advertising_df.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import axes3d\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import scale\n",
    "import sklearn.linear_model as skl_lm\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('seaborn-white')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 200 entries, 0 to 199\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype  \n",
      "---  ------     --------------  -----  \n",
      " 0   TV         200 non-null    float64\n",
      " 1   Radio      200 non-null    float64\n",
      " 2   Newspaper  200 non-null    float64\n",
      " 3   Sales      200 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 6.4 KB\n"
     ]
    }
   ],
   "source": [
    "advertising = pd.read_csv('Data/Advertising.csv', usecols=[1,2,3,4])\n",
    "advertising.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>230.1</td>\n",
       "      <td>37.8</td>\n",
       "      <td>69.2</td>\n",
       "      <td>22.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>44.5</td>\n",
       "      <td>39.3</td>\n",
       "      <td>45.1</td>\n",
       "      <td>10.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.2</td>\n",
       "      <td>45.9</td>\n",
       "      <td>69.3</td>\n",
       "      <td>9.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>151.5</td>\n",
       "      <td>41.3</td>\n",
       "      <td>58.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>180.8</td>\n",
       "      <td>10.8</td>\n",
       "      <td>58.4</td>\n",
       "      <td>12.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      TV  Radio  Newspaper  Sales\n",
       "0  230.1   37.8       69.2   22.1\n",
       "1   44.5   39.3       45.1   10.4\n",
       "2   17.2   45.9       69.3    9.3\n",
       "3  151.5   41.3       58.5   18.5\n",
       "4  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>147.042500</td>\n",
       "      <td>23.264000</td>\n",
       "      <td>30.554000</td>\n",
       "      <td>14.022500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>85.854236</td>\n",
       "      <td>14.846809</td>\n",
       "      <td>21.778621</td>\n",
       "      <td>5.217457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>1.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>74.375000</td>\n",
       "      <td>9.975000</td>\n",
       "      <td>12.750000</td>\n",
       "      <td>10.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.750000</td>\n",
       "      <td>22.900000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>12.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>218.825000</td>\n",
       "      <td>36.525000</td>\n",
       "      <td>45.100000</td>\n",
       "      <td>17.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>296.400000</td>\n",
       "      <td>49.600000</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>27.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               TV       Radio   Newspaper       Sales\n",
       "count  200.000000  200.000000  200.000000  200.000000\n",
       "mean   147.042500   23.264000   30.554000   14.022500\n",
       "std     85.854236   14.846809   21.778621    5.217457\n",
       "min      0.700000    0.000000    0.300000    1.600000\n",
       "25%     74.375000    9.975000   12.750000   10.375000\n",
       "50%    149.750000   22.900000   25.750000   12.900000\n",
       "75%    218.825000   36.525000   45.100000   17.400000\n",
       "max    296.400000   49.600000  114.000000   27.000000"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "advertising.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=advertising)\n",
    "plt.savefig('pics/pairplot');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=advertising.TV, y=advertising.Sales, order=1, ci=None, scatter_kws={'color':'r', 's':9})\n",
    "plt.xlim(-10,310)\n",
    "plt.ylim(ymin=0)\n",
    "plt.savefig('pics/TV_regr_Sales');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "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>TV</th>\n",
       "      <th>Radio</th>\n",
       "      <th>Newspaper</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TV</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.054809</td>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.782224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Radio</th>\n",
       "      <td>0.054809</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>0.576223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Newspaper</th>\n",
       "      <td>0.056648</td>\n",
       "      <td>0.354104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.228299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales</th>\n",
       "      <td>0.782224</td>\n",
       "      <td>0.576223</td>\n",
       "      <td>0.228299</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 TV     Radio  Newspaper     Sales\n",
       "TV         1.000000  0.054809   0.056648  0.782224\n",
       "Radio      0.054809  1.000000   0.354104  0.576223\n",
       "Newspaper  0.056648  0.354104   1.000000  0.228299\n",
       "Sales      0.782224  0.576223   0.228299  1.000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr=advertising.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.triu(np.ones_like(corr, dtype=bool))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,6))\n",
    "fig.suptitle('HeatMap: Sales ~ Radio, TV,  Newspaper', fontsize=20)\n",
    "\n",
    "heatmap = sns.heatmap(corr, \n",
    "                      mask = mask,\n",
    "                      square = True,\n",
    "                      linewidths = .5,\n",
    "                      cmap = 'coolwarm',\n",
    "                      cbar_kws = {'shrink': .4, \n",
    "                                'ticks' : [-1, -.5, 0, 0.5, 1]},\n",
    "                      vmin = -1, \n",
    "                      vmax = 1,\n",
    "                      annot = True,\n",
    "                      annot_kws = {'size': 12})\n",
    "#add the column names as labels\n",
    "ax.set_yticklabels(corr.columns, rotation = 0)\n",
    "ax.set_xticklabels(corr.columns)\n",
    "sns.set_style({'xtick.bottom': True}, {'ytick.left': True})\n",
    "\n",
    "plt.savefig('pics/Heatmap');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14.0225\n",
      "[0.04753664]\n"
     ]
    }
   ],
   "source": [
    "# Regression coefficients (Ordinary Least Squares)\n",
    "regr = skl_lm.LinearRegression()\n",
    "\n",
    "X = scale(advertising.TV, with_mean=True, with_std=False).reshape(-1,1)\n",
    "y = advertising.Sales\n",
    "\n",
    "regr.fit(X,y)\n",
    "print(regr.intercept_)\n",
    "print(regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>   0.612</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.610</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   312.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 May 2021</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:17:15</td>     <th>  Log-Likelihood:    </th> <td> -519.05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1042.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   1049.</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>    7.0326</td> <td>    0.458</td> <td>   15.360</td> <td> 0.000</td> <td>    6.130</td> <td>    7.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0475</td> <td>    0.003</td> <td>   17.668</td> <td> 0.000</td> <td>    0.042</td> <td>    0.053</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.531</td> <th>  Durbin-Watson:     </th> <td>   1.935</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.767</td> <th>  Jarque-Bera (JB):  </th> <td>   0.669</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.089</td> <th>  Prob(JB):          </th> <td>   0.716</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.779</td> <th>  Cond. No.          </th> <td>    338.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.612\n",
       "Model:                            OLS   Adj. R-squared:                  0.610\n",
       "Method:                 Least Squares   F-statistic:                     312.1\n",
       "Date:                Tue, 18 May 2021   Prob (F-statistic):           1.47e-42\n",
       "Time:                        15:17:15   Log-Likelihood:                -519.05\n",
       "No. Observations:                 200   AIC:                             1042.\n",
       "Df Residuals:                     198   BIC:                             1049.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      7.0326      0.458     15.360      0.000       6.130       7.935\n",
       "TV             0.0475      0.003     17.668      0.000       0.042       0.053\n",
       "==============================================================================\n",
       "Omnibus:                        0.531   Durbin-Watson:                   1.935\n",
       "Prob(Omnibus):                  0.767   Jarque-Bera (JB):                0.669\n",
       "Skew:                          -0.089   Prob(JB):                        0.716\n",
       "Kurtosis:                       2.779   Cond. No.                         338.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ TV', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.102530583131351"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RSS with regression coefficients\n",
    "((advertising.Sales - (est.params[0] + est.params[1]*advertising.TV))**2).sum()/1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.032593549127695\n",
      "[0.04753664]\n"
     ]
    }
   ],
   "source": [
    "regr = skl_lm.LinearRegression()\n",
    "\n",
    "X = advertising.TV.values.reshape(-1,1)\n",
    "y = advertising.Sales\n",
    "\n",
    "regr.fit(X,y)\n",
    "print(regr.intercept_)\n",
    "print(regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.611875050850071"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sales_pred = regr.predict(X)\n",
    "r2_score(y, Sales_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>   0.332</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.329</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   98.42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 May 2021</td> <th>  Prob (F-statistic):</th> <td>4.35e-19</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:32:18</td>     <th>  Log-Likelihood:    </th> <td> -573.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1151.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   1157.</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>    9.3116</td> <td>    0.563</td> <td>   16.542</td> <td> 0.000</td> <td>    8.202</td> <td>   10.422</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.2025</td> <td>    0.020</td> <td>    9.921</td> <td> 0.000</td> <td>    0.162</td> <td>    0.243</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>19.358</td> <th>  Durbin-Watson:     </th> <td>   1.946</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td>  21.910</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.764</td> <th>  Prob(JB):          </th> <td>1.75e-05</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.544</td> <th>  Cond. No.          </th> <td>    51.4</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.332\n",
       "Model:                            OLS   Adj. R-squared:                  0.329\n",
       "Method:                 Least Squares   F-statistic:                     98.42\n",
       "Date:                Tue, 18 May 2021   Prob (F-statistic):           4.35e-19\n",
       "Time:                        15:32:18   Log-Likelihood:                -573.34\n",
       "No. Observations:                 200   AIC:                             1151.\n",
       "Df Residuals:                     198   BIC:                             1157.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      9.3116      0.563     16.542      0.000       8.202      10.422\n",
       "Radio          0.2025      0.020      9.921      0.000       0.162       0.243\n",
       "==============================================================================\n",
       "Omnibus:                       19.358   Durbin-Watson:                   1.946\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               21.910\n",
       "Skew:                          -0.764   Prob(JB):                     1.75e-05\n",
       "Kurtosis:                       3.544   Cond. No.                         51.4\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ Radio', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>   0.052</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.047</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   10.89</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 May 2021</td> <th>  Prob (F-statistic):</th>  <td>0.00115</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:32:25</td>     <th>  Log-Likelihood:    </th> <td> -608.34</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   1221.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   1227.</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>   12.3514</td> <td>    0.621</td> <td>   19.876</td> <td> 0.000</td> <td>   11.126</td> <td>   13.577</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th> <td>    0.0547</td> <td>    0.017</td> <td>    3.300</td> <td> 0.001</td> <td>    0.022</td> <td>    0.087</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 6.231</td> <th>  Durbin-Watson:     </th> <td>   1.983</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.044</td> <th>  Jarque-Bera (JB):  </th> <td>   5.483</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.330</td> <th>  Prob(JB):          </th> <td>  0.0645</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.527</td> <th>  Cond. No.          </th> <td>    64.7</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.052\n",
       "Model:                            OLS   Adj. R-squared:                  0.047\n",
       "Method:                 Least Squares   F-statistic:                     10.89\n",
       "Date:                Tue, 18 May 2021   Prob (F-statistic):            0.00115\n",
       "Time:                        15:32:25   Log-Likelihood:                -608.34\n",
       "No. Observations:                 200   AIC:                             1221.\n",
       "Df Residuals:                     198   BIC:                             1227.\n",
       "Df Model:                           1                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept     12.3514      0.621     19.876      0.000      11.126      13.577\n",
       "Newspaper      0.0547      0.017      3.300      0.001       0.022       0.087\n",
       "==============================================================================\n",
       "Omnibus:                        6.231   Durbin-Watson:                   1.983\n",
       "Prob(Omnibus):                  0.044   Jarque-Bera (JB):                5.483\n",
       "Skew:                           0.330   Prob(JB):                       0.0645\n",
       "Kurtosis:                       2.527   Cond. No.                         64.7\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ Newspaper', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>   0.897</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.896</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   570.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sun, 16 May 2021</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>20:07:17</td>     <th>  Log-Likelihood:    </th> <td> -386.18</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   780.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   793.6</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>    2.9389</td> <td>    0.312</td> <td>    9.422</td> <td> 0.000</td> <td>    2.324</td> <td>    3.554</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0458</td> <td>    0.001</td> <td>   32.809</td> <td> 0.000</td> <td>    0.043</td> <td>    0.049</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.1885</td> <td>    0.009</td> <td>   21.893</td> <td> 0.000</td> <td>    0.172</td> <td>    0.206</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Newspaper</th> <td>   -0.0010</td> <td>    0.006</td> <td>   -0.177</td> <td> 0.860</td> <td>   -0.013</td> <td>    0.011</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>60.414</td> <th>  Durbin-Watson:     </th> <td>   2.084</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td> <th>  Jarque-Bera (JB):  </th> <td> 151.241</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-1.327</td> <th>  Prob(JB):          </th> <td>1.44e-33</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 6.332</td> <th>  Cond. No.          </th> <td>    454.</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                  Sales   R-squared:                       0.897\n",
       "Model:                            OLS   Adj. R-squared:                  0.896\n",
       "Method:                 Least Squares   F-statistic:                     570.3\n",
       "Date:                Sun, 16 May 2021   Prob (F-statistic):           1.58e-96\n",
       "Time:                        20:07:17   Log-Likelihood:                -386.18\n",
       "No. Observations:                 200   AIC:                             780.4\n",
       "Df Residuals:                     196   BIC:                             793.6\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      2.9389      0.312      9.422      0.000       2.324       3.554\n",
       "TV             0.0458      0.001     32.809      0.000       0.043       0.049\n",
       "Radio          0.1885      0.009     21.893      0.000       0.172       0.206\n",
       "Newspaper     -0.0010      0.006     -0.177      0.860      -0.013       0.011\n",
       "==============================================================================\n",
       "Omnibus:                       60.414   Durbin-Watson:                   2.084\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              151.241\n",
       "Skew:                          -1.327   Prob(JB):                     1.44e-33\n",
       "Kurtosis:                       6.332   Cond. No.                         454.\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ TV + Radio + Newspaper', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.18799423 0.04575482]\n",
      "2.9210999124051398\n"
     ]
    }
   ],
   "source": [
    "regr = skl_lm.LinearRegression()\n",
    "\n",
    "X = advertising[['Radio', 'TV']].to_numpy()\n",
    "y = advertising.Sales\n",
    "\n",
    "regr.fit(X,y)\n",
    "print(regr.coef_)\n",
    "print(regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a coordinate grid\n",
    "Radio = np.arange(0,50)\n",
    "TV = np.arange(0,300)\n",
    "\n",
    "B1, B2 = np.meshgrid(Radio, TV, indexing='xy')\n",
    "Z = np.zeros((TV.size, Radio.size))\n",
    "\n",
    "for (i,j),v in np.ndenumerate(Z):\n",
    "        Z[i,j] =(regr.intercept_ + B1[i,j]*regr.coef_[0] + B2[i,j]*regr.coef_[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create plot\n",
    "fig = plt.figure(figsize=(10,6))\n",
    "fig.suptitle('Regression: Sales ~ Radio + TV Advertising', fontsize=20)\n",
    "\n",
    "ax = axes3d.Axes3D(fig)\n",
    "\n",
    "ax.plot_surface(B1, B2, Z, rstride=10, cstride=5, alpha=0.4)\n",
    "ax.scatter3D(advertising.Radio, advertising.TV, advertising.Sales, c='r')\n",
    "\n",
    "ax.set_xlabel('Radio')\n",
    "ax.set_xlim(0,50)\n",
    "ax.set_ylabel('TV')\n",
    "ax.set_ylim(ymin=0)\n",
    "ax.set_zlabel('Sales')\n",
    "plt.savefig('pics/Regr_Sales~Radio+TV');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>   0.968</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.967</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   1963.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Tue, 18 May 2021</td> <th>  Prob (F-statistic):</th> <td>6.68e-146</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:19:08</td>     <th>  Log-Likelihood:    </th> <td> -270.14</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   548.3</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   196</td>      <th>  BIC:               </th> <td>   561.5</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>    6.7502</td> <td>    0.248</td> <td>   27.233</td> <td> 0.000</td> <td>    6.261</td> <td>    7.239</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV</th>        <td>    0.0191</td> <td>    0.002</td> <td>   12.699</td> <td> 0.000</td> <td>    0.016</td> <td>    0.022</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Radio</th>     <td>    0.0289</td> <td>    0.009</td> <td>    3.241</td> <td> 0.001</td> <td>    0.011</td> <td>    0.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>TV:Radio</th>  <td>    0.0011</td> <td> 5.24e-05</td> <td>   20.727</td> <td> 0.000</td> <td>    0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>128.132</td> <th>  Durbin-Watson:     </th> <td>   2.224</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>1183.719</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-2.323</td>  <th>  Prob(JB):          </th> <td>9.09e-258</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>13.975</td>  <th>  Cond. No.          </th> <td>1.80e+04</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 1.8e+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:                  Sales   R-squared:                       0.968\n",
       "Model:                            OLS   Adj. R-squared:                  0.967\n",
       "Method:                 Least Squares   F-statistic:                     1963.\n",
       "Date:                Tue, 18 May 2021   Prob (F-statistic):          6.68e-146\n",
       "Time:                        15:19:08   Log-Likelihood:                -270.14\n",
       "No. Observations:                 200   AIC:                             548.3\n",
       "Df Residuals:                     196   BIC:                             561.5\n",
       "Df Model:                           3                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "Intercept      6.7502      0.248     27.233      0.000       6.261       7.239\n",
       "TV             0.0191      0.002     12.699      0.000       0.016       0.022\n",
       "Radio          0.0289      0.009      3.241      0.001       0.011       0.046\n",
       "TV:Radio       0.0011   5.24e-05     20.727      0.000       0.001       0.001\n",
       "==============================================================================\n",
       "Omnibus:                      128.132   Durbin-Watson:                   2.224\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1183.719\n",
       "Skew:                          -2.323   Prob(JB):                    9.09e-258\n",
       "Kurtosis:                      13.975   Cond. No.                     1.80e+04\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 1.8e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "est = smf.ols('Sales ~ TV + Radio + TV*Radio', advertising).fit()\n",
    "est.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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