chapter_2_two_variable_regression_analysis_basic_ideas.ipynb

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      "name": "chapter 2 - two-variable regression analysis - basic ideas.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shengy90/basic-econometrics-/blob/master/chapter_2_two_variable_regression_analysis_basic_ideas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opuxrvnfP-Q0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns; sns.set()\n",
        "import matplotlib.pyplot as plt \n",
        "\n",
        "% matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wcoy9M6SNngU",
        "colab_type": "text"
      },
      "source": [
        "#### **Load data from github**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OQluhvMYN__e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd \n",
        "url10 = \"https://raw.githubusercontent.com/shengy90/basic-econometrics-/master/datasets/table2_10.csv\"\n",
        "url9 = \"https://raw.githubusercontent.com/shengy90/basic-econometrics-/master/datasets/table2_9.csv\"\n",
        "url8 = \"https://raw.githubusercontent.com/shengy90/basic-econometrics-/master/datasets/table2_8.csv\"\n",
        "url7 = \"https://raw.githubusercontent.com/shengy90/basic-econometrics-/master/datasets/table2_7.csv\"\n",
        "table_10 = pd.read_csv(url10, thousands=r',')\n",
        "table_9 = pd.read_csv(url9, thousands=r',')\n",
        "table_8 = pd.read_csv(url8, thousands=r',')\n",
        "table_7 = pd.read_csv(url7, thousands=r',')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wGb8TrU_LtCK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "fa5b32a4-3ffb-491b-8927-0027409bd213"
      },
      "source": [
        "table_10.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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 Income</th>\n",
              "      <th>Number of test takers</th>\n",
              "      <th>Critical Reading (Mean)</th>\n",
              "      <th>Critical Reading (SD)</th>\n",
              "      <th>Mathematics (Mean)</th>\n",
              "      <th>Mathematics (SD)</th>\n",
              "      <th>Writing (Mean)</th>\n",
              "      <th>Writing (SD)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>&lt; 10,000</td>\n",
              "      <td>40610</td>\n",
              "      <td>427</td>\n",
              "      <td>107</td>\n",
              "      <td>451</td>\n",
              "      <td>122</td>\n",
              "      <td>423</td>\n",
              "      <td>104</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10000-20000</td>\n",
              "      <td>72745</td>\n",
              "      <td>453</td>\n",
              "      <td>106</td>\n",
              "      <td>472</td>\n",
              "      <td>113</td>\n",
              "      <td>446</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20000-30000</td>\n",
              "      <td>61244</td>\n",
              "      <td>454</td>\n",
              "      <td>102</td>\n",
              "      <td>465</td>\n",
              "      <td>107</td>\n",
              "      <td>444</td>\n",
              "      <td>97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30000-40000</td>\n",
              "      <td>83685</td>\n",
              "      <td>476</td>\n",
              "      <td>103</td>\n",
              "      <td>485</td>\n",
              "      <td>106</td>\n",
              "      <td>466</td>\n",
              "      <td>98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40000-50000</td>\n",
              "      <td>75836</td>\n",
              "      <td>489</td>\n",
              "      <td>103</td>\n",
              "      <td>486</td>\n",
              "      <td>105</td>\n",
              "      <td>477</td>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Family Income  Number of test takers  ...  Writing (Mean)  Writing (SD)\n",
              "0      < 10,000                  40610  ...             423           104\n",
              "1   10000-20000                  72745  ...             446           102\n",
              "2   20000-30000                  61244  ...             444            97\n",
              "3   30000-40000                  83685  ...             466            98\n",
              "4   40000-50000                  75836  ...             477            99\n",
              "\n",
              "[5 rows x 8 columns]"
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          "metadata": {
            "tags": []
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          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8FaLok-nicrN",
        "colab_type": "text"
      },
      "source": [
        "# **Theoretical Questions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BiTa7J-kP6e0",
        "colab_type": "text"
      },
      "source": [
        "## **Question 1**\n",
        "\n",
        "**What is the conditional expectation function or the population regression function?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opRc4GmoMa4q",
        "colab_type": "text"
      },
      "source": [
        "Ans: The conditional expectation function or population regression function is $\\mathbb{E}(Y|X_{i}) = f(X_{i})$, i.e. the function that describes how the conditional mean of $Y$ given $X_{i}$ varies with $X_{i}$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g9P5gHSncxUU",
        "colab_type": "text"
      },
      "source": [
        "## **Question 2**\n",
        "**What is the difference between the population and sample regression functions? Is this a distinction without difference?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Odq2z2NzNFkO",
        "colab_type": "text"
      },
      "source": [
        "Ans: The sample regression function is given by $\\hat{Y_i} = f(X_i)$ where $\\hat{Y_i}$ is the estimator of $\\mathbb{E}(Y|X_i)$. This distinction is not without difference - in real life, we don't always have the population available for examination - and we only have a sample to work with, where different samples might yield different coefficients for $f(X_i)$. The goal is to find coeffients such that we get $\\hat{Y_i}$ to be as close to the true $Y_i$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kMsBZRWjcySY",
        "colab_type": "text"
      },
      "source": [
        "## **Question 3**\n",
        "**What is the role of the stochastic error term $u_i$ in regression analysis? What is the diffference between the stochastic error term and the residual $\\hat{u_i}$.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "43lLxmhiOUx4",
        "colab_type": "text"
      },
      "source": [
        "Ans: The stochastic error term represents the surrage of all other factors that affect $Y$, but aren't included in the model. The difference between $u_i$ and $\\hat{u_i}$, is that the latter represents the stochastic error term *of the sample*, which is an estimation of the former. However, both should satisfy the condition $\\mathbb{E}(u_i) = 0$."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uQ3wdaU7czdS",
        "colab_type": "text"
      },
      "source": [
        "## **Question 4**\n",
        "**Why do we need regression analysis? Why not simply use the mean of the regressand as its best value?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxeUSw-wPZD2",
        "colab_type": "text"
      },
      "source": [
        "Ans: Using the mean of the regressand simply yields $\\mathbb{E}(Y)$, i.e. mean of the dependent variable of the population regardless of what the regressand's value is. However we know that there is a relationship between the regressand and regressor such the mean of the regressor varies with the value of regressand. \n",
        "\n",
        "Regression analysis therefore allows us to predict with much higher accuracy the real value of the regressor $(Y_i)$ given the value of the regressand $(X_i)$, rather than predicting the average regardless of what the value of the regressand is.\n",
        "\n",
        "For example, we know that height varies with age. Regression analysis allows us to predict with much higher accuracy the height given the age of someone, instead of blindly predicting the same height for everyone."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zzAWDMNwokIL",
        "colab_type": "text"
      },
      "source": [
        "## **Question 5**\n",
        "**What do we mean by a *linear* regression model?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "57B0L4iaQ02G",
        "colab_type": "text"
      },
      "source": [
        "Ans: A linear regression model refers to a model that takes a functional form that is *linear* in parameters. It does not matter if it's linear in variable or not.\n",
        "\n",
        "For example:\n",
        "- $Y = \\beta_1 + \\beta_2 x^2$ is linear in parameter (but not in variable)\n",
        "- $Y = \\beta_1 + \\beta_2^2 x$ is non-linear in parameter (but linear in variable)  \n",
        "- $Y = \\beta_1 + \\beta_2^2 x^2$ is non-linear in both parameter and variable\n",
        "- $Y = \\beta_1 + \\beta_2 x$ is linear in both parameter and variable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqZqCT69QstS",
        "colab_type": "text"
      },
      "source": [
        "## **Question 6**\n",
        "**Determine whether the following models are linear in parameters, or the variables, or both. Which of these models are linear regression models?**\n",
        "\n",
        "**a. $Y_i = \\beta_1 + \\beta_2 (\\frac{1}{x_i}) + u_i$**\n",
        "\n",
        "**b. $Y_i = \\beta_1 + \\beta_2 \\ln(x_i) + u_i$**\n",
        "\n",
        "**c. $\\ln(Y_i) = \\beta_1 + \\beta_2 x_i + u_i$**\n",
        "\n",
        "**d. $\\ln(Y_i) = \\ln(\\beta_1) + \\beta_2 \\ln(x_i) + u_i$**\n",
        "\n",
        "**e. $\\ln(Y_i) = \\beta_1 - \\beta_2 (\\frac{1}{x_i}) + u_i$**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Mad6PxMVSY-o",
        "colab_type": "text"
      },
      "source": [
        "Ans:\n",
        "\n",
        "a. Linear in parameters but not variables.\n",
        "\n",
        "b. Linear in both parameters and variables.\n",
        "\n",
        "c. Linear in both parameters and variables.\n",
        "\n",
        "d. Linear in both parameters and variables.\n",
        "\n",
        "e. Linear in parameters but not variables.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6mVEF-P-QtUE",
        "colab_type": "text"
      },
      "source": [
        "## **Question 7**\n",
        "**Are the following models linear regression models? Why or why not?**\n",
        "\n",
        "**a. $Y_i = e^{\\beta_1 + \\beta_2X_i + u_i}$**\n",
        "\n",
        "**b. $Y_i = \\frac{1}{1 + e^{\\beta_1 + \\beta_2X_i + u_i}}$**\n",
        "\n",
        "**c. $\\ln(Y_i) = \\beta_1 + \\beta_2(\\frac{1}{x_i}) + u_i$**\n",
        "\n",
        "**d. $Y_i = \\beta_1 + (0.75-\\beta_1)e^{-\\beta_2(X_i - 2)} + u_i$**\n",
        "\n",
        "**e. $Y_i = \\beta_1 + \\beta_2^3 X_i + u_i$**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCVrsuaOTjBd",
        "colab_type": "text"
      },
      "source": [
        "Ans:\n",
        "\n",
        "a. This in its current form is not a linear regression model, although it could be transformed into a linear form: $\\ln(Y_i) = \\beta_1 + \\beta_2X_i + u_i$ which is linear in both parameters (though not variables). \n",
        "\n",
        "b. This in its current form is not a linear regression model, although it could be transformed into a linear form: $\\ln(Z_i) = \\beta_1 + \\beta_2X_i + u_i$ where $Z_i = \\frac{1}{Y_i}-1$ which is linear in paramters (though not variables)\n",
        "\n",
        "c. This is a linear regression model - it is linear in paramters (though not in variables)\n",
        "\n",
        "d. This is not a linear regression model, and it can't be transformed into a linear form.\n",
        "\n",
        "e. This is not a linear regression model - although it's linear in variables, it's not linear in paramter."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nIv-qyZxQtvG",
        "colab_type": "text"
      },
      "source": [
        "## **Question 8**\n",
        "**What is meant by an intrinsically linear regression model? If $\\beta_2$ in Exercise 2.7d were 0.8, would it be a linear or nonlinear regression model?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKXDh3ikVNSf",
        "colab_type": "text"
      },
      "source": [
        "A linear regression model is linear in parameter - it does not matter if it's linear in variable or not. Even if $\\beta_2$ takes the value of 0.8, it would still be a nonlinear regression model because $\\beta_1$ is multiplied by a term that contains $\\beta_2$. \n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5siDr7B1QuEW",
        "colab_type": "text"
      },
      "source": [
        "## **Question 9**\n",
        "**Consider the following nonstochastic models (i.e. models without the stochastic error term). Are they linear regression models? If not, is it possible, by suitable algebraic manipulations, to convert them into linear models?**\n",
        "\n",
        "**a. $Y_i = \\frac{1}{\\beta_1 + \\beta_2 X_i}$**\n",
        "\n",
        "**b. $Y_i = \\frac{X_i}{\\beta_1 + \\beta_2 X_i}$**\n",
        "\n",
        "**c. $Y_i = \\frac{1}{1 + \\exp{(-\\beta_1 - \\beta_2 X_i)}}$**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dXnnulX6Wqxw",
        "colab_type": "text"
      },
      "source": [
        "Ans:\n",
        "\n",
        "---\n",
        "\n",
        "a. This in its current form is not a linear regression model, although it could be transformed into a linear form: $Z_i = \\beta_1 + \\beta_2 X_i$ where $Z_i = \\frac{1}{Y_i}$, which is linear in paramter (though not in variables).\n",
        "\n",
        "---\n",
        "\n",
        "b. This in its current form is not a linear regression model, although it could be transformed into a linear form:\n",
        "\n",
        "$$\\beta_1 + \\beta_2 X_i = \\frac{X_i}{Y_i}$$\n",
        "$$\\frac{1}{X_i} \\beta_1 + \\beta_2 = \\frac{1}{Y_i}$$\n",
        "$$A \\beta_1 + \\beta_2 = B$$\n",
        "\n",
        "Where $A = \\frac{1}{X_i}$ and $B = \\frac{1}{Y_i}$. This is linear in parameter (although not in variable).\n",
        "\n",
        "--- \n",
        "\n",
        "c. This in its current form is not a linear regression model, although it could be transformed into a linear form:\n",
        "\n",
        "$$\\exp{(-\\beta_1 - \\beta_2 X_i)} = \\frac{1}{Y_i} - 1$$\n",
        "$$-\\beta_1 - \\beta_2 X_i = ln(\\frac{1}{Y_i} - 1)$$\n",
        "\n",
        "Which is linear in paramters, though not in variables.\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8io4a-JHQuYd",
        "colab_type": "text"
      },
      "source": [
        "## **Question 10**\n",
        "**You are given a scattergram in Figure 2.8 along with the regression line. What general conclusion do you draw from this diagram? Is the regression lin sketched in the diagram a population regression line or the sample regression line?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YrVYXZDeqEE",
        "colab_type": "text"
      },
      "source": [
        "There is a positive correlation between the average annual change in export-GNP ratio and the average growth in real manufacturing wages. The data is a sample regression line because it consists of data for only 50 developing countries, not all developing countries."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1QI6ESyyZmWM",
        "colab_type": "text"
      },
      "source": [
        "## **Question 11**\n",
        "**From the scattergram in Figure 2.9, what general conclusions do you draw? What is the economic theory that underlies this scattergram? (Hint: look up any intenational economics textbook and read up on the Heckscher-Ohlin model of trade)**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MlLEacwhfSnm",
        "colab_type": "text"
      },
      "source": [
        "There is a positive correlation between the skillness of an economy vs the 'sophisitication' of its export:\n",
        "- the less skilled an economy, the less exports consist of manufactured goods and more off raw materials\n",
        "- the more skilled an economy, the less exports consist of raw material and more of manufactured goods\n",
        "\n",
        "According to Heckschner-Ohlin model of trade, countries exploits their comparative advantage when it comes to exporting goods. A highly skilled economy has much better comparative advantage when it comes to manufacturing goods rather than processing raw materials due to the abundance of skilled workers, hence they specialise in manufactured goods (and thus export more). And because manufactured goods require raw materials, quite naturally they'll export less of raw materials as they'd be used for manufacturing.\n",
        "\n",
        "On the other hand, a lowly-skilled economy do not have enough skilled workers to specialise in manufacturing industry, and thus surplus of raw materials which they can then export, i.e. compared to highly-skilled economy, lowly-skilled economy has comparative advantage when it comes to producing raw materials for export. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eR9thQigZpVk",
        "colab_type": "text"
      },
      "source": [
        "## **Question 12**\n",
        "**What does the scattergram in Figure 2.10 regeal? On the basis of this diagram, would you argue that minimum wage laws are good for economic well-being?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xy3mX2TOg8ud",
        "colab_type": "text"
      },
      "source": [
        "There seems to be a negative correlation between the ratio of one year's salary at minimum wage to GNP per capita, and the GNP per capita, i.e. the higher the ratio, the lower the GNP per capita.\n",
        "\n",
        "Correlation does does not imply causation. This negative correlation does not mean that minimum wage is bad for economy! It's quite likely that in developing countries, minimum wage are 'similar' in the sense that it'll be the amount needed for people to 'survive', regardless of how rich a country is. \n",
        "\n",
        "As a result, as GNP per capita increases, the denominator increases despite numerator staying the same, which reduces this ratio.\n",
        "\n",
        "In other words, this negative ratio is a by-product how minimum wage law policy works, rather than the fact that the minimum wage law policy results in GNP per capita decreasing! Simply scrapping minimum wage law doesn't make a 'poor' country rich! "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wv1Hmbj2Zo7u",
        "colab_type": "text"
      },
      "source": [
        "# **Emperical Questions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_vqsZ8PpZoti",
        "colab_type": "text"
      },
      "source": [
        "## **Question 14: You are given the data in `table_7` for the United States for years 1980-2006.** "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8UbZPiTiw8L",
        "colab_type": "text"
      },
      "source": [
        "#### **a. Plot the male civilian labour force participation rate against male civilian unemployment rate. Eyeball a regression line through the scatter points. A priori, what is the expected relationship between the two and what is the underlying economic theory? Does the scattergram support the theory?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z_jM6f8ai_ho",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "ac64fadd-7bdc-46ee-8796-b9f541bccb63"
      },
      "source": [
        "sns.scatterplot(y='UNRM', x='CLFRPM', hue='Year', data=table_7)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa646347898>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "12jQ7MVbjZme",
        "colab_type": "text"
      },
      "source": [
        "`Labour participation rate` refers to the % of the population that are in the labour force (i.e. in employment or actively seeking employment):\n",
        "\n",
        "$$\\frac{\\text{People eligible for work}}{\\text{Total Population}}$$\n",
        "\n",
        "`Unemployment rate` refers to the % of the labour force that is currently out of work:\n",
        "\n",
        "$$\\frac{\\text{Eligible but out of work}}{\\text{Eligible For Work}}$$\n",
        "\n",
        "There are many reasons why the unemployment rate can fall:\n",
        "1. A booming economy where everyone who are eligible for work can find work, i.e. labour force remained the same but people out of work decreased\n",
        "2. A booming economy where people who weren't looking for a job got offered one and entered the job market, i.e. labour force increased, whilst number of people out of work remained the same.\n",
        "3. Structural weakness in an economy where people are dropping out of the labour force, e.g. ageing economy or skill of economy becomes irrelevant\n",
        "\n",
        "How unemployment rate and labour participation rate trends depend on the state of the economy. \n",
        "\n",
        "The data for male labour participation rate vs unemployment rate showed that from 1980 -> 2010, unemployment rate has fallen as labour participation rate falls, which is one of the ways we'd expect unemployment rate could fall according to theory.\n",
        "\n",
        "https://www.investopedia.com/articles/investing/103015/how-labor-force-participation-rate-affects-us-unemployment.asp\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k6f9A1PSm_zn",
        "colab_type": "text"
      },
      "source": [
        "#### **b. Repeat (a) for females.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbCO1I0ep8je",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "7cd26546-e97e-45b1-8c72-f307f48be66c"
      },
      "source": [
        "sns.scatterplot(y='UNRF', x='CLFPRF', hue='Year', data=table_7)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa64613a4e0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soveOTZvqHeV",
        "colab_type": "text"
      },
      "source": [
        "Data shows the contrary that there's a negative relationship between labour participation rates for females and unemployment rate compared to the male conuterpart. \n",
        "\n",
        "This can also be supported by theory:\n",
        "- From 1980 -> 2000, more and more job opportunities have become available for women\n",
        "- a lot of women who previously weren't considering a job were offered jobs and joined the work force, increasing the size of labour force, whilst the number of unemployed remained the same, resulting in a fall in unemployment rate\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VieGtHserEDS",
        "colab_type": "text"
      },
      "source": [
        "#### **c. Now plot both the male and female labour participation rates against average hourly earnings (in 1982 dollars). You may use separate diagrams. Now what do you find and how would you rationalise your findings?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJzbMuj9rNut",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "cfd8a952-42c5-4b57-a7e3-f56aa20064f6"
      },
      "source": [
        "sns.scatterplot(x='CLFRPM', y='AHE82', hue='Year', data=table_7)"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa645ebfe10>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qjDlhYMjriUv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "outputId": "79ebad4a-14cb-437e-c848-5465c3b22f3e"
      },
      "source": [
        "sns.scatterplot(x='CLFPRF', y='AHE82', hue='Year', data=table_7)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa645f56748>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EfKy9xAusJB1",
        "colab_type": "text"
      },
      "source": [
        "There seems to be an opposite relationship between male and female labour participation rates and the average hourly earnings:\n",
        "- over the years, the average earnigs had increased (apart from the late 1990s where it's fell significantly, likely due to recession)\n",
        "- when the economy went into recession, average hourly earnings fell and as a result a lot of men dropped out of the labour force (resulting in falling participation rates). But when the economy recovered, these men didn't come back to the labour force.\n",
        "- On the contrary, when the economy recovered in the 90s, average earning increased and women were the ones that entered the labour force."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Ls2ipgZsiMm",
        "colab_type": "text"
      },
      "source": [
        "#### **d. Can you plot the labour force participation rate against the unemployment rate and the average hourly earnings simultaneously? If not, how would you verbalise the relationship amongst the three variables?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9mjQdrLdrwR5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        },
        "outputId": "9ef02683-9bdf-4533-dc5f-4abdef649d14"
      },
      "source": [
        "qn14d_f = table_7[['CLFPRF', 'AHE82', 'UNRF']]\n",
        "sns.pairplot(qn14d_f)"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7fa649020f60>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 540x540 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iRw4r7DbZoSg",
        "colab_type": "text"
      },
      "source": [
        "## **Question 15:**\n",
        "\n",
        "**`table_8` gives data on expenditure on food and total expenditure, measured in rupees for a sample of 55 rural households from India. (In early 2000, a U.S. dollar was about 40 indian rupees).**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3HW_O3UGxFr9",
        "colab_type": "text"
      },
      "source": [
        "#### **a. Plot the data, using vertical axis for the expenditure on food and the horizontal axis for the total expenditure, and sketch a regression line through the scatter points**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "khxQwzX1xNmr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "eff59fb6-5ea1-4ed6-d35f-e9bf98f76e8f"
      },
      "source": [
        "sns.regplot(y='FOODEXP', x='TOTALEXP', data=table_8)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa644d6ebe0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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eDVUs2swtFijaDgnT4LHxyZqXtDaqpGq3if1GJJNy5e3pgXXlwGEqt3q6kqVEMjbaz1B/45J4K2/+FXYdi4E3MjEqfS2EaIzgjf7urz7F+bkcCctk50CGouNuOBFfq5KqXSb2G51Myq0tB3ZcF9t2OT25yJ9/9jHOTi2t+xSdTgY9t7xksjumyq3Njq7iFiax9ALliyCNsq8NZMQiRENdNTbMPQ+dYpfLqstaYSbiq1VStfLEfpzJpNyO3hTTC3ls2ysFLt/QK2jkmLAMDuxeqdzaN9Kcyq2NRlfNFiaxHG54FEKImqKeiG+lif1gU6xi0SFbsMnnbS+ZNPh111ZuPX1hkaK9/lWHBzJc4VduHWyRyq0oN/8yDDAMA9OEpGlFUlAQZh3LybpfpYxS6n3A+4ErtdbfV0pdD9wBdAEngDdrrc/79616TIjtJOqJ+M0839q5mJ972WUcHO7e0usGDMNbrJgvOuRz9qodFhvFdV0mS5Vbcxw7M1uxciuZMDEN2NGb5ieet48rW+jSYGCrm38ZBpiGQdIySSRNkgkTyzSwTAPTMHBd6O/d2gZi5cLOsTwbLxnciFdqPI3XM+z9Wusfhn0xpdTzgOuBk/7XJnAn8Dat9QNKqd8FPgC8vdaxsK8nRKeIuqVJ2OerNBdzx+ce4w03X7qlS2ZF2yVXKMa28j1s5daRsgn3vu5UY4OKQJjNv0x/FJKwvASSSJhYhoFleYlz7bmP8mcRZoHks4BvAV8D3gtMAPvw+od9Syn1gjULJqs9Txr4CPBG4D7/5ucDWa31A/7Xt+ONTN6+wTEhtpUoWpps5fkqzcXYjrOJuRjXTyY2yzHsYbKULXD88XN8V5/n2MRcxUt7cVZurRVF23xYvfnX7GKekR1d3Hj1Xp59cKc3EjFNLMuoOv/T6IQeZsTy28AntdbvWnP7x5VSf4634dfPh3iePwDu1FqfUEoFtx3AH70AaK0nlVKmUmqo1jGt9XSI1xOio2y2pUkUz1dpLiadtDaciyk6DrmCQzZXaOjIJF+wOXF2vrQw8UzVyq0+vxNwvJVb5eXAR0b7eeSJC5tum7+WYYBlGFx96TDXXr6bpGn4SaT5cz+BMInlJuClVY79D+CrGz2BUuqFwLV4SShWO3f2bulxIyN9EUcSLYmvPhJfOHtHepmZWyZTNheTzRfZO9K7KkbbcckXiuTyDtl8AReLZNoimY62lXvRdjg+McvjJ2bQJ6c5PjGHvWZuJmEZjO3bgTo0yGUHhzi4ty/2N93vj0/yv795Essy6O1Ospgr8rVHJ+jpSpZGf8mESa5g880fnueF11xS8XkM/z9JyyKV8OZFEpZJKmG2dEPKMIllBO8SVCWngDAfoW4CLgeO+6OVS/C6In8IOBjcSSk1DDha62ml1Klqx0K8XsnU1MKmJwVHRvq4cGF+U4+Jk8RXH4kvvJufO8pdX37C3z/Em4sxMXjZtfs4e27O210xZ5Mr2rgNqORyXJczU0ulEcmJs/PrGjgaBuwb7vEvbQ1wzeW7WZhfGVHNXlyOOKqN/csDx8AAyzSxHe/vouOymC3Q35MqVZ+ZhsGF6UWmpxdL1Vmp8ol1w8C0TAy7SL4I+RiK9qL4UBN2B8mK/1601o5SasN/S1rrD+BNvAOglDoBvBL4IfAOpdSL/bmUd7LSl+wRoKvKMSHaTrutdIeVuZivPPwM80t5Du7q5ZbrD7GjO8m3fnCW+779DNMRrvwuVW5N+JVbE3Ms59bvJbhrsKu0luTw3n66ykpvU2Wjq2apVA6csExs28HAG60kEyYuLpfs6mWwPx3bxHocwiSWbqXU16scM/BKgbfET0y3AXcopTL4JcUbHROi3bTLSvdywfqSyw4Mcnh0oLRYcXCwh28++kxkK79nF/OlEcmxiTlmK1Ru7ehNlUYkR/b109/ilVtBOXA6YZFIeJevdg91Ydsug/0ZbMdhOeet2XnxlXtIl5UNt1sSqSRMYvmFDY5/bLMvqrU+VPb/3wCurHK/qseEaCetvNK9nLeHibe+pNbOitVWft/z0MkN+1ctZYscOzNXSiaVCgG6M4mVyq19Awz1pVt6TiEQXM56+XUHuPeR06SSFobhks3bmIbBf7piF09OzHNmconhgQyvvGmMK4+0zs8/KmEWSH4ijkCE6GSNWukexeW1IJnkit58SSHEHiaVLvXYRZuphQI7d7BqFPOKokMqaZXWkpyZXFw3F5NKml7PLf/y1u6hbswWTyRBEgnWiSQTJgnTq87aPdhFJmXxr99a+dn89EuOcNXYcEvNoTVK2AWSA8B/AW7Bm6yfBL4C/JnWeqZx4QnRGRrRwr6ey2suKyOTbL6IXXQ3tSFWpZXfC8tFEpa3qjtfdEpb737qK0+ue7xl+j23/G13L9nV01LlsuUMylqeWJY3sW4ZpYn1anMiVx4Z7sjRSBhhFkjuAx4AFoDPAmeAvXg7R75VKfUirfXphkYpRJuLeuU8bO7y2qpteoN+XHVs01u+8jthGmTzNgXbJWEZFbsAr63cOrinNXpuVWKWJZGkX94btD2ppFXnRJpZLBJmxPIB4N+AXyyvDlNK/T7e7pJ/ArypMeEJ0RmiXjkPG19e28olrjBc12XnQAa1f5DvHZtiKVssJZLyJo4Jy7tMNNCT5B2vfs6qyq1WEFzKsoKRSMIkYZpYFi07egqr2cUiYX7StwJXrC051lq7Sqn3At9vSGRCdJioV86XX14z8MtsDZfR4W7mlwvkCtG1UJldzHPs9CzjE7McPzNfsYtuTyZBwfbmU7rTCX8/E4dbrzvY9KRirr2U5Y9ALMvEqHApq901u1gkzE+7C5itcmwGqL8VphBi015x3QHu/to46ZT3Rr6YLbC4VOCGK/awuFyo67mXc8WV5o2bqNx64umLLbGroWl6o6VU0lulbpnVW550WlKB5m+LECax/AD4KeDTFY69FvhRpBEJ0UEadZ27aDuMXbKDV95wiPu+c5pTZ+fY0Zvmlmv3c3T/5t/I80Wbk6WeW3NMVKvc2tPPlc8aYe9gpmLlljowGHsiWTUnkjIZ6k+TxCm1gd+OGlEsshlhEssfAncppS4B7mZl8v51wO8CtzUuPCHaV5TXuV3X228jV7BXXeLav6uP215+2aZjsx2HZ84v8tTpWY5NzHLq3MK6nlte5VZvacI9qNwaGuphenpx068ZhaABYzJhlXXxXT0n0pVOskB22yYVaEyxyGaEWcfy/ymlfgH4U+CDeJWKBvAM8A6t9RcaG6IQ7ame69yrqrjyNrmCjVNHLy7HdTk7tcT4xCzHTs9x/Owc+cKanlvA6EhPaS3JwT19q8qJ41a+KVVy1aZU7T2xHodGFItsRtheYXcDdyuvg+QwMBlmDxYhtrPNXucOWqjML+WZmct5q963+LHbdV2m53I85U+4H5uYYym7vufWyI4uxvb1c+m+gXU9t+JiAIa5sjXu2pFIJ06uxyHqYpHNCP2vSCl1BHgJfmJRShW01scaFpkQDRRHjf9G17kNwxtJFIqut1ix4LVQ2WGYZAvrt8zdyNxSfmXb3YlZLi5U6bnl99saGx2gvyf+nluG4V1mS/mXsxKmScLflKpSApGk0n7Crrz/X8C78C5/BXMsf6GU+qjW+tcaGJ8QkXv4R+diqfFfe53bcSGdNPnJGw6xlC+S93txuVtcqLicK3KsbNvdCxXaw3dnEt62u6MDXLpvgKH++HtuGQZYlpdIUkmLhF+xtVY7JJB27FBdTSO/lzAr738DeAVwg9b638tuvw74pFLqN7XWH4wkGiFi8Ln7noqlxv+qsWEs0+C+R0+zsFykvzvFc48OMzyQYa7CaGIjK5Vbc4xPzHqVW2vejIPKLa8EOP6eW+X9s1LJ1f2z2l2zFx1Gqdb3cnNM+7H8EvDm8qQCoLV+SCn1FuBv8Sb1hWgL56aXyCRXv9HVW+MffPq7uJBjz1A3N1+7nyOj/ewb6eV1P/6sVR2Cw34yDyq3vL1JalduHfFHJHH23ArmRiwTkgnLSyKWd1mrE0t9m73oMEq1vpebrz9c9/OHSSyXAA9XOfbvwL66oxAiRruHurkwsxRZjf8Pjk/xzw8cpyuTZNdgF7NLee780uPc+p8ObmpNh+O6nJte4ttPTfG9py5w/EyVyq3hHq95476BWCu3glLfdNKivydVSiKV5kY6LalA8xcdRqnR30uYxDIJKCovhLwMmIokEiFi8tM/dikfvfvRumv8bduh4Lh89TsTOC7ML+ZLIwrb8fYsqZVYXNdlej5XWt0+XrVyK+OXAA9wZDSeyq3gktaqUl//Etfwji7cwkqcnZhEKmn2osNAFHMjjf5ewvwL/RTwN0qpn9ZaTwQ3+l2PPw7cFUkkQsTk2st386Zbjob+5fSqt7xEUnRcr3qrYGM73qT78YlZMunEqknxpGVW7Kc1t5Tn2Olgwr1y5dZgX5rDe/v86q0BBhpcuVVe7psq6+ibXNNHK3hDm17IM9SbauuJ661o9qJDiG6ep9HfS5jE8j7gCuAppdRDrFSFXQfc6x8Xoq1Uq/EPkohjOxRdl2LRoVBwKNjempJKn84r7U1SsB0G+9Is54ocPzPnr3Cf4/xMhcqttF+55U+4P+vQTmZmliL9ftd+j8Hq9SCJbHRJq/wNra+rvSeut6rZiw6D145inqfR30uYxDKktX61Uupm4KV461i+BfyR1vreSKIQ21azyzeDT+T5olNaSxK0lw97iWft3iRLuSL5go3rwB/97cPrK7cSJof29pc2udqzc3XlVtTlwOUNGWtVadX6fsvf0Ax/nqVdJ67r0cxFhxDt3Egjv5cwieUJoN9PIvcqpT6ntf6lhkQjtpVmlG8ahndJK1/0RiGFvE3e3vpaEttx6UonOLinn8dPzpDNryxszOa9X3bLNNi/q7c0IrlkpLfiOo4oBHMjqWBuxLJIJKovPgyrkyau21mrzPNsJExiWfvx6ccaEIfYhuIo3wzapJT33cpjMDOX3VLfraByK1iYePzMPLk1q+QNYO9wT6ml/KE9fd5eKQ1iml4iSaUsr0V8he1y651gb5c3tE7XCvM8YYRJLNuk5kPErRGfgoPmjUXHIV9wVibZyxo49rjh/1Gvrdw6NjHHYoXKreGBTGlfkiN7+9d9X1EJJtpLe41UGZFEXalV/oaWsAxyBbvpb2jNvozaDK0wzxNGmH/9CaXUj7Mycln7NVrrf2tEcKKz1fspuHw0UnQcigWHvG3j2NS1BW+pcstfmFipcmugJ7Uy4T7az0BvY/a7Czr8phLeXiPV+mo1uuS3/A1tZiHPYJOrwhq9cryVNXueJ4wwieU8XllxYGrN1y5wJMqgROuL4tNi2GF90KzRtl2KjrfdbcGfbHccb1RRz/tqULkVtEqpVLnVVarc6ufS0QF2DmQa1nPLAJIJg0wq4VVu+WW/5ZqxdiR4QxsZ6ePChfn4AyjT6JXjoj5h9mM5FEMcoo1ENem+dlg/siPD/3H9QZ59eCdFf81IoeCQLwZrRqLZvz1fsHnqmdnSiOR0hZ5byYTJ4b19HPEXJu7d2dieW6ZpkEqYpFMWO3d0kTKi+V47VbsWEzw2Psm9dz/GmQsLLXsZKwrxb74g2l6Uk+5XXzrMc47sLF3OKuQdJi8ub6rcdyO243L6woK3N8npOZ4+P0/RXt9za/+u3tLlrf27Gl+5tbInu0UysdJfK520mJOkUlM7FhMEH8jSKavtm1huRBKL2LStfloM1oysHo0UsYPLWRG9mbquy7mZ5dKEe9XKrZ3dpQn3RlZuBXuyp9YsSFzbqFFGKOG1S3VUueADWSaVoFB0OnotkCQWsWlhPi2unRcpFFfmRaJMIoHpuSzjp2d5yt/kqlbl1tVHd7GrP0V3JhltEPhVW4aBaUE6mSCV3NqCRFFbu1RHlWvXy3dbIYmlzUUxib7Z5yj/tJhJWrhAJmnyky88SLZgMzOXZXJ2ue6RiD41w/3fnWBmPsdgX5obrx4tNXWcX8oz7q8lOTYxV7EvV39PqrSW5MhoPzv8yq2hoR6mpxe3FtQapd0Qk5a/J3uwLzt8/9g0//Ktk23zxtdu2qE6qlzwgSzVRpfvtkoSSxuLYhJ9K89x1dgwlmXw4PfOcoQOF9QAABb3SURBVHE+R3cmwfPVCMM7urg4n8NMJigU6/s4rk/N8PkHj3uXDtIJLi7luftr4xzY1cvUXK5K5ZbFkb0DpZbyww2q3CqfaPdapKwfjXTSplCNtJ3WogQfyLL5IqZhtMXlu62SxNLGophEr/QceeArDz/D1ZcO4zjehlOO45bmRQq2zchAN69+0eqyzigv7Xzt0dPYjkuhaDNbyFMoevuS/OjkxdJ9kgmTQ3v6SvMke4e6Mc3oE0kwKgkubXkjk9otUjppU6hG2W7JN/ie7v3OhFSFidYVxTXbqdksA30pkgnLX3jnffpeWC4wOZfFtqMr860lqNw6NuF1Aj5xdqHi/RKmwUuuGW1o5Vawur00KrHMda+z0fnYTtfTt2o7Jt+rxoa5+frDTV8H1GiSWNrYVksug+qsgu0wdskA0/M5bNslm7cpFh2W80X6u5IU1uxeGKXVlVtzHD8zt65yC7x9TVIpi3TSWyQ40J3ipdfujzwewwDL8hYlphJez616tGM5bNwk+XYuSSxtbDMr18sbMZZ39FX7d5TmMpKWScH2nuPGq0cjj3d6LluacB+fmGNxubDuPsMDmdIuiY8+eYFk0mpYXOVzJSnLJJEwNzUyqzU/EGc5bLvOU0jy7VySWNrY2pLLXTsyvOL6g1xxeCe242DbLnnba8ZYtJ1VjRgD6sAgr4aq1Vf1mF/Kl7oAj1er3OpOrjRvLKvcAji0py/SuIK5ku50AvrSJJPmqrUkm00qteYH4iqHbed5inZciyLCkcTS5q4aG/YSie1S8BsxTs4u4zjhGzGqA4ORJJJsvsjxM/Oc/vZpfjA+ybk6K7eiiMs0DBLBqCThzZUM9mco5rzR0lbnjsLMD8RRDtvO8xTtuBZFhCOJpY0El7SWcwWW8kUKea9CazNJJEqFosOpc/OlEcnpCws4FXpulSq3RvvZu7OnIZVbgaAbcDppkSpNvNe3yVUlrTI/0CpxbFW7rUUR4UhiaUHB5LrtuN7cSHlLeAeKhslchVbujWY7LhOTC6UuwCfPru+5ZRoGh/f1c9DfMbGRPbfKXzORMEknTa8bcMJctTtdI3Juq8wPtEocojM8Nj7JI1/U/Nc3X1vX82y7xNJqE52GgbdWxHW8JBK0Pik6OG5zRiKBoHLr2IRXuXVsonLlVqnn1mg/h/b2s3d3f2Qr2ysxDLAMb7V7Om2V9iiJ81S1yvxAq8Qh2l8wX7d3uKfu54otsSil/gk4DDjAAvBurfWjSqmjwCeAnXh7vbxFa/2k/5iqx7ai2ROdcffP2oqZ+Szjp721JMcm5lioULm1cyCzqlVKTwN6bq21thx47R4lcZ+7VpkfaJU4RPsrzdel6k8LcY5Y3qq1ngVQSr0Gb7Ow5wG3Ax/RWt+plHozcAfwE/5jah3btLgmOlcuZTnYNhRd71JWwbYj7+S7FeU9uPq7kxzc289SzubY6VmmK1Ru9XUnGRtdmXDf0aDdEtcKLnFlUlapmSM0bn5ms1plfiBsHK02WhetpdJ83VbFlliCpOIbAByl1C685HKLf/vfAR9WSo3gvYNUPKa1vrCVGBq1x3owF2L7oxC76FCwnUj3FInK945N8vkHT5Quu80s5Dl5fvVlq0zKKtt2d4CRHY3bLXEt0zRIWSbp9EoVV6udw3bU7NG6aH3BfF0UYp1jUUp9DHgZXtK4FdgPnNZa2wBaa1spNeHfbtQ4tqXEUs9E59oJddtx/EtZrTEKqaZQdDh1ft6bcD89y9PnK7dK6UpZ3HTNPo7s62e0wZVb5Qy8ZJJOWaX2KeV9uFrxnLajdi5LrkZGYNEqzdfl1285sVmxJhat9S8CKKVuAz4I/F6jX3Pnzt7S///cyy7jjs89hu34m+wUbHC920dG+lY9bmioxxt9OA7FouvPhdi4pgEmWFhYKWhW7c3QUOUJNsdxOXl2Dn1yhsdPTDN+erbUwLFcKuldYvLmLAyW8w4/9RPPanh8AcuAdCpBJm2RSiawYkpkgbU/71YTdXzTC3n6uhKrRp4Jy2BmIb+l12r2+Xv4R+f4+3ufIpEwGOhNsZAt8Pf3PsXAQDcjI31Nj28jrRjfzSN9DAx082+PPF33czWlKkxr/Uml1F8CzwD7lFKWPyKxgFHgabwPs9WOhTY1tYDjL644ONzNG26+lC/++ykuLuTZPdjFzddewiWDGU5PXKTguNhFh56+DFPTiy07CinfT8R1Xc5fXC6NSI6fmSObX1+5tWeom7F9/Yw/M0veccgkV3702YLNQHcyskquSvudrF2oaFkmxVyBhdz64oBGGxnpa+kmgI2Ib6g3tW60nivYDPamNv1arXD+Pv2lx8EAyzQp2q73t2Hz6S89zrWX7256fLW0wvmr5uBwd92lxhBTYlFK9QKDWuun/a9fBUwD54FHgTcCd/p/fyeYQ1FKVT22GV41FjiOw2UHhrh0/45SWa/tuEzOZVclkEyPW0pGrWhqdplHHj/P+MQsx07PMV+pcqs/U5onOTLaT2+XV7kV7HOSL9oN7Q1WXhLcyIWKIpxOK0tu94WhnS6uEUsP8BmlVA9g4yWVV2mtXaXUO4FPKKX+b2AGeEvZ42odC2UxW2Q5V8Ru0cn0MBaWC6W1JOPVKre6gp5btSu3GtkbzDAgYZn0dSUx7QyW1fiFiiKcTitLloWhrS2WxKK1PgdcX+XY48B1mz0W1lK2UHGOoZXl8jbHz84x/ozXKuXs9NK6+5Qqt0a9Bo6bqdyKqjcYrC8JTlom/b1pcsvxdwYQtbVKeXQUOm0E1mm23cr7VlS0g55bXquUZ85X6LllmRzc08fYvn6ee/keepNmbJVba5mmQTrhrXpfe4lLRiUiDp02Aus0kliawHFcJiYXGfcvb508O0/BXj2qMg24ZFdvaWHigd19pZ5blSbHGylY9Z5OJrzmjonN7aYoRCN00gis00hiiUF55daxCa9VSq3KrbF9Axze0086ZVV4tniYpkHS8qu4kiYJs7GNJIUQnUMSS4NcXMiVtt0dn5hlfml95dZQf7o0R1JeudUMpYWKfhVXKrF6oaIQQoQliSUiXuXWXKl6a2pufdljX1eSI/tWJtwH++LpuVVNqzV2FEJ0BkksWxRUbh3zRyRnpipXbh3e218qA961oyu2nlvVmIaBlfCSSdrvxSWEEFGSxBLS+sqtxXV7pVimweG9/aWFiaPDPbG3KlnLAAzTICMLFYUQMZHEUkWYyi3w+i1lUhamaWAa8KIr90S2RmSr1l7ikiouIUScJLH4XNflwsUs46dnOXVhEX1yunrl1mg/T52epbCm51a+aHP/dyeakliCZNKVSpBKeiMTsT1J11/RbNs6sYSq3OpLl6q2xvYNlCq3Pvipb5NJrz59SctkpkK7lUYJWqhk0sF8SWtthCXiJ/uuiFawrRLLYtar3AqSSaXKrd6uJJcfHmL/cA9HRvsZ6q/ce2iwL83ccoFUYmWtScF2Gl7pZRiQMA36elKk/RYqcmlLBDpx3xXRfjo+sRybmON741MhKre8MuBdg13s3Nm74cr2G68e9boEQ0O7BEMwMvEruZIWu4a6mZz05nvKk4pcAhHS9Ve0go5PLJ/92jjnZ5ZLXycsw+u55a8l2WrlViO7BMPqOZN0MuFf5gqOrY9XLoEIkK6/ojV0fGIxDNi/q5cxf47kwO4+koloJraj7BIMfjIxDTLpytVctcglEAHS9Ve0ho5PLO/+maswm7wosZby0uB0HdVccglEgHT9Fa2h4xNLOmm13H4sq+dMVl/m2iq5BCIC0vVXNJssdoiJ6W/T29+TYudAhp39GXoyyUiSCniXQGzbIVewcV2XXMGWSyBCiKbo+BFLM5mGQTJhkk5bpb5cjSoNlksgQohWIYklYrWSSaPXm8glECFEK5DEEoEgmWTSXiVXnMlECCFajSSWLTINg5Q/MkklLNn3XQghfJJYNsE0DVKWSSaTWNd+XpKJEEJ4JLFswDS9kYm3aHH1dr2STIQQYj1JLBUYhtdDTJKJEEJsniQWn2FAKmHRlUkwsqObFK4kEyGE2IJtnVhWNXr0t+11XUgmpBW9EEJs1bZLLLV2WpRkIoQQ9dsWiWXtfiYJ2bZXCCEapuMTS293Csv0dl2UbXuFEKLxOj6xdKUsHEeucQkhRFw6PrEIIcKRra1FVDp+suGOf/4+j41PNjsMIVpasLX1xcX8qq2t5XdHbEXHJ5a55YL8ggixgfKtrQ3DIJ20sCyTex461ezQRBvq+MSSSsgviBAbmZzNkkqsfjuQra3FVnV8YgH5BRFiI8MDGfJrtvCWra3FVm2LxCK/IELUJltbiyh1fFVYvii/IEJsRLa2FlHq+MTS35XklluOyi+IEBuQra1FVDo+sfzya54jCySFECJGsSQWpdRO4JPAGJAHngR+WWt9QSl1PXAH0AWcAN6stT7vP67qMSGEEK0prsl7F/gTrbXSWl8JjAMfUEqZwJ3Au7TWR4GvAx8AqHVMCCFE64plxKK1ngbuK7vpW8CvAM8HslrrB/zbb8cbmbx9g2ObIq0qhBAiPrGXG/sjkV8BPg8cAE4Gx7TWk4CplBra4Fhoj5+akVYVQggRo2ZM3v85sAB8GHhto1/swR+cI52yyKS8bzWVtMjmi9z7nQluvv5w1ceNjPQ1OrS6SHz1kfjqI/HVp9Xjq1esiUUp9afAs4BXaa0dpdQp4GDZ8WHA0VpP1zq2mdc8P7WIaRgUylYVm4bBmQsLXLgwX/ExIyN9VY+1AomvPhJffSS++rRDfPWK7VKYUuq/4c2b/JTWOuff/AjQpZR6sf/1O4HPhDgW2lBfWlpVCCFEjGJJLEqpK4DfBkaBbyilHlVK/aPW2gFuA/5CKfUkcBPwWwC1jm3GTc/dJ60qhBAiRnFVhf2AKvsCa62/AVy52WNhXXZgkDfdclSqwoQQIiYdv/IepFWFEELEaVt0NxZCCBEfSSxCCCEiJYlFCCFEpCSxCCGEiFQnT95bAKZZsRhtQ1t9XFwkvvpIfPWR+OrT6vH5rK0+0HDdjt2r5MXA/c0OQggh2tSNwAMb3quCTk4saeAFwBnAbnIsQgjRLixgL/AfQG6D+1bUyYlFCCFEE8jkvRBCiEhJYhFCCBEpSSxCCCEiJYlFCCFEpCSxCCGEiJQkFiGEEJGSxCKEECJSndzSpSal1PuA9wNXaq2/r5S6HrgD6AJOAG/WWp/371v1WIzxucD3gGCf5du01t/z7/sq4IN4P89HgJ/XWi81KK4TQNb/A/AerfUXW+X81YivVc5fBvifwEv9GL+ptX6HUuoo8AlgJzAFvEVr/aT/mKrHYozvBBXOq/+YWH6+SqlDwD+V3bQD6NdaD7XC+dsgvhM0+fz5r/VK4A/xNl40gN/XWn8u6vO3LUcsSqnnAdcDJ/2vTeBO4F1a66PA14EPbHQsrvjK3KC1vsb/E7wp9gJ/BbxKa30pMA/8RiPjA362LI4vttr5Wxtf2e2tcP7+BO/N5ajW+krg9/zbbwc+4p+jj+C90RDiWFzxQYXzGufPV2t9ouz1r8F7E/+Uf7jp52+D+KDJ508pZQCfxPtQdQ3e1u+f8GOI9Pxtu8SilErjnZxfKbv5+UBWax30xbkdeH2IY3HFV8srgIfLPkHcDvxcI2KroWXO3xbEdv78JPYW4Pe01i6A1vqcUmoX8Dzg7/y7/h3wPKXUSK1jccW3wcOa8vNVSqWANwEfb5XzVy2+De4a9/lzgAH//3fgtbwaJuLzt+0SC/AHwJ1a6xNltx2gbHSgtZ4ETKXU0AbH4oovcJ9S6lGl1H/3E9C62IFTwP4GxRa4Syn1mFLqo0qpHWtjaPL5qxRfoNnnbwzvUsL7lFIPK6XuU0q92H+901prG8D/e8K/vdaxuOILVDqvzfj5Arwa77x8m9Y5f9XiCzT1/PkfFl4P/LNS6iTeiOotNOD8bavEopR6IXAt8NFmx1LJBvEd0FpfC7wEeDarL1HE6Uat9dV4DT4N4MNNiqOaavG1wvmzgCPAd/xY3gN8DuhtQiyVVIxPKdVP6/3c387Go4FmWhtf08+fUioB/DbwGq31QeBVwD/QgH9/2yqxADcBlwPH/cm0S4AvApcCB4M7KaWGAUdrPY33CbbasVjiU0q9TGv9NIDWeg74GPAi/zGr4sP7BPR0A2LDf/0gjhxeAnzR2hiaeP6qxUeLnL9TQBH/soLW+iFgElgG9imlLAD/71E/jqdrHIsrvqPVzisx/3z919iH97tyl39TrXMU5/mrFl/Vf5fEe/6uAUa11g/6sTwILOLNqUV6/rZVYtFaf0BrPaq1PqS1PgQ8A7wcryKoq2zY/07gM/7/P1LjWFzx/YdSqgtKnzp+FnjUf9g9wAuUUs8qi+8fGhGfUqpHKTXg/78BvMGPo9Y5iu38VYtPKTXYCufPv8zxVeAWP5ajwC7gCT+eN/p3fSPeqOGCXx1U8ViM8Z2p8nOHGH++Zd4K/G+t9ZQfd9VzFOf5qxZfjd8biPf8PQNcopRSfiyXA7uBJ4n4/G3rtvn+qOCV2ivnvQGv2iHDSsnfOf9+VY/FER/Q57++CySBbwC/rrVe8O/3GrxqHgv4DvA2rfViA+I5AnzWfx0L+CHwa1rrM61w/qrFBxyiBc5fWYwfxyvdLAC/o7X+V6XUZXglnYPADF5Jp/YfU/VYHPEBmio/d/8xsf5+KKWe8F//nrLbWuL8VYqv1u+Nfzy286eUehPwW6yU3b9Pa/1PUZ+/bZ1YhBBCRG9bXQoTQgjReJJYhBBCREoSixBCiEhJYhFCCBEpSSxCCCEiJYlFCCFEpLZt23whAJRSC2VfdgM5wPa//mWt9V1KqWfjdZy9Ce/D2MN460++oZS6EfhX//6G/xzla2CerbU+pZR6P/A+4Hp/RXvw+m8DflFrXd6TKzh2H16X62LZzV/VWr9Kea3+/8p//mn//q/BW9X9bK31rPK2CljCW78zC3wa+M2g75MQjSKJRWxrWutSnyR/Qeovaq2/UnbbGPAg3hv22/AWDf488CWl1C1a6/vxey0pbz+O48AOrXWx7DkMvGZ/0/7fpcQSwv+ltf5Yhbi/oJT6N7y9U97qNzX8C+CdWuvZsrterbV+yl/kdh/eKv/bN/H6QmyaXAoTorb342129Tta62mt9bzW+kN4+1r8PyGf40ZgL14XgDcor6V6FH4NeIVS6uV4CeZrWuvPV7qj1vpx4H7gORG9thBVSWIRorZbqNy76R+AFwU9yDbwVuALrPQge1UUgfm9vf4zXrPDV+Ilmor8y3k34rWsEaKhJLEIUdsw3mZIa53B+/2puW+GUqobeB3wKa11Abgb73JYWB9SSl0s+/OHa45/C2/jpi9VaQz4baXUDF5i+xjwN5t4bSG2ROZYhKhtEu8y1lp78Rr5zWzw+NfiTb7/i//1XcBXlFIjITvs/lqlOZYyfwn8LfDTSqkXaq2/ueb487TWT4V4HSEiIyMWIWr7Ct6IY63X4829LG3w+LfiTe6fUkqdxbuslgT+z3oDU0r9At5Ofr8KvBf4WITzN0JsmYxYhKjt9/H2w/lj4H/gVYW9De9y1stqPdDf8Olm4BXAY2WHft1//P/yvzaUUpnyx2qtsxs89yjePkKv0VrnlFK34+2V8Tt4Zc1CNI2MWISoQWv9JPBi4Gq8vTLOAD8DvDzYia+G24BHtdZf0lqfDf4AHwKuUkoFFVo34O0iWfrjb0gG8GGl1ELZn0f82z8K/L1f7hzsZ/5LwK8rpa6I4FsXYstkPxYhhBCRkhGLEEKISEliEUIIESlJLEIIISIliUUIIUSkJLEIIYSIlCQWIYQQkZLEIoQQIlKSWIQQQkRKEosQQohI/f/KD/acDI0peAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pFY7z4LIxb1Q",
        "colab_type": "text"
      },
      "source": [
        "####**b. What broad conclusions can you draw from this example?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oBjjjlu8xfCA",
        "colab_type": "text"
      },
      "source": [
        "Food expenditure is positively correlated with the total expenditure.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eo_9uqhnxngV",
        "colab_type": "text"
      },
      "source": [
        "#### **c. A priori, would you expect expenditure on food to increase linearly as total expenditure increases regardless of the level of total expenditure? Why or why not? You can use total expenditure as a proxy for total income.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PV8APun8x20I",
        "colab_type": "text"
      },
      "source": [
        "Using total expenditure as a proxy for total income, as a person's income increases, they are more likely to eat better i.e. spend more on food. Therefore we'd expect food expenditure to increase linearly with total expenditure."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DeQ-qE68Zn7M",
        "colab_type": "text"
      },
      "source": [
        "## **Question 16: `table_9` gives data on mean Scholastic Aptitude Test (SAT) scores for college-bound seniors for 1972-2007.**\n",
        "\n",
        "**These data represent the critical reading and mathematics test scores for both male and female students. The writing category was introduced in 2006. Therfore, these data re not included.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EwkDfmnlyTT4",
        "colab_type": "text"
      },
      "source": [
        "#### **a. Use the horizontal axis for years and the vertical axis for SAT scores to plot the critical reading and math scores for males and females separately.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AU1Yd6ZIyiYV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "0963ce23-096f-4fea-b7b0-e300efdcaa69"
      },
      "source": [
        "sns.scatterplot(x='Year', y='Critical Reading (Male)', data=table_9, label='Critical Reading')\n",
        "sns.scatterplot(x='Year', y='Mathematics (Male)', data=table_9, label='Mathematics')"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa645130ba8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1peUzjszCNN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "ba505db7-a53b-4235-8845-092b8d7df0b4"
      },
      "source": [
        "sns.scatterplot(x='Year', y='Creatical Reading (Female)', data=table_9, label='Critical Reading')\n",
        "sns.scatterplot(x='Year', y='Mathematics (Female)', data=table_9, label='Mathematics')"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa644cf2198>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGm65OpFzPyH",
        "colab_type": "text"
      },
      "source": [
        "#### **b. What general conclusions can you draw from these graphs?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QS2aEsoIzWNc",
        "colab_type": "text"
      },
      "source": [
        "The standard of critical reading has stayed the same for both Male and Female seniors, whilst the standard of Mathematics have risen for both Male and Female seniors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwMdI__VzgYl",
        "colab_type": "text"
      },
      "source": [
        "#### **c. Knowing the critical reading scores of males and females, how would you go about predicting their math scores?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a6dJrmOYzs_2",
        "colab_type": "text"
      },
      "source": [
        "That's not possible given there's little correlationsion between mathematics and critical reading scores."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9LOgDmgrz6nc",
        "colab_type": "text"
      },
      "source": [
        "#### **d. Plot the female math scores against the male maths scores. What do you observe?**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "44XEutCmzPSw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "8bfab841-4a6d-4624-9962-435dd42795b9"
      },
      "source": [
        "sns.scatterplot(x='Mathematics (Female)', y='Mathematics (Male)', data=table_9)\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6447b5978>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UmpWx1a_0HAB",
        "colab_type": "text"
      },
      "source": [
        "Mathematics scores are positively correlated between male and females - they both have increased linearly over the years at about the same rate - every 5 score increase for male corresponds to 5 points increase for female. However across the board, male still have higher scores than female by about 30 points."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gLOeR1kjZnWS",
        "colab_type": "text"
      },
      "source": [
        "## **Question 17**\n",
        "**`table_10` presents data on mean SAT reasoning test scores classified by income for three kinds of tests: critical reading, mathematics, and writing. In Example 2.2, we presented Figure 2.7, which plotted mean maths scores on mean family income.**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kB6bHrXb0o4L",
        "colab_type": "text"
      },
      "source": [
        "#### **a. Refer to Figure 2.7 and prepare a similar graph relating average critical reading scores to average family income. Compare your results with those shown in Figure 2.7**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vDG4mbvZ01fa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        },
        "outputId": "54dd69b8-57bb-4bdf-8192-af69ae66d288"
      },
      "source": [
        "q17a = table_10.copy()\n",
        "q17a['Mean Income'] = [5000, 15000, 25000, 35000, 45000, 55000, 65000, 75000, 90000, 150000]\n",
        "sns.regplot(x='Mean Income', y='Critical Reading (Mean)', data=q17a)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa644794080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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fAaq9pXUr8I/GmDAQBo5jb9O9GviYf2ttFPg29pIUIjJpjHkzduGmMPAI8JtVnl8pVYXSYkYT6RwFvfspsKdHZ+gfGObI4Ajj05f7toYc6NlpZ15fu6eDWHT+Rn+N4ji2qB6LhDn9zCTHT43Suzc552Mbsia3iJwEDs+x6zxwcIHjvgR8qV5xKaXmVyh6TKVzZPNFLWgHcHEqy1F/5vXTo5W/W+/acrl43dq08sXrkOMQiYSIx8JEwyGi4RDHh0b47NeF7gXqJg1JGEqptaPoekxn8tpNNoB0tsCxU6P0D6QYOj9B+bera1OCvt4kh3q6SLbPvyhRo4RCDrFIiEQ8QjQcIhJ2Kv5//+U7pwmFQsQi1bc3V0ptENpNNphC0UXOXKR/MMXjp8cqi9eJCAf84vWuLa11n3m9EMeBcMghHosQj4SJRh1CzuUkMfuXgdR4hubEwilBE4ZSG5znQTqv3WQX4noep5+epH8gxaMnRyqK19FwiGv3ddDXk6RnZzvh0PI6wlarvBYRi4WIRkJEZsWy0Igx2Z7g4vTC6+QFShjGmLPAXKfKAk8CXwD+SkQKQZ5PKbUaeGTzLlPpnLbzmMeF0Rn6/ZnXF6cuf5g6DvRc0W6L13s7ia9Q8ToUcoiGQ8TKahHVDmpeftNu7v76E+QK86+AGHSE8RfYSXZ/gZ0LsRv4VeAfgFHgt7CzsH+7ulCVUo1SvjxqLl+c8zfBjWx8OsfRwRT9gynOj1QWr6/obqGvJ8nB/V20NccaHpvjQDjs0ByPQFucSCREOOTUpNZ0cL+9M+rB4xfmfUzQhPFG4MdE5FxpgzHmK8C9InKdMeY/gH9DE4ZSq1rBdZmaY+LdRpfJFTh2cpT+wRSnzlUWrzvb4hzqtcuZdm9ubPHaccBxHOKRMPF4mGjIIRx26NiUoJC1vaZq+f94cH+Svt7uefcHTRjbgalZ26aBHf7XT2An9SmlVqGiawva6UyhYm3njaxQdOl/Ypj7+5/k8dNjFIqXvy/NcVu8Ptzb+OJ1yLFJIRELE42El3WZqdaCJox/xq7t/QFszWIn8G5/O8DzgaGaR6eUWhYPj5lsUden8Lmex5kLpeL1KOns5bJrJOxwzR478/qqXY0rXjuAE7KjiFgsRCwSIhIOrcoRYNCE8cvYdbw/hh1VnMe26Si1Gj8J/FStg1NKVccDsvkiU9M5vfMJeGYsfal4PTaZvbTdcWD/jnYO9XRx3b5OErHG3DhaqkUkYhF7V1Mk+N1MKynQd8dflvVd/p+59j9dy6CUUtUpFbSn/IL2RjYxnePoCTvz+lyqslHfjqQtXr/oObso5hpzc2fpjqZ4LEwsGiIScliwcdMqFDidGrs26yGgol+4iHyy1kEppZau6LpMbfB1tDO5AseH7HKmJ86NV3wfNrfG6Ovtpq8nyZYOW7xub40zOlqfhFG61JSIhonF7CiiVnc0rZSg8zDeA/wucITKBoQeoAlDqRXk4TGdKTCT3pgF7aLrMnB2nEcG7MzrfNG9tK8pHubAlV0c7u1m99b6F69Ll5ri0Qjx6Nq51BRU0BHG/w/cKCJH6xmMUmppMhu0TuF5HmcuTNE/mOLREyPMzCpeX72ng8M9SXp3bSYSrm/xutTIL+GPIup9vpUUNGGkgcfrGYhSKpiNXKcYvugXrwdSjJYXr4F9OzbR15Pk+ivrX7xerJHfehX0u3ob8JfGmNuBimmAIuLOeYRSquZc12UqU9hQnWQnZy4Xr58arixeb+9qtjOve5K0t9R35nXIr0fE43ZuRHk9YqP8XwRNGH/r//3/lW1zsDWMlV8BRKl1zsNjJlPYMCveZfNFjp+yM68Hn3p28fpQj515vbVz/rUblstxIOw4xOO222ssur7qEdUImjD21TUKpdQ8PDJ5l6mZXMVM5PWo6LoMPmmL1z84PUa+cPniRSJmi9d9vUn2bGsjVKfidfnCQqV6xNq68bW+gs7DOL3cExljhoCM/wfgncAp7GTA7UAB+B7wVn+N773AIHCs7GleIiIjy41FqbUgX3SZmsmTXcd1Cs/zeHJ4ikcGbPF6OnO5eB0O2eJ1X08Ss7t+xetQqKxX0waqR1Rj3oRhjPlrEXmz//VdzN3eHBF5wxLO9yoRuZQA/KTwdhF5xBgTAj4HvAP4ff8hF0WkbwnPr9SatxFWvEuNp+kfSHFkcISRicyl7Q6wd/sm+nqTXL+vk6Z4fYrXpXrE5rY4UVycsnHEev2e18JC/xunyr4erMfJRWQIvweViLjGmIeAa+pxLqXWgvGpLCMTmXVZp5hK5zl6YoQjgynOPlPZy3RbZ6l43cXm1nhdzh8KOcSjYRJxe7nJwaElEWVmMrP4wQpYIGGIyB+Wff17NTrf3cYYB7gfeI+IXCztMMY0AW/CNjUs2WSM+T72F4+/A/5YRNbfO0ltcHYho8l0jk046ypZ5PJFjp+2M68Hn7xI+Utrb4lxcL+tS2zvaqnL+UuXmxIJe7mpXrWPjWKhS1IvDvIEIvKNgOe6WUTOGmPiwIeAO7GLMmGMiWATwjdE5B7/8eeBnSLyjDFmC3APMAZ8IuD5AOjqal38QQ3Q3d220iFUba3GvhbizmYLTMzkcSiyKWrfjp2d9fnwrLdS3EXX5fGhMR567Gn6nxiuqME0xSPcYLZw43Xb6N29uS4f4I4DsYgdSSRikUVrH2vh52QuKxG3481zwc4Yc2rWpiuwdYwRoAv7W/+TInLlUk9qjDkA3CMi+4wxYeCz2MmBt843gjDG/Dpwg4jcGvA0e4FTIyNTK/4bW3d3G8PDkysaQ7XWauyrPW7X89t5ZPIV18w7O1sYHZ2e/8BVqqOjmUflGfoHUxw9McJUOn9pXzjkYHZv5lBPkqt3dxCN1LZ4Xbr9NRYLE48tbSSx2n9O5lPPuEMhp/SL9j5mLVux0CWpS7fS+r2kuoDbRGTGGNOMbW0e6I4lY0wLEBGRcf+S1KuBfr/Q/bdAEfil8mThjyrGRCTvn+8W4MtBzqfUauUB6VyB6Zk8xQb9IiNnxrjvyDnGJrN0tMW5+dAOzO6Omjz36ETGtuc4OcqF0crlTPdub7Mzr/d10ZyobfHacWwiSsRte/BoRG9/bYSg/4tvA3aISB7ATxrvBs4Bf7jgkdZW4B/90UQYOA68FfgJ7GWpY8DDtiEuD4jIrwIvBN5vjCkCUWyyuDPoC1NqNXEcyOaKTKbzFfML6k3OjHHPA6cIh20bi4l0nnseOMUtUHXSmM7kedSfeX3mQmXxektHE4d7kxzcn6SjrbbF61Jjv6ZYhFjUjiRUYwVNGNPAjcADZdueS2Xn2nmJyEng8By7/oV5GsKLyBeALwSMT6lVq+Benk/R6Fs27ztyjnA4RCxiGzLEImFy/valJIxcocgPhsboH0wxcHa8oivupuYoN16/nWt2tbOts7mmHWEdByJ+souv88Z+a8FSekl91Rjzz8BZYBfwCuBX6xWYUmtdqU6xkutoj01mScyayxANhypWnZuP63qcODdO/0CKx4ZGyeUvj4zi0TDX7evkcG+Sfds3kUy21qz24jgQjYRt99doiOgqXa50Iwo60/suY8zDwH/GLtH6OPA/ROR4PYNTai3ysAv5TM/kV7zteEdbnIl0/tIIA+wM8vkuF3mex7mRGfoHhjk6OMJkWfE65DhctWszh6+qffG6YiQxK0loslg9Alei/OSgCUKpeazGtuM3H9rBPQ+cIocdWeSLLsWiy82HdlQ8bnQiw5HBEfoHhxm+WDmRbc82W7w+cGUnzYlozWILhRxi4RCxeJh4OEQkoklitVvKEq23AC8CkpTVHZbYGkSpdcl1XSZX4fKoZncHt8Ccd0nNZPI8enKU/oEUpy9U3qLZvTnB4d5uDvV00dGWqFk82iJ8bQu6ROv7gLdgJ9f9PLZh4GuBv69faEqtfpeWR13FbcfN7o5LBe58weUHp8e462vCE2cvVtza29YU5WBPF309SXYkW2pSvNYW4etL0BHGm4AfE5FjxphbReRtxpjPAe+tY2xKrWqZfHFNtB13XY+T5yds8frUaMXM61g0xPX7OjnUk2T/jnZCoRolCZ0jsS4FTRiby7rM5owxURF5yBjzonoFptRqtBrrFHPxPI/zIzMcGUxxZDDFxExl8bp3VzuHe5NcvaejoiC+HJHw5SQRq/FsbrU6BE0YJ4wx14nIY9hJdr9ijBnD9nZSakMouDZRZHONn08R1NhkliODKfoHUzwzlq7Yt3trqy1e7++ipUbF60jIXm7q3BQniqcjiXUuaMJ4L7Y1CNhusncDrdjZ2kqta6thPsVCZjIFjp0aoX8gxdDTlcXrZHuCvl67nGnnptoUr0Mhh0QsTCIasZebHGiKR5lC24Svd0HnYfxr2dcPAj11i0ipVcLDYyZbZDqdX3UF7XzBRc7YmddyprJ43doUtW3De5Jc0V2b4vWltST8yXSOjiU2pKXcVns19g6prSLya8Y2foqLyNG6RafUCvCA7DIL2vVo+Od6HkN+8frYqVEyubLidSTEtXs76etNsv+KdsI1KF6XryVRWnBIbWxBb6v9eeDD2N5OrwV+DWgD7gBeWrfolGog2yDQZSqzvIJ2rRv+nR+Z9ovXI4xP5y5tDznQu3Mzh3qTXLung1h0+cXrUMghFgn5xWtdcEhVCjrCeD/2ttojxphf9LcdAQ7VJyylGqtQ9JhK52rSILAWDf8uTmU5Omg7wj49q234ri2tHOpJcnB/F61Nyy9ez04S5ZPplCoXNGFsAUqXnryyv/XHSq1pRddjOpMnna3dDO1qG/6lswUeOHKO+/ufYuj8RMWbq2uTLV4f6uki2d607BgXShKaLNR8giaMh4HXA58u2/Zq4KGaR6RUA3h4HD0xwtcfOsP5kZmaLiy0lIZ/haKLnLnoF6/HKmomLYkIB/cn6etNsrMGxWtNEmq5giaM3wDuNcb8EtBijPkacBXwsrpFplQdlDrJ9g+k+MdvDRIK1W5hoZLFGv65nsfppyfpH0jx6MmRyuJ1NMQ1ezro60nSs7OdcGh5E+A0SahaCnpb7eP+XVKvwK58dxb4sohMLXykUquF58/QzpEvePz7988SCi1/YaG5zNfwb3NrnK89dIYjgykuTl0uXjsO9FzRTl9PkhfcsJOZqcXXqlhIqQtsIqFJQtXWUtqbzwCfL/3bGPNKY8xtInJjXSJTqgYcB3IFu+JdLl+8VBdYzsJCQZQa/o1P5zg6mOLe753l/Ehl8fqK7hb6/OJ1W3MMgEQswgxLj0FHEqoRFkwYxpitwB8DfcAA9tLUTuAj2HW6/zzoiYwxQ0DG/wPwThH5mjHmedjut03AEPA6EXnGP2befUotZqGC9lIXFlqKTK7AY6dGeWQgxalzlcXrzrY4h/yZ192bl1e81lbhqtEWG2F8BNsC5CPAzwH3AB3AB4BPiUh+gWPn8qqyJoYYY0LAZ4A3isj9xpj3Yud2vGmhfUs8p9pgXM/j4mSWkYnMvDO0gy4sFFSh6PLEWVu8fvx0ZfG6OR6xM697k+za0rqs4vVCk+k0Sah6Wyxh3Az0iMiEMebzwDBwwG9CWAv/CciIyP3+vz+KHUm8aZF9Sj1LqZXHTDpPuxNasJ3HQgsLBeV6HmculIrXo6SzhUv7ouEQ1+ztoK83Se8yi9eXRhLalkOtMMdb4NcSY8yEiGwq+/eYiFRVEfQvSY1jV+u7H3gP8BLgTSLyU2WPm8Fe9vrR+faJyGiAU+4FTlUTq1pbXNdjOp1jOp2nEUtTnE9N89Dxp3nosacZGb/ccM9x4Oo9ndx03Tb6rup+Vo1kKRzHFuKb4mESsQjhsLYLVw23D/tL+iWL/URHjTG3cnlJ1qgxpuI3fBH5ZMCT3ywiZ40xceBDwJ3APwU8tmojI1Mr3jiuu7uN4eHJxR+4Cq107EdPpPjqg2dIjWdItid4+U27Obg/CdhLMNmCbQ6YL7gVx3V2tjA6Ol2zOCZmcpdmXp9LVT7vjuTl4vWmFlu8npnOMjO9tOJ1KOSwrXsT6XSGSDgEBY+ZfKGqInijrfTPSbU07mcLhRy6ulrn3LdYwngQKF+z+3vYCXwlHhAoYYjIWf/vrDHmI9h6yJ8De0qPMcYkAVdERo0xZ+bbFxZC8LoAAB2DSURBVOR8au07eiLF3V9/gnA4RHMiwsXpHH//jUEiEYc929pJZ/J1Xe0ukytwfGiM/oEUJ86NV9QIOtriHOqxxestHdUXry+3Cg8TjYbo2pxgOG9Lg1qTUKvNgglDRH6kFicxxrQAEREZN8Y42Fni/dgZ5E3GmBf6tYq3AP/gH7bQPrUBfPXBM4TDIeLRMOGQw6aWGK4H//rt07zux6+uyzkLRZeBJ8fpH0jxg9OjFQmpKR7hwJWdHO7tZvfW6ovXpcJ1U9wmCa1JqLWi+ousS7MV+EdjTBgIA8eBt4qIa4x5PfAxY0wC/9ZZgIX2qY0hNZ6hvTVGUzxCJBJiaibPdDpPpqy4XAue53HmwhT9gykePTHCTNnzR8LOpZnXvbs220tFVdAusGo9aEjCEJGTwOF59n0bOLDUfWp9cxzYf0U749NZ0tkimYksHrWbKwEwfDFN/2CKIwMpRssm7DnAvh2bONyb5Lp9nSRi1b1NSiOJeDysXWDVutCoEYZSgXhALl9kOlPg2r0dfPG+k4TDoZrMlQCYnMlx9IQtXj81XFm83t7VfKl43d5aXVK6tDKdP5mufCShyUKtdZow1IpzHFs7yOZdZsoK2b07N3PLC/Yte+W6bL7I8aFR+gdSDD5VWbze3BrjUE+SQz1JtnU2VxV/6XJTUyLyrCSh1Hoyb8IwxlwZ5An8y01KLZnreeQKLulMgXzBxZ3jV/BST6alKhYvr3l9fGis4rbbRCzMgSvtzOs929qq+oAvzZNIxMOXivI6glDr3UIjjEHsFYKF3k0etoitNrCF5krM5jiQL3ikcwXS2UJN58h4nseTw1M8MpDisVOjTM5c7lwTDjlcvaeDw71JrqqyeO04EA47NMWjJKIhIuGQ9m5SG8q8CUNEdGqpWtRccyXu/voTABVJw3Gwl5zSebKF5S+DWi41nqZ/wK55PTKRqdi3b/vl4nVTlTOvIyGHeDxCPGqL1yWaJNRGozUMtSzlcyUA4tEwWX/7wf1Je2dT3mU6kydXw0Qxlc7b4vXAME/OKl5v62zm+Qd30Lujjc3LKF7bCXURopEQWpZQKmDCMMZEgLcCLwKSlF2mEpEfrk9oai1IjWdoTlz+MYpFw7Q2RSi6HhMzOXK5IkXXq8ni77l8keOn7czrwScvUn41q70lxqGeLg71JNne1VJVa5DSHU5NMZ1Qp9Rcgo4w/gx4MfDX2NbmvwP8CvB3dYpLrRHJ9gQzuQJtzVGikTD5QpHx6RzxcIiZzPIn2BVdjxNP2ZnXx4dGyc0qXl9/ZRd9PV3s3b6pquJ1yPEn1CV0Qp1SiwmaMH4OeL6InDHG/J6I/Lm/rvfHgNvrFp1a9X7y+Xu454EhxqdzuEWPbKFIsejy4hfsq/o5Pc/jqeFpO6nuxAjT6critdm9mb7ebsyuzUQj1RWvo6U7nCJhImG9w0mpIIImjGbsOt4AaWNMs7/O95yzt9X6ZOdLeOSLLrlckVy+SLK9iRdcv83OlZjOVT1XAmBkIuMXr1OkxiuL13u3t3G4J8n1V3ZVVbwuv8MpHrUTAfUOJ6WWJug77wfAc4GHgO8DtxtjJoCn6hWYWh2Krp0rkS+6ZHNFCnPMl6h2rgTY4vWjJ0c4MpjizIWpin1bOpo43Jvk4P5k1e1ASnc4JWJ25nWJJgmlli5owvhNoOh//Xbgr4A24M31CEqtnNKs63zRI5stknemGZvM1PQDNlco8oOhMY4Mpnji7HhFAtrUHL0083p7V3NVHWHtCnUROtsSeoeTUjUUKGGIyPfKvh4AXlq3iFRDOY6dcZ0veOQKRbK5AsUilz7Em7x4TZKF63qcOGeL148NjZLLXy5ex6Nhrt/XyaHeJFdu30QoVP3M6ya/eF2+roRSqjaC3lb7LuDfyxOHMeZG4EdE5IP1Ck7VT8F1yRX8WkTBrcuqhJ7ncW5khv6BYY4OjjA5q3h91a7N9PUmuXp3R9XF60szryMhIpGQXmpSqo6WcknqL2dtOw58EdCEsco5jq1FFAou2YJLNl+gWPTq9uE6OpHhyOAI/YPDDF+sLF7v2dpGX2+SA1d20pyIVvX8WpdQamUETRgxYPb4PgckahuOqhUP24AvV7DF6nyxPqOIkplMnkdP2o6wpy9UrjXcvbmJvp4kh3q66NxU3Y/M5Ul1OvNaqZUSNGE8jJ3p/aGybW8B/m/NI1JVKd3yWnBtgsjlihS9+o0iAPIFlx+cLhWvL1IsS0htzVEO7U/S17uM4rVOqlNqVQmaMN4GfN1fMvUEsB/YBvzYUk9ojHkfdrLfAWAT8JGy3VuAp0XkBv+xHvAoUKqQvl5EHl3qOdcrz7Mr0OULRbJ5d85bXkvkzNiy15UAW7w+eX7CFq9PjZLNFy/ti0VDtnjdk2T/jvaqi9eXJ9VVdoRVSq2soHdJPWaMuQp4BbAL+ALwZRGZWvjISsaYG4DnAaf95/020Fe2/4vA/bMO+6Glnme9mn3La65QxA0wipAzY9zzwCnCYbum9EQ6zz0PnOIWCJQ0PM/j/MgM/YMpjg6mmChrGx5yHK7a1c6hniTX7O0gFll6t3vHsUXwpniEuF+X0El1Sq0+gafM+h/aVfeOMsbEgQ8DrwG+Ocf+LcDLgF+u9hzrjePYD8xcwZ3zlteg7jtyjnA4dOnDPBYJk/O3L5QwxiazPCTDfPvoOZ4ZS1fs2721lUP+cqYtVRavL3WELdUl/O2aJJRanRZace+rIvJy/+v7YO6Go0voVvt+4DMiMmSMmWv/G4B7ReTCrO3f9LvlfgW4XUSyAc+3RnkUix45f2Z1ruDiLbPb69hklsSsdhrRcIixyWd/K9PZAo+eHKF/IMXQ05XF62R7gr5eO6muq9riteMQi4YuzZfQjrBKrR0LjTA+Xfb1J5ZzEmPM84HnAO9a4GG3Au+etW23iJw1xmwC7gJuA967lHN3dbUu5eF1093dNu++QtElly+SzRXJ5gu4QDgcpjkWpbpVpmedu7OF8akM8bK5Dtm8S3enbQOeLxQ5Opjioccu8NjJ1KU1tQE2tcR4zjVbuem6beze1lZV8doBIuEQTfEwiXiEaBWXraqx0Pd8NdO4G0vjDs7xAoz/jTE3iciDc2y/UUQeCnD8u4DfwN6KC7ATuADcKiL3GmOeB/wTsEtE5uyJbYx5JfB2EfnRRQO29gKnRkam6no7aRDd3W0MD1f+tl4ouuSKLrlskVydb3ktr2FEwyHyRZdCociN12xjdCLDsVOjZHJlxetIiOv84vVzD2xn/GJ6gWefm0NpUt2z6xKNMNf3fC3QuBtL4362UMgp/aK9Dxgq3xe0hvF17B1Ns30V6FzsYBG5A7ij9G9jzBDwChE55m96E3BXebIwxnQAGRFJ+5ekXgX0B4x31XE9v8tr3rW1CLe+t7yWM7s7uAVbsxi+mCYUCpEvetz7vbOXHhNyoHfnZg71Jrl2TwcxfwW9cGhpM7C1LqHU+rVgwjDGhLC/LDrGGAcqLjjvB5a9Qo4xpgn4ReCmWbuuBj7m31obBb6NvSS1Jnj4o4iCizc6Q2o8s2IjnYtTWS6MppnJFplMV/6X7dpyuXjd2lR98ToW0bqEUuvdYiOMApeL3bOTg4tdfW/JRGRv2ddpoH2Ox3wHOFjN86+EudaKKE2ci8ajDU8W6WyBY6dG6R8YZuj8ZEXRvGtTgkM9XfT1Jkm2N1X1/KX5Ek3xMDFdhEipDWGxhLEPO6r4FlB+N5QHDPsf9hvWpYlzC6wV0UiFooucuUj/YAo5M1ZRvG5JRDjoz7ze2d1SXfG61OwvpvMllNqIFkwYInLa/3JPA2JZAy7f8prLuWQLRbw6t99YjOt5nH56kv6BFI+eHKkoXkcjIa7d20FfT5Kene1LrkeUaF1CKQULz8P4axF5s//1p+d7nIi8oR6BrQaN7vK6FE+PznBk0C5nenEqd2m740DvTjvz+tq9ncSj1d3CWurj1N4aI4qrdQml1IIjjFNlX5+odyCrRXmxOteALq9LMT6d4+hgiv7BFOdHZir27exu8duGd9HWHKvq+efq49TaFCM9tc7nSiqlApk3YYjIHwIYY8LAWeCzIpKZ7/FrVXl/plyuSDYfrD9To2RyBR47NcojAylOnZuoKF53tsU51JukrydJ9+bqi9dal1BKBbHoPAwRKRpj/lREPtmIgOrt8mUmj2yhSC5foLBKLjOVFIouT5y1xevHT1cWr5vjEQ7s7+Jwb5JdW1qrKl7D5UWI4tEwsYguQqSUWlzQiXv/bIx5pYj8c12jqZPyxYRyufrPrK6G63mcuVAqXo+Szl6+izkaDnHN3g76epP0LrN4HS/dCltlbUMptXEFTRgJ4H8bY76DvTx16dN2tRe9p9J5ZtL5ui8mVK0LYzMcGUhx5MRIRTNAx4H9O9rp601y3d5O4rHqPuAdIBJxaEpESUTDhEM6X0IpVZ2gCeOY/2fNSWcLFFbZaGJiOsfREyP0Dwxzblbxekeyhb6eJAd7uthUZfEaLneFbY5HiUX1kpNSavmCLqD0e/UOZL3L5AocHxqjfyDFiXPjFR/cHW1xDvlrXm/tqL4/reOUusLa2oTOvlZK1dJivaReANwiIu+cY98dwBdF5Lv1Cm6tK7ouA2fHOX7fSfqfGK4oXjfFIxy4spPDvd3s3lp98RpsoohHwzQnosQil1t+abJQStXSYiOM91C55na5bwG/A7yyphGtcZ7ncebCFP2DKR49McJMWfE6Ena4ek8Hh3u76d3ZTiRcXfG6JOQva9oUb3z7cKXUxrNYwujDtjCfy9eB/1XbcNauZy6mbfF6MMVoefEaMHs6uG5vB9ft6yQRC7wq7rzCIYfmRIRELEI4pKMJpVRjLPbptQmIAXM1GYwCa3OpqhqZnPGL14Mpnhqerti3vauZvt4kB/cn2berg9HR6XmeJZjZdzuFlnEJSymlqrFYwngceBnwpTn2vczfv6Fk80WOnxqlfzDF4FOVxevNrTEO9diZ11s7l168ljNj3HfkHGOTWTra4tx8aAdX7+kgHvHrE9HlXcJSSqnlWCxh/Bl2EaMwtsDt+osq/QzwYeDt9Q5wNSi6HoNP2pnXj50arShexyIhmyR6k+zZ1lb1b/7ly6gm4hFyRZcHjj1N9+YmrtvXqZeclFIrbrH25p81xmwDPgXEjTEpIAlkgfeJyOcaEOOK8DyPJ4en6B8Y4eiJFNOZyvWjErEw0UiIcAiu3dvBvu1zrWAb3H1HzhEJh2hvjdOciOAAF6dyfPnbQ1y7d9FVcJVSqu6C9JL6U2PMJ4DnA13ACPAdEZmo5oTGmPcBtwMHROSYvwTro9gV/ABeLyKP+o99JfBHfpwPA7eKyMyzn7V2UuNp+gdSHBkcYWTicq9FB4jH7NyG1qYYIb/YnCsUue/IOczujmWdN1902dHdSrHoMp0uUCi6eJ5Hanzd9XtUSq1RQSfuTQBfW+7JjDE3AM8DTs/a9UMiMjXrsa3Ax4GbRWTAT1rvAN6/3Dhmm0rnL828fnJW8XpbZ/Olmdcfv+cxEvFIxZyJaDhU0dJjqSJhh5amKFs6mnh6NF1xq22u4JJsT1T93EopVUvLv8czIGNMHFv3eA3wzQCH/ATwfREZ8P/9UeylsZokjFy+yPHTdub14JMXKe8e0t4S41BPF4d6kmzvarm0vaMtzkQ6Tyxyua9TvujS0RZf8vkjIYeW5iiJaATHgZsP7uDurz9B0fWIRULkCi7FosvLb9q9rNeplFK10rCEgf2g/4yIDBljZu/7pjEmAnwFuF1EssBuKkciZ4Bdywmg6HqceGqc/oEUx4dGyRXcS/sSsTDXX9lFX08Xe7dvmrN4ffOhHdzzwCly2JFFvmg/1G8+tCNwDOGQQ3NTlOZ4uGIVu4P7kwB89cEzpMYzJNsTvPym3Ze2K6XUSmtIwjDGPB94DvCuOXbvFpGzxphNwF3AbcB7a3Xu9vYmTp6b4KHHnub7P7jAxPTl5UwjYYfr9ye58dptHOjpIhpZuCPs8ztbaGtLcO+DZxgZT9PV3sTLbtrN9QE+1Lu6WmiOR2ltjs47w/sl3W285Hn7lvYCG6C7e21Ot9G4G0vjbqyViLtRI4wXAdcAp/zRxU7ga8aYW0XkXrB1Er9OUbpV9wzwo2XPsRvbWn1J/uTuh5EzFyu27d3exuGeJNdf2UVT3H4LJieCFZd3dDTxxpdXjpAWmpQXchx2bNtENp0jn8kxlsnN+9jVqLu7jeHhyZUOY8k07sbSuBurnnGHQg5dXa1z7mtIwhCRO4A7Sv82xgwBrwCeMsY0iUjavyT1KqDff9hXgTuNMb1+HeMtwOeXeu5SQXpLRxOH/ZnX1dQclirkOCTiYVoTETo2JRjO5ut+TqWUqqdG1jDmcjV2YqCHbTXybewlKURk0hjzZuDL/sTBR4DfXOoJnnv1Fq7csYltnc3L6ggblOPYTrQtiUjVK+MppdRqtCIJQ0T2lv3z4AKP+xJztyUJ7EcOX0G+rLhdT/FomJamKPGodo5VSq0/Kz3CWBciETuZLxENAbpokVJqfdKEsQzhkJ101xzXb6NSav3TT7oqhPz1KJrjEW0zrpTaMDRhLEHIcWjyE0Vp4SKllNooNGEEoHc+KaWUJowFOY6986m1KUYkrCMKpdTGpgljHrFomFa9RVYppS7RhDFLJOLQ1hQjHrV9pTRZKKWUpQnDp7fIKqXUwjb8p2OpoN3aFNVbZJVSagEbNmE4+AXtZi1oK6VUEBsyYUT9Vh5xv5WHUkqpxW2ohHFpWdRYRNOEUkot0YZIGKGQQ0siQnMiUrEsqlJKqeDWfcJoTti5FFrQVkqp5Vn3fS5aEtogUCmlamHdJ4yPfekYR0+kVjoMpZRa89Z9wphI57n7609o0lBKqWVqeA3DGPM+4HbgAJADPgZsBwrA94C3ikjaGLMXGASOlR3+EhEZWcr5YpEw4XCIrz54hoP7kzV4BUoptTE1NGEYY24Angec9jflgLeLyCPGmBDwOeAdwO/7+y+KSN9yzxuLhEiNZ5b7NEoptaE17JKUMSYOfBj4ldI2ERkSkUf8r13gIWBPrc+dK7gk2xO1flqllNpQGjnCeD/wGREZMsY8a6cxpgl4E/Duss2bjDHfx07H/jvgj0VkSf1jc4UixaLLy2/aXX3kSimlGpMwjDHPB54DvGue/RFsQviGiNzjbz4P7BSRZ4wxW4B7gDHgE0s5d2d7E6968VU855qtVcdfC93dbSt6/uVYq7Fr3I2lcTfWSsTteA1Y8MEY8y7gN7A1C4CdwAXgVuDfgc8CaeDW+UYQxphfB24QkVsDnnYvcGpkZArXXdlFLbq72xgenlzRGKq1VmPXuBtL426sesYdCjl0dbUC7AOGyvc1ZIQhIncAd5T+bYwZAl4BHAc+BRSBXypPFv6oYkxE8saYZuAW4MuNiFcppdSzrfQ8jJ8AXoe9xfZhY0y/MebD/r4XAo8YY44A3wceAe5cmTCVUkqtSC8pEdnrf3mMefqLi8gXgC80KiallFILW+kRhlJKqTViPXerDYMt4KwGqyWOaqzV2DXuxtK4G6tecZc9b3j2vobcJbVCXgjct9JBKKXUGnUzcH/5hvWcMOLAc7HzOYorHItSSq0VYWx/v+8B2fId6zlhKKWUqiEteiullApEE4ZSSqlANGEopZQKRBOGUkqpQDRhKKWUCkQThlJKqUA0YSillApkPbcGqRljTBdwF7Afu6bHAPDLIjJsjHke8DGgCds7/nUi8ox/XM33LeM1vA+4HTggIsdWe9zGmATwZ8BLgQzwHRF5szHmKmxL/C5gBHiDiAz4x9R8XxVxvwK7Jr3j//k9EfnCaovbGPPHwH/GrhtzQESO1SuWWr6GueJe6P3pH7Mqftbn+56X7a94j66m2Et0hBGMB3xQRIyIHABOAHcYY0LAZ4BfFZGrgP+Dv+5HPfZVyxhzA/A84HS9YqtD3B/EJoqr/O/5bf72jwIf9s/xYewPPnXcF5gxxsF+cL1eRPqA1wOf8r83qy3uLwI/jP8zUedYavka5op7zvcnrLqf9fm+5896j67C2AFNGIGIyKiIfLNs03eBPcB/AjIiUuq38lHgF/yv67FvyYwxcewb8VfKNq/quI0xrcAbgNtKi2qJyAV/Ua0bgM/5D/0ccIMxprse+6qJHXCBdv/rzdjWNMnVFreI3C8iZ8u3Nfr7W81rmCvuBd6fsIp+1ueKHeZ9j66q2Es0YSyRn6F/BbvG+G7KfiMQkRQQMsZ01mlfNd4PfEZEhsq2rfa492MvT7zPGPN9Y8w3jTEvBHYBT4lI0T9HETjnb6/HviXxk9svAF8yxpzG/kb5htUed5lGx1nz1zDr/Qmr/2cd5n6PrsrYNWEs3V8CU6yB1f+MMc8HngN8ZKVjWaIwcCXwiIg8B3gndjGt1hWNahHGmAjwbuCnRWQP8Erg86zyuNeZNfP+hLX3HtWEsQR+0aoX+EURcYEzXB76YoxJAq6IjNZp31K9CLgGOOWvo74T+BrQs8rjPgMU8C9ViMiDQApIA1cYY8L+OcLADuCs/6fW+5aqD9ghIg/4cT8ATGNrMas57pJ6xNKw1zDH+xPW6HvUGPOy1Ri7JoyAjDF/gL3+9zMiUmr5+zDQ5F8uAXgL8A913LckInKHiOwQkb1il8V9Evhx4I9Wedwp4D+AH4NLd9JsAZ4A+oHX+A99DXYUMiz2Lo+a7qsi9CeBncYY48d9DbAVe9fOao4bgHrE0qjXMM/7E9boe1RE7l2NsWt78wCMMddh1x9/AvtbLsApEflZY8wPYe/sSHD5FrUL/nE137fM1zEEvELsrYirOm5jzJXAJ7G3W+aB3xGRrxhjrsbehtkBjGFvwxT/mJrvqyLu/wK8C1v8BnifiHxxtcVtjPkL4OeAbdjR24iIXNfoOJf6GuaKG1s3mvP96R+zKn7W5/uez3rMEP57dDXFXqIJQymlVCB6SUoppVQgmjCUUkoFoglDKaVUIJowlFJKBaIJQymlVCCaMJRSSgWi7c3VuuXf074DO/s6Vbb9Eeys7H1z9O+pZzw/gu0ZtLNR51SqlnSEoda7U1yeSYwx5gDQvHLhKLV26QhDrXd3YTvG/qX/7/8KfBr4H6UHGNte+gPYGcNx4J+At4lI2hjT4T/HTdj3ywPAW0TkSf/YbwL3AS8GDgLfAV5bPqKZz2LH+u0bPghcC0xi273/rTGm3X89PwHMAB8H/kBEXGPMG4H/BjwE3AqMAq8DrsIu7BQH/ruIfGqx177od1ZtODrCUOvdd4FNxphr/OZ2r8YuIFPuDuwHah+2MeMVwO/6+0LA32Abtu3Gtp6Y3Qn1tdgP5y1ADHjHEuKb81hjzB7gK9jE0O3H1u8f85fYNTeuxDave4P/HCU3AUexbVU+C/wd8Fz/tb0OuNPYNUcWe+1KVdARhtoISqOMbwE/AJ4q7TB2lbw3AwdLHTv9RnafBd4tIiPAP5Y9/gPYxojl/kZEnvD3fx64ZQmxzXfsa4F/E5HS4kIjwEhZ0usTkUlg0hjzJ9jV/f6X/9hTIvI3/nP+PfA7wPv9pnz3GmNyQI8x5shCr30Jr0FtEJow1EZwF3Ypyn3Yy1HlurE1jYf9JrNg1+Iutdxuxq4t/nJsgzyANmNMWPyFf4Cny55vhqWtfzHfsbuwS43OlgSiVC7zeRo7MigpbySXBrti4axtrSzy2pWaTROGWvdE5LQx5hTwk8AvzdpdWmfjOhF56lkHw28BBrhJRJ42xvQBj2A/WOvpLHDjHNtT2O69e4Dj/rbdlI2almCx165UBa1hqI3il4AXi8h0+UZ/oZ2PA39m7BrTGGOuMMb8uP+QNuyH6kVjl7F8X4PivRt4qTHmF4wxEWNMlzGmzx/VfB74gDGmza91vJ1n12UWFeC1K1VBE4baEETkhIh8f57d7wQGge8aYyaAf8OOKgA+BDRhfxv/LvDVescKICJnsCOi38Le6dQPHPJ3/zp2Jb+TwP3YmsMnqzzVQq9dqQq6HoZSSqlAdIShlFIqEE0YSimlAtGEoZRSKhBNGEoppQLRhKGUUioQTRhKKaUC0YShlFIqEE0YSimlAtGEoZRSKpD/B8eiZmc4sHomAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1IBF8TVV1e0q",
        "colab_type": "text"
      },
      "source": [
        "#### **b. Repeat (a), relating average writing scores to average family income and compare your results with the other two graphs.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uWIiOYe61lSt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 305
        },
        "outputId": "b2e8f70e-6418-4569-da98-7d74ad3e2272"
      },
      "source": [
        "sns.regplot(x='Mean Income', y='Writing (Mean)', data=q17a)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fa6446b9be0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LZIX2RbcP9r4jAyRSE6U6Vhb6YJt2mpuqU6rDwh24DvoJhZyB61qtz/DCa8JYLSKH3I/TxpigiPzSGPPKagWmlFpYhYV3o+7Cu6Ws0Ae7V+KcvljcBxs6NzmL6+baB3s2luUs4guHnMdNQb+POsoRJbwmjGPGmJeIyK+AQ8DvGWOGgKHqhaaUWijLoZGRbducPH+Ff3rmFAcm9cFuWRmmuzPGnh3t8+6DPZVCknDWSPgJBnzUaY4o4TVh3A20uh9/HHgIpwjhB6sRlFJqYdjYjKdyS3o9RSKVZf8RZ3Hd5D7YL9nS4vbBXlmVR0HFA9eLJUkU8zqt9odFHz8DbCv3QsaYk0DS/QPwUeAEzgD6WiAL9AAfFJGEMWYzcBTnjqbgNSIyWO61lVKzS2ZyjI6nS3pELxW2bXPi/Ai9fXEOnRgseY9rmqN074yxe1v7rKUx5mJi4DrgJInFliWKlDOtthNnhtQaEfkPbrnzcJkFCd9SNBaCmxQ+LCL7jDE+4OvAR4A/dQ+5LCK7y3h9pVQZlvo4xch4mn2HB+iVOAPDRX2wAz5u3trKa27bxMqwv+KlOgpVYCORQN0NXM+H12m1vwXcD3wbZzrtf8Bp1Xov8Nq5XlxETgIn3Y/zxphf4rR/VUpVWTbvJIpUOrekxikKfbB7+/p54dRQyRTgjbEVdHXGuPn6VsIhf0U71xXXbgr6fVerwC4lXu8wPo0zrfaAMea33W0HcBoqleMhY4wFPAF8QkQuF3YYY6I403c/XnT8SmNML85ss28AnxGRsv4JWltXzH5QDbS3Ny10CHO2WGPXuKeWyeYZHU+TSWVpaPTTUKHqFS0t1S+DMZNLw0mefO4cTx08x9CViVIdDZEAt73kOl6xax0bYtd+becTtwUE/D6ikQDRcKCqazImW4jvb68JIwYUHj3ZRX+X88v7DhE5Y4wJA5/Fae36TgBjTAAnIfxURB52jz8PbBCRuDEmBjyMMyvri2Vck8HB0QUfvGtvb6K/f2T2A+vQYo1d476WbbvrKRKZkgJ5lbBQPaazuTx9p4bolThHzlzbB7urM8ZL3D7YwDUxziVuy3KSRCQcIBLw4bNskmM5kmOp2U+ukGp+n/h81rT/0faaMPYC7wK+UrTtbcAvvQYhImfcv1PGmM/hJACMMX6cWVdDwIeKjk8BcffjuDHmIeAVlJkwlFJOx7uRRJrsEul41+/2wX72yABjiYlSHSuiQXdxXYzWVZVbXGdZ4PdbRN21EgG/RaFC0lJ77DQTrwnjQ8Cjxpj3Ao3GmB8DO4DXeznZGNMIBERk2H0k9TZgvzvQ/XdADnhv8eMm965iSEQyxpgG4E7g+x7jVWrZW2od7zLZPIeOD9IjcU6eL+2DvWPDarp3xjAdq/H7KvdYqHgabChQu8dN9crrtNo+d5bUm3B+aZ8Bvi8iozOfedUa4Fvu3YQfeB5nDccbcR5LHQL2OhOveFJE/j1wO/BpY0wOCLrXvc/rG1NqOcvn84wms0ti4d35wTF6Xoizf1If7NUrQtxqYtxq2lk9Qx/qchXXbwoG/It6GmyleZ5WKyLjwDfnchEROQ7cMsWuH8DUa1dE5Ns4s7KUUh7ZOAUCx5PZBR+7m49kOstzx5w+2Gf7J/XB3tRMV2eMbetXVaxUx+TudEtlGmylzZgwjDE/ne0FROTVlQtHKTUXhUZGi7lAoG3bnImP0vNCnOcm9cFuWxWhe2eMW7a3V6wPts+yCPp9rG4KX9OdTk1ttjuM3wAEZ1D6fNWjUUqVxbIglS40MsrPfkIdGktmrpbqiA+V9sG+8foWunfG2LSmqSKL65wZThbRcJBw0EespYH+fucxlyaL2c2WMHYD/wanF8ZBnFlS3xGR5IxnKaWqLpuzGU2kSWUW38K7vG1z/NwVel6I8/zJSyXTfNe2NtDdGWPXtjai4cqU6vD5LCIhpzyHDl7P3Yz/Gm7Zjz8yxvwx8JvAXcBfGmN+ANwtIhdqEKNSqkihkdF4MrPoEsWVsTTPHu6nty/OpUl9sHdta6W7M8b69sostvX5LMIBP5Gw08bUWnSl/uqP11lSeeBHxphjOGVBPgh8CdCEoVQNjaWyi66SbC5vc+TMZacP9ukhikPftKaJ7p0xbtzSUtJRbq6Wag2nejFrwjDGNANvx7m7aAYeBLa5daCUUjWQzOQYXWQL7y5dSbJX+tkrca4U9cFucPtgd5kYseb598G2LAi6dxLhgH9J1nCqF7PNkvoW8DLgu8AfisgvahKVUqpk4V1qkVSSzeacPti9ff0cPTtcsm/b+lV076xMH2wLd+V1OEA45HSpKyQJTRbVM9sdxr8C+oE3A29yF9aVEJGOKsSl1LKWz+cZSWRJphfHwrtzA6P80zOn2Hd4gPHiPtgNwauL61pWzr9UR/HgdXEDosXwNVoKZksYr6pJFEopAHK5PGPJDGOLYOFdOpPj4HFncd3kPtimw+2DvXE1/nkuris8coqG/YSDfh2XWECzzZL6Wa0CUWp5cwoE9l8eZ6ToeX8lyOkhHj9wjqGRFM1NYe7YtQ7T0Tzn1zvbP0pPX5wDRwdLHpW1NIXpcvtgr2ycXx/smR45qYUzbcIwxnwIeMCtGjvdMWHg/SLyP6sRnFLLQTZvMzqeJpXO0RysbItQOT3Ew0+ewO+W476SyPDwkye4E8pKGolUlgNHB+jti3NucKIPtt/n9MF+dXcHbU2hef/vXx851beZvjuvA44aY34I/AxnxfcITqe9HTirwN9IaclzpZRHV+s+JbIlXeEq6fED5/D7fYQCzpTVUMBP2t0+W8KwbZuTF5w+2AePl/bBjjVH6e6Mccv2NhoiwXn1w9BHTovHtAlDRD5hjPlL4HeB9wI3Aatx+lY8B/wQp2veYA3iVGoJsUlm8oyOpate92loJEVk0mrpoN/H0Mj0zX5GExn2He6np+/aPtg3bXUW122MrZhXqY6r/SXCQSJBHwF95LQozDaGMQB8xv2jlJqnbM55/JSs0TTZ5qYwVxKZq3cYAJlcnuam0nLg+bzN0bPD9PTFeeFkaR/sDe2NdHfGuHlrG+HQ/BbXTddfQpPF4lDZB6ZKqSktVDmPO3at4+EnT5DGubPI5PLkcnnu2LUOgOHRFL3u4rrLo+mr50VCfnZvb6O7M8ba1vn16vb5LMJBP1HtL7HoacJQqsqSmVxNHj9NxXQ0cyeUzJJ6xU1ryeVtvvyjPg6/eLkkgW1Z20RXZ4wbt7Re7YM9FzousTRpwlCqCgqrtEfd9qgLyXQ0YzqaGRhO0NvXz3ceP8HopD7Ye3a00WVitK2ee6mOkr7XOhV2SdKEoVSF1VN71Ew2z69OXKKn7yInivtgA9s3rqa7M0bnpvn1wQ74LBrCAayVEYJ+HZdYyjwlDGPMlN9NbhVbpRT11R71wqVxtw92P4nUxB3OqsYQXZ3z74NdqAobjQYIB/w0r4yQTVV2waGqP17vMLI4XSBLGGOywDmc3tv3iMjo5GOUWurqpT1qKp3jueOD9LxwkReL+mD7LIudm5vpMu1s37B6zn2wLcAf0Kmwy5nXhPH7wL8E7gXOAB3A/wv8AGdB3z3AZ4F/O90LGGNOAkn3D8BHReTHxpiXAg8AUeAk8E4RibvnTLtPqYVnu+MUaTILVHbctm1e7Hf7YB8bJD2pD3aXu7iuqWHupTqm61anyWL58ZowPgzsEZFCveLDxpheYK+IbDXGHAT2enidt4jIocIn7qOurwK/KyJPGGPuxklK75lpn8eYlaqKQh/t0eTCDWiPJ7PsO+J0rrtY1Ac74Le46fpWujpjbL5u7n2wLctZFR7RWU6qiNeEsRJoAIoL3DcAq9yPL+DcBZTrViApIk+4n38e507iPbPsU6rmCjOfxhMZUtna99HO2zYnzk/0wS4u1bG2tYEuE2P39rn3wbYsJ+FEwkEiAR+BgD5yUqW8fmd9BfiJMeZ/4DyS2gD8AfBld//rcR5NzeYhY4wFPAF8AufR1qnCThEZMMb4jDEtM+0TkUse46a1tTL9geervb1poUOYs8UaeyXjTmdyXBlLg52joclPwyzHHzo2wKPPnGZgOEHbqiivv62DG7e2ebpWS0vpQrnh0RRPHzzPkwfO0X954m4iEvLTfcMabt+1no553E34LYiEg0RDfkIh/5xfR79Pamsh4vaaMP4YOAK8DVgHnAfuB77g7v9n4LFZXuMOETnjVrj9LHAf8J1yAy7X4ODogs9YaW9vor9/ZPYD69Bijb2ScZfbR7u4Qmw44OPSlQQPPfICd75iy6wF/wpF/PJ5m8MvXqa3L07fqdI+2BtjK+jujHHT1lbCbh/soaHxaV5xasXjEr6Aj0wyTSY5+3nT0e+T2qpm3D6fNe1/tD0lDHf67OfdP1Ptn/VbTUTOuH+njDGfAx4G/gewqXCMMaYNyIvIJWPM6en2eYlZqflxBrRH5tBHez4VYgcuJ/in3jPslX7njsYVDQfYs72Nrs4Ya1pmu7+ZmmVBOOAnEgkQDvqw0HEJVR7PDzuNMa8HdgMlqUdE/sTDuY1AQESG3UdSbwP24wyUR40xt7tjFR8A/sE9baZ9SlVNoUBgKpO7di65B+VWiM3m8rxwaojevjhHXxwuuea29avo6mznhs0tc+qDreMSqpK8Lty7D3grzqOn4ntfr996a4BvGWP8gB94HvigiOSNMe8CHjDGRHCnzoJzVzPdPqWqIZe3GUtm5r1C22uF2PjlBL19cZ493M94srQP9h4To2uOfbAtnMZG4XCAiFuio0CThZoPr3cY7wB2FR4rlUtEjgO3TLPvKZxeG2XtU6pSbGzGk9mK9dGeqUJsOpvj0PFL9PTFOXWhqFSHBWZjM6/u3sja5uic+mBfHZcIBggGffrASVWc14QxAFyuZiBK1drzJwd54rnznB0YI+T3cfs8e10XTFUh9obNLfSdvsw3/uloSR/s5qaw07luRzurGkNld67zWRahoNN+VcclVLV5TRj/HWdK7H8DLhbvcO8elFo0LAsOnhjkOz87xlgyh99nkUjPrdf1dExHM5uua2L/0QF6+/r5/tNXZ4jj91ncsLmF7s4Y169fWfaiOGdcwkc04tRxCvgtfdSkasJrwvj/3L/fNGm7jTMmodSikM07Jcf/8YkTjCSyc5rJNBPbtjl9cZSevoscPHaJTG6iVEesOUqXiXHLjjYaI8GyX9vns4i64xKhosFrTRaqVrxOq5177WOl6kCh410imSVv2wxcTpbd63omo4kM+48M0NMXL1lcFwz4uPn6Vrp3zq0PduFuoiESKCnRoUlCLQTth6GWvHF34V2uaEDb60ymmeRtm2NFfbCLX399WyNdnTF2bWslEir/x8zns4gE/UTCpQX/lFpI034nG2MeEZE3uB8/zjRTaEXk16sUm1Jzls/bTmvU8XRJzaWC2Xpdz2R4NMXew/3slf6SO5JIyM/ubc7iunVt5ffBtiwnlpUrQkS04J+qQzP91+crRR9/sdqBKFUpqUyO/qFxLs/weGmqmUx3zDBLKpfPI6edUh1yprQP9ua1TXSbGDdeX34fbAunrWkkHCAS9BNraaC/f2Fbuio1nWkThoh8rejTPhF5ZvIxxphfq0pUSpXJdkt5jCezpDM5WoKzPwYq9LqeyeCVpLO4TvoZKeqD3RgJsGdHO12dMdrn0Ae7uJZTMKBrJtTi4PXh6k9wSpxP9gjQUrlwlCr13LEBHnnmNAPDSdpWRXjDbR3cXFT1NZfPM57KkUplK9btLpPN8/xJZ3Hd8XNXrm53+mCvosvE6NzUXHapDp/PbWsaccYldM2EWmxmTBhuEyMLsNwaUMXf4VtxWrcqVRXPHRvgoZ8cxu/OEro8lnY+91mYjhbGUxmSqRz5Ck0ZunBpnN6+OPuODJBITXxrr2oMcatp51YTK2tQHNxxiYCfqNuIyO/TNRNq8ZrtDqO4l/fk5JAH/qziESnleuSZ006JcLeE94pokGDAx2P7z9G2OlqRX7ypTI6Dxwbp6YtzJj7Rkt5nWezc1ExXZ/l9sAvjEtFwgLBby0nXTKilYLaEsQXn+/9nQPFsKBvoF5HElGcpVQEDw0kaIgH8PovGaADLsrgylubCwNi8fvE6fbDH6O2Lc+DYAJxb9oEAABrrSURBVOnMxOK61lURukw7e3a0l90H++pU2FBpLSdNEmqpmDFhiEihnsGmmY5TqhrWtETJ5PI0hIOMJjLOgHY2V/ZjoYLxZNYt1RHnwqWJossBv8WNW5w+2FvWlte5bvIjJ50Kq5aymdZh/P8i8j73469Md5yI3FWNwNTylrdtXtu1kf/12FGGrqQIlLlWosB2+2D39vVz6MRgyZqM61oa6OqMsXtbGw0R74vrLMt95BS69pGTUkvZTD8lJ4o+PlbtQJSyLKeZ0HgqRyKVZU1zA6+9daPntRLFhkdT/Gz/WXr7+hm8MtEQMhT0sWurs7huQ3tjWXcTAZ/ldqvTHhNqeZppHcZ/A3CbHp0BvualFatSc5HO5Eikc6TSpbOevKyVKMjnbY68eJmevjh9py+X9LaYqg+2F9ONSyi1HM16Hy4iOWPMX4rIl2oRkFo+0tk8qcz811AMjaTYK3H2Sj/Dk/pg3+L2wb6ujD7YxeXDtUSHUhO8Prj9R2PMm0XkH6sajVrycnmbVCZHIpUhm7Pn/Dgnm8vTd2qInin6YF+/biWv6tpIR1tjWaU6AiVtTScvO1JKeU0YEeB/GWOexnk8dfXnUwe91WyrsS3LuZtIpLLzXmjXX9QHe6yoD3ZTNMge006XidG6KuK5c53PcldfR51mREqp6XlNGIfcP0qVmGk19s7NLU6icKfDzjVPFPpg9/bFOTmpD/aOjavp7oxhOlbj95V3NxGJBIiG/AR0lpNSnsxWGuStwM9F5D9X6oLGmHuATwE34dSn+lzR7hhwQUT2uMfawEGcVeUA7xKRg5WKRc1fYTV2JOgnFPSzstGHDTy2/xztzQ0lA8/lOjfgLK7bf3SAZHqiguvqFSG6OmPcuqOdVSu8r8ko9L+OhgMlA9+aLJTyZrY7jP8CbDXGHAN+jrPi+2cicnouFzPG7AFeCpwCEJGngN1F+78LPDHptJeLyCiqLo0ls7StihAM+ElncoynsqRSOcaTmTkli2Q6y4Gjg/RKnLP9E4+UnD7YzXR1xti6fpXngejCmomGcJBwUPtfKzUfs6303mGMuQ64A6c0yB8Bf2uMOYubQETEU68MY0wYuB94O/DYFPtjwOuB95fzBtTCyObypLN5WlaGGbySxM5PDGyVuxq70Ae7ty/Oc8cHyWQnSnW0r47Q3bmG3dvbWBH13gfbsqAhEiASKu1Yp8lCqbnzMq32AvAP7h+MMc3AvwM+DLwD782VPg18VUROGmOm2n8X8KiIXJy0/TFjTAD4EfApEZlb02U1LzZO2e9UJkcqkyXnznDava2Nh588gd/vK7tz3Vgyw77DA/RKnPhQUR9sv4+btrbQ3bmGjjXe+2AXynQ0RALEmhsY0uygVEXNmjDcsua7ce4wfh14OXAO+CbwuJeLGGNeBnQBH5vhsHcDH5+0rUNEzhhjVgIPAp8E7vZyzYLW1hXlHF417e1NCx1C2XJ5m/FkhkA4SDKdhYCfcMBPODpRlO9lLY00NUV49JnTDA4naF0V5fW3dXBj0SypYnnbRk4N8eSBs+w/3F9SqmPjmiZu37WOX7vhOqJllOrwWRANB4mE/SX9sxfj1xw07lrTuL2z7Bn+F2aM+QFwCyA4YwtPAE+JyMi0J039Oh8DPgQUVlVtAC4C7xaRR40xLwW+A2wUkSl7bBhj3gx8WERe5fGym4ETg4Oj8xp4rYT29ib6+8v6ki0Iy3KSRCaXJ5XKkczkWL26wdP01NkMj6V5VvrplXhJH+xw0M9ud3Hd+jL6YPt8FuGAn4hb9G+yxfI1n0zjri2N+1o+n1X4j/YW4GTxvtn+G7cDSOHUlToGHC03WQCIyL3AvYXPjTEngTeJSGGq7nuAB4uThfvoKykiCfeR1FuA/eVeW83Mqd/kJIlkKks6m69Ygs3lbQ6fHqKnrx85M1QyfrBpTRPdO2PceH0LIY/rH7QZkVILa7ZB7+2TBr3/0BjTBjyJ8zjqCRGZ1y9xY0wU+G3gtkm7OoEH3Km1QeApnEdSat5ssnmbdCZPMp0jm81XrGsdwCW3D/bew/2MjE/0wW4o9ME2MWLN3vtg+30WkXCAaCiAk1ucMQ1NFkrV1nwGve8G2oGyl8eKyOaijxPAqimOeRq4udzXVlMrDFpnsjkS6YlB60rJ5pw+2L19/Rw9O1yyb9v6VXR1xrhhs/c+2MUD2OGg9r9Wqh7MZdD7dmA10AtoQcI6l87kSWVzpNJZcvnKJgmAi24f7Gcn9cFeWeiDvaOdlpURz69XqOcUDQUIBvSRk1L1ZLaV3j8EXgaEgGdwFu7dBzytpc7rT2HQOp3Nk0rnSGVyVRnwT2dyPHfMWVx3+mJxH2ynHHn3zhg7yuiDXajnFHGrwxZoslCqvsx2h/FznNXePSKSmeVYtQAKTYfSWWdmUzo39aC1nB6aUyOiAtu2OeuW6jhwdJBUZqJUR8vKMF0mxh7Tzsoy+mAHApYzHTbo03pOSi0Csw163zvTfrVwsrk8KfdOYrZBazk9dHVxXSQc4Eoiw8NPnuBOmDVpjCUzPP2rC/T2xTk/WNoH+4bNLXTvjLFl7UrPpToK9ZwawgGCQf/VkQlNFkrVP++ro9SCKgxap7M5kmUOWj9+4Bx+v+/q9NVQwE/a3T5VwrBtm5MXRujti3PoxKWSUh1rmqN074yxe1t7WX2wAz7LaUik1WGVWrQ0YdQxG5tMxnYGrVNZcvbcBq2HRlJEwqX/1EG/r2QBHcBoIsOzh/vp7YszMFzUBzvg4+ZtbXSZdjbGvJfq0LEJpZYWTRh1pGTQOpUjla3MoHVzU5griUzJArlMLk9zU5h83ubo2WF6+uK8cHKo5NHWxtgKfn3PBrZd10Q45H1xnd+vYxNKLUWaMBaY10Hr+bhj1zoefvIEabhaIDCTydHU1shnvrGPy6MTfbAjIT+3bG+nq7Odta2N3jvX+SwiIb9Wh1VqCdOEsUCKB60z8+hG54XpaOZO4Of7zxK/nCRv2yRSOQ4cHbx6zJa1K+nujPGSLS2e+2AXP3LSxXVKLX2aMGpkPoPW8zUwnODE+SvEh1MlfbBXRINuqY522lZ7L9UR8FtEI/rISanlRhNGFTmD1nkuDScZvJyY86D1XGSyeQ6dGKS3L86J85P6YG9YTVdnjM5N3vtgW5ZTWVanwyq1fGnCqKDpKr+2BANka1Ri/fzgGD19cfYfubYP9q0mxq2mndVl9MG2LGiMBomG/QT1bkKpZU0TRgUUBq2rUfnVi1Q6x3PHBujpi/NiUR9sn2Wxc3Mz3Z0xtq1f5blUBziPnRoiQdpXN3DZfT+aLJRa3jRhzIFtO9NSF2I8YiIGmzPxUXr64hw8Nki6aHFd26oI3Z0xdm9vo6mMUh2W5Szqa4gECLmPnbwOgCullj5NGB6UdKJL50inc07l1wWIZTyZYd8R526iuA92wG9x0/WtdHXG2Hxdk+fFdeBMiY1qhVil1Cw0YUyjFusjvMrbNifOXaGnL86vTlwiVxTH2tYGujtj7NrWRjTs/Z/Twin+1xAJEgn5r06J1WShlJqOJoxJMoXxiFSWbC6/oL9Ar4xP9MG+dKW0D/auba10d8ZY376irNecKP4XJBTUx01KKe+WfcIoqddUpSZD5cjlbY6cuUxPXxw5PUTxTU3HmhV0d8a46fpWQsHyGh0GfBaRSICoFv9TSs3RsksY1arXNF9DI0l6+/rZe7ifK2MTpToawgFu2dFGl4mxpqWhrNe0LAj4fTS4VWL1sZNSaj6WScKwyRYlicwCjkcUc/pgD9HbF+fY2eGSQfSt651SHTdsbvHcB7vA57MIB/1Ew6V1nZRSaj6WfMIYT2UZS6TJLsDU1+nEhxJuH+x+xotKdaxsCLLHxOgy5fXBLggELBrCQcJBP/4y1lwopZQXNU8Yxph7gE8BN4nIIWOMDRwECgsJ3iUiB91j3wz8hRvnXuDdIjJ+7atObyyRIZNd+EyRzuQ4eHyQ3r5+Tl2cKNVR6IPd1Rljx8bVZf+itywIBvw0Fq2dUEqpaqhpwjDG7AFeCpyatOvlIjI66dgVwBeAO0TkiDHmi8BHgE/XJNgKOTswxiM9Z3jm0IXSPthNYbo6Y+zZ0c7KRu+L6wqu1naKBAkFLNBUoZSqspolDGNMGLgfeDvwmIdT3gj0isgR9/PPA19mESSMZDrL/qMD9L4Q51xRH2y/z+IlW1ro7oyxZZ33PtjFCn0nGsJBXWSnlKqpWt5hfBr4qoicNMZM3veYMSYA/Aj4lIikgA5K70ROAxvLvejq1Q0lC92qxbZtjr04zBMHzrG372JJH+y1bY3cvmsdt924lhXR4Jxe32dBQyRIQyRAMFDelNr5am9vqun1KkXjri2Nu7YWIu6aJAxjzMuALuBjU+zuEJEzxpiVwIPAJ4G7K3Xty5fHS355V9poIsO+w/30TOqDHQz4uHmrs7huV+cahobGSSfSXEqkZ3i1awUCFiuiIUJBP+lEmnSZ589Xe3sT/f0jsx9YZzTu2tK4a6uacft8Fq2tUy8IrtUdxiuBncAJ9+5iA/BjY8y7ReRRABG54o5TfNg95zTwqqLX6ADO1CjeacnpIX6+/yz9l5PYQCKVLVlct6G9ka7OGDdvbSUScr685dR1Kgj4LRqjQSLBAHM4XSmlKq4mCUNE7gXuLXxujDkJvAk4a4yJikjCfST1FmC/e9gjwH3GmO3uOMYHgG/WIt7pPHs4zvefOkU6kytJEsGAj1t3tNO9M8ba1sZ5XSPgs2hoCNIQWvIznpVSi8xC/1bqBB5wp9YGgadwHkkhIiPGmPcB3zfG+IF9wB/UOsBcPo+cLpTquFyyLxTwEQr5aVsZ5s7bt8zrOn6fxbnBMZ587jxn4qM0N4V5w20d3Ly1bV6vq5RSlbIgCUNENhd9evMMx30P+F7VA5rC4HCSXonzrPQzkshc3X518DkcIBDwYds2w6NzH1fw+SwaIgFOX7zCN396BBuLcMjP5bE0D/3kMIAmDaVUXVjoO4y6ksnm+dXJS/T2xTl+7srV7RawfeNqLo8kydk24eDEly2Ty9Pc5L3laYHPsoiE/ayIBPD5fHz/yVNOsnCLCoaDflLAI8+c1oShlKoLmjCAC5fG3T7Y/SRSE4vrVjWG6Oqc6IMtp4d4+MkTpLM5gn4fmVyeXC7PHbvWeb5WIVE0RgL4fRN1ngaGkzRESv85QgFfycwrpZRaSMs2YaTSOZ47PkjPCxev7YO9qZnundf2wTYdzdwJPH7gHEMjKZqbwtyxax2mo3nW61kWRMOBaxJFQduqCJfH0lfvMADS2Txtq8qvKaWUUtWwrBKGbdu82D9GT1+c544NkM6U9sHu6oxxyyx9sE1Hs6cEUWBZEA74aV0ZnXFl9htu6+ChnxwmhXNnkc46dy9vuK3D87WUUqqalkXCGE9m2X+0n96+fi5cmijVEfBb3LjF6YO9ZW15fbC9CAX9rIgGaWuO0t+fnbGMR2Gc4pFnTjMwnKRtVURnSSml6sqSTxjff/okTz53nmxu4rf1dS1OH+zd28vrg+1VYXV2pMyueDdvbdMEoZSqW0s+YbxwcohsziYU9LFra5vbB7ux4ncT4KylaIw6U26VUmqpWfK/2da1NXL7TWu5aWtryYByJfksi4ZogIZwYE4VaJVSajFY8gnjd163o2rFBwsznwprKZRSailb8gmjGiychXWNDUFCAZ/2pFBKLQuaMMoUDPhoigYJBX2ANjBSSi0fmjA8CvgsGhuCREIBbYaqlFqWNGHMolAcsDESwNJUoZRaxjRhTMOyIBIKsCI6dSkPpZRabjRhTGLhrNBujAYJB3VAWymlCjRhFNEBbaWUmp4mDHRAWymlvFjWCUNXaCullHfLMmFYlrPwbkVDkIAOaCullCfLLmEEAxZN0dDVcQqllFLe1DxhGGPuAT4F3ASkgQeAtUAW6AE+KCIJY8xm4ChwqOj014jI4Fyu63fHKaI6TqGUUnNS04RhjNkDvBQ45W5KAx8WkX3GGB/wdeAjwJ+6+y+LyO75XNOyoDEapDGi4xRKKTUfNXuAb4wJA/cDv1fYJiInRWSf+3Ee+CWwqZLX/UnPi5y6cEWThVJKzVMt7zA+DXxVRE4aY67ZaYyJAu8BPl60eaUxphdnsOEbwGdEpKzVEecvjXHg6ADv/9dRunaumXv089Te3rRg156vxRq7xl1bGndtLUTcNUkYxpiXAV3Ax6bZH8BJCD8VkYfdzeeBDSISN8bEgIeBIeCL5Vzb7/OBBX//aB+b2hrm/B7mo729if7+kQW59nwt1tg17trSuGurmnH7fBatrSum3leVK17rlcBO4IQx5iSwAfixMeb1xhg/8BBOMvhQ4QQRSYlI3P047h7zirlcPBTwMTCcnNcbUEqp5a4mdxgici9wb+FzN2m8CXge+DKQA95b/LjJvasYEpGMMaYBuBP4/lyun87maVsVmXP8SimlajjoPY03Au/EmWK71xiz3xhzv7vvdmCfMeYA0AvsA+4r9wLpbI5cLs8bbuuoVMxKKbUsLcjCPRHZ7H54iGlWz4nIt4Fvz/daK6NBXve6Hdy8tW2+L6WUUsvakl/p/f5/cSP5vJadVUqp+VroR1JKKaUWCU0YSimlPNGEoZRSyhNNGEoppTxZyoPefnBWLdaDeoljLhZr7Bp3bWnctVWtuIte1z95n2Uv3cbVtwOPL3QQSim1SN0BPFG8YSknjDDQjVOTKrfAsSil1GLhx+lR1AOkincs5YShlFKqgnTQWymllCeaMJRSSnmiCUMppZQnmjCUUkp5oglDKaWUJ5owlFJKeaIJQymllCeaMJRSSnmylGtJVYwxphV4ENgKpIEjwPtFpN8Y81LgASAKnATeKSJx97yK75vHe7gH+BRwk4gcqve4jTER4K+A1wJJ4GkReZ8xZgdOH/hWYBC4S0SOuOdUfN8c4n4T8Kc4nSQt4D+LyLfrLW5jzGeA/wfYjPs9Ua1YKvkepop7pp9P95y6+F6f7mtetL/kZ7SeYi/QOwxvbODPRcSIyE3AMeBeY4wP+Crw70VkB/Bz4F6AauybK2PMHuClwKlqxVaFuP8cJ1HscL/mn3S3fx64373G/Tjf+FRxn2fGGAvnF9e7RGQ38C7gy+7Xpt7i/i7w67jfE1WOpZLvYaq4p/z5hLr7Xp/ua37Nz2gdxg5owvBERC6JyGNFm34BbAJuBZIiUijQ9Xngre7H1dhXNmNMGOcH8feKNtd13MaYFcBdwCdFxAYQkYvGmBiwB/i6e+jXgT3GmPZq7JtL7EAeWOV+vBqnlllbvcUtIk+IyJnibbX++s7lPUwV9ww/n1BH3+tTxQ7T/ozWVewFmjDK5Gbo3wMeBjoo+h+BiAwAPmNMS5X2zcWnga+KyMmibfUe91acxxP3GGN6jTGPGWNuBzYCZ0Uk514jB5xzt1djX1nc5PZW4HvGmFM4/6O8q97jLlLrOCv+Hib9fEL9f6/D1D+jdRm7Jozy/TUwCty30IHMxhjzMqAL+NxCx1ImP3A9sE9EuoCPAt8GVixoVLMwxgSAjwP/QkQ2AW8Gvkmdx73ELJqfT1h8P6OaMMrgDlptB35bRPLAaSZufTHGtAF5EblUpX3leiWwEzhhjDkJbAB+DGyr87hPA1ncRxUi8gwwACSA9cYYv3sNP7AOOOP+qfS+cu0G1onIk27cTwJjOGMx9Rx3QTViqdl7mOLnExbpz6gx5vX1GLsmDI+MMf8V5/nfvxSRQo34vUDUfVwC8AHgH6q4rywicq+IrBORzSKyGXgR+E3gL+o87gHgn4HXwdWZNDHgMLAfeLt76Ntx7kL6xZnlUdF9cwj9RWCDMca4ce8E1uDM2qnnuAGoRiy1eg/T/HzCIv0ZFZFH6zF27YfhgTHmJcAhnF9YCXfzCRH5V8aYl+PM7IgwMUXtontexffN832cBN4kzlTEuo7bGHM98CWc6ZYZ4D+JyI+MMZ040zCbgSGcaZjinlPxfXOI+3eAj+EMfgPcIyLfrbe4jTH/E/jXwHU4d2+DIvKSWsdZ7nuYKm6ccaMpfz7dc+rie326r/mkY07i/ozWU+wFmjCUUkp5oo+klFJKeaIJQymllCeaMJRSSnmiCUMppZQnmjCUUkp5oglDKaWUJ1reXC1Z7pz2dTirrweKtu/DWZW9ZYr6PdWM5zdwagZtqNU1laokvcNQS90JJlYSY4y5CWhYuHCUWrz0DkMtdQ/iVIz9a/fzfwN8BfgvhQOMU176z3BWDIeB7wD/UUQSxphm9zVuw/l5eRL4gIi86J77GPA48GrgZuBp4B3FdzTTme1ct3zDnwM3ACM45d7/zhizyn0/bwTGgS8A/1VE8saY3wX+HfBL4N3AJeCdwA6cxk5h4I9F5MuzvfdZv7Jq2dE7DLXU/QJYaYzZ6Ra3extOA5li9+L8Qt2NU5hxPfAn7j4f8Lc4Bds6cEpPTK6E+g6cX84xIAR8pIz4pjzXGLMJ+BFOYmh3Y9vvnvPXOD03rscpXneX+xoFtwHP4ZRV+RrwDaDbfW/vBO4zTs+R2d67UiX0DkMtB4W7jJ8BLwBnCzuM0yXvfcDNhYqdbiG7rwEfF5FB4FtFx/8ZTmHEYn8rIofd/d8E7iwjtunOfQfwv0Wk0FxoEBgsSnq7RWQEGDHG/Hec7n5/4x57QkT+1n3Nvwf+E/Bptyjfo8aYNLDNGHNgpvdexntQy4QmDLUcPIjTinILzuOoYu04Yxp73SKz4PTiLpTcbsDpLf4GnAJ5AE3GGL+4jX+AC0WvN055/S+mO3cjTqvRydqAIKVtPk/h3BkUFBeSS4DTsXDSthXM8t6VmkwThlryROSUMeYE8H8B7520u9Bn4yUicvaak+GPAAPcJiIXjDG7gX04v1ir6Qzwa1NsH8Cp3rsJeN7d1kHRXVMZZnvvSpXQMQy1XLwXeLWIjBVvdBvtfAH4K+P0mMYYs94Y85vuIU04v1QvG6eN5T01ivch4LXGmLcaYwLGmFZjzG73ruabwJ8ZY5rcsY4Pc+24zKw8vHelSmjCUMuCiBwTkd5pdn8UOAr8whhzBfjfOHcVAJ8Fojj/G/8F8Ei1YwUQkdM4d0R/hDPTaT+wy939+zid/I4DT+CMOXxpjpea6b0rVUL7YSillPJE7zCUUkp5oglDKaWUJ5owlFJKeaIJQymllCeaMJRSSnmiCUMppZQnmjCUUkp5oglDKaWUJ/8HzFJWRRf55qUAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "83MpPI-H1sTZ",
        "colab_type": "text"
      },
      "source": [
        "#### **c. Looking at the three graphs, what general conclusion can you draw?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R9oGZuUo1xT3",
        "colab_type": "text"
      },
      "source": [
        "Average scores increases as the mean income of families increase."
      ]
    }
  ]
}