{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab 3.6: Linear Regression\n",
    "\n",
    "## 3.6.2 Simple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "%matplotlib inline\n",
    "pd.set_option('display.max_rows', 10)\n",
    "pd.set_option('display.float_format', '{:20,.2f}'.format) # get rid of scientific notation\n",
    "plt.style.use('seaborn') # pretty matplotlib plots"
   ]
  },
  {
   "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>crim</th>\n",
       "      <th>zn</th>\n",
       "      <th>indus</th>\n",
       "      <th>chas</th>\n",
       "      <th>nox</th>\n",
       "      <th>rm</th>\n",
       "      <th>age</th>\n",
       "      <th>dis</th>\n",
       "      <th>rad</th>\n",
       "      <th>tax</th>\n",
       "      <th>ptratio</th>\n",
       "      <th>black</th>\n",
       "      <th>lstat</th>\n",
       "      <th>medv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.01</td>\n",
       "      <td>18.00</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.54</td>\n",
       "      <td>6.58</td>\n",
       "      <td>65.20</td>\n",
       "      <td>4.09</td>\n",
       "      <td>1</td>\n",
       "      <td>296</td>\n",
       "      <td>15.30</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "      <td>24.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>6.42</td>\n",
       "      <td>78.90</td>\n",
       "      <td>4.97</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.80</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "      <td>21.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>7.18</td>\n",
       "      <td>61.10</td>\n",
       "      <td>4.97</td>\n",
       "      <td>2</td>\n",
       "      <td>242</td>\n",
       "      <td>17.80</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "      <td>34.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>7.00</td>\n",
       "      <td>45.80</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.70</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "      <td>33.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>7.15</td>\n",
       "      <td>54.20</td>\n",
       "      <td>6.06</td>\n",
       "      <td>3</td>\n",
       "      <td>222</td>\n",
       "      <td>18.70</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "      <td>36.20</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.59</td>\n",
       "      <td>69.10</td>\n",
       "      <td>2.48</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.00</td>\n",
       "      <td>391.99</td>\n",
       "      <td>9.67</td>\n",
       "      <td>22.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.12</td>\n",
       "      <td>76.70</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.00</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.08</td>\n",
       "      <td>20.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.98</td>\n",
       "      <td>91.00</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.00</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.64</td>\n",
       "      <td>23.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.79</td>\n",
       "      <td>89.30</td>\n",
       "      <td>2.39</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.00</td>\n",
       "      <td>393.45</td>\n",
       "      <td>6.48</td>\n",
       "      <td>22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.93</td>\n",
       "      <td>0</td>\n",
       "      <td>0.57</td>\n",
       "      <td>6.03</td>\n",
       "      <td>80.80</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1</td>\n",
       "      <td>273</td>\n",
       "      <td>21.00</td>\n",
       "      <td>396.90</td>\n",
       "      <td>7.88</td>\n",
       "      <td>11.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    crim                   zn                indus  chas  \\\n",
       "1                   0.01                18.00                 2.31     0   \n",
       "2                   0.03                 0.00                 7.07     0   \n",
       "3                   0.03                 0.00                 7.07     0   \n",
       "4                   0.03                 0.00                 2.18     0   \n",
       "5                   0.07                 0.00                 2.18     0   \n",
       "..                   ...                  ...                  ...   ...   \n",
       "502                 0.06                 0.00                11.93     0   \n",
       "503                 0.05                 0.00                11.93     0   \n",
       "504                 0.06                 0.00                11.93     0   \n",
       "505                 0.11                 0.00                11.93     0   \n",
       "506                 0.05                 0.00                11.93     0   \n",
       "\n",
       "                     nox                   rm                  age  \\\n",
       "1                   0.54                 6.58                65.20   \n",
       "2                   0.47                 6.42                78.90   \n",
       "3                   0.47                 7.18                61.10   \n",
       "4                   0.46                 7.00                45.80   \n",
       "5                   0.46                 7.15                54.20   \n",
       "..                   ...                  ...                  ...   \n",
       "502                 0.57                 6.59                69.10   \n",
       "503                 0.57                 6.12                76.70   \n",
       "504                 0.57                 6.98                91.00   \n",
       "505                 0.57                 6.79                89.30   \n",
       "506                 0.57                 6.03                80.80   \n",
       "\n",
       "                     dis  rad  tax              ptratio                black  \\\n",
       "1                   4.09    1  296                15.30               396.90   \n",
       "2                   4.97    2  242                17.80               396.90   \n",
       "3                   4.97    2  242                17.80               392.83   \n",
       "4                   6.06    3  222                18.70               394.63   \n",
       "5                   6.06    3  222                18.70               396.90   \n",
       "..                   ...  ...  ...                  ...                  ...   \n",
       "502                 2.48    1  273                21.00               391.99   \n",
       "503                 2.29    1  273                21.00               396.90   \n",
       "504                 2.17    1  273                21.00               396.90   \n",
       "505                 2.39    1  273                21.00               393.45   \n",
       "506                 2.50    1  273                21.00               396.90   \n",
       "\n",
       "                   lstat                 medv  \n",
       "1                   4.98                24.00  \n",
       "2                   9.14                21.60  \n",
       "3                   4.03                34.70  \n",
       "4                   2.94                33.40  \n",
       "5                   5.33                36.20  \n",
       "..                   ...                  ...  \n",
       "502                 9.67                22.40  \n",
       "503                 9.08                20.60  \n",
       "504                 5.64                23.90  \n",
       "505                 6.48                22.00  \n",
       "506                 7.88                11.90  \n",
       "\n",
       "[506 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "boston = pd.read_csv('../datasets/Boston.csv', index_col=0)\n",
    "boston"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(34.55384087938311, array([-0.95004935]))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import linear_model\n",
    "\n",
    "# ols model with intercept\n",
    "ols_sl = linear_model.LinearRegression(fit_intercept=True) \n",
    "\n",
    "# fitted ols model (.values.reshape(-1, 1) is required for single predictor?)\n",
    "x_train = boston['lstat'].values.reshape(-1, 1)\n",
    "y_true = boston['medv']\n",
    "ols_sl.fit(x_train, y_true)\n",
    "\n",
    "# summary\n",
    "ols_sl.intercept_, ols_sl.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 : 0.5441462975864799\n",
      "Ex. Var : 0.5441462975864798\n",
      "MSE : 38.48296722989414\n"
     ]
    }
   ],
   "source": [
    "# metrics\n",
    "from sklearn.metrics import mean_squared_error, explained_variance_score, r2_score\n",
    "\n",
    "y_pred = ols_sl.predict(boston['lstat'].values.reshape(-1, 1))\n",
    "\n",
    "ols_sl_summary = {'R2': r2_score(y_true, y_pred), \n",
    "                  'Ex. Var': explained_variance_score(y_true, y_pred), \n",
    "                  'MSE': mean_squared_error(y_true, y_pred)}\n",
    "\n",
    "for k, v in ols_sl_summary.items():\n",
    "    print(k, ':', v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29.80359411, 25.05334734, 20.30310057])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# out-of-sample predictions\n",
    "ols_sl.predict(np.array([5, 10, 15]).reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using statsmodels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.544</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.543</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   601.6</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>5.08e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:25</td>     <th>  Log-Likelihood:    </th> <td> -1641.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3287.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   504</td>      <th>  BIC:               </th> <td>   3295.</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>   34.5538</td> <td>    0.563</td> <td>   61.415</td> <td> 0.000</td> <td>   33.448</td> <td>   35.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th> <td>   -0.9500</td> <td>    0.039</td> <td>  -24.528</td> <td> 0.000</td> <td>   -1.026</td> <td>   -0.874</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>137.043</td> <th>  Durbin-Watson:     </th> <td>   0.892</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 291.373</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.453</td>  <th>  Prob(JB):          </th> <td>5.36e-64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.319</td>  <th>  Cond. No.          </th> <td>    29.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<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:                   medv   R-squared:                       0.544\n",
       "Model:                            OLS   Adj. R-squared:                  0.543\n",
       "Method:                 Least Squares   F-statistic:                     601.6\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):           5.08e-88\n",
       "Time:                        13:12:25   Log-Likelihood:                -1641.5\n",
       "No. Observations:                 506   AIC:                             3287.\n",
       "Df Residuals:                     504   BIC:                             3295.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         34.5538      0.563     61.415      0.000      33.448      35.659\n",
       "lstat         -0.9500      0.039    -24.528      0.000      -1.026      -0.874\n",
       "==============================================================================\n",
       "Omnibus:                      137.043   Durbin-Watson:                   0.892\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              291.373\n",
       "Skew:                           1.453   Prob(JB):                     5.36e-64\n",
       "Kurtosis:                       5.319   Cond. No.                         29.7\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using statsmodels\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# predictor & dependent var\n",
    "x_train = boston['lstat']\n",
    "y_true = boston['medv']\n",
    "\n",
    "# ols model with intercept added to predictor\n",
    "ols_sm = sm.OLS(y_true, sm.add_constant(x_train))\n",
    "\n",
    "# fitted model and summary\n",
    "ols_sm_results = ols_sm.fit()\n",
    "ols_sm_results.summary()\n",
    "\n",
    "# robust SE\n",
    "#ols_sm_robust = sm.RLM(boston['medv'], X, M=sm.robust.norms.LeastSquares())\n",
    "#ols_sm_robust.fit(cov='H2').summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([29.80359411, 25.05334734, 20.30310057])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# out-of-sample predictions\n",
    "ols_sm_results.predict(sm.add_constant([5, 10, 15]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>x_train</th>\n",
       "      <th>y_pred</th>\n",
       "      <th>y_true</th>\n",
       "      <th>lwr</th>\n",
       "      <th>upr</th>\n",
       "      <th>pred_se</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.98</td>\n",
       "      <td>29.82</td>\n",
       "      <td>24.00</td>\n",
       "      <td>17.58</td>\n",
       "      <td>42.06</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9.14</td>\n",
       "      <td>25.87</td>\n",
       "      <td>21.60</td>\n",
       "      <td>13.64</td>\n",
       "      <td>38.10</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.03</td>\n",
       "      <td>30.73</td>\n",
       "      <td>34.70</td>\n",
       "      <td>18.48</td>\n",
       "      <td>42.97</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.94</td>\n",
       "      <td>31.76</td>\n",
       "      <td>33.40</td>\n",
       "      <td>19.51</td>\n",
       "      <td>44.01</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.33</td>\n",
       "      <td>29.49</td>\n",
       "      <td>36.20</td>\n",
       "      <td>17.25</td>\n",
       "      <td>41.73</td>\n",
       "      <td>6.23</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>9.67</td>\n",
       "      <td>25.37</td>\n",
       "      <td>22.40</td>\n",
       "      <td>13.14</td>\n",
       "      <td>37.59</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>9.08</td>\n",
       "      <td>25.93</td>\n",
       "      <td>20.60</td>\n",
       "      <td>13.70</td>\n",
       "      <td>38.15</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>5.64</td>\n",
       "      <td>29.20</td>\n",
       "      <td>23.90</td>\n",
       "      <td>16.96</td>\n",
       "      <td>41.43</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>6.48</td>\n",
       "      <td>28.40</td>\n",
       "      <td>22.00</td>\n",
       "      <td>16.16</td>\n",
       "      <td>40.63</td>\n",
       "      <td>6.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>7.88</td>\n",
       "      <td>27.07</td>\n",
       "      <td>11.90</td>\n",
       "      <td>14.84</td>\n",
       "      <td>39.30</td>\n",
       "      <td>6.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 x_train               y_pred               y_true  \\\n",
       "1                   4.98                29.82                24.00   \n",
       "2                   9.14                25.87                21.60   \n",
       "3                   4.03                30.73                34.70   \n",
       "4                   2.94                31.76                33.40   \n",
       "5                   5.33                29.49                36.20   \n",
       "..                   ...                  ...                  ...   \n",
       "502                 9.67                25.37                22.40   \n",
       "503                 9.08                25.93                20.60   \n",
       "504                 5.64                29.20                23.90   \n",
       "505                 6.48                28.40                22.00   \n",
       "506                 7.88                27.07                11.90   \n",
       "\n",
       "                     lwr                  upr              pred_se  \n",
       "1                  17.58                42.06                 6.23  \n",
       "2                  13.64                38.10                 6.22  \n",
       "3                  18.48                42.97                 6.23  \n",
       "4                  19.51                44.01                 6.23  \n",
       "5                  17.25                41.73                 6.23  \n",
       "..                   ...                  ...                  ...  \n",
       "502                13.14                37.59                 6.22  \n",
       "503                13.70                38.15                 6.22  \n",
       "504                16.96                41.43                 6.23  \n",
       "505                16.16                40.63                 6.23  \n",
       "506                14.84                39.30                 6.22  \n",
       "\n",
       "[506 rows x 6 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# prediction intervals\n",
    "from statsmodels.sandbox.regression.predstd import wls_prediction_std\n",
    "from collections import OrderedDict\n",
    "\n",
    "y_pred = ols_sm_results.predict(sm.add_constant(x_train))\n",
    "prstd, iv_l, iv_u = wls_prediction_std(ols_sm_results)\n",
    "\n",
    "pred_dict = OrderedDict({'x_train': x_train,\n",
    "                         'y_pred': y_pred, \n",
    "                         'y_true': y_true, \n",
    "                         'lwr': iv_l, \n",
    "                         'upr': iv_u, \n",
    "                         'pred_se': prstd})\n",
    "\n",
    "pd.DataFrame(pred_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('lstat', 'medv', data=boston);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from statsmodels.graphics.regressionplots import *\n",
    "\n",
    "ols_sm_resid = ols_sm_results.resid # residuals\n",
    "ols_sm_resid_stud = ols_sm_resid / prstd # studentized residuals\n",
    "\n",
    "f, axes = plt.subplots(2, 2, sharex=False, sharey=False) \n",
    "f.set_figheight(10)\n",
    "f.set_figwidth(15)\n",
    "\n",
    "sns.regplot('lstat', 'medv', data=boston, ax=axes[0, 0], scatter_kws={'alpha': 0.5}) # regression plot\n",
    "sns.residplot(y_pred, 'medv', data=boston, ax=axes[0, 1], scatter_kws={'alpha': 0.5}) # residual plot\n",
    "\n",
    "#plot_leverage_resid2(ols_sm_results, ax=axes[1, 0], color='red') # leverage plot\n",
    "\n",
    "# custom leverage plot instead of above\n",
    "from statsmodels.stats.outliers_influence import OLSInfluence\n",
    "from scipy.stats import zscore\n",
    "norm_resid = zscore(ols_sm_resid)\n",
    "leverage = OLSInfluence(ols_sm_results).hat_matrix_diag\n",
    "axes[1, 0].autoscale(enable=True, axis='y', tight=True)\n",
    "axes[1, 0].scatter(norm_resid ** 2, leverage, alpha=0.5, color='red')\n",
    "\n",
    "# studentized residual plot\n",
    "axes[1, 1].scatter(y_pred, ols_sm_resid_stud, alpha=0.5, color='magenta')\n",
    "axes[1, 1].axhline(0, ls=\":\", c=\".2\")\n",
    "axes[1, 1].axhline(-3, ls=\":\", c=\".6\")\n",
    "axes[1, 1].axhline(3, ls=\":\", c=\".6\")\n",
    "axes[1, 1].set_ylim(-5, 5)\n",
    "\n",
    "x = y_pred[np.logical_or(ols_sm_resid_stud > 3, ols_sm_resid_stud < -3)]\n",
    "y = ols_sm_resid_stud[np.logical_or(ols_sm_resid_stud > 3, ols_sm_resid_stud < -3)]\n",
    "\n",
    "for i, x, y in zip(x.index, x, y):\n",
    "    axes[1, 1].annotate(i, xy=(x, y));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(374, 0.026865166510283502)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# item with the highest leverage (0-indexed)\n",
    "\n",
    "leverage.argmax(), leverage.max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6.3 Multiple Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   309.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>2.98e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:26</td>     <th>  Log-Likelihood:    </th> <td> -1637.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3281.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   503</td>      <th>  BIC:               </th> <td>   3294.</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>   33.2228</td> <td>    0.731</td> <td>   45.458</td> <td> 0.000</td> <td>   31.787</td> <td>   34.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th> <td>   -1.0321</td> <td>    0.048</td> <td>  -21.416</td> <td> 0.000</td> <td>   -1.127</td> <td>   -0.937</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>   <td>    0.0345</td> <td>    0.012</td> <td>    2.826</td> <td> 0.005</td> <td>    0.011</td> <td>    0.059</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>124.288</td> <th>  Durbin-Watson:     </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 244.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.362</td>  <th>  Prob(JB):          </th> <td>1.02e-53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.038</td>  <th>  Cond. No.          </th> <td>    201.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<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:                   medv   R-squared:                       0.551\n",
       "Model:                            OLS   Adj. R-squared:                  0.549\n",
       "Method:                 Least Squares   F-statistic:                     309.0\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):           2.98e-88\n",
       "Time:                        13:12:26   Log-Likelihood:                -1637.5\n",
       "No. Observations:                 506   AIC:                             3281.\n",
       "Df Residuals:                     503   BIC:                             3294.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const         33.2228      0.731     45.458      0.000      31.787      34.659\n",
       "lstat         -1.0321      0.048    -21.416      0.000      -1.127      -0.937\n",
       "age            0.0345      0.012      2.826      0.005       0.011       0.059\n",
       "==============================================================================\n",
       "Omnibus:                      124.288   Durbin-Watson:                   0.945\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              244.026\n",
       "Skew:                           1.362   Prob(JB):                     1.02e-53\n",
       "Kurtosis:                       5.038   Cond. No.                         201.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predictors & dependent var\n",
    "x_train = boston[['lstat', 'age']]\n",
    "y_true = boston['medv']\n",
    "\n",
    "# ols model with intercept added to predictor\n",
    "ols_sm = sm.OLS(y_true, sm.add_constant(x_train))\n",
    "\n",
    "# fitted model and summary\n",
    "ols_sm_results = ols_sm.fit()\n",
    "ols_sm_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.551</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.549</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   309.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>2.98e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:26</td>     <th>  Log-Likelihood:    </th> <td> -1637.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3281.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   503</td>      <th>  BIC:               </th> <td>   3294.</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>Intercept</th> <td>   33.2228</td> <td>    0.731</td> <td>   45.458</td> <td> 0.000</td> <td>   31.787</td> <td>   34.659</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>     <td>   -1.0321</td> <td>    0.048</td> <td>  -21.416</td> <td> 0.000</td> <td>   -1.127</td> <td>   -0.937</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.0345</td> <td>    0.012</td> <td>    2.826</td> <td> 0.005</td> <td>    0.011</td> <td>    0.059</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>124.288</td> <th>  Durbin-Watson:     </th> <td>   0.945</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 244.026</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.362</td>  <th>  Prob(JB):          </th> <td>1.02e-53</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.038</td>  <th>  Cond. No.          </th> <td>    201.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<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:                   medv   R-squared:                       0.551\n",
       "Model:                            OLS   Adj. R-squared:                  0.549\n",
       "Method:                 Least Squares   F-statistic:                     309.0\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):           2.98e-88\n",
       "Time:                        13:12:26   Log-Likelihood:                -1637.5\n",
       "No. Observations:                 506   AIC:                             3281.\n",
       "Df Residuals:                     503   BIC:                             3294.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     33.2228      0.731     45.458      0.000      31.787      34.659\n",
       "lstat         -1.0321      0.048    -21.416      0.000      -1.127      -0.937\n",
       "age            0.0345      0.012      2.826      0.005       0.011       0.059\n",
       "==============================================================================\n",
       "Omnibus:                      124.288   Durbin-Watson:                   0.945\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              244.026\n",
       "Skew:                           1.362   Prob(JB):                     1.02e-53\n",
       "Kurtosis:                       5.038   Cond. No.                         201.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf # R-style formula api\n",
    "\n",
    "# ols model with intercept\n",
    "ols_smf = smf.ols(formula='medv ~ lstat + age', data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.734</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   108.1</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>6.72e-135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:26</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3026.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   492</td>      <th>  BIC:               </th> <td>   3085.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</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>   36.4595</td> <td>    5.103</td> <td>    7.144</td> <td> 0.000</td> <td>   26.432</td> <td>   46.487</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>crim</th>      <td>   -0.1080</td> <td>    0.033</td> <td>   -3.287</td> <td> 0.001</td> <td>   -0.173</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zn</th>        <td>    0.0464</td> <td>    0.014</td> <td>    3.382</td> <td> 0.001</td> <td>    0.019</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>indus</th>     <td>    0.0206</td> <td>    0.061</td> <td>    0.334</td> <td> 0.738</td> <td>   -0.100</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>chas</th>      <td>    2.6867</td> <td>    0.862</td> <td>    3.118</td> <td> 0.002</td> <td>    0.994</td> <td>    4.380</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nox</th>       <td>  -17.7666</td> <td>    3.820</td> <td>   -4.651</td> <td> 0.000</td> <td>  -25.272</td> <td>  -10.262</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rm</th>        <td>    3.8099</td> <td>    0.418</td> <td>    9.116</td> <td> 0.000</td> <td>    2.989</td> <td>    4.631</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>    0.0007</td> <td>    0.013</td> <td>    0.052</td> <td> 0.958</td> <td>   -0.025</td> <td>    0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dis</th>       <td>   -1.4756</td> <td>    0.199</td> <td>   -7.398</td> <td> 0.000</td> <td>   -1.867</td> <td>   -1.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rad</th>       <td>    0.3060</td> <td>    0.066</td> <td>    4.613</td> <td> 0.000</td> <td>    0.176</td> <td>    0.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tax</th>       <td>   -0.0123</td> <td>    0.004</td> <td>   -3.280</td> <td> 0.001</td> <td>   -0.020</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ptratio</th>   <td>   -0.9527</td> <td>    0.131</td> <td>   -7.283</td> <td> 0.000</td> <td>   -1.210</td> <td>   -0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>black</th>     <td>    0.0093</td> <td>    0.003</td> <td>    3.467</td> <td> 0.001</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>     <td>   -0.5248</td> <td>    0.051</td> <td>  -10.347</td> <td> 0.000</td> <td>   -0.624</td> <td>   -0.425</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>178.041</td> <th>  Durbin-Watson:     </th> <td>   1.078</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 783.126</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.521</td>  <th>  Prob(JB):          </th> <td>8.84e-171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.281</td>  <th>  Cond. No.          </th> <td>1.51e+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, 1.51e+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:                   medv   R-squared:                       0.741\n",
       "Model:                            OLS   Adj. R-squared:                  0.734\n",
       "Method:                 Least Squares   F-statistic:                     108.1\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):          6.72e-135\n",
       "Time:                        13:12:26   Log-Likelihood:                -1498.8\n",
       "No. Observations:                 506   AIC:                             3026.\n",
       "Df Residuals:                     492   BIC:                             3085.\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     36.4595      5.103      7.144      0.000      26.432      46.487\n",
       "crim          -0.1080      0.033     -3.287      0.001      -0.173      -0.043\n",
       "zn             0.0464      0.014      3.382      0.001       0.019       0.073\n",
       "indus          0.0206      0.061      0.334      0.738      -0.100       0.141\n",
       "chas           2.6867      0.862      3.118      0.002       0.994       4.380\n",
       "nox          -17.7666      3.820     -4.651      0.000     -25.272     -10.262\n",
       "rm             3.8099      0.418      9.116      0.000       2.989       4.631\n",
       "age            0.0007      0.013      0.052      0.958      -0.025       0.027\n",
       "dis           -1.4756      0.199     -7.398      0.000      -1.867      -1.084\n",
       "rad            0.3060      0.066      4.613      0.000       0.176       0.436\n",
       "tax           -0.0123      0.004     -3.280      0.001      -0.020      -0.005\n",
       "ptratio       -0.9527      0.131     -7.283      0.000      -1.210      -0.696\n",
       "black          0.0093      0.003      3.467      0.001       0.004       0.015\n",
       "lstat         -0.5248      0.051    -10.347      0.000      -0.624      -0.425\n",
       "==============================================================================\n",
       "Omnibus:                      178.041   Durbin-Watson:                   1.078\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              783.126\n",
       "Skew:                           1.521   Prob(JB):                    8.84e-171\n",
       "Kurtosis:                       8.281   Cond. No.                     1.51e+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, 1.51e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def ols_formula(df, dependent_var, *excluded_cols):\n",
    "    '''\n",
    "    Generates the R style formula for statsmodels (patsy) given\n",
    "    the dataframe, dependent variable and optional excluded columns\n",
    "    as strings\n",
    "    '''\n",
    "    df_columns = list(df.columns.values)\n",
    "    df_columns.remove(dependent_var)\n",
    "    for col in excluded_cols:\n",
    "        df_columns.remove(col)\n",
    "    return dependent_var + ' ~ ' + ' + '.join(df_columns)\n",
    "\n",
    "# ols model with intercept\n",
    "ols_smf = smf.ols(formula=ols_formula(boston, 'medv'), data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crim \t: 1.79\n",
      "zn \t: 2.30\n",
      "indus \t: 3.99\n",
      "chas \t: 1.07\n",
      "nox \t: 4.39\n",
      "rm \t: 1.93\n",
      "age \t: 3.10\n",
      "dis \t: 3.96\n",
      "rad \t: 7.48\n",
      "tax \t: 9.01\n",
      "ptratio : 1.80\n",
      "black \t: 1.35\n",
      "lstat \t: 2.94\n"
     ]
    }
   ],
   "source": [
    "# variance inflation factors\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
    "\n",
    "# don't forget to add constant if the ols model includes intercept\n",
    "boston_exog = sm.add_constant(boston.drop('medv', axis=1))\n",
    "\n",
    "# too fancy for printing results?\n",
    "for i, col in enumerate(boston_exog.columns):\n",
    "    if col == 'const':\n",
    "        pass\n",
    "    elif len(col) > 6:\n",
    "        print(col, ':', \"{0:.2f}\".format(vif(boston_exog.values, i)))\n",
    "    else:\n",
    "        print(col, '\\t:', \"{0:.2f}\".format(vif(boston_exog.values, i)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.741</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.734</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   117.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>6.08e-136</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:26</td>     <th>  Log-Likelihood:    </th> <td> -1498.8</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3024.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   493</td>      <th>  BIC:               </th> <td>   3079.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</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>   36.4369</td> <td>    5.080</td> <td>    7.172</td> <td> 0.000</td> <td>   26.456</td> <td>   46.418</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>crim</th>      <td>   -0.1080</td> <td>    0.033</td> <td>   -3.290</td> <td> 0.001</td> <td>   -0.173</td> <td>   -0.043</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>zn</th>        <td>    0.0463</td> <td>    0.014</td> <td>    3.404</td> <td> 0.001</td> <td>    0.020</td> <td>    0.073</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>indus</th>     <td>    0.0206</td> <td>    0.061</td> <td>    0.335</td> <td> 0.738</td> <td>   -0.100</td> <td>    0.141</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>chas</th>      <td>    2.6890</td> <td>    0.860</td> <td>    3.128</td> <td> 0.002</td> <td>    1.000</td> <td>    4.378</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>nox</th>       <td>  -17.7135</td> <td>    3.679</td> <td>   -4.814</td> <td> 0.000</td> <td>  -24.943</td> <td>  -10.484</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rm</th>        <td>    3.8144</td> <td>    0.408</td> <td>    9.338</td> <td> 0.000</td> <td>    3.012</td> <td>    4.617</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>dis</th>       <td>   -1.4786</td> <td>    0.191</td> <td>   -7.757</td> <td> 0.000</td> <td>   -1.853</td> <td>   -1.104</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>rad</th>       <td>    0.3058</td> <td>    0.066</td> <td>    4.627</td> <td> 0.000</td> <td>    0.176</td> <td>    0.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>tax</th>       <td>   -0.0123</td> <td>    0.004</td> <td>   -3.283</td> <td> 0.001</td> <td>   -0.020</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>ptratio</th>   <td>   -0.9522</td> <td>    0.130</td> <td>   -7.308</td> <td> 0.000</td> <td>   -1.208</td> <td>   -0.696</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>black</th>     <td>    0.0093</td> <td>    0.003</td> <td>    3.481</td> <td> 0.001</td> <td>    0.004</td> <td>    0.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>     <td>   -0.5239</td> <td>    0.048</td> <td>  -10.999</td> <td> 0.000</td> <td>   -0.617</td> <td>   -0.430</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>178.343</td> <th>  Durbin-Watson:     </th> <td>   1.078</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 786.386</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.523</td>  <th>  Prob(JB):          </th> <td>1.73e-171</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 8.294</td>  <th>  Cond. No.          </th> <td>1.48e+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, 1.48e+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:                   medv   R-squared:                       0.741\n",
       "Model:                            OLS   Adj. R-squared:                  0.734\n",
       "Method:                 Least Squares   F-statistic:                     117.3\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):          6.08e-136\n",
       "Time:                        13:12:26   Log-Likelihood:                -1498.8\n",
       "No. Observations:                 506   AIC:                             3024.\n",
       "Df Residuals:                     493   BIC:                             3079.\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     36.4369      5.080      7.172      0.000      26.456      46.418\n",
       "crim          -0.1080      0.033     -3.290      0.001      -0.173      -0.043\n",
       "zn             0.0463      0.014      3.404      0.001       0.020       0.073\n",
       "indus          0.0206      0.061      0.335      0.738      -0.100       0.141\n",
       "chas           2.6890      0.860      3.128      0.002       1.000       4.378\n",
       "nox          -17.7135      3.679     -4.814      0.000     -24.943     -10.484\n",
       "rm             3.8144      0.408      9.338      0.000       3.012       4.617\n",
       "dis           -1.4786      0.191     -7.757      0.000      -1.853      -1.104\n",
       "rad            0.3058      0.066      4.627      0.000       0.176       0.436\n",
       "tax           -0.0123      0.004     -3.283      0.001      -0.020      -0.005\n",
       "ptratio       -0.9522      0.130     -7.308      0.000      -1.208      -0.696\n",
       "black          0.0093      0.003      3.481      0.001       0.004       0.015\n",
       "lstat         -0.5239      0.048    -10.999      0.000      -0.617      -0.430\n",
       "==============================================================================\n",
       "Omnibus:                      178.343   Durbin-Watson:                   1.078\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              786.386\n",
       "Skew:                           1.523   Prob(JB):                    1.73e-171\n",
       "Kurtosis:                       8.294   Cond. No.                     1.48e+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, 1.48e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ols model with intercept\n",
    "ols_smf = smf.ols(formula=ols_formula(boston, 'medv', 'age'), data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6.4 Interaction Terms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.556</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.553</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   209.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>4.86e-88</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:26</td>     <th>  Log-Likelihood:    </th> <td> -1635.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3278.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   502</td>      <th>  BIC:               </th> <td>   3295.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     3</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>   36.0885</td> <td>    1.470</td> <td>   24.553</td> <td> 0.000</td> <td>   33.201</td> <td>   38.976</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>     <td>   -1.3921</td> <td>    0.167</td> <td>   -8.313</td> <td> 0.000</td> <td>   -1.721</td> <td>   -1.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>age</th>       <td>   -0.0007</td> <td>    0.020</td> <td>   -0.036</td> <td> 0.971</td> <td>   -0.040</td> <td>    0.038</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat:age</th> <td>    0.0042</td> <td>    0.002</td> <td>    2.244</td> <td> 0.025</td> <td>    0.001</td> <td>    0.008</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>135.601</td> <th>  Durbin-Watson:     </th> <td>   0.965</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 296.955</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.417</td>  <th>  Prob(JB):          </th> <td>3.29e-65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.461</td>  <th>  Cond. No.          </th> <td>6.88e+03</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, 6.88e+03. 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:                   medv   R-squared:                       0.556\n",
       "Model:                            OLS   Adj. R-squared:                  0.553\n",
       "Method:                 Least Squares   F-statistic:                     209.3\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):           4.86e-88\n",
       "Time:                        13:12:26   Log-Likelihood:                -1635.0\n",
       "No. Observations:                 506   AIC:                             3278.\n",
       "Df Residuals:                     502   BIC:                             3295.\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     36.0885      1.470     24.553      0.000      33.201      38.976\n",
       "lstat         -1.3921      0.167     -8.313      0.000      -1.721      -1.063\n",
       "age           -0.0007      0.020     -0.036      0.971      -0.040       0.038\n",
       "lstat:age      0.0042      0.002      2.244      0.025       0.001       0.008\n",
       "==============================================================================\n",
       "Omnibus:                      135.601   Durbin-Watson:                   0.965\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              296.955\n",
       "Skew:                           1.417   Prob(JB):                     3.29e-65\n",
       "Kurtosis:                       5.461   Cond. No.                     6.88e+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, 6.88e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ols model with intercept\n",
    "ols_smf = smf.ols(formula='medv ~ lstat * age', data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6.5 Non-linear Transformations of Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.641</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.639</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   448.5</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>1.56e-112</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:26</td>     <th>  Log-Likelihood:    </th> <td> -1581.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3169.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   503</td>      <th>  BIC:               </th> <td>   3181.</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>Intercept</th>          <td>   42.8620</td> <td>    0.872</td> <td>   49.149</td> <td> 0.000</td> <td>   41.149</td> <td>   44.575</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>              <td>   -2.3328</td> <td>    0.124</td> <td>  -18.843</td> <td> 0.000</td> <td>   -2.576</td> <td>   -2.090</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.power(lstat, 2)</th> <td>    0.0435</td> <td>    0.004</td> <td>   11.628</td> <td> 0.000</td> <td>    0.036</td> <td>    0.051</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>107.006</td> <th>  Durbin-Watson:     </th> <td>   0.921</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 228.388</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.128</td>  <th>  Prob(JB):          </th> <td>2.55e-50</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 5.397</td>  <th>  Cond. No.          </th> <td>1.13e+03</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, 1.13e+03. 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:                   medv   R-squared:                       0.641\n",
       "Model:                            OLS   Adj. R-squared:                  0.639\n",
       "Method:                 Least Squares   F-statistic:                     448.5\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):          1.56e-112\n",
       "Time:                        13:12:26   Log-Likelihood:                -1581.3\n",
       "No. Observations:                 506   AIC:                             3169.\n",
       "Df Residuals:                     503   BIC:                             3181.\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "Intercept             42.8620      0.872     49.149      0.000      41.149      44.575\n",
       "lstat                 -2.3328      0.124    -18.843      0.000      -2.576      -2.090\n",
       "np.power(lstat, 2)     0.0435      0.004     11.628      0.000       0.036       0.051\n",
       "==============================================================================\n",
       "Omnibus:                      107.006   Durbin-Watson:                   0.921\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              228.388\n",
       "Skew:                           1.128   Prob(JB):                     2.55e-50\n",
       "Kurtosis:                       5.397   Cond. No.                     1.13e+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, 1.13e+03. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ols model with intercept\n",
    "ols_smf = smf.ols(formula='medv ~ lstat + np.power(lstat, 2)', data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>df_resid</th>\n",
       "      <th>ssr</th>\n",
       "      <th>df_diff</th>\n",
       "      <th>ss_diff</th>\n",
       "      <th>F</th>\n",
       "      <th>Pr(&gt;F)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>504.00</td>\n",
       "      <td>19,472.38</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>503.00</td>\n",
       "      <td>15,347.24</td>\n",
       "      <td>1.00</td>\n",
       "      <td>4,125.14</td>\n",
       "      <td>135.20</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              df_resid                  ssr              df_diff  \\\n",
       "0               504.00            19,472.38                 0.00   \n",
       "1               503.00            15,347.24                 1.00   \n",
       "\n",
       "               ss_diff                    F               Pr(>F)  \n",
       "0                  NaN                  NaN                  NaN  \n",
       "1             4,125.14               135.20                 0.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# anova of the two models\n",
    "ols_smf = smf.ols(formula='medv ~ lstat', data=boston).fit()\n",
    "ols_smf2 = smf.ols(formula='medv ~ lstat + np.power(lstat, 2)', data=boston).fit()\n",
    "\n",
    "sm.stats.anova_lm(ols_smf, ols_smf2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x1440 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axes = plt.subplots(4, 2, sharex=False, sharey=False)\n",
    "f.set_figheight(20)\n",
    "\n",
    "for axis, order in ((axes[0,0], 1), (axes[0,1], 2), (axes[1,0], 3), (axes[1,1], 5)):\n",
    "    sns.regplot('lstat', 'medv', data=boston, ax=axis, order=order, line_kws={'color': 'gray'}, scatter_kws={'alpha': 0.5})\n",
    "\n",
    "for axis, order in ((axes[2,0], 1), (axes[2,1], 2), (axes[3,0], 3), (axes[3,1], 5)):\n",
    "    sns.residplot('lstat', 'medv', data=boston, ax=axis, order=order, line_kws={'color': 'gray'}, scatter_kws={'alpha': 0.5})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.682</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.679</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   214.2</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>8.73e-122</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:28</td>     <th>  Log-Likelihood:    </th> <td> -1550.6</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3113.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   500</td>      <th>  BIC:               </th> <td>   3139.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     5</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>   67.6997</td> <td>    3.604</td> <td>   18.783</td> <td> 0.000</td> <td>   60.618</td> <td>   74.781</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>lstat</th>         <td>  -11.9911</td> <td>    1.526</td> <td>   -7.859</td> <td> 0.000</td> <td>  -14.989</td> <td>   -8.994</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(lstat ** 2)</th> <td>    1.2728</td> <td>    0.223</td> <td>    5.703</td> <td> 0.000</td> <td>    0.834</td> <td>    1.711</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(lstat ** 3)</th> <td>   -0.0683</td> <td>    0.014</td> <td>   -4.747</td> <td> 0.000</td> <td>   -0.097</td> <td>   -0.040</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(lstat ** 4)</th> <td>    0.0017</td> <td>    0.000</td> <td>    4.143</td> <td> 0.000</td> <td>    0.001</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>I(lstat ** 5)</th> <td>-1.632e-05</td> <td> 4.42e-06</td> <td>   -3.692</td> <td> 0.000</td> <td> -2.5e-05</td> <td>-7.63e-06</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>144.085</td> <th>  Durbin-Watson:     </th> <td>   0.987</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 494.545</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 1.292</td>  <th>  Prob(JB):          </th> <td>4.08e-108</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.096</td>  <th>  Cond. No.          </th> <td>1.37e+08</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, 1.37e+08. 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:                   medv   R-squared:                       0.682\n",
       "Model:                            OLS   Adj. R-squared:                  0.679\n",
       "Method:                 Least Squares   F-statistic:                     214.2\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):          8.73e-122\n",
       "Time:                        13:12:28   Log-Likelihood:                -1550.6\n",
       "No. Observations:                 506   AIC:                             3113.\n",
       "Df Residuals:                     500   BIC:                             3139.\n",
       "Df Model:                           5                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=================================================================================\n",
       "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
       "---------------------------------------------------------------------------------\n",
       "Intercept        67.6997      3.604     18.783      0.000      60.618      74.781\n",
       "lstat           -11.9911      1.526     -7.859      0.000     -14.989      -8.994\n",
       "I(lstat ** 2)     1.2728      0.223      5.703      0.000       0.834       1.711\n",
       "I(lstat ** 3)    -0.0683      0.014     -4.747      0.000      -0.097      -0.040\n",
       "I(lstat ** 4)     0.0017      0.000      4.143      0.000       0.001       0.003\n",
       "I(lstat ** 5) -1.632e-05   4.42e-06     -3.692      0.000    -2.5e-05   -7.63e-06\n",
       "==============================================================================\n",
       "Omnibus:                      144.085   Durbin-Watson:                   0.987\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              494.545\n",
       "Skew:                           1.292   Prob(JB):                    4.08e-108\n",
       "Kurtosis:                       7.096   Cond. No.                     1.37e+08\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, 1.37e+08. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# polynomial ols model with intercept\n",
    "# numpy way\n",
    "#ols_smf = smf.ols(formula='medv ~ lstat + np.power(lstat, 2) + np.power(lstat, 3) + np.power(lstat, 4) + np.power(lstat, 5)',\n",
    "#                  data=boston)\n",
    "# patsy way\n",
    "ols_smf = smf.ols(formula='medv ~ lstat + I(lstat**2) + I(lstat**3) + I(lstat**4) + I(lstat**5)',\n",
    "                  data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>medv</td>       <th>  R-squared:         </th> <td>   0.436</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.435</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   389.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>1.22e-64</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:28</td>     <th>  Log-Likelihood:    </th> <td> -1695.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   506</td>      <th>  AIC:               </th> <td>   3395.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   504</td>      <th>  BIC:               </th> <td>   3403.</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>Intercept</th>  <td>  -76.4878</td> <td>    5.028</td> <td>  -15.213</td> <td> 0.000</td> <td>  -86.366</td> <td>  -66.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>np.log(rm)</th> <td>   54.0546</td> <td>    2.739</td> <td>   19.732</td> <td> 0.000</td> <td>   48.672</td> <td>   59.437</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>117.102</td> <th>  Durbin-Watson:     </th> <td>   0.681</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 584.336</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.916</td>  <th>  Prob(JB):          </th> <td>1.30e-127</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 7.936</td>  <th>  Cond. No.          </th> <td>    38.9</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<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:                   medv   R-squared:                       0.436\n",
       "Model:                            OLS   Adj. R-squared:                  0.435\n",
       "Method:                 Least Squares   F-statistic:                     389.3\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):           1.22e-64\n",
       "Time:                        13:12:28   Log-Likelihood:                -1695.4\n",
       "No. Observations:                 506   AIC:                             3395.\n",
       "Df Residuals:                     504   BIC:                             3403.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept    -76.4878      5.028    -15.213      0.000     -86.366     -66.610\n",
       "np.log(rm)    54.0546      2.739     19.732      0.000      48.672      59.437\n",
       "==============================================================================\n",
       "Omnibus:                      117.102   Durbin-Watson:                   0.681\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              584.336\n",
       "Skew:                           0.916   Prob(JB):                    1.30e-127\n",
       "Kurtosis:                       7.936   Cond. No.                         38.9\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# polynomial ols model with intercept\n",
    "ols_smf = smf.ols(formula='medv ~ np.log(rm)', data=boston)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot('rm', \n",
    "            'medv', \n",
    "            data=boston, \n",
    "            logx=True, \n",
    "            line_kws={'color': 'gray'}, \n",
    "            scatter_kws={'alpha': 0.5});"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6.6 Qualitative Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Sales</th>\n",
       "      <th>CompPrice</th>\n",
       "      <th>Income</th>\n",
       "      <th>Advertising</th>\n",
       "      <th>Population</th>\n",
       "      <th>Price</th>\n",
       "      <th>ShelveLoc</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>Urban</th>\n",
       "      <th>US</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9.50</td>\n",
       "      <td>138</td>\n",
       "      <td>73</td>\n",
       "      <td>11</td>\n",
       "      <td>276</td>\n",
       "      <td>120</td>\n",
       "      <td>Bad</td>\n",
       "      <td>42</td>\n",
       "      <td>17</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11.22</td>\n",
       "      <td>111</td>\n",
       "      <td>48</td>\n",
       "      <td>16</td>\n",
       "      <td>260</td>\n",
       "      <td>83</td>\n",
       "      <td>Good</td>\n",
       "      <td>65</td>\n",
       "      <td>10</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.06</td>\n",
       "      <td>113</td>\n",
       "      <td>35</td>\n",
       "      <td>10</td>\n",
       "      <td>269</td>\n",
       "      <td>80</td>\n",
       "      <td>Medium</td>\n",
       "      <td>59</td>\n",
       "      <td>12</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.40</td>\n",
       "      <td>117</td>\n",
       "      <td>100</td>\n",
       "      <td>4</td>\n",
       "      <td>466</td>\n",
       "      <td>97</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>14</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4.15</td>\n",
       "      <td>141</td>\n",
       "      <td>64</td>\n",
       "      <td>3</td>\n",
       "      <td>340</td>\n",
       "      <td>128</td>\n",
       "      <td>Bad</td>\n",
       "      <td>38</td>\n",
       "      <td>13</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>396</th>\n",
       "      <td>12.57</td>\n",
       "      <td>138</td>\n",
       "      <td>108</td>\n",
       "      <td>17</td>\n",
       "      <td>203</td>\n",
       "      <td>128</td>\n",
       "      <td>Good</td>\n",
       "      <td>33</td>\n",
       "      <td>14</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>397</th>\n",
       "      <td>6.14</td>\n",
       "      <td>139</td>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>37</td>\n",
       "      <td>120</td>\n",
       "      <td>Medium</td>\n",
       "      <td>55</td>\n",
       "      <td>11</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>7.41</td>\n",
       "      <td>162</td>\n",
       "      <td>26</td>\n",
       "      <td>12</td>\n",
       "      <td>368</td>\n",
       "      <td>159</td>\n",
       "      <td>Medium</td>\n",
       "      <td>40</td>\n",
       "      <td>18</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>5.94</td>\n",
       "      <td>100</td>\n",
       "      <td>79</td>\n",
       "      <td>7</td>\n",
       "      <td>284</td>\n",
       "      <td>95</td>\n",
       "      <td>Bad</td>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>400</th>\n",
       "      <td>9.71</td>\n",
       "      <td>134</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>120</td>\n",
       "      <td>Good</td>\n",
       "      <td>49</td>\n",
       "      <td>16</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>400 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Sales  CompPrice  Income  Advertising  Population  Price  \\\n",
       "1                   9.50        138      73           11         276    120   \n",
       "2                  11.22        111      48           16         260     83   \n",
       "3                  10.06        113      35           10         269     80   \n",
       "4                   7.40        117     100            4         466     97   \n",
       "5                   4.15        141      64            3         340    128   \n",
       "..                   ...        ...     ...          ...         ...    ...   \n",
       "396                12.57        138     108           17         203    128   \n",
       "397                 6.14        139      23            3          37    120   \n",
       "398                 7.41        162      26           12         368    159   \n",
       "399                 5.94        100      79            7         284     95   \n",
       "400                 9.71        134      37            0          27    120   \n",
       "\n",
       "    ShelveLoc  Age  Education Urban   US  \n",
       "1         Bad   42         17   Yes  Yes  \n",
       "2        Good   65         10   Yes  Yes  \n",
       "3      Medium   59         12   Yes  Yes  \n",
       "4      Medium   55         14   Yes  Yes  \n",
       "5         Bad   38         13   Yes   No  \n",
       "..        ...  ...        ...   ...  ...  \n",
       "396      Good   33         14   Yes  Yes  \n",
       "397    Medium   55         11    No  Yes  \n",
       "398    Medium   40         18   Yes  Yes  \n",
       "399       Bad   50         12   Yes  Yes  \n",
       "400      Good   49         16   Yes  Yes  \n",
       "\n",
       "[400 rows x 11 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "carseats = pd.read_csv('../datasets/Carseats.csv', index_col=0)\n",
    "carseats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>Sales</td>      <th>  R-squared:         </th> <td>   0.876</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.872</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   210.0</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 09 Jan 2022</td> <th>  Prob (F-statistic):</th> <td>6.14e-166</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>13:12:28</td>     <th>  Log-Likelihood:    </th> <td> -564.67</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   400</td>      <th>  AIC:               </th> <td>   1157.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   386</td>      <th>  BIC:               </th> <td>   1213.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    13</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>    6.5756</td> <td>    1.009</td> <td>    6.519</td> <td> 0.000</td> <td>    4.592</td> <td>    8.559</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Urban[T.Yes]</th>           <td>    0.1402</td> <td>    0.112</td> <td>    1.247</td> <td> 0.213</td> <td>   -0.081</td> <td>    0.361</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>US[T.Yes]</th>              <td>   -0.1576</td> <td>    0.149</td> <td>   -1.058</td> <td> 0.291</td> <td>   -0.450</td> <td>    0.135</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(ShelveLoc)[T.Good]</th>   <td>    4.8487</td> <td>    0.153</td> <td>   31.724</td> <td> 0.000</td> <td>    4.548</td> <td>    5.149</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(ShelveLoc)[T.Medium]</th> <td>    1.9533</td> <td>    0.126</td> <td>   15.531</td> <td> 0.000</td> <td>    1.706</td> <td>    2.201</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CompPrice</th>              <td>    0.0929</td> <td>    0.004</td> <td>   22.567</td> <td> 0.000</td> <td>    0.085</td> <td>    0.101</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Income</th>                 <td>    0.0109</td> <td>    0.003</td> <td>    4.183</td> <td> 0.000</td> <td>    0.006</td> <td>    0.016</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Advertising</th>            <td>    0.0702</td> <td>    0.023</td> <td>    3.107</td> <td> 0.002</td> <td>    0.026</td> <td>    0.115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Population</th>             <td>    0.0002</td> <td>    0.000</td> <td>    0.433</td> <td> 0.665</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price</th>                  <td>   -0.1008</td> <td>    0.007</td> <td>  -13.549</td> <td> 0.000</td> <td>   -0.115</td> <td>   -0.086</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Age</th>                    <td>   -0.0579</td> <td>    0.016</td> <td>   -3.633</td> <td> 0.000</td> <td>   -0.089</td> <td>   -0.027</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Education</th>              <td>   -0.0209</td> <td>    0.020</td> <td>   -1.063</td> <td> 0.288</td> <td>   -0.059</td> <td>    0.018</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Income:Advertising</th>     <td>    0.0008</td> <td>    0.000</td> <td>    2.698</td> <td> 0.007</td> <td>    0.000</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Price:Age</th>              <td>    0.0001</td> <td>    0.000</td> <td>    0.801</td> <td> 0.424</td> <td>   -0.000</td> <td>    0.000</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.281</td> <th>  Durbin-Watson:     </th> <td>   2.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.527</td> <th>  Jarque-Bera (JB):  </th> <td>   1.147</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.129</td> <th>  Prob(JB):          </th> <td>   0.564</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.050</td> <th>  Cond. No.          </th> <td>1.31e+05</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, 1.31e+05. 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:                  Sales   R-squared:                       0.876\n",
       "Model:                            OLS   Adj. R-squared:                  0.872\n",
       "Method:                 Least Squares   F-statistic:                     210.0\n",
       "Date:                Sun, 09 Jan 2022   Prob (F-statistic):          6.14e-166\n",
       "Time:                        13:12:28   Log-Likelihood:                -564.67\n",
       "No. Observations:                 400   AIC:                             1157.\n",
       "Df Residuals:                     386   BIC:                             1213.\n",
       "Df Model:                          13                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==========================================================================================\n",
       "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------------\n",
       "Intercept                  6.5756      1.009      6.519      0.000       4.592       8.559\n",
       "Urban[T.Yes]               0.1402      0.112      1.247      0.213      -0.081       0.361\n",
       "US[T.Yes]                 -0.1576      0.149     -1.058      0.291      -0.450       0.135\n",
       "C(ShelveLoc)[T.Good]       4.8487      0.153     31.724      0.000       4.548       5.149\n",
       "C(ShelveLoc)[T.Medium]     1.9533      0.126     15.531      0.000       1.706       2.201\n",
       "CompPrice                  0.0929      0.004     22.567      0.000       0.085       0.101\n",
       "Income                     0.0109      0.003      4.183      0.000       0.006       0.016\n",
       "Advertising                0.0702      0.023      3.107      0.002       0.026       0.115\n",
       "Population                 0.0002      0.000      0.433      0.665      -0.001       0.001\n",
       "Price                     -0.1008      0.007    -13.549      0.000      -0.115      -0.086\n",
       "Age                       -0.0579      0.016     -3.633      0.000      -0.089      -0.027\n",
       "Education                 -0.0209      0.020     -1.063      0.288      -0.059       0.018\n",
       "Income:Advertising         0.0008      0.000      2.698      0.007       0.000       0.001\n",
       "Price:Age                  0.0001      0.000      0.801      0.424      -0.000       0.000\n",
       "==============================================================================\n",
       "Omnibus:                        1.281   Durbin-Watson:                   2.047\n",
       "Prob(Omnibus):                  0.527   Jarque-Bera (JB):                1.147\n",
       "Skew:                           0.129   Prob(JB):                        0.564\n",
       "Kurtosis:                       3.050   Cond. No.                     1.31e+05\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, 1.31e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ols model with intercept\n",
    "\n",
    "form = ols_formula(carseats, 'Sales', 'ShelveLoc') + ' + Income:Advertising + Price:Age + C(ShelveLoc)'\n",
    "ols_smf = smf.ols(formula=form, data=carseats)\n",
    "\n",
    "# fitted model and summary\n",
    "ols_smf_results = ols_smf.fit()\n",
    "ols_smf_results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [1., 0.],\n",
       "       [0., 1.]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from patsy.contrasts import Treatment\n",
    "\n",
    "Treatment(reference=0).code_without_intercept(list(carseats['ShelveLoc'].unique())).matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.6.7 Writing Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "come on..."
   ]
  }
 ],
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    "version": 3
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   "name": "python",
   "nbconvert_exporter": "python",
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  },
  "toc": {
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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