ex35.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import Markdown as md\n",
    "np.random.seed(42)\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "beauty_df = pd.read_csv(\"./ARM_Data_extra/examples/beauty/ProfEvaltnsBeautyPublic.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>tenured</th>\n",
       "      <th>profnumber</th>\n",
       "      <th>minority</th>\n",
       "      <th>age</th>\n",
       "      <th>beautyf2upper</th>\n",
       "      <th>beautyflowerdiv</th>\n",
       "      <th>beautyfupperdiv</th>\n",
       "      <th>beautym2upper</th>\n",
       "      <th>beautymlowerdiv</th>\n",
       "      <th>beautymupperdiv</th>\n",
       "      <th>...</th>\n",
       "      <th>nonenglish</th>\n",
       "      <th>onecredit</th>\n",
       "      <th>percentevaluating</th>\n",
       "      <th>profevaluation</th>\n",
       "      <th>students</th>\n",
       "      <th>tenuretrack</th>\n",
       "      <th>blkandwhite</th>\n",
       "      <th>btystdvariance</th>\n",
       "      <th>btystdavepos</th>\n",
       "      <th>btystdaveneg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "      <td>463.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.546436</td>\n",
       "      <td>45.434125</td>\n",
       "      <td>0.138229</td>\n",
       "      <td>48.365011</td>\n",
       "      <td>5.213823</td>\n",
       "      <td>3.963283</td>\n",
       "      <td>5.019438</td>\n",
       "      <td>4.751620</td>\n",
       "      <td>3.412527</td>\n",
       "      <td>4.146868</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060475</td>\n",
       "      <td>0.058315</td>\n",
       "      <td>74.427788</td>\n",
       "      <td>4.174730</td>\n",
       "      <td>55.177106</td>\n",
       "      <td>0.779698</td>\n",
       "      <td>0.168467</td>\n",
       "      <td>1.842626</td>\n",
       "      <td>0.282413</td>\n",
       "      <td>-0.370763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.498377</td>\n",
       "      <td>27.508902</td>\n",
       "      <td>0.345513</td>\n",
       "      <td>9.802742</td>\n",
       "      <td>2.018224</td>\n",
       "      <td>1.873936</td>\n",
       "      <td>1.934437</td>\n",
       "      <td>1.575266</td>\n",
       "      <td>1.637102</td>\n",
       "      <td>2.110586</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238623</td>\n",
       "      <td>0.234592</td>\n",
       "      <td>16.756311</td>\n",
       "      <td>0.543865</td>\n",
       "      <td>75.072800</td>\n",
       "      <td>0.414899</td>\n",
       "      <td>0.374685</td>\n",
       "      <td>1.261574</td>\n",
       "      <td>0.480691</td>\n",
       "      <td>0.425478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>10.416670</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.085029</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.538840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.696165</td>\n",
       "      <td>3.800000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.828371</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.744618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>76.923080</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.565791</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.156363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>70.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>87.249170</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.682287</td>\n",
       "      <td>0.457254</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>581.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.791667</td>\n",
       "      <td>1.881670</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          tenured  profnumber    minority         age  beautyf2upper  \\\n",
       "count  463.000000  463.000000  463.000000  463.000000     463.000000   \n",
       "mean     0.546436   45.434125    0.138229   48.365011       5.213823   \n",
       "std      0.498377   27.508902    0.345513    9.802742       2.018224   \n",
       "min      0.000000    1.000000    0.000000   29.000000       1.000000   \n",
       "25%      0.000000   20.000000    0.000000   42.000000       4.000000   \n",
       "50%      1.000000   44.000000    0.000000   48.000000       5.000000   \n",
       "75%      1.000000   70.500000    0.000000   57.000000       6.000000   \n",
       "max      1.000000   94.000000    1.000000   73.000000      10.000000   \n",
       "\n",
       "       beautyflowerdiv  beautyfupperdiv  beautym2upper  beautymlowerdiv  \\\n",
       "count       463.000000       463.000000     463.000000       463.000000   \n",
       "mean          3.963283         5.019438       4.751620         3.412527   \n",
       "std           1.873936         1.934437       1.575266         1.637102   \n",
       "min           1.000000         1.000000       1.000000         1.000000   \n",
       "25%           2.000000         4.000000       4.000000         2.000000   \n",
       "50%           4.000000         5.000000       5.000000         3.000000   \n",
       "75%           5.000000         7.000000       6.000000         5.000000   \n",
       "max           8.000000         9.000000       9.000000         7.000000   \n",
       "\n",
       "       beautymupperdiv  ...  nonenglish   onecredit  percentevaluating  \\\n",
       "count       463.000000  ...  463.000000  463.000000         463.000000   \n",
       "mean          4.146868  ...    0.060475    0.058315          74.427788   \n",
       "std           2.110586  ...    0.238623    0.234592          16.756311   \n",
       "min           1.000000  ...    0.000000    0.000000          10.416670   \n",
       "25%           3.000000  ...    0.000000    0.000000          62.696165   \n",
       "50%           4.000000  ...    0.000000    0.000000          76.923080   \n",
       "75%           5.000000  ...    0.000000    0.000000          87.249170   \n",
       "max           9.000000  ...    1.000000    1.000000         100.000000   \n",
       "\n",
       "       profevaluation    students  tenuretrack  blkandwhite  btystdvariance  \\\n",
       "count      463.000000  463.000000   463.000000   463.000000      463.000000   \n",
       "mean         4.174730   55.177106     0.779698     0.168467        1.842626   \n",
       "std          0.543865   75.072800     0.414899     0.374685        1.261574   \n",
       "min          2.300000    8.000000     0.000000     0.000000        0.085029   \n",
       "25%          3.800000   19.000000     1.000000     0.000000        0.828371   \n",
       "50%          4.300000   29.000000     1.000000     0.000000        1.565791   \n",
       "75%          4.600000   60.000000     1.000000     0.000000        2.682287   \n",
       "max          5.000000  581.000000     1.000000     1.000000        5.791667   \n",
       "\n",
       "       btystdavepos  btystdaveneg  \n",
       "count    463.000000    463.000000  \n",
       "mean       0.282413     -0.370763  \n",
       "std        0.480691      0.425478  \n",
       "min        0.000000     -1.538840  \n",
       "25%        0.000000     -0.744618  \n",
       "50%        0.000000     -0.156363  \n",
       "75%        0.457254      0.000000  \n",
       "max        1.881670      0.000000  \n",
       "\n",
       "[8 rows x 64 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <td>4.1</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1.421450</td>\n",
       "      <td>0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 64 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   tenured  profnumber  minority  age  beautyf2upper  beautyflowerdiv  \\\n",
       "0        0           1         1   36              6                5   \n",
       "1        1           2         0   59              2                4   \n",
       "2        1           3         0   51              5                5   \n",
       "3        1           4         0   40              4                2   \n",
       "4        0           5         0   31              9                7   \n",
       "\n",
       "   beautyfupperdiv  beautym2upper  beautymlowerdiv  beautymupperdiv  ...  \\\n",
       "0                7              6                2                4  ...   \n",
       "1                4              3                2                3  ...   \n",
       "2                2              3                2                3  ...   \n",
       "3                5              2                3                3  ...   \n",
       "4                9              6                7                6  ...   \n",
       "\n",
       "   nonenglish  onecredit  percentevaluating  profevaluation  students  \\\n",
       "0           0          0           55.81395             4.7        43   \n",
       "1           0          0           85.00000             4.6        20   \n",
       "2           0          0          100.00000             4.1        55   \n",
       "3           0          0           86.95652             4.5        46   \n",
       "4           0          0           87.50000             4.8        48   \n",
       "\n",
       "   tenuretrack  blkandwhite  btystdvariance  btystdavepos  btystdaveneg  \n",
       "0            1            0        2.129806      0.201567      0.000000  \n",
       "1            1            0        1.386081      0.000000     -0.826081  \n",
       "2            1            0        2.537435      0.000000     -0.660333  \n",
       "3            1            0        1.760577      0.000000     -0.766312  \n",
       "4            1            0        1.693100      1.421450      0.000000  \n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beauty_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>    <td>courseevaluation</td> <th>  R-squared:         </th> <td>   0.036</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   17.08</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 04 Mar 2020</td> <th>  Prob (F-statistic):</th> <td>4.25e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:09:00</td>     <th>  Log-Likelihood:    </th> <td> -375.32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   463</td>      <th>  AIC:               </th> <td>   754.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   461</td>      <th>  BIC:               </th> <td>   762.9</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>    4.0100</td> <td>    0.026</td> <td>  157.205</td> <td> 0.000</td> <td>    3.960</td> <td>    4.060</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>btystdave</th> <td>    0.1330</td> <td>    0.032</td> <td>    4.133</td> <td> 0.000</td> <td>    0.070</td> <td>    0.196</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>15.399</td> <th>  Durbin-Watson:     </th> <td>   1.410</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  16.405</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.453</td> <th>  Prob(JB):          </th> <td>0.000274</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.831</td> <th>  Cond. No.          </th> <td>    1.29</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:       courseevaluation   R-squared:                       0.036\n",
       "Model:                            OLS   Adj. R-squared:                  0.034\n",
       "Method:                 Least Squares   F-statistic:                     17.08\n",
       "Date:                Wed, 04 Mar 2020   Prob (F-statistic):           4.25e-05\n",
       "Time:                        11:09:00   Log-Likelihood:                -375.32\n",
       "No. Observations:                 463   AIC:                             754.6\n",
       "Df Residuals:                     461   BIC:                             762.9\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const          4.0100      0.026    157.205      0.000       3.960       4.060\n",
       "btystdave      0.1330      0.032      4.133      0.000       0.070       0.196\n",
       "==============================================================================\n",
       "Omnibus:                       15.399   Durbin-Watson:                   1.410\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               16.405\n",
       "Skew:                          -0.453   Prob(JB):                     0.000274\n",
       "Kurtosis:                       2.831   Cond. No.                         1.29\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdl_5a = sm.OLS(exog = sm.add_constant(beauty_df[['btystdave']]), endog=beauty_df.courseevaluation).fit()\n",
    "mdl_5a.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.rc_context():\n",
    "    plt.rc(\"figure\", figsize=(20,7))\n",
    "    sm.graphics.plot_regress_exog(mdl_5a, \"btystdave\");\n",
    "    sm.qqplot(mdl_5a.resid, line='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next exercise is pretty open-ended with a goal of trying out different variables. Instead I wanted to try a variable selection approach described [here](https://planspace.org/20150423-forward_selection_with_statsmodels/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "\n",
    "def forward_selected(data, response):\n",
    "    \"\"\"Linear model designed by forward selection.\n",
    "\n",
    "    Parameters:\n",
    "    -----------\n",
    "    data : pandas DataFrame with all possible predictors and response\n",
    "\n",
    "    response: string, name of response column in data\n",
    "\n",
    "    Returns:\n",
    "    --------\n",
    "    model: an \"optimal\" fitted statsmodels linear model\n",
    "           with an intercept\n",
    "           selected by forward selection\n",
    "           evaluated by adjusted R-squared\n",
    "    \"\"\"\n",
    "    remaining = set(data.columns)\n",
    "    remaining.remove(response)\n",
    "    selected = []\n",
    "    current_score, best_new_score = 0.0, 0.0\n",
    "    while remaining and current_score == best_new_score:\n",
    "        scores_with_candidates = []\n",
    "        for candidate in remaining:\n",
    "            formula = \"{} ~ {} + 1\".format(response,\n",
    "                                           ' + '.join(selected + [candidate]))\n",
    "            score = smf.ols(formula, data).fit().rsquared_adj\n",
    "            scores_with_candidates.append((score, candidate))\n",
    "        scores_with_candidates.sort()\n",
    "        best_new_score, best_candidate = scores_with_candidates.pop()\n",
    "        if current_score < best_new_score:\n",
    "            remaining.remove(best_candidate)\n",
    "            selected.append(best_candidate)\n",
    "            current_score = best_new_score\n",
    "    formula = \"{} ~ {} + 1\".format(response,\n",
    "                                   ' + '.join(selected))\n",
    "    model = smf.ols(formula, data).fit()\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "elimd_model = forward_selected(beauty_df, \"courseevaluation\")"
   ]
  },
  {
   "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>courseevaluation</td> <th>  R-squared:         </th> <td>   0.901</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.895</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   152.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Wed, 04 Mar 2020</td> <th>  Prob (F-statistic):</th> <td>8.81e-201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>11:09:37</td>     <th>  Log-Likelihood:    </th> <td>  151.87</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   463</td>      <th>  AIC:               </th> <td>  -249.7</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   436</td>      <th>  BIC:               </th> <td>  -138.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    26</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>Intercept</th>         <td>-2.812e+05</td> <td>  1.1e+05</td> <td>   -2.553</td> <td> 0.011</td> <td>-4.98e+05</td> <td>-6.47e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>profevaluation</th>    <td>    0.9298</td> <td>    0.017</td> <td>   54.246</td> <td> 0.000</td> <td>    0.896</td> <td>    0.964</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>percentevaluating</th> <td>    0.0013</td> <td>    0.001</td> <td>    2.298</td> <td> 0.022</td> <td>    0.000</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>               <td>    0.0035</td> <td>    0.001</td> <td>    3.398</td> <td> 0.001</td> <td>    0.001</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>profnumber</th>        <td>    0.0007</td> <td>    0.000</td> <td>    2.069</td> <td> 0.039</td> <td> 3.66e-05</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nonenglish</th>        <td>   -0.1781</td> <td>    0.038</td> <td>   -4.679</td> <td> 0.000</td> <td>   -0.253</td> <td>   -0.103</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class12</th>           <td>   -0.2612</td> <td>    0.108</td> <td>   -2.427</td> <td> 0.016</td> <td>   -0.473</td> <td>   -0.050</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class27</th>           <td>    0.3447</td> <td>    0.130</td> <td>    2.650</td> <td> 0.008</td> <td>    0.089</td> <td>    0.600</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class14</th>           <td>    0.3115</td> <td>    0.109</td> <td>    2.849</td> <td> 0.005</td> <td>    0.097</td> <td>    0.526</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class18</th>           <td>    0.3083</td> <td>    0.094</td> <td>    3.266</td> <td> 0.001</td> <td>    0.123</td> <td>    0.494</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class19</th>           <td>   -0.1449</td> <td>    0.080</td> <td>   -1.815</td> <td> 0.070</td> <td>   -0.302</td> <td>    0.012</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beautym2upper</th>     <td>    0.0127</td> <td>    0.008</td> <td>    1.668</td> <td> 0.096</td> <td>   -0.002</td> <td>    0.028</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class8</th>            <td>    0.2985</td> <td>    0.131</td> <td>    2.285</td> <td> 0.023</td> <td>    0.042</td> <td>    0.555</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>onecredit</th>         <td>    0.0650</td> <td>    0.039</td> <td>    1.674</td> <td> 0.095</td> <td>   -0.011</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class26</th>           <td>    0.2167</td> <td>    0.108</td> <td>    2.009</td> <td> 0.045</td> <td>    0.005</td> <td>    0.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class3</th>            <td>   -0.1037</td> <td>    0.069</td> <td>   -1.504</td> <td> 0.133</td> <td>   -0.239</td> <td>    0.032</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class17</th>           <td>    0.1553</td> <td>    0.073</td> <td>    2.136</td> <td> 0.033</td> <td>    0.012</td> <td>    0.298</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class29</th>           <td>   -0.1620</td> <td>    0.130</td> <td>   -1.245</td> <td> 0.214</td> <td>   -0.418</td> <td>    0.094</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beautyfupperdiv</th>   <td>    0.0150</td> <td>    0.007</td> <td>    2.044</td> <td> 0.042</td> <td>    0.001</td> <td>    0.029</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>btystdml</th>          <td>   -0.0281</td> <td>    0.013</td> <td>   -2.141</td> <td> 0.033</td> <td>   -0.054</td> <td>   -0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>female</th>            <td>   -0.0354</td> <td>    0.019</td> <td>   -1.853</td> <td> 0.064</td> <td>   -0.073</td> <td>    0.002</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class5</th>            <td>    0.1384</td> <td>    0.093</td> <td>    1.495</td> <td> 0.136</td> <td>   -0.044</td> <td>    0.320</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>multipleclass</th>     <td>   -0.0358</td> <td>    0.022</td> <td>   -1.665</td> <td> 0.097</td> <td>   -0.078</td> <td>    0.006</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class2</th>            <td>    0.1363</td> <td>    0.131</td> <td>    1.043</td> <td> 0.298</td> <td>   -0.121</td> <td>    0.393</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>beautymupperdiv</th>   <td> 6.355e+04</td> <td> 2.49e+04</td> <td>    2.553</td> <td> 0.011</td> <td> 1.46e+04</td> <td> 1.12e+05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>btystdmu</th>          <td>-1.384e+05</td> <td> 5.42e+04</td> <td>   -2.553</td> <td> 0.011</td> <td>-2.45e+05</td> <td>-3.18e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>class23</th>           <td>    0.0996</td> <td>    0.084</td> <td>    1.183</td> <td> 0.237</td> <td>   -0.066</td> <td>    0.265</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>88.713</td> <th>  Durbin-Watson:     </th> <td>   1.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 235.914</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.934</td> <th>  Prob(JB):          </th> <td>5.92e-52</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.956</td> <th>  Cond. No.          </th> <td>1.53e+09</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 smallest eigenvalue is 2.06e-12. This might indicate that there are<br/>strong multicollinearity problems or that the design matrix is singular."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:       courseevaluation   R-squared:                       0.901\n",
       "Model:                            OLS   Adj. R-squared:                  0.895\n",
       "Method:                 Least Squares   F-statistic:                     152.8\n",
       "Date:                Wed, 04 Mar 2020   Prob (F-statistic):          8.81e-201\n",
       "Time:                        11:09:37   Log-Likelihood:                 151.87\n",
       "No. Observations:                 463   AIC:                            -249.7\n",
       "Df Residuals:                     436   BIC:                            -138.0\n",
       "Df Model:                          26                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept         -2.812e+05    1.1e+05     -2.553      0.011   -4.98e+05   -6.47e+04\n",
       "profevaluation        0.9298      0.017     54.246      0.000       0.896       0.964\n",
       "percentevaluating     0.0013      0.001      2.298      0.022       0.000       0.002\n",
       "age                   0.0035      0.001      3.398      0.001       0.001       0.006\n",
       "profnumber            0.0007      0.000      2.069      0.039    3.66e-05       0.001\n",
       "nonenglish           -0.1781      0.038     -4.679      0.000      -0.253      -0.103\n",
       "class12              -0.2612      0.108     -2.427      0.016      -0.473      -0.050\n",
       "class27               0.3447      0.130      2.650      0.008       0.089       0.600\n",
       "class14               0.3115      0.109      2.849      0.005       0.097       0.526\n",
       "class18               0.3083      0.094      3.266      0.001       0.123       0.494\n",
       "class19              -0.1449      0.080     -1.815      0.070      -0.302       0.012\n",
       "beautym2upper         0.0127      0.008      1.668      0.096      -0.002       0.028\n",
       "class8                0.2985      0.131      2.285      0.023       0.042       0.555\n",
       "onecredit             0.0650      0.039      1.674      0.095      -0.011       0.141\n",
       "class26               0.2167      0.108      2.009      0.045       0.005       0.429\n",
       "class3               -0.1037      0.069     -1.504      0.133      -0.239       0.032\n",
       "class17               0.1553      0.073      2.136      0.033       0.012       0.298\n",
       "class29              -0.1620      0.130     -1.245      0.214      -0.418       0.094\n",
       "beautyfupperdiv       0.0150      0.007      2.044      0.042       0.001       0.029\n",
       "btystdml             -0.0281      0.013     -2.141      0.033      -0.054      -0.002\n",
       "female               -0.0354      0.019     -1.853      0.064      -0.073       0.002\n",
       "class5                0.1384      0.093      1.495      0.136      -0.044       0.320\n",
       "multipleclass        -0.0358      0.022     -1.665      0.097      -0.078       0.006\n",
       "class2                0.1363      0.131      1.043      0.298      -0.121       0.393\n",
       "beautymupperdiv    6.355e+04   2.49e+04      2.553      0.011    1.46e+04    1.12e+05\n",
       "btystdmu          -1.384e+05   5.42e+04     -2.553      0.011   -2.45e+05   -3.18e+04\n",
       "class23               0.0996      0.084      1.183      0.237      -0.066       0.265\n",
       "==============================================================================\n",
       "Omnibus:                       88.713   Durbin-Watson:                   1.976\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              235.914\n",
       "Skew:                          -0.934   Prob(JB):                     5.92e-52\n",
       "Kurtosis:                       5.956   Cond. No.                     1.53e+09\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The smallest eigenvalue is 2.06e-12. This might indicate that there are\n",
       "strong multicollinearity problems or that the design matrix is singular.\n",
       "\"\"\""
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elimd_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'courseevaluation ~ profevaluation + percentevaluating + age + profnumber + nonenglish + class12 + class27 + class14 + class18 + class19 + beautym2upper + class8 + onecredit + class26 + class3 + class17 + class29 + beautyfupperdiv + btystdml + female + class5 + multipleclass + class2 + beautymupperdiv + btystdmu + class23 + 1'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elimd_model.model.formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "with plt.rc_context():\n",
    "    plt.rc(\"figure\", figsize=(20,7))\n",
    "    sm.qqplot(elimd_model.resid, line='r');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Worked nicely, especially in comparison to the original model with one variable ($R^2$ went up to 0.901 from 0.036)"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}