{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple linear regression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grading the professor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Many college courses conclude by giving students the opportunity to evaluate the course and the instructor anonymously. However, the use of these student evaluations as an indicator of course quality and teaching effectiveness is often criticized because these measures may reflect the influence of non-teaching related characteristics, such as the physical appearance of the instructor. The article titled, \"Beauty in the classroom: instructors' pulchritude and putative pedagogical productivity\" (Hamermesh and Parker, 2005) found that instructors who are viewed to be better looking receive higher instructional ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In this lab we will analyze the data from this study in order to learn what goes into a positive professor evaluation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The data were gathered from end of semester student evaluations for a large sample of professors from the University of Texas at Austin. In addition, six students rated the professors' physical appearance. (This is aslightly modified version of the original data set that was released as part of the replication data for Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman and Hill, 2007).) The result is a data frame where each row contains a different course and columns represent variables about the courses and professors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import io\n",
    "import requests\n",
    "\n",
    "df_url = 'https://raw.githubusercontent.com/akmand/datasets/master/openintro/evals.csv'\n",
    "url_content = requests.get(df_url, verify=False).content\n",
    "evals = pd.read_csv(io.StringIO(url_content.decode('utf-8')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "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>score</th>\n",
       "      <th>rank</th>\n",
       "      <th>ethnicity</th>\n",
       "      <th>gender</th>\n",
       "      <th>language</th>\n",
       "      <th>age</th>\n",
       "      <th>cls_perc_eval</th>\n",
       "      <th>cls_did_eval</th>\n",
       "      <th>cls_students</th>\n",
       "      <th>cls_level</th>\n",
       "      <th>...</th>\n",
       "      <th>cls_credits</th>\n",
       "      <th>bty_f1lower</th>\n",
       "      <th>bty_f1upper</th>\n",
       "      <th>bty_f2upper</th>\n",
       "      <th>bty_m1lower</th>\n",
       "      <th>bty_m1upper</th>\n",
       "      <th>bty_m2upper</th>\n",
       "      <th>bty_avg</th>\n",
       "      <th>pic_outfit</th>\n",
       "      <th>pic_color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.7</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>english</td>\n",
       "      <td>36</td>\n",
       "      <td>55.81395</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.1</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>english</td>\n",
       "      <td>36</td>\n",
       "      <td>68.80000</td>\n",
       "      <td>86</td>\n",
       "      <td>125</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.9</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>english</td>\n",
       "      <td>36</td>\n",
       "      <td>60.80000</td>\n",
       "      <td>76</td>\n",
       "      <td>125</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.8</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>english</td>\n",
       "      <td>36</td>\n",
       "      <td>62.60163</td>\n",
       "      <td>77</td>\n",
       "      <td>123</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>5.000</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>tenured</td>\n",
       "      <td>not minority</td>\n",
       "      <td>male</td>\n",
       "      <td>english</td>\n",
       "      <td>59</td>\n",
       "      <td>85.00000</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3.000</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>4.5</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>not minority</td>\n",
       "      <td>male</td>\n",
       "      <td>english</td>\n",
       "      <td>32</td>\n",
       "      <td>60.46511</td>\n",
       "      <td>52</td>\n",
       "      <td>86</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>6.833</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>459</th>\n",
       "      <td>3.5</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>non-english</td>\n",
       "      <td>42</td>\n",
       "      <td>57.14286</td>\n",
       "      <td>48</td>\n",
       "      <td>84</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>460</th>\n",
       "      <td>4.4</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>non-english</td>\n",
       "      <td>42</td>\n",
       "      <td>77.61194</td>\n",
       "      <td>52</td>\n",
       "      <td>67</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>461</th>\n",
       "      <td>4.4</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>non-english</td>\n",
       "      <td>42</td>\n",
       "      <td>81.81818</td>\n",
       "      <td>54</td>\n",
       "      <td>66</td>\n",
       "      <td>upper</td>\n",
       "      <td>...</td>\n",
       "      <td>multi credit</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>462</th>\n",
       "      <td>4.1</td>\n",
       "      <td>tenure track</td>\n",
       "      <td>minority</td>\n",
       "      <td>female</td>\n",
       "      <td>non-english</td>\n",
       "      <td>42</td>\n",
       "      <td>80.00000</td>\n",
       "      <td>28</td>\n",
       "      <td>35</td>\n",
       "      <td>lower</td>\n",
       "      <td>...</td>\n",
       "      <td>one credit</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5.333</td>\n",
       "      <td>not formal</td>\n",
       "      <td>color</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>463 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     score          rank     ethnicity  gender     language  age  \\\n",
       "0      4.7  tenure track      minority  female      english   36   \n",
       "1      4.1  tenure track      minority  female      english   36   \n",
       "2      3.9  tenure track      minority  female      english   36   \n",
       "3      4.8  tenure track      minority  female      english   36   \n",
       "4      4.6       tenured  not minority    male      english   59   \n",
       "..     ...           ...           ...     ...          ...  ...   \n",
       "458    4.5  tenure track  not minority    male      english   32   \n",
       "459    3.5  tenure track      minority  female  non-english   42   \n",
       "460    4.4  tenure track      minority  female  non-english   42   \n",
       "461    4.4  tenure track      minority  female  non-english   42   \n",
       "462    4.1  tenure track      minority  female  non-english   42   \n",
       "\n",
       "     cls_perc_eval  cls_did_eval  cls_students cls_level  ...   cls_credits  \\\n",
       "0         55.81395            24            43     upper  ...  multi credit   \n",
       "1         68.80000            86           125     upper  ...  multi credit   \n",
       "2         60.80000            76           125     upper  ...  multi credit   \n",
       "3         62.60163            77           123     upper  ...  multi credit   \n",
       "4         85.00000            17            20     upper  ...  multi credit   \n",
       "..             ...           ...           ...       ...  ...           ...   \n",
       "458       60.46511            52            86     upper  ...  multi credit   \n",
       "459       57.14286            48            84     upper  ...  multi credit   \n",
       "460       77.61194            52            67     upper  ...  multi credit   \n",
       "461       81.81818            54            66     upper  ...  multi credit   \n",
       "462       80.00000            28            35     lower  ...    one credit   \n",
       "\n",
       "    bty_f1lower  bty_f1upper  bty_f2upper  bty_m1lower  bty_m1upper  \\\n",
       "0             5            7            6            2            4   \n",
       "1             5            7            6            2            4   \n",
       "2             5            7            6            2            4   \n",
       "3             5            7            6            2            4   \n",
       "4             4            4            2            2            3   \n",
       "..          ...          ...          ...          ...          ...   \n",
       "458           6            6            9            7            8   \n",
       "459           3            8            7            4            6   \n",
       "460           3            8            7            4            6   \n",
       "461           3            8            7            4            6   \n",
       "462           3            8            7            4            6   \n",
       "\n",
       "     bty_m2upper  bty_avg  pic_outfit pic_color  \n",
       "0              6    5.000  not formal     color  \n",
       "1              6    5.000  not formal     color  \n",
       "2              6    5.000  not formal     color  \n",
       "3              6    5.000  not formal     color  \n",
       "4              3    3.000  not formal     color  \n",
       "..           ...      ...         ...       ...  \n",
       "458            5    6.833  not formal     color  \n",
       "459            4    5.333  not formal     color  \n",
       "460            4    5.333  not formal     color  \n",
       "461            4    5.333  not formal     color  \n",
       "462            4    5.333  not formal     color  \n",
       "\n",
       "[463 rows x 21 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAAAABwSP8H3+444rAa0dUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 319,
       "width": 611
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "%config InlineBackend.figure_format = 'retina'\n",
    "plt.style.use('ggplot')\n",
    "plt.rcParams['figure.figsize'] = (10,5)\n",
    "\n",
    "plt.scatter(evals.bty_avg, evals.score)\n",
    "plt.xlabel('bty_avg')\n",
    "plt.ylabel('score')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercise 1: Fit a linear model called m_bty to predict average professor score by average beauty rating and add the line to your plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  score   R-squared:                       0.035\n",
      "Model:                            OLS   Adj. R-squared:                  0.033\n",
      "Method:                 Least Squares   F-statistic:                     16.73\n",
      "Date:                Thu, 04 May 2023   Prob (F-statistic):           5.08e-05\n",
      "Time:                        11:35:29   Log-Likelihood:                -366.22\n",
      "No. Observations:                 463   AIC:                             736.4\n",
      "Df Residuals:                     461   BIC:                             744.7\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      3.8803      0.076     50.961      0.000       3.731       4.030\n",
      "bty_avg        0.0666      0.016      4.090      0.000       0.035       0.099\n",
      "==============================================================================\n",
      "Omnibus:                       33.007   Durbin-Watson:                   1.267\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               38.797\n",
      "Skew:                          -0.709   Prob(JB):                     3.76e-09\n",
      "Kurtosis:                       3.007   Cond. No.                         14.9\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "formula_string = \"score ~ bty_avg\"\n",
    "\n",
    "model = sm.formula.ols(formula = formula_string, data = evals)\n",
    "model_fitted = model.fit()\n",
    "\n",
    "print(model_fitted.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 302,
       "width": 595
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = evals['bty_avg']\n",
    "y = evals['score']\n",
    "\n",
    "y_pred = model_fitted.predict(x)\n",
    "\n",
    "plt.scatter(evals['bty_avg'], evals['score'])\n",
    "plt.plot(x, y_pred, color = 'orange')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 319,
       "width": 602
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(evals.bty_f1lower, evals.bty_avg)\n",
    "plt.xlabel('bty_f1lower')\n",
    "plt.ylabel('bty_avg')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8439111692147886"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evals.bty_avg.corr(evals.bty_f1lower)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1260x1260 with 56 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 1238,
       "width": 1238
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.pairplot(evals.iloc[:,12:19])\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In order to see if beauty is still a significant predictor of professor score after we've accounted for the gender of the professor, we can add the gender term into the model.\n",
    "\n",
    "### Since gender is a nominal category feature, we first need to convert gender from having the values of female and male to being an indicator variable called gender_integer that takes a value of 0 for females and a value of 1 for males (Such variables are often referred to as \"dummy\" variables.). We can use the replace() function for integer-encoding. Before using the replace() function, we need define a mapping between the levels and the integers using a dictionary as below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "level_mapping = {'female': 0, 'male': 1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_integer = evals['gender'].copy()\n",
    "gender_integer = gender_integer.replace(level_mapping)\n",
    "evals['gender_integer'] = gender_integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Slope: [[0.07415537 0.17238955]]\n",
      "Intercept: [3.74733824]\n",
      "R-squared: 0.059122790336817665\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "x = evals[['bty_avg','gender_integer']].copy()\n",
    "y = np.array(evals.score).reshape((-1, 1))\n",
    "\n",
    "# model initialization\n",
    "mlinear_model = LinearRegression()\n",
    "# fit the data\n",
    "mlinear_model.fit(x, y)\n",
    "# predict\n",
    "y_pred = mlinear_model.predict(x)\n",
    "print('Slope:', mlinear_model.coef_)\n",
    "print('Intercept:', mlinear_model.intercept_)\n",
    "print('R-squared:', r2_score(y, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 319,
       "width": 611
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(evals.bty_avg, evals.score, label = '')\n",
    "plt.xlabel('bty_avg')\n",
    "plt.ylabel('score')\n",
    "\n",
    "# male\n",
    "y_male = 3.74733824 + 0.07415537*evals.bty_avg + 0.17238955*1\n",
    "plt.plot(evals.bty_avg, y_male, color = 'C8', label = 'male')\n",
    "\n",
    "# female\n",
    "y_female = 3.74733824 + 0.07415537*evals.bty_avg + 0.17238955*0\n",
    "plt.plot(evals.bty_avg, y_female, color = 'C4', label = 'female')\n",
    "\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The search for the best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  score   R-squared:                       0.187\n",
      "Model:                            OLS   Adj. R-squared:                  0.162\n",
      "Method:                 Least Squares   F-statistic:                     7.366\n",
      "Date:                Thu, 04 May 2023   Prob (F-statistic):           6.55e-14\n",
      "Time:                        11:58:05   Log-Likelihood:                -326.52\n",
      "No. Observations:                 463   AIC:                             683.0\n",
      "Df Residuals:                     448   BIC:                             745.1\n",
      "Df Model:                          14                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=============================================================================================\n",
      "                                coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------------\n",
      "Intercept                     4.0952      0.291     14.096      0.000       3.524       4.666\n",
      "rank[T.tenure track]         -0.1476      0.082     -1.798      0.073      -0.309       0.014\n",
      "rank[T.tenured]              -0.0973      0.066     -1.467      0.143      -0.228       0.033\n",
      "ethnicity[T.not minority]     0.1235      0.079      1.571      0.117      -0.031       0.278\n",
      "gender[T.male]                0.2109      0.052      4.071      0.000       0.109       0.313\n",
      "language[T.non-english]      -0.2298      0.111     -2.063      0.040      -0.449      -0.011\n",
      "cls_level[T.upper]            0.0605      0.058      1.051      0.294      -0.053       0.174\n",
      "cls_profs[T.single]          -0.0147      0.052     -0.282      0.778      -0.117       0.088\n",
      "cls_credits[T.one credit]     0.5020      0.116      4.330      0.000       0.274       0.730\n",
      "pic_outfit[T.not formal]     -0.1127      0.074     -1.525      0.128      -0.258       0.033\n",
      "pic_color[T.color]           -0.2173      0.072     -3.039      0.003      -0.358      -0.077\n",
      "age                          -0.0090      0.003     -2.872      0.004      -0.015      -0.003\n",
      "cls_perc_eval                 0.0053      0.002      3.461      0.001       0.002       0.008\n",
      "cls_students                  0.0005      0.000      1.205      0.229      -0.000       0.001\n",
      "bty_avg                       0.0400      0.018      2.287      0.023       0.006       0.074\n",
      "==============================================================================\n",
      "Omnibus:                       30.719   Durbin-Watson:                   1.410\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               35.095\n",
      "Skew:                          -0.666   Prob(JB):                     2.40e-08\n",
      "Kurtosis:                       3.218   Cond. No.                     1.47e+03\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, 1.47e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "m_full = sm.formula.ols(formula = 'score ~ rank + ethnicity + gender + language + age + cls_perc_eval + cls_students + cls_level + cls_profs + cls_credits + bty_avg + pic_outfit + pic_color', data = evals)\n",
    "multi_reg = m_full.fit()\n",
    "print(multi_reg.summary())"
   ]
  },
  {
   "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",
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