Dummy_variables_with_comments.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dummy variables"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the relevant libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data from a .csv in the same folder. \n",
    "raw_data = pd.read_csv('Dummies.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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",
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       "      <th>...</th>\n",
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       "      <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": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what's inside this data frame\n",
    "raw_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Map the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <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",
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       "      <td>2.52</td>\n",
       "      <td>0</td>\n",
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       "    <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",
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       "      <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": 6,
     "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",
    "\n",
    "data = raw_data.copy()\n",
    "data['Attendance'] = data['Attendance'].map({'Yes': 1, 'No': 0})\n",
    "\n",
    "# what's inside\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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",
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      ],
      "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": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics\n",
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "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>Tue, 04 Oct 2022</td> <th>  Prob (F-statistic):</th> <td>2.19e-15</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:40:25</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/>Notes:<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:                Tue, 04 Oct 2022   Prob (F-statistic):           2.19e-15\n",
       "Time:                        14:40:25   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",
       "Notes:\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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add a constant\n",
    "x = sm.add_constant(x1)\n",
    "# Fit the model, according to the OLS  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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 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 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": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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",
    "\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": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 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",
    "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()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}