Chapter3_notes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import seaborn as sns; sns.set()\n",
    "import statsmodels.formula.api as sm\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the Advertising example, this data set consists of the TV advertising budget and product sales in n = 200 different markets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</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>1</td>\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>2</td>\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>3</td>\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>4</td>\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>5</td>\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": [
       "   Unnamed: 0     TV  radio  newspaper  sales\n",
       "0           1  230.1   37.8       69.2   22.1\n",
       "1           2   44.5   39.3       45.1   10.4\n",
       "2           3   17.2   45.9       69.3    9.3\n",
       "3           4  151.5   41.3       58.5   18.5\n",
       "4           5  180.8   10.8       58.4   12.9"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"http://www-bcf.usc.edu/~gareth/ISL/Advertising.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.drop(columns=['Unnamed: 0'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        vertical-align: top;\n",
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       "\n",
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       "</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": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x720 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danielruiz/anaconda2/envs/gstore/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 3.1\n",
    "sns.lmplot(x='TV', y='sales', data=df, height=8, aspect=1.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04753664]\n",
      "7.0325935491276965\n"
     ]
    }
   ],
   "source": [
    "# sklearn \n",
    "\n",
    "model = LinearRegression(fit_intercept=True)\n",
    "\n",
    "x=df['TV']\n",
    "y=df['sales']\n",
    "\n",
    "reg = model.fit(x[:, np.newaxis], y)\n",
    "print(reg.coef_)\n",
    "print(reg.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "# statsmodel\n",
    "\n",
    "lm = sm.ols('sales~TV', data=df).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>Thu, 11 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>1.47e-42</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:50:23</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:                Thu, 11 Oct 2018   Prob (F-statistic):           1.47e-42\n",
       "Time:                        14:50:23   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": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 3.1\n",
    "lm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RSE calculated: 3.258656368650463\n"
     ]
    }
   ],
   "source": [
    "# Resididual Standard Error calculation\n",
    "print(\"RSE calculated: \" + str(np.sqrt(lm.ssr/lm.df_resid)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.258656368650463"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# equivalent way to calculate RSE\n",
    "np.sqrt(lm.scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2102.5305831313517"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RSS (ssr)\n",
    "lm.ssr\n",
    "\n",
    "# available attributes from statsmodel regressionresults\n",
    "# https://www.statsmodels.org/dev/generated/statsmodels.regression.linear_model.RegressionResults.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    1.406300e-35\n",
       "TV           1.467390e-42\n",
       "dtype: float64"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.pvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    0.457843\n",
       "TV           0.002691\n",
       "dtype: float64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.bse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "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>Tue, 09 Oct 2018</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:23:10</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:                Tue, 09 Oct 2018   Prob (F-statistic):           1.58e-96\n",
       "Time:                        14:23:10   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": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 3.4\n",
    "lm = sm.ols('sales~TV+radio+newspaper', data=df).fit()\n",
    "lm.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.6855103734147439"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate RSE\n",
    "np.sqrt(lm.scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14.0225"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate mean 'sales'\n",
    "df.describe().loc['mean']['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate % error (RSE/mean of sales)\n",
    "# 3.4 The Marketing Plan question #2\n",
    "np.sqrt(lm.scale)/df.describe().loc['mean']['sales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      20.523974\n",
       "1      12.337855\n",
       "2      12.307671\n",
       "3      17.597830\n",
       "4      13.188672\n",
       "5      12.478348\n",
       "6      11.729760\n",
       "7      12.122953\n",
       "8       3.727341\n",
       "9      12.550849\n",
       "10      7.032299\n",
       "11     17.285129\n",
       "12     10.577121\n",
       "13      8.826300\n",
       "14     18.434366\n",
       "15     20.819300\n",
       "16     12.823657\n",
       "17     23.224957\n",
       "18      9.951682\n",
       "19     14.166073\n",
       "20     18.100767\n",
       "21     14.740538\n",
       "22      6.489150\n",
       "23     16.545933\n",
       "24      8.146519\n",
       "25     15.610039\n",
       "26     14.989514\n",
       "27     17.051673\n",
       "28     19.410538\n",
       "29      9.144024\n",
       "         ...    \n",
       "170     7.394980\n",
       "171    14.358274\n",
       "172     7.607692\n",
       "173    11.970939\n",
       "174    13.744357\n",
       "175    24.786870\n",
       "176    19.979373\n",
       "177    12.162046\n",
       "178    16.010997\n",
       "179    12.384555\n",
       "180    10.587200\n",
       "181    13.928099\n",
       "182     6.554670\n",
       "183    24.133100\n",
       "184    18.538521\n",
       "185    20.803011\n",
       "186     9.691373\n",
       "187    17.076442\n",
       "188    18.644306\n",
       "189     6.051624\n",
       "190    12.489159\n",
       "191     8.424019\n",
       "192     4.466230\n",
       "193    18.486958\n",
       "194    16.495300\n",
       "195     5.370342\n",
       "196     8.165312\n",
       "197    12.785921\n",
       "198    23.767321\n",
       "199    15.173196\n",
       "Length: 200, dtype: float64"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.fittedvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/danielruiz/anaconda2/envs/gstore/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# similar to Figure 3.11 and 3.12 (Residual plots) residuals v fitted values\n",
    "#sns.regplot(lm.predict(), lm.resid)\n",
    "sns.regplot(lm.fittedvalues, lm.resid)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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>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": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Table 3.5\n",
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.6"
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 },
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