Dummy variables MLR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy variables or how to deal with categorical predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# We can override the default matplotlib styles with those of Seaborn\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data from a .csv in the same folder. Since we will do some preprocessing, the variable is not called 'data' just yet!\n",
    "raw_data=pd.read_csv(r'D:\\Stu\\Data Science\\Exercises Regression\\1.03. Dummies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1714</td>\n",
       "      <td>2.40</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1664</td>\n",
       "      <td>2.52</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1760</td>\n",
       "      <td>2.54</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1685</td>\n",
       "      <td>2.74</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1693</td>\n",
       "      <td>2.83</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1936</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1810</td>\n",
       "      <td>3.71</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1987</td>\n",
       "      <td>3.73</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1962</td>\n",
       "      <td>3.76</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2050</td>\n",
       "      <td>3.81</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SAT   GPA Attendance\n",
       "0   1714  2.40         No\n",
       "1   1664  2.52         No\n",
       "2   1760  2.54         No\n",
       "3   1685  2.74         No\n",
       "4   1693  2.83         No\n",
       "..   ...   ...        ...\n",
       "79  1936  3.71        Yes\n",
       "80  1810  3.71        Yes\n",
       "81  1987  3.73         No\n",
       "82  1962  3.76        Yes\n",
       "83  2050  3.81        Yes\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's check what's inside this data frame\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1714</td>\n",
       "      <td>2.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1664</td>\n",
       "      <td>2.52</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1760</td>\n",
       "      <td>2.54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1685</td>\n",
       "      <td>2.74</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1693</td>\n",
       "      <td>2.83</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>1936</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1810</td>\n",
       "      <td>3.71</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1987</td>\n",
       "      <td>3.73</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1962</td>\n",
       "      <td>3.76</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2050</td>\n",
       "      <td>3.81</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SAT   GPA  Attendance\n",
       "0   1714  2.40           0\n",
       "1   1664  2.52           0\n",
       "2   1760  2.54           0\n",
       "3   1685  2.74           0\n",
       "4   1693  2.83           0\n",
       "..   ...   ...         ...\n",
       "79  1936  3.71           1\n",
       "80  1810  3.71           1\n",
       "81  1987  3.73           0\n",
       "82  1962  3.76           1\n",
       "83  2050  3.81           1\n",
       "\n",
       "[84 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Map all 'No' entries with 0, and all 'Yes' entries with 1. Put that in a new variable called 'data'\n",
    "# Note data is a copy of raw_data, because of how pointers in Python work\n",
    "data = raw_data.copy()\n",
    "data['Attendance'] = data['Attendance'].map({'Yes': 1, 'No': 0})\n",
    "\n",
    "# Let's see what's inside\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAT</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Attendance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "      <td>84.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1845.273810</td>\n",
       "      <td>3.330238</td>\n",
       "      <td>0.464286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>104.530661</td>\n",
       "      <td>0.271617</td>\n",
       "      <td>0.501718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1634.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1772.000000</td>\n",
       "      <td>3.190000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1846.000000</td>\n",
       "      <td>3.380000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1934.000000</td>\n",
       "      <td>3.502500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2050.000000</td>\n",
       "      <td>3.810000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SAT        GPA  Attendance\n",
       "count    84.000000  84.000000   84.000000\n",
       "mean   1845.273810   3.330238    0.464286\n",
       "std     104.530661   0.271617    0.501718\n",
       "min    1634.000000   2.400000    0.000000\n",
       "25%    1772.000000   3.190000    0.000000\n",
       "50%    1846.000000   3.380000    0.000000\n",
       "75%    1934.000000   3.502500    1.000000\n",
       "max    2050.000000   3.810000    1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This method gives us very nice descriptive statistics.\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Following the regression equation, our dependent variable (y) is the GPA\n",
    "y = data ['GPA']\n",
    "# Similarly, our independent variable (x) is the SAT score\n",
    "x1 = data [['SAT','Attendance']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>           <td>GPA</td>       <th>  R-squared:         </th> <td>   0.565</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   52.70</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 15 Jul 2020</td> <th>  Prob (F-statistic):</th> <td>2.19e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:47:36</td>     <th>  Log-Likelihood:    </th> <td>  25.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    84</td>      <th>  AIC:               </th> <td>  -45.60</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    81</td>      <th>  BIC:               </th> <td>  -38.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</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>    0.6439</td> <td>    0.358</td> <td>    1.797</td> <td> 0.076</td> <td>   -0.069</td> <td>    1.357</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SAT</th>        <td>    0.0014</td> <td>    0.000</td> <td>    7.141</td> <td> 0.000</td> <td>    0.001</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Attendance</th> <td>    0.2226</td> <td>    0.041</td> <td>    5.451</td> <td> 0.000</td> <td>    0.141</td> <td>    0.304</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19.560</td> <th>  Durbin-Watson:     </th> <td>   1.009</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  27.189</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.028</td> <th>  Prob(JB):          </th> <td>1.25e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 4.881</td> <th>  Cond. No.          </th> <td>3.35e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.35e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                    GPA   R-squared:                       0.565\n",
       "Model:                            OLS   Adj. R-squared:                  0.555\n",
       "Method:                 Least Squares   F-statistic:                     52.70\n",
       "Date:                Wed, 15 Jul 2020   Prob (F-statistic):           2.19e-15\n",
       "Time:                        20:47:36   Log-Likelihood:                 25.798\n",
       "No. Observations:                  84   AIC:                            -45.60\n",
       "Df Residuals:                      81   BIC:                            -38.30\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          0.6439      0.358      1.797      0.076      -0.069       1.357\n",
       "SAT            0.0014      0.000      7.141      0.000       0.001       0.002\n",
       "Attendance     0.2226      0.041      5.451      0.000       0.141       0.304\n",
       "==============================================================================\n",
       "Omnibus:                       19.560   Durbin-Watson:                   1.009\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               27.189\n",
       "Skew:                          -1.028   Prob(JB):                     1.25e-06\n",
       "Kurtosis:                       4.881   Cond. No.                     3.35e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.35e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a constant. Esentially, we are adding a new column (equal in lenght to x), which consists only of 1s\n",
    "x = sm.add_constant(x1)\n",
    "# Fit the model, according to the OLS (ordinary least squares) method with a dependent variable y and an idependent x\n",
    "results = sm.OLS(y,x).fit()\n",
    "# Print a nice summary of the regression.\n",
    "results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the regression line(s) on the scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a scatter plot of SAT and GPA\n",
    "plt.scatter(data['SAT'],y)\n",
    "# Define the two regression equations, depending on whether they attended (yes), or didn't (no)\n",
    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
    "# Plot the two regression lines\n",
    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837')\n",
    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026')\n",
    "# Name your axes :)\n",
    "plt.xlabel('SAT', fontsize = 20)\n",
    "plt.ylabel('GPA', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the regression line(s) on the scatter plot and color the data points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In this code I want to colour the points depending on attendance\n",
    "# Note: This code would have been very easy in Seaborn\n",
    "\n",
    "# Create one scatter plot which contains all observations\n",
    "# Use the series 'Attendance' as color, and choose a colour map of your choice\n",
    "# The colour map we've chosen is completely arbitrary\n",
    "plt.scatter(data['SAT'],data['GPA'], c=data['Attendance'],cmap='RdYlGn_r')\n",
    "\n",
    "# Define the two regression equations (one with a dummy = 1, the other with dummy = 0)\n",
    "# We have those above already, but for the sake of consistency, we will also include them here\n",
    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
    "\n",
    "# Plot the two regression lines\n",
    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837')\n",
    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026')\n",
    "plt.xlabel('SAT', fontsize = 20)\n",
    "plt.ylabel('GPA', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Add the original regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Same as above, this time we are including the regression line WITHOUT the dummies.\n",
    "\n",
    "# In this code I want to color the points depending on attendance\n",
    "# Note: This code would have been very easy in Seaborn\n",
    "\n",
    "# Create one scatter plot which contains all observations\n",
    "# Use the series 'Attendance' as color, and choose a colour map of your choice\n",
    "# The colour map we've chosen is completely arbitrary\n",
    "plt.scatter(data['SAT'],data['GPA'], c=data['Attendance'],cmap='RdYlGn_r')\n",
    "\n",
    "# Define the two regression equations (one with a dummy = 1, the other with dummy = 0)\n",
    "# We have those above already, but for the sake of consistency, we will also include them here\n",
    "yhat_no = 0.6439 + 0.0014*data['SAT']\n",
    "yhat_yes = 0.8665 + 0.0014*data['SAT']\n",
    "# Original regression line\n",
    "yhat = 0.0017*data['SAT'] + 0.275\n",
    "\n",
    "# Plot the two regression lines\n",
    "fig = plt.plot(data['SAT'],yhat_no, lw=2, c='#006837', label ='regression line1')\n",
    "fig = plt.plot(data['SAT'],yhat_yes, lw=2, c='#a50026', label ='regression line2')\n",
    "# Plot the original regression line\n",
    "fig = plt.plot(data['SAT'],yhat, lw=3, c='#4C72B0', label ='regression line')\n",
    "\n",
    "plt.xlabel('SAT', fontsize = 20)\n",
    "plt.ylabel('GPA', fontsize = 20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}