Linear_Regression.ipynb

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  "metadata": {
    "colab": {
      "name": "Linear Regression",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TheHouseOfVermeulens/Financial-Engineering/blob/master/Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qP0r4e9zNX8P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Useful Functions\n",
        "def linreg(X,Y):\n",
        "    # Running the linear regression\n",
        "    X = sm.add_constant(X)\n",
        "    model = regression.linear_model.OLS(Y, X).fit()\n",
        "    a = model.params[0]\n",
        "    b = model.params[1]\n",
        "    X = X[:, 1]\n",
        "\n",
        "    # Return summary of the regression and plot results\n",
        "    X2 = np.linspace(X.min(), X.max(), 100)\n",
        "    Y_hat = X2 * b + a\n",
        "    plt.scatter(X, Y, alpha=0.3) # Plot the raw data\n",
        "    plt.plot(X2, Y_hat, 'r', alpha=0.9);  # Add the regression line, colored in red\n",
        "    plt.xlabel('X Value')\n",
        "    plt.ylabel('Y Value')\n",
        "    return model.summary()\n",
        "\n",
        "# Useful Libraries\n",
        "import math\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from statsmodels import regression\n",
        "from statsmodels.stats import diagnostic\n",
        "import statsmodels.regression as smr\n",
        "import statsmodels.api as sm\n",
        "from statsmodels.stats.diagnostic import het_breushpagan\n",
        "\n",
        "import scipy as sp\n",
        "import scipy.stats\n",
        "import seaborn\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1SHiRQVONcxz",
        "colab_type": "text"
      },
      "source": [
        "##1: Temperatures\n",
        "\n",
        "Given this set of Fahrenheit and Celsius values, find a model that expresses the relationship between the two temperature scales."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YV2cXU0aN0tA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 716
        },
        "outputId": "f24c2545-400f-453c-f4c7-32444e1c7640"
      },
      "source": [
        "fahrenheit = [-868, -778, -688, -598, -508, -418, -328, -238, -144, -58, 32, 122, 212, 302, 392, 482, \n",
        "              572, 662, 752, 842, 932]\n",
        "celsius    = [-500, -450, -400, -350, -300, -250, -200, -150, -100, -50, 0, 50, 100, 150, 200, 250, \n",
        "              300, 350, 400, 450, 500]\n",
        "\n",
        "## Your code goes here\n",
        "linreg(celsius, fahrenheit)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>OLS Regression Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>            <td>y</td>        <th>  R-squared:         </th> <td>   1.000</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   1.000</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>7.818e+06</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>             <td>Thu, 09 Jan 2020</td> <th>  Prob (F-statistic):</th> <td>8.33e-55</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                 <td>16:51:12</td>     <th>  Log-Likelihood:    </th> <td> -26.373</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>No. Observations:</th>      <td>    21</td>      <th>  AIC:               </th> <td>   56.75</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Residuals:</th>          <td>    19</td>      <th>  BIC:               </th> <td>   58.83</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "    <td></td>       <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>const</th> <td>   32.1905</td> <td>    0.195</td> <td>  165.172</td> <td> 0.000</td> <td>   31.783</td> <td>   32.598</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>x1</th>    <td>    1.7998</td> <td>    0.001</td> <td> 2795.998</td> <td> 0.000</td> <td>    1.798</td> <td>    1.801</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Omnibus:</th>       <td>53.344</td> <th>  Durbin-Watson:     </th> <td>   2.112</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 281.704</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Skew:</th>          <td> 4.210</td> <th>  Prob(JB):          </th> <td>6.74e-62</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Kurtosis:</th>      <td>18.844</td> <th>  Cond. No.          </th> <td>    303.</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
            ],
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                            OLS Regression Results                            \n",
              "==============================================================================\n",
              "Dep. Variable:                      y   R-squared:                       1.000\n",
              "Model:                            OLS   Adj. R-squared:                  1.000\n",
              "Method:                 Least Squares   F-statistic:                 7.818e+06\n",
              "Date:                Thu, 09 Jan 2020   Prob (F-statistic):           8.33e-55\n",
              "Time:                        16:51:12   Log-Likelihood:                -26.373\n",
              "No. Observations:                  21   AIC:                             56.75\n",
              "Df Residuals:                      19   BIC:                             58.83\n",
              "Df Model:                           1                                         \n",
              "Covariance Type:            nonrobust                                         \n",
              "==============================================================================\n",
              "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "const         32.1905      0.195    165.172      0.000      31.783      32.598\n",
              "x1             1.7998      0.001   2795.998      0.000       1.798       1.801\n",
              "==============================================================================\n",
              "Omnibus:                       53.344   Durbin-Watson:                   2.112\n",
              "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              281.704\n",
              "Skew:                           4.210   Prob(JB):                     6.74e-62\n",
              "Kurtosis:                      18.844   Cond. No.                         303.\n",
              "==============================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
              "\"\"\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        },
        {
          "output_type": "display_data",
          "data": {
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LWYFe/YxUmtWuxQFTp1D3qUmUNKxP6n33wfnnawoQkSSipCH7ZWFuPvUzUmnx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\nlDRERKTClDRERKTClDRERKTC/j8KHBqkjsj+6wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U-KRfha2N_EJ",
        "colab_type": "text"
      },
      "source": [
        "##2 : Confidence Intervals\n",
        "####a. Visualizing Confidence Intervals\n",
        "\n",
        "Using the lecture series and the seaborn library, plot the regression line between the parameters and the 95%\n",
        "confidence interval."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NYgNfWhYN44V",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = '2014-01-01'\n",
        "end = '2015-01-01'\n",
        "asset = get_pricing('KO', fields='price', start_date=start, end_date=end)\n",
        "benchmark = get_pricing('PEP', fields='price', start_date=start, end_date=end)\n",
        "\n",
        "returns1 = asset.pct_change()[1:]\n",
        "returns2 = benchmark.pct_change()[1:]\n",
        "\n",
        "## Your code goes here\n",
        "seaborn.regplot(returns1.values, returns2.values, ci=95);"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iAWrnaFXOM8_",
        "colab_type": "text"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6srx5-L-OTxB",
        "colab_type": "text"
      },
      "source": [
        "##b. Calculating Confidence Levels of Parameters.\n",
        "\n",
        "Let's directly calculate the 95%\n",
        "\n",
        "confidence intervals of our parameters. The formula for a given parameter is:\n",
        "\n",
        "##CI=(β−z⋅sn−−⎷,β+z⋅sn−−⎷)\n",
        "\n",
        "Where, β\n",
        "is the coefficient, z is the critical value(t-statistic required to obtain a probability less than the alpha significance level), and SEi,i is the Standard Error Matrix."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7AAedpdiOJcL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "start = '2014-01-01'\n",
        "end = '2015-01-01'\n",
        "asset = get_pricing('KO', fields='price', start_date=start, end_date=end)\n",
        "benchmark = get_pricing('PEP', fields='price', start_date=start, end_date=end)\n",
        "\n",
        "X = asset.pct_change()[1:]\n",
        "Y = benchmark.pct_change()[1:]\n",
        "\n",
        "result = sm.OLS(Y,X).fit()\n",
        "\n",
        "# Convert X to Matrix (adding columns of one)\n",
        "X = np.vstack((X, np.ones( X.size ) ))\n",
        "X = np.matrix( X )\n",
        "\n",
        "# Matrix Multiplication and inverse calculation\n",
        "C = np.linalg.inv( X * X.T )\n",
        "C *= result.mse_resid\n",
        "SE = np.sqrt(C) # Calucaltion of Standard Error. \n",
        "\n",
        "# Critical Values of the t-statistic\n",
        "N = result.nobs\n",
        "P = result.df_model\n",
        "dof = N - P - 1\n",
        "z = scipy.stats.t(dof).ppf(0.975)\n",
        "\n",
        "i = 0\n",
        "\n",
        "# Fetch values of Beta and parameters of SE from the matrix\n",
        "beta = result.params[i]\n",
        "c = SE[i,i]\n",
        "\n",
        "print (beta - z * c, beta + z * c)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xnIzqAroOpus",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "0.343562222943 0.530358115519\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wkKg8ZWMOu--",
        "colab_type": "text"
      },
      "source": [
        "##3 : Value\n",
        "\n",
        "R^2 is the measure of how closely your data points are to the regression line, and is defined as\n",
        "##R2=1−Σ((ypredicted−(yactual))2)Σ(ypredicited−Σyactuallen(yactual)2\n",
        "\n",
        "Given the information from exercise 1, calculate the value of R2 manually. You can start by expressing f as a function of c from the data obtained from Exercise 1 (these are the predicted values of y)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RwdrEKoOjDx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Creat an empty numpy array (float values). \n",
        "# Find the predicted value of f for every c in celsius (given by f = 32 + 1.8c)\n",
        "fpred = np.array([])\n",
        "f = [32 + 1.8*a for a in celsius] ## Your code goes here (fill in the values of Beta, and X1)\n",
        "ypredicted = np.append(f, fpred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfUZ1c04PTJB",
        "colab_type": "text"
      },
      "source": [
        "Using the values of ypredicted and yactual, calculate the squared element by element difference of the two lists, and sum them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R59A63RuPHUU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calucate the difference between the predicted values of y and the actual values of y, \n",
        "# Find the square of the difference\n",
        "# Sum the Squares\n",
        "\n",
        "ypred_yact = [a - b for a, b in zip(ypredicted, fahrenheit)] \n",
        "diff1squared = [a**2 for a in ypred_yact] \n",
        "sumsquares1 = sum(diff1squared) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3EjFSxf0PZdF",
        "colab_type": "text"
      },
      "source": [
        "Using the values of ypredicted and mean, calculate the mean of the predicted values, along with the difference between ypredicted−mean. Square the values in the list obtained from the difference and sum them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2LOJFASPP6S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Calucate the difference between the predicted values of y and mean of y. \n",
        "# Find the square of the difference\n",
        "# Sum the Squares\n",
        "\n",
        "mean = sum(fahrenheit)/len(fahrenheit) \n",
        "ypred_mean = [x - mean for x in ypredicted] \n",
        "ypred_meansquared = [a**2 for a in ypred_mean] \n",
        "sumsquares2 = sum(ypred_meansquared) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6zcDUjRIPpYs",
        "colab_type": "text"
      },
      "source": [
        "We can now calculate the R-Squared by subtracting one to the ratio of the two sums."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FGgX6pAhPmSn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9c497e35-63ad-490e-96f8-923b3d6aabed"
      },
      "source": [
        "## Your code goes here\n",
        "r = 1 - sumsquares1/sumsquares2\n",
        "\n",
        "print ('R-squared = ', r)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "R-squared =  0.9999974346644147\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZByUnnu9P1Qm",
        "colab_type": "text"
      },
      "source": [
        "##4 : Residuals\n",
        "\n",
        "Defintion : In statistics, the residuals are differences between the predicted values and the actual values:\n",
        "\n",
        "###e=y−ŷ\n",
        "\n",
        "##a. Residual Analysis I\n",
        "\n",
        "    Model the data given bellow as a linear regression.\n",
        "    Calculate and plot the residual of the data sets (remember to use the coefficient and the value of x1 to find the predicted values of y)\n",
        "    Print the sum of the residuals.\n",
        "    Discuss the choice of regression model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YvwQ5FGFPvst",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "asset1 = get_pricing('SPY', fields='price', start_date='2005-01-01', end_date='2010-01-01')\n",
        "asset2 = get_pricing('GS', fields='price', start_date='2005-01-01', end_date='2010-01-01')\n",
        "\n",
        "returns1 = asset1.pct_change()[1:]\n",
        "returns2 = asset2.pct_change()[1:]\n",
        "\n",
        "results = smr.linear_model.OLS(returns1.values, sm.add_constant(returns2.values)).fit()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u7rVVplrQHp3",
        "colab_type": "text"
      },
      "source": [
        "Run the Breush-Pagan test to check for heteroskedasticity in the residuals. Note that the residuals of the model should have constant variance, presence of heteroskedasticity would indicate our choice of model is not optimal."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "avZm2qneQAKf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "lm, p_lm, fv, p_fv = het_breushpagan(results.resid, results.model.exog)\n",
        "print ('p-value for f-statistic of the breush-pagan test:', p_fv)\n",
        "print ('====' ) \n",
        "print (\"Since the p-value obtained is greater than alpha (0.05), \\\n",
        "we can't reject the null hypothesis of the breush-pagan test, and state that there is \\\n",
        "no presence of heteroskedasticity\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WIojlzPYQWZY",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "```\n",
        "p-value for f-statistic of the breush-pagan test: 0.664407993179\n",
        "====\n",
        "Since the p-value obtained is greater than alpha (0.05), we can't reject the null hypothesis of the breush-pagan test, and state that there is no presence of heteroskedasticity\n",
        "\n",
        "```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMPRoJDiQNik",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Predicted values of asset1\n",
        "y = results.params[0] + results.params[1]*returns1.values\n",
        "\n",
        "plt.scatter(y, results.resid)\n",
        "plt.title('Scatter plot of Residuals to predicted model')\n",
        "plt.xlabel('Predicted Model')\n",
        "plt.ylabel('Residuals');\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fji2Yqm_Qgf_",
        "colab_type": "text"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jHo8LZ-DQmEO",
        "colab_type": "text"
      },
      "source": [
        "Here we can clearly see the plot of the residuals obeys a random pattern, which would indicate that the choice of model was good. This is further accentuated by the near 0 value of the sum of residuals."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6j_weTgsQr12",
        "colab_type": "text"
      },
      "source": [
        "##b. Residual Analysis II\n",
        "\n",
        "    Run the linear regression function for x and y\n",
        "    Find and plot the residual of the two data points.\n",
        "    Discuss the choice in model.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XpgQv-8CQdMY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "p1 = get_pricing('SPY', start_date = '2005-01-01', \n",
        "                 end_date = '2010-01-01', \n",
        "                 fields = 'price').pct_change()[1:]\n",
        "p2 = get_pricing('XLF', start_date = '2005-01-01', \n",
        "                 end_date = '2010-01-01', \n",
        "                 fields = 'price').pct_change()[1:]\n",
        "\n",
        "## Your code goes here\n",
        "results2 = smr.linear_model.OLS(p1, sm.add_constant(p2)).fit()\n",
        "\n",
        "y = results2.params[0] + results2.params[1]*p1\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ro2QjH1vQnC-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "res = results2.resid\n",
        "\n",
        "plt.scatter(y, res)\n",
        "plt.title('Scatter plot of Residuals to predicted model')\n",
        "plt.xlabel('Predicted Model')\n",
        "plt.ylabel('Residuals')\n",
        "\n",
        "lm, p_lm, fv, p_fv = het_breushpagan(results2.resid, results2.model.exog)\n",
        "print ('p-value for f-statistic of the breush-pagan test:', p_fv)\n",
        "print ('====') \n",
        "print (\"Since the p-value obtained is less than alpha (0.05), \\\n",
        "we reject the null hypothesis of the breush-pagan test, and state that there is \\\n",
        "presence of heteroskedasticity\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UGpDBqp2QyO4",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "\n",
        "```\n",
        "p-value for f-statistic of the breush-pagan test: 5.66764037947e-08\n",
        "====\n",
        "Since the p-value obtained is less than alpha (0.05), we reject the null hypothesis of the breush-pagan test, and state that there is presence of heteroskedasticity\n",
        "```\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DiYXIUpsRQGE",
        "colab_type": "text"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nKA1Q3_fRcXe",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "\n",
        "Here, we can clearly see that the choice of model was not adapt for our data as the plot of the residuals gives us an exponential looking function (not random) and the sum of the residuals is much greater than 0.\n",
        "\n",
        "While checking for residual is a good way of checking the accuracy of our model choice, we must also check fot heteroscedasticity (checking if there are sub-populations that have different variabilities from others).\n",
        "An assumption of the linear regression model is that there is no heteroscedasticity, OLS estimators are no longer the Best Linear Unbiased Estimators if this assumption is broken.\n",
        "Read more about heteroscedasticity here https://en.wikipedia.org/wiki/Heteroscedasticity#Consequences\n"
      ]
    }
  ]
}