",
        "      <td>12</td>\n",
-       "      <td>0.387097</td>\n",
        "      <td>0.006101</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1638,7 +1761,6 @@
        "      <td>black</td>\n",
        "      <td>3712</td>\n",
        "      <td>980</td>\n",
-       "      <td>0.264009</td>\n",
        "      <td>0.730565</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1646,7 +1768,6 @@
        "      <td>hispanic</td>\n",
        "      <td>1140</td>\n",
        "      <td>317</td>\n",
-       "      <td>0.278070</td>\n",
        "      <td>0.224365</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1654,7 +1775,6 @@
        "      <td>other</td>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.600000</td>\n",
        "      <td>0.000984</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -1662,7 +1782,6 @@
        "      <td>white</td>\n",
        "      <td>190</td>\n",
        "      <td>66</td>\n",
-       "      <td>0.347368</td>\n",
        "      <td>0.037394</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -1670,16 +1789,16 @@
        "</div>"
       ],
       "text/plain": [
-       "  driver_race  num_searched  num_hit  prop_hit  prop_searched\n",
-       "0   am_indian             3        1  0.333333       0.000590\n",
-       "1       asian            31       12  0.387097       0.006101\n",
-       "2       black          3712      980  0.264009       0.730565\n",
-       "3    hispanic          1140      317  0.278070       0.224365\n",
-       "4       other             5        3  0.600000       0.000984\n",
-       "5       white           190       66  0.347368       0.037394"
+       "  driver_race  num_searched  num_hit  prop_searched\n",
+       "0   am_indian             3        1       0.000590\n",
+       "1       asian            31       12       0.006101\n",
+       "2       black          3712      980       0.730565\n",
+       "3    hispanic          1140      317       0.224365\n",
+       "4       other             5        3       0.000984\n",
+       "5       white           190       66       0.037394"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1691,7 +1810,39 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(searched['driver_race'], searched[\"prop_searched\"])\n",
+    "plt.xlabel(\"Driver's Race\")\n",
+    "plt.ylabel(\"Proportion of Searches\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see from this barchart that Black drivers are searched much more often than drivers of any other race. Over 70% of searches conducted in 2020 were of Black drivers!\n",
+    "\n",
+    "Are these searches successful? If these were necessary searches, officers should be finding contraband more often when searching Black drivers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
@@ -1718,8 +1869,8 @@
        "      <th>driver_race</th>\n",
        "      <th>num_searched</th>\n",
        "      <th>num_hit</th>\n",
-       "      <th>prop_hit</th>\n",
        "      <th>prop_searched</th>\n",
+       "      <th>prop_hit</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -1728,64 +1879,64 @@
        "      <td>am_indian</td>\n",
        "      <td>3</td>\n",
        "      <td>1</td>\n",
-       "      <td>0.333333</td>\n",
        "      <td>0.000590</td>\n",
+       "      <td>0.333333</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>asian</td>\n",
        "      <td>31</td>\n",
        "      <td>12</td>\n",
-       "      <td>0.387097</td>\n",
        "      <td>0.006101</td>\n",
+       "      <td>0.387097</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>black</td>\n",
        "      <td>3712</td>\n",
        "      <td>980</td>\n",
-       "      <td>0.264009</td>\n",
        "      <td>0.730565</td>\n",
+       "      <td>0.264009</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>hispanic</td>\n",
        "      <td>1140</td>\n",
        "      <td>317</td>\n",
-       "      <td>0.278070</td>\n",
        "      <td>0.224365</td>\n",
+       "      <td>0.278070</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>other</td>\n",
        "      <td>5</td>\n",
        "      <td>3</td>\n",
-       "      <td>0.600000</td>\n",
        "      <td>0.000984</td>\n",
+       "      <td>0.600000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td>white</td>\n",
        "      <td>190</td>\n",
        "      <td>66</td>\n",
-       "      <td>0.347368</td>\n",
        "      <td>0.037394</td>\n",
+       "      <td>0.347368</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
-       "  driver_race  num_searched  num_hit  prop_hit  prop_searched\n",
-       "0   am_indian             3        1  0.333333       0.000590\n",
-       "1       asian            31       12  0.387097       0.006101\n",
-       "2       black          3712      980  0.264009       0.730565\n",
-       "3    hispanic          1140      317  0.278070       0.224365\n",
-       "4       other             5        3  0.600000       0.000984\n",
-       "5       white           190       66  0.347368       0.037394"
+       "  driver_race  num_searched  num_hit  prop_searched  prop_hit\n",
+       "0   am_indian             3        1       0.000590  0.333333\n",
+       "1       asian            31       12       0.006101  0.387097\n",
+       "2       black          3712      980       0.730565  0.264009\n",
+       "3    hispanic          1140      317       0.224365  0.278070\n",
+       "4       other             5        3       0.000984  0.600000\n",
+       "5       white           190       66       0.037394  0.347368"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1795,11 +1946,186 @@
     "searched"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(searched['driver_race'], searched[\"prop_hit\"])\n",
+    "plt.xlabel(\"Driver's Race\")\n",
+    "plt.ylabel(\"Proportion of Successful Searches\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "American Indian, Asian, and other drivers all have small numbers of searches. Focusing on the 3 races with enough searches to analyze, we see that, despite being searched more often, searches of Black drivers result in  a hit slightly less often than searches of White drivers. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We see a slightly taller bar for White drivers in the previous barchart. It would be interesting to know if contraband is found on White drivers significantly more often than Black drivers. Let's add a confidence interval to the point estimates shown in the plot. We will focus on the three most common races in our data: Black, Hispanic, and White."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First, we create 5000 bootstrap samples and calculate hit proportions for White, Black, and Hispanic drivers in each sample."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "props_white = np.array([])\n",
+    "props_black = np.array([])\n",
+    "props_hispanic = np.array([])\n",
+    "for i in np.arange(5000):\n",
+    "    bootstrap_sample = idot_20_abbrv.sample(len(idot_20_abbrv),replace=True)\n",
+    "    searched = bootstrap_sample.groupby(\"driver_race\")[[\"any_search\",\"search_hit\"]].sum().reset_index().rename(columns={\"any_search\":\"num_searched\", \"search_hit\":\"num_hit\"})\n",
+    "    searched['prop_hit'] = searched['num_hit'] / searched['num_searched']\n",
+    "    resampled_white = searched[searched.driver_race == \"white\"][\"prop_hit\"]\n",
+    "    resampled_black = searched[searched.driver_race == \"black\"][\"prop_hit\"]\n",
+    "    resampled_hispanic = searched[searched.driver_race == \"hispanic\"][\"prop_hit\"]\n",
+    "    props_white = np.append(props_white, resampled_white)\n",
+    "    props_black = np.append(props_black, resampled_black)\n",
+    "    props_hispanic = np.append(props_hispanic, resampled_hispanic)\n",
+    "\n",
+    "white_ci = np.percentile(props_white, [2.5,97.5])\n",
+    "black_ci = np.percentile(props_black, [2.5,97.5])\n",
+    "hispanic_ci = np.percentile(props_hispanic, [2.5,97.5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that we have our 3 95% bootstrap confidence intervals, we need to get them in the correct format to make error bars on our graph. The method `.T` transposes a 2-D array or matrix. In this case, we needed all lower limits in one row and all upper limits in another, so we transposed our array and then made it back into a list. The argument that creates error bars also needs the upper and lower limits in the format of distances from the top of the bar in a barchart so we subtract to find those distances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2    0.017515\n",
+       "3    0.005375\n",
+       "5    0.076838\n",
+       "Name: prop_hit, dtype: float64"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "searched3 = searched.loc[searched.driver_race.isin(['white','black','hispanic'])]\n",
+    "\n",
+    "errors = np.array([black_ci, hispanic_ci, white_ci]).T.tolist()\n",
+    "low = searched3[\"prop_hit\"] - errors[0]\n",
+    "high = errors[1] - searched3[\"prop_hit\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can plot our barchart and include the confidence interval we calculated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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IiDRJcNJobGwss+jF1NQUAJCYmChXV09PD0+fPhXaFQDg6tWrGDZsGGxtbWFlZYVevXohMDBQ6fNDQkLw2WefoX379rC1tUW9evXQvn17TJs2DQ8fPlR4jlgsLvHP5MmTVboeIiIioqpE8O1pKysrJCcnS79u3Lgxjh8/jrCwMHz00UfS8pSUFDx8+BA1a9YUHGR4eDiGDBkCfX19DB48GEZGRggKCoKPjw8SExMxffr0Mts4efIkLl++jHbt2qFXr17Q1dXFvXv3EBAQgH379mHv3r1wc3OTO8/GxgajRo2SK2/RooXg6yEiIiKqagQnjR07dsSWLVuQkpICCwsLDBgwAKtWrcL8+fOhq6uLzp07IyUlBQsWLEBeXh569eolqJ+CggJ8/fXX0NLSwtGjR9GyZUsAwKxZs+Du7o6FCxfCy8sLtra2pbazcOFCLFu2TK787Nmz8PLyws8//4zTp0/LHbe1tcXcuXMFxU5ERERUXQi+Pd2nTx8UFRXhxIkTAIA2bdrgk08+wevXr/Hdd9/BxcUFgwcPxrVr12BoaIjvv/9eUD/h4eGIi4vD0KFDpQkj8PZ2uK+vL/Ly8hAQEFBmO/r6+grL3dzcIBaLERsbKyg+IiIiog+B4JnG3r17IzExEXp6etKy9evXo0mTJggICMDjx49Rs2ZNuLi44Pvvv0fTpk0F9XP+/HkAQM+ePeWOubu7AwAiIiIEtQ0Aly5dQkZGBjp37qzweGZmJrZu3Yq0tDTUqlULHTt2RLNmzQT3R0RERFQVqbQjjKGhoczX2tra+Oabb/DNN9+oFNS7YmJiAACOjo5yxywsLGBkZFSuWcKwsDBERUUhLy8PMTExOHHiBMzMzEp8HdCtW7cwY8YMmbJevXphw4YNMDc3V6rP3NxcpeMj9cnLy5P5L1FVxzFN9OGp6ByipDuxiqi893RFy8rKAgCYmJgoPG5sbCyto4ywsDCsXbtW+rWDgwO2bNmCVq1aydWdNm0aBg4ciIYNG0JXVxf//PMPli1bhpCQEAwfPhwhISHSVw2V5smTJygsLFQ6RlKvlJQUTYdApFYc01WNgaYDoCosISGhwtrW1taGg4OD0vXVkjQWFBTg+vXrSEpKwqtXrzBy5Eh1NFshFi1ahEWLFiEnJwf37t3Dr7/+ir59+2Lt2rUYNmyYXN13dejQAXv27IGnpyciIiJw9OjRUl9uXszKykqt10DKycvLky7UevcxCqKqimO6qkrTdABUhdnY2Gg6BCmVkkaJRIIVK1Zg3bp1yMzMlJa/mzR+9dVXOHv2LA4ePIgGDRqUu4/iGcaSZhOzs7MhFovL3a6RkRHatm2LnTt3onv37pgxYwZ69OiBOnXqlHqelpYWxo0bh4iICERFRSmVNJZn6pfUT09Pj/8PqFrhmCb6cFSmz7rg1dMSiQSffvopFi9ejMzMTNjY2MDIyEiunru7O+Lj43HkyBFB/RQ/y1j8bOO7UlJSkJOTU66p1X/T0dFBt27d8PLlS1y7dk2pc8zMzAAAr169EtwvERERUVUiOGkMCAjAkSNH0KBBA5w+fRo3btxQuKq4V69e0NLSQkhIiKB+XFxcALx9FvHfQkNDZeoIVfyScl1dXaXqX7lyBQDKfDckERERUXUhOGncsWMHRCIRNm/eDGdn5xLrGRoaws7ODvfv3xfUj5ubG+zt7bF//35ER0dLyzMzM7Fy5Uro6elhxIgR0vLk5GTcv39f5nY5gBJnEUNDQ3HkyBGYmpqiffv20vLbt28jPz9frn5UVBT++9//QldXF97e3oKuiYiIiKiqEfxM4+3bt2FlZaVw1fG/mZmZKdyTWhk6OjpYvXo1hgwZAg8PD5ltBBMSErBw4ULY2dlJ68+fPx8BAQFYt24dRo8eLS3v0aMHnJyc0KxZM1hZWeHVq1e4desWIiMjoauri7Vr18q8Qmjt2rU4efIkOnXqBGtra+jq6uLu3bsICwuDSCTC8uXLBT2jSURERFQVCU4a37x5I5OslSY3Nxc1atQQ2hVcXV0RHByMJUuWIDAwEPn5+XBycsL8+fMxePBgpdr48ccfce7cOUREROD58+fQ0tJC/fr1MX78eEyePBmNGzeWqd+/f39kZmbi1q1bOHPmDPLy8mBhYYEhQ4Zg8uTJaNu2reDrISIiIqpqRBkZGRIhJ7Zs2RIZGRmIj4+XlvXr1w9RUVFIT0+XluXm5qJBgwawt7dHZGSk6hETKSk3NxcJCQmwsbGpVKvPiITimK6axP5Jmg6h8vltFPD4hmyZnTMwY5dm4qnEMnysNR2ClOBnGrt06YKcnBzs3bu31Hr+/v7Izc1Ft27dhHZFRERERBomOGmcPHkyAGD27Nk4duyYwjrbt2/H/Pnzoauri4kTJwrtioiIiIg0TPAzjc7Ozvj555/x008/YcyYMbCwsMDr168BAAMGDMDdu3eRnp4OiUSCpUuXomHDhmoLmoiIiIjeL8EzjcDb3V42b94Ma2trJCcnIzMzExKJBBEREUhLS4OlpSU2btyISZMmqSteIiIiItIAlfeeHjx4MLy8vHDlyhXcunULGRkZMDQ0hJOTEzp37qz0C7OJiIiIqPJSOWkEAG1tbXTs2BEdO3ZUR3NEREREVMmodHuaiIiIiD4Mak0ar1y5gmHDhsHe3h7W1tZwdXXF1q1b1dkFEREREWmA0knjiRMnYG5uDk9PT4XHw8LC4OHhgdDQUGRmZuLVq1e4efMmfH19MWvWLLUFTERERETvn9JJ44ULF1BQUCCzn3OxvLw8fPXVV8jLy4O+vj6mTp2KlStXYtiwYZBIJNi0aRMuXbqk1sCJiIiI6P1ReiHMpUuXIBKJ8PHHH8sdO3HiBJKSkiASibBr1y50794dAODj4wNLS0usWbMGu3btQocOHdQWOBERERG9P0rPND558gS2trYQi8Vyx8LCwgAA7du3lyaMxaZPnw4dHR3ONBIRERFVYUonjWlpaTA3N1d47MqVKxCJROjVq5fcMXNzc1hZWSExMVF4lERERESkUUonjQUFBcjMzJQrz8vLw7179wAAbdq0UXiuubk5Xr16JTBEIiIiItI0pZNGc3NzJCYm4uXLlzLlV65cQX5+PoCSk8acnBwYGBioECYRERERaZLSSWPbtm2Rm5sLf39/mfKdO3cCAJo1a4ZatWrJnZefn4+4uDhYW1urGCoRERERaYrSq6fHjBmDQ4cOYcGCBYiPj4eTkxMiIyOxd+9eiEQijBo1SuF5kZGRyMvLQ8uWLdUWNBERERG9X0onjb169cLw4cOxZ88ebNq0SeZYkyZN8Nlnnyk8b/fu3RCJROjRo4dqkRIRERGRxpRrG8H169dj4cKFaNSoEXR1dVG7dm2MGjUKQUFBqFGjhlz91NRUnD59GrVr12bSSERERFSFKT3TCABaWlqYNm0apk2bplR9c3Nz/PPPP4ICIyIiIqLKo1wzjURERET0YWLSSERERERlYtJIRERERGVi0khEREREZWLSSERERERlYtJIRERERGVi0khEREREZWLSSERERERlYtJIRERERGVi0khEREREZVJqG0FnZ2eVOxKJRLh+/brg869evYolS5YgKioKBQUFcHJywtSpUzFo0CClzg8JCUFAQABu3ryJlJQU5Ofno379+ujYsSNmzJiBhg0bKjwvNDQUK1asQHR0NEQiEZydnTFz5ky4ubkJvhYiIiKiqkappDE+Pl7ljkQikeBzw8PDMWTIEOjr62Pw4MEwMjJCUFAQfHx8kJiYiOnTp5fZxsmTJ3H58mW0a9cOvXr1gq6uLu7du4eAgADs27cPe/fulUsE9+zZg0mTJqFOnToYOXIkACAwMBDe3t7YunUrvLy8BF8TERERUVUiysjIkJRV6fz582rprGvXruU+p6CgAO3bt8eTJ08QEhKCli1bAgAyMzPh7u6O+Ph4XLlyBba2tqW2k5ubC319fbnys2fPwsvLC61bt8bp06el5RkZGXB2doaOjg7Cw8NhbW0NAEhKSoKrqysA4Pr16zA2Ni73NdH7kZubi4SEBNjY2Cj8f0+kScnJyUhOTi7XOXl5eUhJSYGFhQX09PTKda6lpSUsLS3LdQ6ph9g/SdMhVD6/jQIe35Ats3MGZuzSTDyVWIaPtaZDkFJqplFIsqcu4eHhiIuLw+jRo6UJIwCYmprC19cXU6ZMQUBAAGbPnl1qOyUlDW5ubhCLxYiNjZUpP3jwIDIzMzF37lxpwggA1tbWmDBhApYuXYojR45IZyCJiMrD398ffn5+762/2bNnY+7cue+tPyKqfpRKGjWpeJazZ8+ecsfc3d0BABEREYLbv3TpEjIyMtC5c+dy9bt06VJEREQwaSQiQXx8fNCvXz+l6k6ZMgV37tyRKXNycsL69euV7o+zjESkqkqfNMbExAAAHB0d5Y5ZWFjAyMhIbpawNGFhYYiKikJeXh5iYmJw4sQJmJmZYfHixUr3W1xWXKcsubm5SsdH6pOXlyfzX6LKRCwWQywWK1W3Zs2aCsuaNGlSrj75s4io6qnoz215Ht8SnDQGBASU+xwhs3JZWVkAABMTE4XHjY2NpXWUERYWhrVr10q/dnBwwJYtW9CqVSul+y1+jlHZfp88eYLCwkKlYyT1SklJ0XQIRCpR9ItPXl4eEhISNBANlZ+BpgOgKqwiP+fa2tpwcHBQur7gpHHKlCnlXhFdGW7lLlq0CIsWLUJOTg7u3buHX3/9FX379sXatWsxbNiwCunTysqqQtql0qmyaICoMlE0fvX09GBjY6OBaKj80jQdAFVhlelzLjhp7NKlS4lJ46tXrxATE4OsrCzo6emhffv2ggMsnukraVYvOztb6Vs87zIyMkLbtm2xc+dOdO/eHTNmzECPHj1Qp04duX5r164t1+e7dcrClbuapaenx/8HVKVpacnvw6ClpcVxTfQBqEyfc8FJ49GjR8uss3fvXvznP/+Bg4MDVq9eLaifd58f/Pct5JSUFOTk5KBNmzaC2gYAHR0ddOvWDbdu3cK1a9fQu3dvab/Xrl1DTEyMXNJY2vOORERERNVRhW4j+Mknn2Dz5s3YsWMHdu7cKagNFxcXAG+fRfy30NBQmTpCFb8rTVdX9732S0RERFRVVPje025ubrC2tsbmzZsFn29vb4/9+/cjOjpaWp6ZmYmVK1dCT08PI0aMkJYnJyfj/v37yMzMlGnn2rVrCtsPDQ3FkSNHYGpqKnMbfdCgQTAxMcEff/yBpKT/ezFrUlISNm7cCDMzMwwYMEDQNRERERFVNe/llTtmZma4f/++oHN1dHSwevVqDBkyBB4eHjLbCCYkJGDhwoWws7OT1p8/fz4CAgKwbt06jB49Wlreo0cPODk5oVmzZrCyssKrV69w69YtREZGQldXF2vXroWhoaG0vlgsxrJlyzBp0iS4ublJ97gODAxEeno6/P39uRsMERERfTAqPGl8/fo1YmJiFD7IrSxXV1cEBwdjyZIlCAwMRH5+PpycnDB//nwMHjxYqTZ+/PFHnDt3DhEREXj+/Dm0tLRQv359jB8/HpMnT0bjxo3lzhk+fDjMzMywYsUK7Nq1CyKRCM7Ozpg5cya6d+8u+HqIiIiIqpoKTRqfP3+O7777Djk5OejWrZtKbbVt2xb79+8vs96GDRuwYcMGuXJfX1/4+vqWu99evXqhV69e5T6PiIiIqDoRnDR6enqWeEwikSA1NRWPHz9GXl4etLW18e233wrtioiIiIg0THDSWLw3c1lsbW2xePFiuLm5Ce2KiIiIiDRMcNK4bt26Eo+JRCIYGBjA0dERzZo1K/fOMURERERUuQhOGkeNGqXOOIiIiIioElNqSXPt2rXRv39/mTI/Pz/BL+wmIiIioqpFqaRRIpFAIpHIlC1duhQ7duyokKCIiIiIqHJR6va0vr4+srKyKjoWIiIiqqoyU4GsVOXqvnmluCzhjvL9mZgDpubK1yeVKZU0NmjQAHfv3sWhQ4fQt29f6OvrV3RcREREVJVE7gVOrBd+fvIDYOUw5ev3nQJ8PFV4f1RuSiWNo0ePxrx58+Dj4yNTHhUVhdq1ayvVkUgkQlpaWvkjJCIiosqv8ydAsx7vrz8TzjK+b0oljVOmTEFWVhY2b94sk/j9+znH0pSnLtG/JScnIzk5uVzn5OXlISUlBS9evICenl65zrW0tISlpWW5ziEi+qCZ8nZxdadU0igSiTB37lzMnTsXz58/x6tXr+Ds7Iw2bdrA39+/omMkgr+/P/z8/N5bf7Nnz8bcuXPfW39ERESVXbnf01inTh3p3/X09GBra6vWgIgU8fHxQb9+/ZSqO2XKFNy5I/swtZOTE9avV/5ZG84yEhERyRL8cu8bN25wQQy9N+W5XWxoaKiwrFWrVmqOioiI6MMhOGksa4YxIyMDT548gaOjI2rUqCG0GyIiIiKqBJR6ubciN27cwC+//IKwsDCZ8tevX+Pzzz+Hg4MDunbtiiZNmuDQoUMqB0pEREREmiM4adyxYwdWrFghtyp68eLFOHDggHQXmYyMDEyYMEHuGTMiIiIiqjoEJ40XLlyAvr4+evT4v3cy5eXlYdu2bdDV1cXevXvx6NEjTJo0Cfn5+fj999/VEjARERERvX+Ck8Znz56hXr160NL6vyYuXbqE7Oxs9OvXD71794apqSl++uknGBoaIiIiQi0BExEREdH7JzhpzMjIQK1atWTKLl26BJFIBHd3d2lZzZo1YW9vjydPngiPkoiIiIg0SnDSWLNmTTx//lymLDIyEgDQsWNHmXI9PT2ZGUkiIiIiqloEv3KnUaNGuHr1Kv755x80bdoUaWlpOHfuHMzMzNC4cWOZuk+fPpV5KTipj9g/SdMhVD7P8uSKLj/L4/eqBBk+1poOgYiIqgDB03/e3t6QSCQYNmwYvv/+e3h6eiIvLw+DBw+WqZeQkIDk5GQ4ODioHCwRERERaYbgpHHixIno0qULkpKSsH79evzzzz9o2LAhZs+eLVMvMDAQANCtWzfVIiUiIiIijRF8e1pPTw+HDx/G8ePH8eDBA9jY2MDDw0Nua0FtbW18+eWX8PLyUjlYIiIiItIMwUkjAGhpacHDw6PUOlOnTlWlCyIiIiKqBLikmYiIiIjKJDhpfP78Oc6ePYuHDx/KHfP394eLiwscHBwwbNgwPHjwQKUgiYiIiEizBCeNv//+OwYNGoTLly/LlG/duhXffvst7ty5gxcvXuDUqVPw9PREenq6ysESERERkWYIThrPnTsHbW1teHp6ypSvXLkSADB9+nTs2LEDnTt3xrNnz7B+/XrVIiUiIiIijRG8ECYhIQEWFhYwMjKSlt28eRMJCQno1KkTFixYAABo164dmjdvjhMnTmDevHmqR0xE1Rpfwq4AX1hfLnxhPVHFEDzTmJ6eDktLS5myixcvAgD69+8vLbOwsICDgwMePXoktCsAwNWrVzFs2DDY2trCysoKvXr1kr4DsiwSiQQhISHw9fVFly5dYGtri3r16sHFxQUrVqxAbm6uwvPEYnGJfyZPnqzS9RARERFVJYJnGrW0tJCTkyNTdunSJYhEInTq1Emm3MTERKWkMTw8HEOGDIG+vj4GDx4MIyMjBAUFwcfHB4mJiZg+fXqp57958wbDhg1DjRo10LVrV7i7uyM3NxdhYWFYuHAhjh49iiNHjsDAwEDuXBsbG4waNUquvEWLFoKvh4iIiKiqEZw02traIjY2Fi9evECtWrWQn5+PsLAw1KxZE61bt5apm5aWBjMzM0H9FBQU4Ouvv4aWlhaOHj2Kli1bAgBmzZoFd3d3LFy4EF5eXrC1tS2xDW1tbcybNw9ffPEFxGKxtDw/Px9jx45FcHAwNm3ahK+++krhdc6dO1dQ7EREJcpMBbJSlav75pXisoQ7yvdnYg6Ymitfn4joXwQnjT179sS9e/fw+eefY8KECTh06BDS09Ph6ekJHZ3/azYzMxOPHj1C27ZtBfUTHh6OuLg4jB49WpowAoCpqSl8fX0xZcoUBAQEyG1f+C5dXV189913Cst9fX0RHByMiIgIhUkjEVGFiNwLnFBhgWDyA2DlMOXr950CfMzNFohIOMFJ44wZM3DgwAGcPn0aZ86cgUQigb6+vlzyFhwcDIlEgs6dOwvq5/z58wDeJqn/5u7uDgCIiIgQ1DbwNnEE3s5GKpKZmYmtW7ciLS0NtWrVQseOHdGsWTPB/RERAQA6fwI06/H++jPhLCMRqUZw0li3bl2EhYVh9erVePjwIWxsbPDll1+icePGMvUiIyPRvHlz9O3bV1A/MTExAABHR0e5Y8Wrt2NjYwW1DQA7duwAoDgpBYBbt25hxowZMmW9evXChg0bYG6u3A/hkhbaUDnwVl6F4fjUENMPZ4y9bxzTVJ1U9HjW19dXuq5Ke09bWVlh6dKlpdb57bffVOkCWVlZAN4uplHE2NhYWqe8QkJC4O/vj8aNG2Ps2LFyx6dNm4aBAweiYcOG0NXVxT///INly5YhJCQEw4cPR0hISIkzlO968uQJCgsLBcVYNvnFO9USb+VVmISEBE2H8C8fyJimCsMxTdVJRY5nbW1tODg4KF1fpaSxKrt69So+++wzmJiYYOvWrahRo4ZcnUWLFsl83aFDB+zZsweenp6IiIjA0aNHMXDgwDL7srKyUlvc8tIqsO1KhLfyKoyNjY2mQ/iXD2RMU4XhmKbqpDKNZ7UkjdeuXcPZs2eRlJSE169fY+3atdJjycnJyM/PF3zRxTOMJc0mZmdny6yIVjbeQYMGQSQS4cCBA2jatKnS52ppaWHcuHGIiIhAVFSUUkljeaZ+qQS8lVdhOD6puuGYpuqkMo1nlZLGlJQUTJo0CeHh4QDevkRbJBLJJI2LFi3Crl27cOLECbRv377cfRQ/yxgTE4NWrVrJ9Z+Tk4M2bdoo3d61a9fg7e0NiUSCAwcOlOvcYsWvD3r1SsGzc0RERETVkOAdYXJycuDp6YmzZ8+iXr16GDlyJKyt5bduGjFiBCQSCY4dOyaoHxcXFwBAWFiY3LHQ0FCZOmUpThiLioqwf/9+tGvXTlBMV65cAYBS3w1JREREVJ0IThrXrVuHBw8eoE+fPoiKisK6desU3oLu3LkzatSogbNnzwrqx83NDfb29ti/fz+io6Ol5ZmZmVi5ciX09PQwYsQIaXlycjLu37+PzMxMmXauX78Ob29vFBYWYt++fejQoUOp/d6+fRv5+fly5VFRUfjvf/8LXV1deHt7C7omIiIioqpG8O3poKAg6OjoYM2aNTAyMiqxXvHKnLi4OGEB6uhg9erVGDJkCDw8PGS2EUxISMDChQthZ2cnrT9//nwEBARg3bp1GD16NADgxYsX8Pb2RmZmJnr16oXTp0/j9OnTMv2YmppiypQp0q/Xrl2LkydPolOnTrC2toauri7u3r2LsLAwiEQiLF++HA0aNBB0TURERERVjeCk8dGjR3BwcEDdunXLrGtkZCS3T3V5uLq6Ijg4GEuWLEFgYCDy8/Ph5OSE+fPnY/DgwWWen5WVhYyMDADAqVOncOrUKbk6NjY2Mklj//79kZmZiVu3buHMmTPIy8uDhYUFhgwZgsmTJwve4YaIiIioKhKcNIpEIqXrZmRkwNDQUGhXAIC2bdti//79ZdbbsGEDNmzYIFNmZ2cnTRqV5enpCU9Pz3KdQ0RERFRdCX6m0dbWFo8fPy5zBfGzZ88QExODRo0aCe2KiIiIiDRMcNLYq1cv5OXllbnjy+LFiyGRSNCnTx+hXRERERGRhglOGqdOnQojIyMsX74c//nPf/Dw4UOZ47dv38akSZOwbds2mJmZ4YsvvlA5WCIiIiLSDMHPNFpYWODPP//E2LFj8fvvv+P333+XHjMzM4NEIoFEIoGxsTG2bt1a7l1biIiIiKjyEDzTCLx9h+LZs2cxdOhQ1KxZU5ooFhUVQU9PDwMHDkRYWJjSL98mIiIiospJ5b2nGzRogD/++AMFBQWIiYmRrpRu2LBhpdovkYiIiIiEUzlplDako4PGjRurqzkiIiIiqkRUuj1NRERERB8GwUnj7t27Ubt2bSxZsqTUekuWLEHt2rXx119/Ce2KiIiIiDRMcNIYFBQEABg7dmyp9UaPHg2JRIKDBw8K7YqIiIiINExw0nj79m2Ym5ujfv36pdaztbVF3bp1cevWLaFdEREREZGGCU4aU1JSykwYi1lbWyMlJUVoV0RERESkYYKTRn19fWRmZipVNysrCzo6aluoTURERETvmeCksWHDhoiNjcWjR49KrRcXF4eYmBg4ODgI7YqIiIiINExw0ti3b19IJBJ8/fXXePPmjcI6eXl5mDFjBkQiEfr16yc4SCIiIiLSLMFJ48SJE2FhYYFz587Bzc0N27dvx927d/H06VPcvXsX27dvh6urK8LDw2FhYYFJkyapM24iIiIieo8EP2hoamqK3bt345NPPsG9e/cwY8YMuToSiQR169ZFQEAAxGKxCmESERERkSaptCNMq1atEBERgSlTpqB+/fqQSCTSPzY2Npg+fToiIiLQqlUrNYVLRERERJqg8pJmc3Nz/PLLL/jll1+Qk5OD7OxsGBsbw8jISB3xEREREVEloNb34BgZGTFZJCIiIqqGBN+ezs/PR0JCAtLT00utl56ejoSEBBQUFAjtioiIiIg0THDSuGPHDjg7O2Pnzp2l1tu5cyecnZ0REBAgtCsiIiIi0jDBSePhw4chEokwatSoUuuNGDECABAUFCS0KyIiIiLSMMFJ4/3792FpaQkzM7NS65mbm6NevXq4d++e0K6IiIiISMMEJ42pqamoV6+eUnUtLS2RmpoqtCsiIiIi0jDBSaOhoSGePXumVN3U1FTUqFFDaFdEREREpGGCk8amTZsiMTER0dHRpdaLjo5GQkICmjRpIrQrIiIiItIwwUmjp6cnJBIJpk6dWuJrd168eIGpU6dCJBLB09NTcJBEREREpFmCX+49fvx4bNmyBbdv30bHjh0xbtw4dOjQAaampsjMzMSlS5ewfft2pKamolGjRvj888/VGTcRERERvUeCk0Z9fX3s3bsXw4cPx/3797Fy5Uq5OhKJBE2aNEFAQAD09fVVCvTq1atYsmQJoqKiUFBQACcnJ0ydOhWDBg0q81yJRIJTp07h+PHjuHjxIhITE5Gfnw8HBwcMHjwYU6dOLTG+0NBQrFixAtHR0RCJRHB2dsbMmTPh5uam0vUQERERVSWijIwMiSoN5ObmYtu2bTh8+DDu3Lkj3Xu6WbNm8PLywtixY1VeBBMeHo4hQ4ZAX18fgwcPhpGREYKCgpCQkICFCxdi+vTpZcZoaWmJGjVqoGvXrnByckJubi7CwsIQExODNm3a4MiRIzAwMJA5b8+ePZg0aRLq1KkjTU4DAwORlpaGrVu3wsvLS6XrUgexf5KmQ6AqLsPHWtMhyOCYJlVxTFN1UpnGs8pJY0UrKChA+/bt8eTJE4SEhKBly5YAgMzMTLi7uyM+Ph5XrlyBra1tiW3k5+fjv//9L7744guIxWKZ8rFjxyI4OBgLFizAV199JT2WkZEBZ2dn6OjoIDw8HNbWb/+nJSUlwdXVFQBw/fp1GBsbV8BVK48/jEhVlekHEsAxTarjmKbqpDKNZ8ELYd6X8PBwxMXFYejQodKEEQBMTU3h6+uLvLy8Mrco1NXVxXfffSeTMBaX+/r6AgAiIiJkjh08eBCZmZmYOHGiNGEEAGtra0yYMAFpaWk4cuSIildHREREVDVU+qTx/PnzAICePXvKHXN3dwcgn/CVh66uLgBAW1v7vfZLREREVJUIXggzderUctUXiURYu3ZtufuJiYkBADg6Osods7CwgJGREWJjY8vdbrEdO3YAkE8OS+u3uKy4DhEREVF1Jzhp3LVrF0QiESQSxY9EikQi6d8lEongpDErKwsAYGJiovC4sbGxtE55hYSEwN/fH40bN8bYsWOV7rf4OUZl+83NzRUUH9H7wPFJ1Q3HNFUnFT2ey/N2G8FJ4+zZs0s89urVKzx8+BBhYWGQSCSYOHEiDA0NhXZVIa5evYrPPvsMJiYm2Lp1a4Vuc/jkyRMUFhZWUOsGZVchKkVCQoKmQ/gXjmlSDcc0VScVOZ61tbXh4OCgdH3BSeOcOXPKrPPo0SP4+PggPDwcJ06cENRP8UxfSbN62dnZcgtcynLt2jUMGjQIIpEIBw4cQNOmTUvtt3bt2nJ9vlunLFZWVuWKr3zSKrBt+hDY2NhoOoR/4Zgm1XBMU3VSmcaz4KRRGfb29tiyZQvatm2LFStW4Pvvvy93G+8+P9iqVSuZYykpKcjJyUGbNm2Ubu/atWvw9vaGRCLBgQMHSjzX0dER165dQ0xMjFzSWNrzjoqo+mJzoorE8UnVDcc0VSeVaTxX+OrpBg0aoHHjxggMDBR0vouLCwAgLCxM7lhoaKhMnbIUJ4xFRUXYv38/2rVr9176JSIiIqrq3ssrd0QiERITEwWd6+bmBnt7e+zfvx/R0dHS8szMTKxcuRJ6enoYMWKEtDw5ORn3799HZmamTDvXr1+Ht7c3CgsLsW/fPnTo0KHUfgcNGgQTExP88ccfSEr6vxezJiUlYePGjTAzM8OAAQMEXRMRERFRVVOht6cBID4+Hg8fPoSpqamg83V0dLB69WoMGTIEHh4eCrcRtLOzk9afP38+AgICsG7dOowePRoA8OLFC3h7eyMzMxO9evXC6dOncfr0aZl+TE1NMWXKFOnXYrEYy5Ytw6RJk+Dm5iazjWB6ejr8/f01vhsMERER0ftSYUljamoqLl26hEWLFqGgoADdu3cX3JarqyuCg4OxZMkSBAYGIj8/H05OTpg/fz4GDx5c5vlZWVnIyMgAAJw6dQqnTp2Sq2NjYyOTNALA8OHDYWZmhhUrVkhfMeTs7IyZM2eqdD1EREREVY3gvaf/vTikJBKJBGZmZggJCUGDBg2EdEWl4J6mpKrKtK8pwDFNquOYpuqkMo1nwc80SiSSMv+YmppixIgRCAsLY8JIREREVIUJvj1948aNEo+JRCIYGhoqPRtJRERERJWb4KTR1tZWnXEQERERUSX2Xl65Q0RERERVm1pXTxcVFeHQoUO4fPky8vPz4ejoiCFDhsDc3Fyd3RARERHRe6Z00vjgwQOsXbsWFhYW+M9//iN3PC0tDYMHD8bNmzdlypcuXYpt27bBzc1N9WiJiIiISCOUvj194sQJ/PnnnxCJRAqPT5kyBdHR0ZBIJKhTpw7atm0LQ0NDZGZmYty4cUhL44btRERERFWV0kljZGQkAMDb21vu2M2bN3Hy5EmIRCJ88cUXuHv3LkJCQhAdHY127dohKysL/v7+aguaiIiIiN4vpZPG4q0AmzZtKnfs8OHDAIB69erhl19+gZbW22Zr1aqFRYsWQSKRICwsTE0hExEREdH7pnTS+Pz5c5k9nt8VFRUFkUiEvn37Qk9PT+ZYx44dUadOHTx48EC1SImIiIhIY5ROGrOysiCRKN5xMDo6GgDQpUsXhcetrKyQlZUlIDwiIiIiqgyUThqNjY2RlCS/f2ZsbCwyMjIAAM7OzgrPlUgk0lvWRERERFT1KJ3JNW7cGOnp6Th//rxM+bFjxwAAZmZm+OijjxSem5SUBDMzMxXCJCIiIiJNUjpp7N+/PyQSCb755htcv34deXl5OHv2LH777TeIRCJ4enoqPC82Nhbp6elo1KiR2oImIiIiovdL6Zd7f/7559i4cSNiYmLQs2dPablEIoG+vj6mTZum8LyDBw9CJBKhW7duqkdLRERERBqh9EyjgYEBAgMD4eTkBIlEIv1jYmKC//3vf3BwcJA7Jz8/H1u2bAEA9OjRQ31RExEREdF7Va69px0dHXH+/HlcvXoVcXFxMDY2RufOnWFsbKyw/suXL7FixQqIRCK0atVKHfESERERkQaUK2ks1qZNG7Rp06bMemKxGH379hXSBRERERFVInwPDhERERGViUkjEREREZWJSSMRERERlYlJIxERERGViUkjEREREZWJSSMRERERlUmppDEhIQGpqakVHQsRERERVVJKJY0tW7bEuHHjZMqmTp2KVatWVUhQRERERFS5KH17WiKRyHy9a9cunDx5Uu0BEREREVHlo1TSaGBggLS0tIqOhYiIiIgqKaW2EWzcuDGuX7+ONWvWoG/fvqhZsyYAIC8vDwkJCUp3ZmNjIyxKIiIiItIoUUZGhqSsSrt27cLUqVMhEomkZRKJRObrMjsSiThbWQHE/kmaDoGquAwfa02HIINjmlTFMU3VSWUaz0rdnh41ahTWrl2Lli1bQl9fX5owSiQSpf8UFRWpFOjVq1cxbNgw2NrawsrKCr169UJgYKDS58fFxWHJkiUYMWIEmjZtCrFYjBYtWpR6jlgsLvHP5MmTVboeIiIioqpEqdvTADB69GiMHj1a+nWtWrXQqVMnHD9+vEICe1d4eDiGDBkCfX19DB48GEZGRggKCoKPjw8SExMxffr0Mtu4cOEC/Pz8oK2tjcaNGyMlJUWpvm1sbDBq1Ci58rISTiIiIqLqROmkUVMKCgrw9ddfQ0tLC0ePHkXLli0BALNmzYK7uzsWLlwILy8v2NraltqOi4sLQkJC0Lx5c9SsWRMWFhZK9W9ra4u5c+eqfB1EREREVZngHWFevHjx3mYZ4+LiMHToUGnCCACmpqbw9fVFXl4eAgICymzH3t4e7du3ly7iISIiIiLlqW2msaCgAPHx8cjOzoaxsTFsbW2ho6N68+fPnwcA9OzZU+6Yu7s7ACAiIkLlfkqSmZmJrVu3Ii0tDbVq1ULHjh3RrFmzCuuPiIiIqDJSOau7evUqli1bhrNnzyI3N1darq+vjx49euC7775D69atBbcfExMDAHB0dJQ7ZmFhASMjI8TGxgpuvyy3bt3CjBkzZMp69eqFDRs2wNzcXKk23v2+EFU2HJ9U3XBMU3VS0eNZX19f6boqJY3btm3Dd999h8LCQrkdY16/fo1jx47h5MmTWLFiBT799FNBfWRlZQEATExMFB43NjaW1lG3adOmYeDAgWjYsCF0dXXxzz//YNmyZQgJCcHw4cMREhICbW3tMtt58uQJCgsLKyRGwKCC2qUPRXnetfp+cEyTajimqTqpyPGsra0NBwcHpesLThpv3LiBb7/9FoWFhejcuTOmT58OJycnWFpaIjk5GXfu3MGaNWsQGRkJX19fODs7w9nZWWh3GrFo0SKZrzt06IA9e/bA09MTEREROHr0KAYOHFhmO1ZWVhUVIgC++5JUU/leus8xTarhmKbqpDKNZ8FJ49q1a1FYWIhp06Zh4cKFMsfs7OxgZ2eHfv364ccff8SaNWuwbt06/PHHH+Xup3iGsaTZxOzsbIjF4nK3K5SWlhbGjRuHiIgIREVFKZU0lmfql+h94/ik6oZjmqqTyjSeBa+evnDhAkxNTfHjjz+WWu+HH36AiYmJ4MUqxc8yFj/b+K6UlBTk5OSUa2pVHczMzAAAr169eq/9EhEREWmK4KQxNTUVjo6O0NXVLbWerq4uGjZsiOfPnwvqx8XFBQAQFhYmdyw0NFSmzvty5coVACjz3ZBERERE1YXgpNHIyEjpXVVSUlJgaGgoqB83NzfY29tj//79iI6OlpZnZmZi5cqV0NPTw4gRI6TlycnJuH//PjIzMwX1V+z27dvIz8+XK4+KisJ///tf6OrqwtvbW6U+iIiIiKoKwc80tmzZEuHh4Th27Bj69+9fYr2jR48iKSkJbm5uwgLU0cHq1asxZMgQeHh4yGwjmJCQgIULF8LOzk5af/78+QgICMC6detktj1MS0vDvHnzpF/n5+cjPT1dZg/pRYsWSW89r127FidPnkSnTp1gbW0NXV1d3L17F2FhYRCJRFi+fDkaNGgg6JqIiIiIqhrBSeOYMWNw9uxZTJw4EXPmzMFnn30GA4P/e63Aq1evsHnzZvj5+UEkEmHs2LGCg3R1dUVwcDCWLFmCwMBA5Ofnw8nJCfPnz8fgwYOVaiMnJ0du55iXL1/KlM2ZM0eaNPbv3x+ZmZm4desWzpw5g7y8PFhYWGDIkCGYPHky2rZtK/h6iIiIiKoaUUZGhqTsaoqNGzcOQUFBEIlE0NfXh62tLerWrYtnz54hPj4eubm5kEgk8PLywtatW9UYNhUT+ydpOgSq4jJ8rDUdggyOaVIVxzRVJ5VpPAt+phEAtmzZgtmzZ8PIyAivX7/GvXv3cO7cOdy7dw+vX7+GkZER5syZg82bN6srXiIiIiLSAJV2hNHW1sacOXPw1VdfITIyEg8ePEBOTg6MjIzQqFEjdOrUSeaWNRERERFVTSrvPQ0ABgYGcHd3h7u7uzqaIyIiIqJKRqXb00RERET0YWDSSERERERlYtJIRERERGVi0khEREREZWLSSERERERlYtJIRERERGVi0khEREREZWLSSERERERlYtJIRERERGVSaUeYoqIi7N69G8HBwYiNjUVOTg4kEonCuiKRCNevX1elOyIiIiLSEMFJY3Z2NoYOHYrLly+XmCi+SyQSCe2KiIiIiDRMcNLo5+eHS5cuwcDAAGPGjEGHDh1gbm4OLS3e8SYiIiKqbgQnjUFBQdDS0kJAQABcXV3VGRMRERERVTKCpwVTUlJgY2PDhJGIiIjoAyA4aTQzM0OtWrXUGQsRERERVVKCk8aePXvi7t27yM7OVmc8RERERFQJCU4a58yZgxo1amD27NkoLCxUZ0xEREREVMkIXgjz+PFjzJ07Fz/88AOuXbuGTz/9FA0bNoSBgUGJ57i4uAjtjoiIiIg0SHDSOGDAAOm7F+/evYvvv/++1PoikQhpaWlCuyMiIiIiDRKcNNavX58v7CYiIiL6QAhOGm/evKnOOIiIiIioEuP2LURERERUJiaNRERERFQmwben33Xjxg2cPHkSDx48QHZ2NoyNjdGoUSP07t0bzs7O6uiCiIiIiDRIpaQxPT0dU6ZMwcmTJwEAEolEekwkEmHx4sX4+OOPsXbtWtSuXVu1SImIiIhIYwQnjW/evMGgQYNw8+ZNSCQStGzZEk5OTrC0tERycjLu3LmD6OhoBAcHY/DgwThx4gRq1KihztiJiIiI6D0RnDRu3LgR0dHRsLa2xrp16+Dm5iZXJzw8HFOnTkV0dDQ2bdqEqVOnqhQsEREREWmG4IUwgYGBEIlE2LVrl8KEEQBcXV2xc+dOSCQSHDhwQHCQAHD16lUMGzYMtra2sLKyQq9evRAYGKj0+XFxcViyZAlGjBiBpk2bQiwWo0WLFmWeFxoaiv79+6N+/fqwsbHBgAEDcPbsWVUuhYiIiKjKETzT+ODBA3z00Udo2bJlqfVatmyJRo0a4cGDB0K7Qnh4OIYMGQJ9fX0MHjwYRkZGCAoKgo+PDxITEzF9+vQy27hw4QL8/Pygra2Nxo0bIyUlpcxz9uzZg0mTJqFOnToYOXIkgLfJsre3N7Zu3QovLy/B10RERERUlYgyMjIkZVeTV69ePTRu3Bhnzpwps2737t1x7949PH36tNz9FBQUoH379njy5AlCQkKkSWpmZibc3d0RHx+PK1euwNbWttR2Hj16hNTUVDRv3hw1a9aEhYUF6tatW+JLyjMyMuDs7AwdHR2Eh4fD2toaAJCUlARXV1cAwPXr12FsbFzua1InsX+SRvunqi/Dx1rTIcjgmCZVcUxTdVKZxrPg29PW1ta4e/cuMjIySq334sUL3L17F1ZWVoL6CQ8PR1xcHIYOHSozq2lqagpfX1/k5eUhICCgzHbs7e3Rvn171KxZU6l+Dx48iMzMTEycOFGaMAJvr3vChAlIS0vDkSNHyn9BRERERFWQ4KSxR48eePPmDaZMmYLc3FyFdXJzczFlyhTk5eWhV69egvo5f/48AKBnz55yx9zd3QEAERERgtqujP0SERERVUaCn2mcMWMG9uzZg+DgYLRs2RLjx4+Hk5MT6tati2fPnuHOnTvYunUrnj9/DmNjY3z11VeC+omJiQEAODo6yh2zsLCAkZERYmNjhV6GoH6Ly4rrlKWkpJqoMuD4pOqGY5qqk4oez/r6+krXFZw0WltbY9euXRg/fjxSU1OxfPlyuToSiQR16tTB1q1bZW7xlkdWVhYAwMTEROFxY2NjaR11Kq3f4ucYle33yZMnKCwsVF9wMgwqqF36UCQkJGg6hH/hmCbVcExTdVKR41lbWxsODg5K11dpR5iuXbvi0qVL2LRpE0JCQvDgwQPk5OTAyMgIjRo1Qp8+ffDZZ5998LvBCH2eUzlpFdg2fQhsbGw0HcK/cEyTajimqTqpTONZ5b2na9eujVmzZmHWrFnqiEdO8UxfSbN62dnZEIvFFdrvv5Pe7OxsmTplKc/UL9H7xvFJ1Q3HNFUnlWk8C14I876U9vxgSkoKcnJyyjW1qo5+S3vekYiIiKg6qvRJo4uLCwAgLCxM7lhoaKhMnerQLxEREVFlpNTLvf38/AAAZmZm+OKLL2TKymP27NnlPqegoADt2rXD06dPS3y59+XLl2FnZwcASE5ORlZWFiwsLGBqalpiu8q83Ltly5bQ1dXly72pWqtML44FOKZJdRzTVJ1UpvGsVNJYq1YtiEQifPTRR4iKipIpU4ZEIoFIJEJ6erqgIEvaRjAhIQELFy6U2UZw8uTJCAgIwLp16zB69GhpeVpaGubNmyf9es+ePahZsyYGDhwoLVu0aBHMzMxk6hRvIzho0CAAb7cRTEtLg7+/P7y9vQVdjzrxhxGpqjL9QAI4pkl1HNNUnVSm8azUQpgRI0ZAJBLB0tJSrux9cHV1RXBwMJYsWYLAwEDk5+fDyckJ8+fPx+DBg5VqIycnR27nmJcvX8qUzZkzRyZpHD58OMzMzLBixQrs2rULIpEIzs7OmDlzJrp3766WayMiIiKqCgTvPU2VA3+DJVVVpt9iAY5pUh3HNFUnlWk8V/qFMERERESkeYKTxqlTp2LVqlVK1f3tt98wdepUoV0RERERkYYJThp37dqFkydPKlX31KlTcs8TEhEREVHV8V5uTxcVFb23RTNEREREpH7vJWl8+vQpDA0N30dXRERERFQBlN57OiEhAfHx8TJlWVlZiIiIKPGc169f4+zZs3j06BHat28vPEoiIiIi0iilk8adO3fi119/lSn7559/4OnpWep5EsnbN/qMHz++/NERERERUaWgdNJoamqK+vXrS79OTEyEnp4e6tatq7C+SCSCgYEBGjRogBEjRsjsvEJEREREVYvSSePkyZMxefJk6de1atVC69atcfz48QoJjIiIiIgqD6WTxn9bt24dLCws1BkLEREREVVSgpPGpUuXwsjICF27dkWNGjXUGRMRERERVTKCX7mTmpqKGjVqMGEkIiIi+gAIThrr16+P3NxcdcZCRERERJWU4KSxf//+uH//Ph49eqTGcIiIiIioMhKcNH777bewt7fHuHHjkJiYqM6YiIiIiKiSEbwQZsOGDXB3d8eWLVvQrl07uLm5oUmTJjAwMCjxnNmzZwvtjoiIiIg0SJSRkSERcmKtWrUgEomkO74Ab1/orYhEIoFIJEJ6erqwKKlEYv8kTYdAVVyGj7WmQ5DBMU2q4pim6qQyjWfBM40jRowoMUkkIiIioupFpdvTRERERPRhELwQhoiIiIg+HEwaiYiIiKhMgm9PF8vOzsb27dtx8uRJPHjwADk5OTAyMkKjRo3Qt29fjBkzBsbGxuqIlYiIiIg0RKWk8erVq/j000/x5MkTmVXU2dnZePr0KcLDw7Fu3Tr8+eefaN26tcrBEhEREZFmCE4aU1JSMGzYMKSnp8PY2Bhjx46Fk5MTLC0tkZycjDt37mDHjh1ISkrCsGHDEBERAQsLC3XGTkRERETvieCkcfXq1UhPT4ebmxu2bt0KsVgsV2fWrFkYP348zp49izVr1mDRokWqxEpEREREGiJ4IUxISAj09PSwadMmhQkjAJiamuKPP/6Ajo4OTp48KbQrIiIiItIwwUljYmIimjZtijp16pRaz9zcHE2bNuX+1ERERERVmOCkUUdHB2/evFGqbl5eHnR0VF6oTUREREQaIjhpdHR0xP3793Hv3r1S6929exf37t2Do6Oj0K6IiIiISMMEJ40DBw5EUVERxo4di+vXryusc/36dYwZMwYA4OXlJbQrIiIiItIwwfeMJ02ahD179uDevXvo2bMnOnXqBCcnJ9StWxfPnj3DnTt3cPHiRUgkEjRt2hSTJk1SKdCrV69iyZIliIqKQkFBAZycnDB16lQMGjRI6TbevHmD3377DXv27EFSUhJq1aqFvn37Yt68eTA3N5ep+/jxYzg7O5fY1uzZszF37lzB10NERERUlQhOGg0MDHDw4EF88cUXiIiIQGRkJC5evCg9Xvyy765du2Ljxo2oWbOm4CDDw8MxZMgQ6OvrY/DgwTAyMkJQUBB8fHyQmJiI6dOnl9lGUVERRo0ahdDQULRv3x4DBw5ETEwMtm/fjrNnz+LUqVMKF/U0b94cHh4ecuVdu3YVfD1EREREVY1Kq1MsLS1x5MgRREZGKtxGsE+fPujUqZNKARYUFODrr7+GlpYWjh49ipYtWwJ4+w5Id3d3LFy4EF5eXrC1tS21nV27diE0NBRDhw7Fxo0bIRKJAABbtmyBr68vFi1ahN9++03uvBYtWnBGkYiIiD54alnS3LlzZ3Tu3FkdTckJDw9HXFwcRo8eLU0YgbfvgPT19cWUKVMQEBCA2bNnl9rO9u3bAQA//vijNGEEAB8fH6xevRr79u3DkiVLVJoRJSIiIqquBC+EeV/Onz8PAOjZs6fcMXd3dwBAREREqW3k5ubiypUr+Oijj+RmJEUiEXr06IGXL1/i2rVrcucmJydj48aNWLFiBbZv3464uDihl0JERERUZallpjE6OhonT57E/fv3kZ2dDWNjYzRq1Ai9e/cudTGJMmJiYgBA4St7LCwsYGRkhNjY2FLbiIuLQ1FRERwcHBQeLy6PiYlBly5dZI6dPn0ap0+fln4tEokwbNgwrFq1CoaGhkpdQ25urlL1iDSB45OqG45pqk4qejzr6+srXVelpDE1NRXTpk1DSEgIgP9b/AK8Ta4WL16M3r17Y/Xq1bCwsBDUR1ZWFgDAxMRE4XFjY2NpnbLaMDU1VXi8uO132zEwMMDMmTPh4eGBBg0aQCKR4MaNG1i4cCH27t2L169f488//1TqGp48eYLCwkKl6pafQQW1Sx+KhIQETYfwLxzTpBqOaapOKnI8a2trlzihpojgpDEzMxP9+/dHTEwMJBIJ2rRpgyZNmkhfuXP37l1cvXoVISEhGDBgAE6dOlVi0lYZmZub4/vvv5cpc3NzQ/v27eHm5obDhw/j+vXraNWqVZltWVlZVVCUAJBWgW3Th8DGxkbTIfwLxzSphmOaqpPKNJ4FJ43Lly/Hw4cP0aBBA2zYsAEdO3aUq3Pp0iVMnjwZMTExWL58ORYuXFjufhTNAr4rOzsbYrFYqTYyMzMVHi9rNvNdBgYGGD58OBYtWoSoqCilksbyTP0SvW8cn1TdcExTdVKZxrPghTBBQUHQ0tJCQECAwoQRADp06IBdu3ZJ6wtR/Cxj8bON70pJSUFOTk6ZU6v29vbQ0tIq8dnH4nJltzo0MzMDALx69Uqp+kRERERVneCkMTk5GY0bN0bjxo1Lrde4cWM0adIEKSkpgvpxcXEBAISFhckdCw0NlalTkpo1a6Jt27Z48OAB4uPjZY5JJBKcPn0ahoaGaN26tVIxXblyBQDKfDckERERUXUhOGmsU6cOdHSUu7uto6MjnZ0rLzc3N9jb22P//v2Ijo6WlmdmZmLlypXQ09PDiBEjpOXJycm4f/++3K3ocePGAQAWLFggs2DH398fjx49wrBhw2Te0Xjjxg2ZesWCgoIQEBAAsViMXr16CbomIiIioqpG8DON3bt3x969e/H06VPUq1evxHpPnjzBP//8I5PYlStAHR2sXr0aQ4YMgYeHh8w2ggkJCVi4cCHs7Oyk9efPn4+AgACsW7cOo0ePlpaPGjUKgYGB2L9/Px4/fgwXFxfExsbi8OHDsLOzw7x582T6/c9//oNHjx6hffv2sLKyQmFhIaKjoxEZGYkaNWpg/fr1VWphDxEREZEqBM80/uc//4GpqSnGjRuHpKQkhXWSkpLg4+MDsVis0lZ8rq6uCA4ORseOHREYGIgtW7agbt262LJli1L7TgOAlpYWdu3ahTlz5uD58+dYv349oqKiMHbsWISEhMjtOz18+HA0bdoUV65cgb+/P7Zu3YqnT5/i008/xblz59C/f3/B10NERERU1YgyMjLk78EqISAgAI8ePcKqVasAAAMGDEDTpk1lXrlz5MgRAMA333wjMxv4rpEjRwoMnQBA7K84YSdSVoaPtaZDkMExTarimKbqpDKNZ8FJY61atSASiaTP/b27n3Ox0o4VS09PF9I9/X/8YUSqqkw/kACOaVIdxzRVJ5VpPAt+prFLly6lJoNEREREVH0IThqPHj2qzjiIiIiIqBITvBCGiIiIiD4cTBqJiIiIqEyCb08Xk0gkOHLkCE6ePIkHDx4gOzsbxsbGaNSoEfr06QMPDw8++0hERERUxamUND569Aiffvopbt26BQAyO6hcunQJO3bsQIsWLbBt2zbY29urFCgRERERaY7gpDErKwteXl6Ij4+HtrY2+vXrBycnJ1haWiI5ORl37tzB8ePHER0djUGDBuHs2bMwMTFRZ+xERERE9J4IThrXrVuH+Ph4tGjRAv7+/nB0dJSrExsbi/Hjx+PWrVtYv3495syZo1KwRERERKQZghfCHDlyBNra2ti+fbvChBEAHBwcsH37dohEIhw+fFhwkERERESkWYKTxsePH6Nx48ZlPqtob2+PJk2a4PHjx0K7IiIiIiINE5w0SiQSaGkpd/q72w0SERERUdUjOGm0s7PD3bt38eTJk1LrJSYm4u7du7CzsxPaFRERERFpmOCk8eOPP0ZBQQF8fHyQnJyssM7Tp0/x2WefoaioCP379xccJBERERFpluDV09OnT8fu3btx+fJltGrVCt7e3nByckLdunXx7Nkz3LlzBwcPHsSbN29gZWWFadOmqTNuIiIiInqPBCeNtWrVwoEDBzB27Fg8fPgQe/fulTle/AzjRx99hO3bt0MsFqsUKBERERFpjko7wjRp0gQREREIDAyUbiOYk5MDIyMj6TaCgwYNgq6urrriJSIiIiINEJw0RkREAAA6dOiA4cOHY/jw4WoLioiIiIgqF8FJ44ABA2BtbS3dd5qIiIiIqi/Bq6fFYjEsLS3VGQsRERERVVKCk0YnJyckJiaqMxYiIiIiqqQEJ40+Pj5ISUnBX3/9pc54iIiIiKgSEvxM49ChQ3H16lVMmzYNCQkJGDt2LMzMzNQZGxERERFVEoKTRmdnZwBAfn4+FixYgAULFsDMzAwGBgYK64tEIly/fl1od0RERESkQYKTxvj4eLmy58+fl1hfJBIJ7YqIiIiINExw0nj48GF1xkFERERElZjgpLFr167qjIOIiIiIKjFBSWNmZibi4uIAAA0aNICpqalagyIiIiKiyqVcSWNKSgp8fX1x4sQJFBUVAQC0tLTw8ccfY8WKFbCwsKiQIImIiIhIs5R+T+OrV6/g4eGB48ePo7CwEBKJBBKJBIWFhTh27Bg8PT3x+vXrCgv06tWrGDZsGGxtbWFlZYVevXohMDCwXG28efMGfn5+aNOmDSwsLNCkSRN8/fXXSE1NLfGcvXv3omfPnrCysoKdnR2GDx/OVeBERET0wVE6ady0aRNiYmJgYGCAn3/+GadPn0ZYWBh++uknGBgY4OHDh9i0aVOFBBkeHo6+ffvi4sWLGDRokPTF4j4+PlizZo1SbRQVFWHUqFFYsmQJzMzMMHnyZLRv3x7bt29H7969Fa78Xr58OSZOnIjU1FT4+PjA29sbFy5ckMZCRERE9KEQZWRkSJSp2LdvX1y+fBnbtm2Dp6enzLFDhw5h/Pjx6NixI4KDg9UaYEFBAdq3b48nT54gJCQELVu2BPD2uUp3d3fEx8fjypUrsLW1LbWdHTt2YNq0aRg6dCg2btwofQXQli1b4Ovri/Hjx+O3336T1o+JiUHHjh1hb2+P0NBQ6XOb0dHR6N27N+zt7REZGQktLcGb6qiF2D9Jo/1T1ZfhY63pEGRwTJOqOKapOqlM41npjOf+/fswMzOTSxgBwMvLC2ZmZrh3755agwPezjLGxcVh6NCh0oQRAExNTeHr64u8vDwEBASU2c727dsBAD/++KPMOyN9fHxgb2+Pffv2ydxe37lzJwoKCvDtt9/KLPRp2bIlhgwZgnv37iEyMlIdl0hERERU6Sm9ECYrKwtt2rQp8bi9vX2FPOt3/vx5AEDPnj3ljrm7uwMAIiIiSm0jNzcXV65cwUcffSQ3IykSidCjRw/4+/vj2rVr6NKli1L97tq1CxEREXBxcSn/RamRWQ3NznQSqRvHNFU3HNNUXSidNBYVFUFHp+Tqurq60hXV6hQTEwMAcHR0lDtmYWEBIyMjxMbGltpGXFwcioqK4ODgoPB4cXlMTIw0aYyJiYGRkZHCFeHFsRTHpkkxo+ppOgQiteKYpuqGY5qqi0r/609WVhYAwMTEROFxY2NjaZ2y2ijpfZLFbb/bTlZWVql9/rs+ERERUXVWrvc0JiYmws/PT+GxhIQEACjxOADMnj27PN0RERERUSVRrqQxKSmpxKRQInm7CFvdSaOiWcB3ZWdnQywWK9VGZmamwuOKZjNNTExK7fPf9YmIiIiqM6WTxi5dusisOn5f3n1+sFWrVjLHUlJSkJOTU+oCHeDtIh0tLa0Sn30sLn/3uUlHR0dcunQJKSkpcs81lvacJREREVF1pHTSePTo0YqMo0QuLi5YuXIlwsLCMGTIEJljoaGh0jqlqVmzJtq2bYvLly8jPj5eZgW1RCLB6dOnYWhoiNatW8v0e+nSJYSFhWHkyJGC+iUiIiKqLir9Qhg3NzfY29tj//79iI6OlpZnZmZi5cqV0NPTw4gRI6TlycnJuH//vtyt6HHjxgEAFixYIL2VDgD+/v549OgRhg0bhpo1a0rLR48eDR0dHaxYsUKmrejoaPz1119o3LgxOnfurPbrJSIiIqqMKn3SqKOjg9WrV6OoqAgeHh74+uuv8f3336Nr1654+PAhfvjhB9jZ2Unrz58/Hx06dMCRI0dk2hk1ahTc3d2xf/9+9OnTBz///DM+/fRTfPvtt7Czs8O8efNk6jds2BBz5szBw4cP0bVrV3z//ff4+uuv4eHhAQD473//q/HdYKqDc+fOQSwWY8mSJUrV9/DwKPMZVnV63/1R5VHesdmiRQu0aNGigqN6fzj2SVXl/QwB1e9zVN1UiazH1dUVwcHB6NixIwIDA7FlyxbUrVsXW7ZswfTp05VqQ0tLC7t27cKcOXPw/PlzrF+/HlFRURg7dixCQkJQp04duXO+++47/PHHH6hTpw62bNmCwMBAdO7cGSdOnECnTp3UfZlERET0L0uWLIFYLMa5c+c0HcoHr1yrpzWpbdu22L9/f5n1NmzYgA0bNig8VqNGDcyZMwdz5sxRut9PPvkEn3zyidL1iejDFBQUpOkQ1Or333+X2VqV6H2obp+j6qbKJI1ERJVZgwYNNB2CWtnY2Gg6BPoAVbfPUXVTJW5P04chMjISHh4eqF+/PmxtbTF27Ngyt4gE3i6K+u2339C/f380adIE5ubmaNKkCSZNmoS4uDiF50gkEuzYsQP9+vWDra0t6tWrhzZt2mDGjBnSF9WX5sCBA6hbty5cXFyQnJxc7mulquXatWvw9vaWjs3Ro0fj8ePHMnUUPYuVm5uLNWvWwMXFBba2trCyskKLFi0wfvx43Lx5U1pv586dEIvF2LlzJ44ePYqePXuiXr16cHR0xNSpU/Hs2TO5mA4fPozPP/8crVu3Rr169WBra4t+/frh0KFDcnUfP34MsViMyZMnIzY2FqNHj4adnR2srKzg5eUlE0ux0p5pPHr0KAYNGoQGDRrAwsICLVq0wMSJE3Hnzh1lvp1URWRkZKB27doYPny4THl0dDTEYjHEYrHcz2gPDw9YWlrizZs3MuXKfIYA+c+Rh4eH9P3Pnp6e0n7//VlLTU3F3Llz0bp1a9StWxcODg4YO3Ysx6SacaaRKoUrV65g1apVcHd3x8SJE/HPP//gyJEjiIyMxKlTp2Bvb1/iuffv38fixYvRrVs3DBgwAAYGBrh//z7279+PkydP4uzZszKvWSoqKoKPjw8OHToEKysrDB06FMbGxoiPj0dgYCB69epV6izL//73P8yZMwedO3dGQEBAidtTUvVw7do1rF69Gt26dcP48eMRHR2No0eP4s6dO4iMjIS+vn6J506ePBmBgYFo1qwZRo0ahRo1aiApKQnnzp3DtWvX5P7hCwoKQlhYGLy8vNC9e3dcvnwZO3fuRGRkJMLCwmSSuAULFkBXVxedOnWCpaUlnj9/juPHj2PcuHHw8/PDpEmT5OKJj49Hr1690KRJE4wZMwZxcXE4duwYPD09cenSJdStW7fM78f333+PdevWoVatWvDw8IC5uTmSkpJw9uxZtGrVCk5OTsp/c6lSE4vFaN68OSIjI1FYWAhtbW0AkHm28Ny5c3BwcADw9pekK1euoEOHDqhRo4a0jiqfoVGjRgEAIiIiMHLkSOnP8nd/7sbFxWHAgAFISkpCz5494eHhgdTUVBw+fBhhYWE4dOgQ2rVrp75vzAeMSSNVCqGhoVi1ahV8fHykZf7+/vjmm28we/Zs7Nmzp8RzGzVqhHv37qFWrVoy5eHh4fD29sby5cuxevVqafmmTZtw6NAhuLm5Yffu3TKvWnr9+jVyc3NL7GvhwoVYsWIFBgwYgE2bNpX6w46qh5MnT2LLli0YPHiwtGzSpEnYs2cPjh49Kvf+2GKZmZk4ePAgWrVqhdDQUOk/uABQWFgo3VnqXSdOnMBff/0Fd3d3adn8+fOxatUq/PLLL1i2bJm0fN++fXK/TOXk5KBPnz745ZdfMHbsWBgYGMgcj4iIwM8//4wZM2ZIyxYtWoTly5dj586d+Oabb0r9XgQHB2PdunVwcnLCkSNHULt2bemxgoICpKenl3o+VT3dunVDdHQ0rl+/jrZt2wJ4myg2bNgQubm5OHfunPSVdlFRUXjz5g26desm04bQzxDw9vV38fHxiIiIwKhRo+TaBoAvv/wSycnJcp+dmTNnokePHvjqq69w4cIFlb4P9BZvT1Ol0LBhQ+kPnmLjxo2Do6MjTp48iefPn5d4rqmpqVzCCLxddd+kSROcOXNGpnzz5s3Q1tbGypUrZRJG4O2L4BW1VVhYiOnTp2PFihUYN24ctm3bxoTxA9GlSxeZf+wAYMyYMQCAq1evlnieSCSCRCKBvr6+3Ou5tLW1Fd767d69u8w/egDw7bffwtTUFHv27EFRUZG0XNHsu5GREUaNGoWsrCyFsdnZ2eGrr76SKRs7dmyZ11Js8+bNAIClS5fKJIzA29ejKTNTSVVLcZIWHh4O4O3PwgsXLqBbt27o2rWr3KwjAHTt2lWmDaGfIWXcuHEDUVFRGDlypNxnp2HDhvj0009x584d3qZWE840UqXQsWNHuX9YtbS00LFjR8TExODWrVvo3r17ieefO3cOGzZswN9//420tDQUFBRIj+np6Un/npOTg3v37sHBwaFc20COHTsWx44dw3fffSf3Tk+q3v69fSkAWFtbAyh5P3vg7d70ffr0wcmTJ+Hq6gpvb2907doVbdq0ga6ursJzFG0YYGRkhBYtWuD8+fN49OiR9FZgamoqVq1ahVOnTiEhIUFupbOiZ21btGgh9zlT5lqK/f3336hRo4ZcUkDVV5cuXaCtrY1z587hm2++QXR0NLKysuDq6opXr15h9+7duHfvHho3bozz58+jZs2acreChX6GlHHlyhUAbz8Pit4H+eDBA+l/+eiE6pg0UqVQ0gxFcXlpP1gOHjwIHx8fGBkZoWfPnrC1tUXNmjUhEomwa9cumYUtWVlZAIB69eqVK74LFy5AX18fvXv3Ltd5VPUZGxvLlRXfai4sLCz13K1bt2LlypXYt28fFi5cCOBtMjlq1Cj8+OOPcrePy/ocFI/fFy9eoEePHkhMTESnTp3g5uYGU1NTaGtr4+bNmzh27JjcQoSSrkVHR0epaynuv169etzY4ANiYmICZ2dnREVFIT8/H+fOnYNIJEK3bt3w6tUrAG9/abexscHff/8NFxcXmV/UAdU+Q2V58eIFgLePdpw4caLEei9fvlSpH3qLSSNVCopWh75bXtpik6VLl0JfXx9nzpyRmz08cOCAzNcmJiYAgKdPn5YrvkOHDsHb2xtDhw7F/v370bFjx3KdTx8mAwMDzJs3D/PmzcOjR49w7tw5+Pv74/fff0dubi5+++03mfplfQ6Kx++ff/6JxMREfP/995g5c6ZM3VWrVuHYsWPqvxi8/Rw+e/YMRUVFTBw/IN26dcPVq1fx999/4/z582jatKl0Qww7OzucO3cOjo6OyM/PV/jMYUUqTkh//fVXTJw48b32/SHip54qhaioKJnntYC3q5wvXboEkUiE5s2bl3huXFwcGjVqJJcwJicn49GjRzJlRkZGaNKkCR4/foyYmBil43N2dkZQUBB0dXUxdOhQXLx4UelziYC3zyCOHTsWR48ehZGREY4fPy5XJzIyUq4sJycHN2/ehImJifQ5xuJXSfXv31+pNtSlbdu2ePPmDc6fP19hfVDlU5wIhoWFITIyUiYxdHV1xfnz56XPPFbEowvFs5L//jcCgPRW+OXLl9XeL8lj0kiVwsOHD7Ft2zaZsm3btuHhw4fo06ePwm0ei9nY2CAuLk5mliY3Nxe+vr7Iz8+Xq//FF1+gsLAQ3377rdxzYLm5udLbHf/WokULBAUFQU9PD0OHDq3Qf5yp6nv+/LnCh+8zMjLw5s0bmVeSFDtz5gxCQ0NlylasWIHMzEwMHz5cOrtX/Eqof//ysm/fPpw8eVJdlyDniy++AADMmTNH7nNSUFBQ4kwpVW2dOnWCjo4OtmzZguzsbLi6ukqPdevWDWlpafjzzz9haGiINm3aqL3/4sWJiYmJcsfatm2Ldu3aYf/+/XJ3loC3iSZ/yVEf3p6mSsHd3R2zZ8/GyZMn0bRpU/zzzz8IDg6GmZmZ9MWuJZk4cSJmzZoFV1dXDBw4EIWFhTh9+jQkEgmaN2+OW7duydT//PPPERERgcDAQLRt2xb9+vWDsbExEhMTERoaijVr1mDAgAEK+2revDmCgoLg5eWFYcOGYe/evejSpYvavg9UfTx58gSurq5o3rw5mjVrBisrK6Snp+PYsWPIz8/H9OnT5c7p27cvRowYAS8vL9ja2uLy5cs4d+4cGjRogO+//15ab/jw4fjtt98wa9Ys6fNkt27dwtmzZ+Hp6YnDhw9XyDX16dMH06dPx5o1a9CmTRsMGDAA5ubmePLkCcLDwzFt2jRMmTKlQvomzTEyMkKbNm1w6dIlaGlpwcXFRXqseNbx+fPncHd3L3GRlyq6desGkUiEhQsX4u7duzAxMYGpqan0dvSmTZvg6emJzz77DBs2bICzszP09fWRmJiIy5cv4/nz50hJSVF7XB8izjRSpdCuXTscOnQIWVlZ+N///oeIiAh4eHggJCSk1Bd7A8CECROwatUq1KpVC9u3b8eRI0fg4uKCkJAQhc9CikQibNmyBatXr4a1tTV2796NP/74A9euXcOgQYMUrvR7V7NmzRAUFISaNWti2LBhiIiIUOHKqbqytbXFnDlzYGJigrNnz2LdunU4efIknJ2dsX//fkyYMEHunIEDB2Lr1q2IjY3Fhg0bcPv2bYwaNQrBwcEyr+ixtrbG0aNH4ebmhjNnzmDr1q3Iy8tDYGAgPv744wq9roULF2L79u1o3rw5Dh06hHXr1klfwdKjR48K7Zs0pzg5bNmypcxYrFevHho2bAigYm5NA0CTJk2wbt061K5dG3/88Qd++eUXrFmzRnrc3t4e586dw3fffYeXL19i586d2Lp1K27evIkuXbpg06ZNFRLXh0iUkZEh0XQQREQfsp07d2Lq1KlYt24dRo8erelwiIgU4kwjEREREZWJSSMRERERlYlJIxERERGVic80EhEREVGZONNIRERERGVi0khEREREZWLSSERERERlYtJIRERERGVi0khEH4zHjx9DLBbL7GhBRETKYdJIRBrl4eEhTeSK/1hYWOCjjz6Ci4sLJk+ejF27duHVq1eaDlUjlixZArFYjCVLllRoPzt37pT7/yAWi2FlZYU2bdrgyy+/xN9//12hMRBR5aaj6QCIiACgfv36qF+/PgCgoKAAWVlZiImJwe3btxEQEIA5c+bg559/xmeffSa4D11dXXz00UfqCrlaqlGjBlq3bi39+tmzZ4iPj0dsbCz27t0LPz8/hftmE1H1x6SRiCqF0aNHY+7cuTJl+fn5uHTpEtauXYvjx4/D19cX9+/fx9KlSwX1YWVlhcuXL6sj3Gqrbt26CA4Olil78uQJpk2bhrCwMMydOxe9e/eGvb29ZgIkIo3h7WkiqrR0dXXh4uKCgIAA/PjjjwCA33//HYcPH9ZwZB8WKysrbNq0Cfr6+igoKEBQUJCmQyIiDWDSSERVgq+vL7p37w4A8PPzkzte/Aze48eP8ffff+PTTz9Fo0aNULt2benzgCUthGndujXEYjH27dtXYv+ZmZmwtLSEWCzGtWvX5I6Hh4dj3LhxaNq0KczNzdGgQQMMHjwYR48eVdhe8TOEHh4eKCoqwqZNm9CzZ0/Y2tpKr6MssbGx+Prrr9G6dWtYWFigXr16aN68OQYMGIDly5fj5cuXZbahrNq1a6Nhw4YAgEePHskdT0pKwoYNGzBkyBC0atUKlpaWsLGxgZubG5YtW4bs7Owyr2XmzJno0KEDrK2tUb9+fbRv3x7Tpk1DRESEwnOuX7+OL7/8Ei1atICFhQVsbW3Rr18/7Ny5E0VFRSpfMxHJYtJIRFXGpEmTAAC3bt1CQkKCwjpBQUHo27cvwsLCYGVlBQcHB4hEolLbHT58OABg9+7dJdY5ePAgcnNz0aRJE5ln/iQSCWbNmoWBAwfi0KFDeP36NZo2bQpdXV2EhYVh9OjRmDlzZontSiQSjBs3Dt999x2ePXuGhg0bwszMrNR4AeDGjRtwc3PDtm3b8PTpUzRo0ACNGzdGfn4+Lly4gEWLFiElJaXMdsrj9evXAAADAwO5Yxs2bMDcuXNx4cIFSCQSODk5wczMDLdu3cIvv/yC3r17IyMjQ2G7O3fuRKdOnbBx40bExsbCzs4O9vb2SElJwY4dO7B48WK5c1avXo0ePXpg9+7dyMjIwEcffQRjY2NERkZi6tSpGDt2LAoLC9V6/UQfOj7TSERVRufOnSESiSCRSHD58mXY2NjI1fn5558xefJkzJs3D/r6+gD+L9kpyYgRI+Dn54czZ84gOTkZlpaWcnWKE8oRI0bIlK9evRp//PEHrK2tsWLFCnz88cfSY6Ghofjyyy+xceNGtG3bVu5cAIiKioKxsTEOHDiAnj17Ani7EKjY3Llz5Z71BN7OtmZnZ+OTTz7B8uXLYWJiIj32/PlzBAYGwtjYuNTrLo9bt24hLi4OAODs7Cx3vHfv3vj444/RuXNnaGtrS8sTExMxc+ZMHD9+HPPnz8eqVatkzjt79iymT5+OoqIiTJgwAd9//73MTPCVK1dw/fp1mXMOHDiAH3/8Eaampli6dCmGDx8OLa23cyBXr17FF198gaNHj2LFihWYNWuWmr4DRMSZRiKqMsRisTQRevbsmcI6bm5uWLRokTRhBICaNWuW2q69vT06deqEwsJChbeoHz16hIsXL0JLSwuffPKJtDwjIwPLli2DtrY2duzYIZMwAoC7uztWrFgBAHLJUrHCwkIsW7ZMmjACgI6ODnR0Sv+d/sGDBwCA6dOnyySMAFCnTh1MmDAB5ubmpbahjNTUVBw6dAijR49GUVERGjduDG9vb7l6bm5u6Nq1q0zCCLxdFb9582bo6upi3759crN/P/74I4qKijBixAgsW7ZM7tGBdu3a4YsvvpB+XVBQgJ9++gkAsHbtWowcOVKaMAJAmzZtsGXLFohEIqxbtw55eXkqfgeIqBhnGomoSjEyMkJWVhZycnIUHh87dqygdkeMGIHIyEjs3r0b06dPlzm2d+9eSCQSdO/eHVZWVtLykydPIicnB+3atZO5Zf2ufv36QVdXF/fu3VM4i2lsbIxBgwaVO14bGxs8ePAABw4cQLNmzWQSJ1UkJCQofPm5lpYWvLy8sGzZMujq6io8NysrC4GBgYiKikJycjJev34NiUQiPT8nJwcxMTFo1KgRgLfPmN64cQMA8N133ykV35UrV5CQkAALCwt4enoqrNOqVSvY2NggPj4e169fR4cOHZRqm4hKx6SRiKqU4mTx37NrxZo0aSKoXW9vb8yePRu3b99GdHQ0WrZsKT22Z88eAPK3pm/dugXgbfLz71nGdxU/U5mUlCSXNDZs2LDMWUVFvvrqK5w5cwarVq3C7t270bNnT3To0AGdO3eWJmVCvPuexvz8fCQkJODZs2fQ09NDq1atULduXYXnRUREYPz48UhNTS21/fT0dOnf79y5A0B2kU1Zir/nr1+/LvV7/uLFCwBvv+dEpB5MGomoynjx4gWysrIAoMTkRdEiDWWYmpqif//+OHDgAHbv3i1NGi9fvoyYmBgYGxvLzWwVL+xITU0tM1kCoHBXG6Hxdu/eHUFBQVixYgXOnz+PnTt3YufOnQDeJs5z586Fl5dXudv993saJRIJDh06hC+//BLz58+HoaEhJk6cKHNOVlYWxo0bh+fPn8PNzQ3ffPMNmjVrBrFYLJ2VbN68ORITE5Gfny89r3hFtampqdLxFX/Ps7KycPHixTLrf6g7CRFVBCaNRFRlXLhwQfr39u3bq739ESNG4MCBA9i/fz8WLlwIbW1t6QIYT09PuQTP0NBQet7vv/+u9njK0rVrV3Tt2hWvXr3C5cuXcfHiRQQFBeH27dsYN24c9u3bh969e6vUh0gkgre3N9LT0+Hr64uffvoJ/fv3l+7eAwAhISF4/vw56tevj927d8s9QyqRSBSunC5+PjUzM1PpeIq/5126dMGxY8cEXBERCcWFMERUZfzvf/8D8Hb1rrW1tdrbd3d3R926dfHs2TOEhoYiLy8PBw4cAACMHDlSrr6TkxMA4Pbt22qPpTwMDAzg5uaG2bNn4/z589IZxk2bNqmtj/Hjx8PJyQmvX7/GL7/8InOs+J2SrVu3Vrjo6M6dOwqfQW3WrBmAt7esHz58qFQcxd/zu3fv8l2MRO8Zk0YiqhJWrlyJ8PBwAMDs2bMrpA9tbW0MHToUwNvnGE+cOIEXL17AxsYGXbt2lav/8ccfo2bNmrh58yZOnz5dITGVl0gkQseOHQEAT58+VVu7Wlpa0u/73r17ERMTIz1WnCiW9F7I1atXKyy3tbWVPj+5cuVKpeLo3Lkz6tWrh/T0dPz5559Kx09EqmPSSESVVkFBAS5cuICRI0diwYIFAIBp06ahf//+FdZn8WKXY8eOSWfqhg8frvAF4ebm5tJVv+PGjUNAQIDMOxaBt89hBgQE4IcfflBrnOPGjUNQUJDcM3txcXHYtm0bgLevn1GngQMHwsnJCYWFhfj111+l5V26dAEAXLp0CVu3bpWW5+XlYdGiRdi3bx/09PQUtjl//nxoaWlh165dmD17ttxt7L///ltmxlRPT086FmbNmoX169fLvYczJycHhw4dklsFT0SqEWVkZEg0HQQRfbg8PDwQERGB+vXrS5+TKywsRFZWFuLj46UJgampKRYsWIBx48YpbKf4NTE3btyAnZ2dwjqPHz+Wvpi6pN1JgLdJUPHKXuBt4uLo6KiwrkQiwU8//SSdTTMyMoKjoyN0dHTw7NkzJCYmQiKRwMXFRWZLwZ07d2Lq1Kly5cqytbVFVlYWdHR00KBBA5iamuLFixeIjY2FRCKBo6Mjjh8/XuKCoX8rjsfGxgY3b94ssd6hQ4cwbtw4aGtrIyoqSrrqedKkSdJV5vXq1YOlpSViYmKQlZWFefPmYdu2bUhISMDhw4fRrVs3mTZ37NiBb775Bvn5+dDV1UWjRo0gEokQHx+PrKwshd+jDRs24IcffkBBQQH09fXRsGFD6OvrIy0tDY8fP0ZRUVGZ10JE5cOFMERUKSQmJiIxMRHA29kkExMTODg4oGXLlnB1dYW3t3eZL+lWl5EjR0pnBtu3b19iwgi8vR28YMECeHt7Y/Pmzbhw4QLu3buHwsJC1KlTB+7u7ujTpw88PDzUGuPvv/+O06dPIyoqCk+fPkVcXBwMDAzQunVreHh4YOLEiWrdEaZY8WzjnTt38Ouvv+KPP/4AAKxfvx5OTk7YsWMHHj16hNevX8PZ2RmTJk3CgAEDpLOfiowZMwYdO3bE+vXrcebMGcTExEBPTw/16tWDt7e3wudJJ0+eDHd3d2zcuBHh4eGIi4vDmzdvULt2bXTp0gW9e/fGgAED1H79RB8yzjQSERERUZn4TCMRERERlYlJIxERERGViUkjEREREZWJSSMRERERlYlJIxERERGViUkjEREREZWJSSMRERERlYlJIxERERGViUkjEREREZWJSSMRERERlYlJIxERERGViUkjEREREZWJSSMRERERlYlJIxERERGV6f8BDTbZZC/S6uYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.bar(searched3['driver_race'], searched3[\"prop_hit\"], yerr=[low,high], capsize=10)\n",
+    "plt.xlabel(\"Driver's Race\")\n",
+    "plt.ylabel(\"Proportion of Successful Searches\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The 95% bootstrap confidence intervals for Black and Hispanic and Hispanic and White seem to overlap, but those for Black and White don't, indicating there is a significant difference between the two. Let's double check this with a hypothesis test for the difference in proportion of successful searches between Black and White drivers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "differences = np.array([])\n",
+    "shuffled_dat = idot_20_abbrv.loc[idot_20_abbrv.driver_race.isin(['white','black'])].copy()\n",
+    "\n",
+    "for i in np.arange(5000):\n",
+    "    shuffled_dat['shuffled_races'] = np.random.permutation(shuffled_dat[\"driver_race\"])\n",
+    "    shuffle_searched = shuffled_dat.groupby(\"shuffled_races\")[[\"any_search\",\"search_hit\"]].sum().reset_index().rename(columns={\"any_search\":\"num_searched\", \"search_hit\":\"num_hit\"})\n",
+    "    shuffle_searched['prop_hit'] = shuffle_searched['num_hit'] / shuffle_searched['num_searched']\n",
+    "    diff = shuffle_searched.loc[shuffle_searched['shuffled_races'] == 'white','prop_hit'].iloc[0] - shuffle_searched.loc[shuffle_searched['shuffled_races'] == 'black','prop_hit'].iloc[0]\n",
+    "    differences = np.append(differences, diff)\n",
+    "\n",
+    "data_diff = searched3.loc[searched3['driver_race'] == 'white','prop_hit'].iloc[0] - searched3.loc[searched3['driver_race'] == 'black','prop_hit'].iloc[0]\n",
+    "plt.hist(differences)\n",
+    "plt.scatter(data_diff, -1, color='red', s=30)\n",
+    "plt.title('5000 simulated datasets');\n",
+    "plt.xlabel(\"Difference in proportions of hits\");\n",
+    "plt.ylabel(\"Frequency\");"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here, we can see that Black and Hispanic drivers are also searched more often than other races despite all races having similar hit rates. In other words, though contraband is found in similar rates across all races, police search Black and Hispanic drivers for contraband more often than other drivers. This type of investigation is called an **outcomes analysis** since we are comparing the outcomes (hits) of searches. "
+    "Our permutation test shows that the difference in proportions we see in our data is not consistent with what we would expect if there was no difference between the two groups. In fact, the red dot does not overlap with our histogram at all indicating our p-value is 0. So, we can reject the null hypothesis that Black and White drivers have similar proportions of search hits, and conclude that contraband is found on Black drivers at a significantly lower rate than White drivers!"
    ]
   },
   {
diff --git a/textbook/17/1/prediction.ipynb b/textbook/17/1/prediction.ipynb
index 1edfea3d..ecff6e62 100644
--- a/textbook/17/1/prediction.ipynb
+++ b/textbook/17/1/prediction.ipynb
@@ -153,7 +153,7 @@
     }
    ],
    "source": [
-    "housing_df = pd.read_csv(\"Housing.csv\")\n",
+    "housing_df = pd.read_csv(\"../../data/Housing.csv\")\n",
     "housing_df.head()"
    ]
   },
diff --git a/textbook/17/2/correlation.ipynb b/textbook/17/2/correlation.ipynb
index 62ba85c0..bdcdf3dd 100644
--- a/textbook/17/2/correlation.ipynb
+++ b/textbook/17/2/correlation.ipynb
@@ -249,7 +249,7 @@
     }
    ],
    "source": [
-    "heights_df = pd.read_csv(\"../../12/3/galton.csv\")\n",
+    "heights_df = pd.read_csv(\"../../data/galton.csv\")\n",
     "heights_df.head()"
    ]
   },
diff --git a/textbook/_toc.yml b/textbook/_toc.yml
index 8d595612..fa057717 100644
--- a/textbook/_toc.yml
+++ b/textbook/_toc.yml
@@ -3,7 +3,7 @@ format: jb-book
 root: intro
 
 parts:
-- caption: "Data Science I"
+- caption: "Part I: Exploring Data"
   chapters:
   
   - title: "1. What is Data Science?"
@@ -40,6 +40,8 @@ parts:
       file: 04/2/Dictionaries
     - title: "4.3 Arrays"
       file: 04/3/Arrays-Intro
+    - title: "4.4 Array Indexing and Slicing"
+      file: 04/4/Arrays-Slicing
     - title: "4.5 Assignment for Mutable Data Types"
       file: 04/5/assignment
 
@@ -98,16 +100,12 @@ parts:
   - title: "9. Data Visualization"
     file: 09/data-visualization
     sections:
-    - title: "9.1 Introduction to Matplotlib & Pyplot"
-      file: 09/1/Intro-to-Matplotlib
-    - title: "9.2 Numerical Data"
-      file: 09/2/Numerical_Data
-    - title: "9.3 Categorical Data"
-      file: 09/3/Categorical_Data
-    - title: "9.4 Other Visualization Techniques"
-      file: 09/4/other-viz
-      
-
+    - title: "9.1 Libraries"
+      file: 09/1/Libraries
+    - title: "9.2 Categorical Data"
+      file: 09/2/Categorical_Data
+    - title: "9.3 Numerical Data"
+      file: 09/3/Numerical_Data
 
   - title: "10. Data Collection"
     file: 10/data-collection
@@ -174,13 +172,13 @@ parts:
     sections:
     - title: "15.1 Data Ethics and the Law" 
       file: 15/1/ethics-and-law
-    - title: "15.2 Pillar 1: Mitigating Unintended Consequences" 
+    - title: "15.2 Pillar 1: Data Transparency & Accountability" 
       file: 15/2/pillar1
     - title: "15.3 Pillar 2: Data Privacy" 
       file: 15/3/pillar2
     - title: "15.4 Pillar 3: Informed Consent" 
       file: 15/4/pillar3
-    - title: "15.5 Pillar 4: Data Transparency & Accountability" 
+    - title: "15.5 Pillar 4: Mitigating Unintended Consequences" 
       file: 15/5/pillar4
 
   - title: "16. Traffic Stops Case Study"
@@ -191,7 +189,7 @@ parts:
     - title: "16.2 Investigating Traffic Stops"
       file: 16/2/investigation
 
-- caption: "Data Science II"
+- caption: "Part II: Using Data to Understand Our World"
   chapters:
   - title: "17. Prediction and Correlation"
     file: 17/prediction-and-correlation
diff --git a/textbook/07/data/Data_Science_Jobs_Salaries.csv b/textbook/data/Data_Science_Jobs_Salaries.csv
similarity index 100%
rename from textbook/07/data/Data_Science_Jobs_Salaries.csv
rename to textbook/data/Data_Science_Jobs_Salaries.csv
diff --git a/textbook/17/1/Housing.csv b/textbook/data/Housing.csv
similarity index 100%
rename from textbook/17/1/Housing.csv
rename to textbook/data/Housing.csv
diff --git a/textbook/08/5/Names_2010Census.csv b/textbook/data/Names_2010Census.csv
similarity index 100%
rename from textbook/08/5/Names_2010Census.csv
rename to textbook/data/Names_2010Census.csv
diff --git a/textbook/13/1/Natality2016.csv b/textbook/data/Natality2016.csv
similarity index 100%
rename from textbook/13/1/Natality2016.csv
rename to textbook/data/Natality2016.csv
diff --git a/textbook/08/6/SINGLE.txt b/textbook/data/SINGLE.txt
similarity index 100%
rename from textbook/08/6/SINGLE.txt
rename to textbook/data/SINGLE.txt
diff --git a/textbook/14/Salaries.csv b/textbook/data/Salaries.csv
similarity index 100%
rename from textbook/14/Salaries.csv
rename to textbook/data/Salaries.csv
diff --git a/textbook/11/4/US_births_2000-2014_SSA.csv b/textbook/data/US_births_2000-2014_SSA.csv
similarity index 100%
rename from textbook/11/4/US_births_2000-2014_SSA.csv
rename to textbook/data/US_births_2000-2014_SSA.csv
diff --git a/textbook/08/1/address_list.csv b/textbook/data/address_list.csv
similarity index 100%
rename from textbook/08/1/address_list.csv
rename to textbook/data/address_list.csv
diff --git a/textbook/16/2/adjusted_population_beat.csv b/textbook/data/adjusted_population_beat.csv
similarity index 100%
rename from textbook/16/2/adjusted_population_beat.csv
rename to textbook/data/adjusted_population_beat.csv
diff --git a/textbook/13/3/diabetes.csv b/textbook/data/diabetes.csv
similarity index 100%
rename from textbook/13/3/diabetes.csv
rename to textbook/data/diabetes.csv
diff --git a/textbook/12/3/galton.csv b/textbook/data/galton.csv
similarity index 100%
rename from textbook/12/3/galton.csv
rename to textbook/data/galton.csv
diff --git a/textbook/13/4/gss.csv b/textbook/data/gss.csv
similarity index 100%
rename from textbook/13/4/gss.csv
rename to textbook/data/gss.csv
diff --git a/textbook/16/2/idot_abbrv.csv b/textbook/data/idot_abbrv.csv
similarity index 100%
rename from textbook/16/2/idot_abbrv.csv
rename to textbook/data/idot_abbrv.csv
diff --git a/textbook/08/3/nametable.csv b/textbook/data/nametable.csv
similarity index 100%
rename from textbook/08/3/nametable.csv
rename to textbook/data/nametable.csv
diff --git a/textbook/10/1/simpsons_paradox_covid.csv b/textbook/data/simpsons_paradox_covid.csv
similarity index 100%
rename from textbook/10/1/simpsons_paradox_covid.csv
rename to textbook/data/simpsons_paradox_covid.csv
diff --git a/textbook/07/data/student_scores_data.csv b/textbook/data/student_scores_data.csv
similarity index 100%
rename from textbook/07/data/student_scores_data.csv
rename to textbook/data/student_scores_data.csv
diff --git a/textbook/intro.md b/textbook/intro.md
index 49d85e69..cd33ca46 100644
--- a/textbook/intro.md
+++ b/textbook/intro.md
@@ -1,6 +1,6 @@
-# Introduction to Data Science I & II
+# Introduction
 
-[Dan L. Nicolae](https://www.stat.uchicago.edu/~nicolae/), [Michael J. Franklin](https://cs.uchicago.edu/people/michael-franklin/), [Amanda R. Kube Jotte](https://amandakube.github.io/), Evelyn Campbell, Susanna Lange, Jesse London, and Will Trimble
+[Dan L. Nicolae](https://www.stat.uchicago.edu/~nicolae/), [Michael J. Franklin](https://cs.uchicago.edu/people/michael-franklin/), [Amanda R. Kube Jotte](https://amandakube.github.io/), Evelyn Campbell, Susanna Lange, Will Trimble, and Jesse London
 
 
 Forthcoming...