diff --git a/.ipynb_checkpoints/Answers_mh-checkpoint.ipynb b/.ipynb_checkpoints/Answers_mh-checkpoint.ipynb
new file mode 100644
index 0000000..c7a7a96
--- /dev/null
+++ b/.ipynb_checkpoints/Answers_mh-checkpoint.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>1</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>2</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>3</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>4</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>5</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",
+       "1  230.1   37.8       69.2   22.1\n",
+       "2   44.5   39.3       45.1   10.4\n",
+       "3   17.2   45.9       69.3    9.3\n",
+       "4  151.5   41.3       58.5   18.5\n",
+       "5  180.8   10.8       58.4   12.9"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Advertising.csv', index_col=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QqhelZULiTtXFYas4wZj2eqRMjJzvwziP3imOPKz7Q/8+46LXsvhxPbiQX8yl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xwTuyUbGZc4uGMuZdNiLrFRzjQo7unJ973DoAQGXe6LvWjT5k5gI+lbc992r8+gcXLS/HJXuVfB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PpCM7shpnjaJUnHd1upz3TMzOxioId9BypU3f38OGNbjOjb6KnsOxDhRnf/SIyhLUpWt9di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\nGt6VF1gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc4b8fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, alpha=0.2, figsize=(12,12))\n",
+    "None "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Coef</th>\n",
+       "      <th>Columns</th>\n",
+       "      <th>Int</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0.0482245128152]</td>\n",
+       "      <td>TV</td>\n",
+       "      <td>7.066583</td>\n",
+       "      <td>0.623689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[0.223774515044]</td>\n",
+       "      <td>radio</td>\n",
+       "      <td>9.290417</td>\n",
+       "      <td>0.081757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0.0658344742479]</td>\n",
+       "      <td>newspaper</td>\n",
+       "      <td>12.602571</td>\n",
+       "      <td>-0.111407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[0.0447396196487, 0.199355464099]</td>\n",
+       "      <td>TV, radio</td>\n",
+       "      <td>2.867355</td>\n",
+       "      <td>0.859348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[0.0472036046549, 0.0507723040181]</td>\n",
+       "      <td>TV, newspaper</td>\n",
+       "      <td>5.673310</td>\n",
+       "      <td>0.643582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[0.216625857637, 0.0156987409941]</td>\n",
+       "      <td>radio, newspaper</td>\n",
+       "      <td>8.980343</td>\n",
+       "      <td>0.071047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[0.0446651206327, 0.196630062826, 0.0060743865...</td>\n",
+       "      <td>TV, radio, newspaper</td>\n",
+       "      <td>2.758072</td>\n",
+       "      <td>0.855557</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                Coef               Columns  \\\n",
+       "0                                  [0.0482245128152]                    TV   \n",
+       "1                                   [0.223774515044]                 radio   \n",
+       "2                                  [0.0658344742479]             newspaper   \n",
+       "3                  [0.0447396196487, 0.199355464099]             TV, radio   \n",
+       "4                 [0.0472036046549, 0.0507723040181]         TV, newspaper   \n",
+       "5                  [0.216625857637, 0.0156987409941]      radio, newspaper   \n",
+       "6  [0.0446651206327, 0.196630062826, 0.0060743865...  TV, radio, newspaper   \n",
+       "\n",
+       "         Int     Score  \n",
+       "0   7.066583  0.623689  \n",
+       "1   9.290417  0.081757  \n",
+       "2  12.602571 -0.111407  \n",
+       "3   2.867355  0.859348  \n",
+       "4   5.673310  0.643582  \n",
+       "5   8.980343  0.071047  \n",
+       "6   2.758072  0.855557  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,11):\n",
+    "    combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n",
+    "    for j,com in enumerate(combos):\n",
+    "        y = df.sales\n",
+    "        X = pd.DataFrame(df, columns=com)\n",
+    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+    "        model = linear_model.LinearRegression(fit_intercept=True).fit(X_train, y_train)\n",
+    "        score = model.score(X_test, y_test)\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Columns':s, 'Coef':model.coef_,'Int':model.intercept_})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Coef       [0.0447396196487, 0.199355464099]\n",
+       "Columns                            TV, radio\n",
+       "Int                                  2.86735\n",
+       "Score                               0.859348\n",
+       "Name: 3, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[df1.Score.idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final Answer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best R-squared is 0.859348."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The equation is y-hat=0.048tv+0.199radio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.92"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat=(0.048*199 + 0.199*32)\n",
+    "y_hat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The predicted sales for  TV=199, Radio=32, Newspaper=88 is 15.92"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Coef</th>\n",
+       "      <th>Columns</th>\n",
+       "      <th>Int</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0.0836113918268]</td>\n",
+       "      <td>TV</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.003590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[0.506253594753]</td>\n",
+       "      <td>radio</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.971840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0.349023408698]</td>\n",
+       "      <td>newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2.281992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[0.0539244890605, 0.241431961061]</td>\n",
+       "      <td>TV, radio</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.795534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[0.0665655550235, 0.108210290382]</td>\n",
+       "      <td>TV, newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.351303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[0.383738723667, 0.116026824632]</td>\n",
+       "      <td>radio, newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.883228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[0.052290638823, 0.22477230256, 0.0233762837797]</td>\n",
+       "      <td>TV, radio, newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.789118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Coef               Columns  \\\n",
+       "0                                 [0.0836113918268]                    TV   \n",
+       "1                                  [0.506253594753]                 radio   \n",
+       "2                                  [0.349023408698]             newspaper   \n",
+       "3                 [0.0539244890605, 0.241431961061]             TV, radio   \n",
+       "4                 [0.0665655550235, 0.108210290382]         TV, newspaper   \n",
+       "5                  [0.383738723667, 0.116026824632]      radio, newspaper   \n",
+       "6  [0.052290638823, 0.22477230256, 0.0233762837797]  TV, radio, newspaper   \n",
+       "\n",
+       "   Int     Score  \n",
+       "0  0.0  0.003590  \n",
+       "1  0.0 -0.971840  \n",
+       "2  0.0 -2.281992  \n",
+       "3  0.0  0.795534  \n",
+       "4  0.0  0.351303  \n",
+       "5  0.0 -0.883228  \n",
+       "6  0.0  0.789118  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for i in range(1,11):\n",
+    "    combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n",
+    "    for j,com in enumerate(combos):\n",
+    "        y = df.sales\n",
+    "        X = pd.DataFrame(df, columns=com)\n",
+    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+    "        model = linear_model.LinearRegression(fit_intercept=False, normalize = True).fit(X_train, y_train)\n",
+    "        score = model.score(X_test, y_test)\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Columns':s, 'Coef':model.coef_,'Int':model.intercept_})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Coef       [0.0539244890605, 0.241431961061]\n",
+       "Columns                            TV, radio\n",
+       "Int                                        0\n",
+       "Score                               0.795534\n",
+       "Name: 3, dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[df1.Score.idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>Thu, 11 Jan 2018</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>08:52:51</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>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",
+       "<tr>\n",
+       "  <th>const</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",
+       "</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>"
+      ],
+      "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:                Thu, 11 Jan 2018   Prob (F-statistic):           1.58e-96\n",
+       "Time:                        08:52:51   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",
+       "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",
+       "const          2.9389      0.312      9.422      0.000       2.324       3.554\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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking using OLS\n",
+    "y = df ['sales']\n",
+    "X = df[['TV','radio', 'newspaper']].astype(float) \n",
+    "X['const'] = 1\n",
+    "model = sm.OLS(y,X).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>   0.982</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.982</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3566.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 11 Jan 2018</td> <th>  Prob (F-statistic):</th> <td>2.43e-171</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>08:54:31</td>     <th>  Log-Likelihood:    </th> <td> -423.54</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   853.1</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   197</td>      <th>  BIC:               </th> <td>   863.0</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>TV</th>        <td>    0.0538</td> <td>    0.001</td> <td>   40.507</td> <td> 0.000</td> <td>    0.051</td> <td>    0.056</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>radio</th>     <td>    0.2222</td> <td>    0.009</td> <td>   23.595</td> <td> 0.000</td> <td>    0.204</td> <td>    0.241</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>newspaper</th> <td>    0.0168</td> <td>    0.007</td> <td>    2.517</td> <td> 0.013</td> <td>    0.004</td> <td>    0.030</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 5.982</td> <th>  Durbin-Watson:     </th> <td>   2.038</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.050</td> <th>  Jarque-Bera (JB):  </th> <td>   7.039</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.232</td> <th>  Prob(JB):          </th> <td>  0.0296</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.794</td> <th>  Cond. No.          </th> <td>    12.6</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  sales   R-squared:                       0.982\n",
+       "Model:                            OLS   Adj. R-squared:                  0.982\n",
+       "Method:                 Least Squares   F-statistic:                     3566.\n",
+       "Date:                Thu, 11 Jan 2018   Prob (F-statistic):          2.43e-171\n",
+       "Time:                        08:54:31   Log-Likelihood:                -423.54\n",
+       "No. Observations:                 200   AIC:                             853.1\n",
+       "Df Residuals:                     197   BIC:                             863.0\n",
+       "Df Model:                           3                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "TV             0.0538      0.001     40.507      0.000       0.051       0.056\n",
+       "radio          0.2222      0.009     23.595      0.000       0.204       0.241\n",
+       "newspaper      0.0168      0.007      2.517      0.013       0.004       0.030\n",
+       "==============================================================================\n",
+       "Omnibus:                        5.982   Durbin-Watson:                   2.038\n",
+       "Prob(Omnibus):                  0.050   Jarque-Bera (JB):                7.039\n",
+       "Skew:                          -0.232   Prob(JB):                       0.0296\n",
+       "Kurtosis:                       3.794   Cond. No.                         12.6\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df ['sales']\n",
+    "X = df[['TV', 'radio','newspaper']].astype(float) \n",
+    "#X['const'] = 1\n",
+    "model = sm.OLS(y,X).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.295"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat=0.0538*199 + 0.2222*32 + 0.0168*88\n",
+    "y_hat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>   0.981</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.981</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5206.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 11 Jan 2018</td> <th>  Prob (F-statistic):</th> <td>6.73e-172</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>08:57:34</td>     <th>  Log-Likelihood:    </th> <td> -426.71</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   857.4</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   864.0</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>TV</th>    <td>    0.0548</td> <td>    0.001</td> <td>   42.962</td> <td> 0.000</td> <td>    0.052</td> <td>    0.057</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>radio</th> <td>    0.2356</td> <td>    0.008</td> <td>   29.909</td> <td> 0.000</td> <td>    0.220</td> <td>    0.251</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 6.047</td> <th>  Durbin-Watson:     </th> <td>   2.080</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.049</td> <th>  Jarque-Bera (JB):  </th> <td>   8.829</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.112</td> <th>  Prob(JB):          </th> <td>  0.0121</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 4.005</td> <th>  Cond. No.          </th> <td>    9.37</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  sales   R-squared:                       0.981\n",
+       "Model:                            OLS   Adj. R-squared:                  0.981\n",
+       "Method:                 Least Squares   F-statistic:                     5206.\n",
+       "Date:                Thu, 11 Jan 2018   Prob (F-statistic):          6.73e-172\n",
+       "Time:                        08:57:34   Log-Likelihood:                -426.71\n",
+       "No. Observations:                 200   AIC:                             857.4\n",
+       "Df Residuals:                     198   BIC:                             864.0\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "TV             0.0548      0.001     42.962      0.000       0.052       0.057\n",
+       "radio          0.2356      0.008     29.909      0.000       0.220       0.251\n",
+       "==============================================================================\n",
+       "Omnibus:                        6.047   Durbin-Watson:                   2.080\n",
+       "Prob(Omnibus):                  0.049   Jarque-Bera (JB):                8.829\n",
+       "Skew:                          -0.112   Prob(JB):                       0.0121\n",
+       "Kurtosis:                       4.005   Cond. No.                         9.37\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df ['sales']\n",
+    "X = df[['TV', 'radio']].astype(float) \n",
+    "#X['const'] = 1\n",
+    "model = sm.OLS(y,X).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "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": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Advertising.csv b/Advertising.csv
new file mode 100644
index 0000000..9547f43
--- /dev/null
+++ b/Advertising.csv
@@ -0,0 +1,201 @@
+,TV,radio,newspaper,sales
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diff --git a/Answers_mh.ipynb b/Answers_mh.ipynb
new file mode 100644
index 0000000..c7a7a96
--- /dev/null
+++ b/Answers_mh.ipynb
@@ -0,0 +1,883 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\anaconda\\anaconda3\\lib\\site-packages\\statsmodels\\compat\\pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
+      "  from pandas.core import datetools\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import statsmodels.api as sm\n",
+    "from sklearn import linear_model\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from pandas.plotting import scatter_matrix\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\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>1</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>2</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>3</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>4</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>5</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",
+       "1  230.1   37.8       69.2   22.1\n",
+       "2   44.5   39.3       45.1   10.4\n",
+       "3   17.2   45.9       69.3    9.3\n",
+       "4  151.5   41.3       58.5   18.5\n",
+       "5  180.8   10.8       58.4   12.9"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.read_csv('Advertising.csv', index_col=0)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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QqhelZULiTtXFYas4wZj2eqRMjJzvwziP3imOPKz7Q/8+46LXsvhxPbiQX8yl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xwTuyUbGZc4uGMuZdNiLrFRzjQo7unJ973DoAQGXe6LvWjT5k5gI+lbc992r8+gcXLS/HJXuVfB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PpCM7shpnjaJUnHd1upz3TMzOxioId9BypU3f38OGNbjOjb6KnsOxDhRnf/SIyhLUpWt9di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\nGt6VF1gAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0xc4b8fd0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "scatter_matrix(df, alpha=0.2, figsize=(12,12))\n",
+    "None "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Coef</th>\n",
+       "      <th>Columns</th>\n",
+       "      <th>Int</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0.0482245128152]</td>\n",
+       "      <td>TV</td>\n",
+       "      <td>7.066583</td>\n",
+       "      <td>0.623689</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[0.223774515044]</td>\n",
+       "      <td>radio</td>\n",
+       "      <td>9.290417</td>\n",
+       "      <td>0.081757</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0.0658344742479]</td>\n",
+       "      <td>newspaper</td>\n",
+       "      <td>12.602571</td>\n",
+       "      <td>-0.111407</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[0.0447396196487, 0.199355464099]</td>\n",
+       "      <td>TV, radio</td>\n",
+       "      <td>2.867355</td>\n",
+       "      <td>0.859348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[0.0472036046549, 0.0507723040181]</td>\n",
+       "      <td>TV, newspaper</td>\n",
+       "      <td>5.673310</td>\n",
+       "      <td>0.643582</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[0.216625857637, 0.0156987409941]</td>\n",
+       "      <td>radio, newspaper</td>\n",
+       "      <td>8.980343</td>\n",
+       "      <td>0.071047</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[0.0446651206327, 0.196630062826, 0.0060743865...</td>\n",
+       "      <td>TV, radio, newspaper</td>\n",
+       "      <td>2.758072</td>\n",
+       "      <td>0.855557</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                Coef               Columns  \\\n",
+       "0                                  [0.0482245128152]                    TV   \n",
+       "1                                   [0.223774515044]                 radio   \n",
+       "2                                  [0.0658344742479]             newspaper   \n",
+       "3                  [0.0447396196487, 0.199355464099]             TV, radio   \n",
+       "4                 [0.0472036046549, 0.0507723040181]         TV, newspaper   \n",
+       "5                  [0.216625857637, 0.0156987409941]      radio, newspaper   \n",
+       "6  [0.0446651206327, 0.196630062826, 0.0060743865...  TV, radio, newspaper   \n",
+       "\n",
+       "         Int     Score  \n",
+       "0   7.066583  0.623689  \n",
+       "1   9.290417  0.081757  \n",
+       "2  12.602571 -0.111407  \n",
+       "3   2.867355  0.859348  \n",
+       "4   5.673310  0.643582  \n",
+       "5   8.980343  0.071047  \n",
+       "6   2.758072  0.855557  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from itertools import combinations\n",
+    "rows = []\n",
+    "for i in range(1,11):\n",
+    "    combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n",
+    "    for j,com in enumerate(combos):\n",
+    "        y = df.sales\n",
+    "        X = pd.DataFrame(df, columns=com)\n",
+    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+    "        model = linear_model.LinearRegression(fit_intercept=True).fit(X_train, y_train)\n",
+    "        score = model.score(X_test, y_test)\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Columns':s, 'Coef':model.coef_,'Int':model.intercept_})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Coef       [0.0447396196487, 0.199355464099]\n",
+       "Columns                            TV, radio\n",
+       "Int                                  2.86735\n",
+       "Score                               0.859348\n",
+       "Name: 3, dtype: object"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[df1.Score.idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Final Answer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The best R-squared is 0.859348."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The equation is y-hat=0.048tv+0.199radio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "15.92"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat=(0.048*199 + 0.199*32)\n",
+    "y_hat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The predicted sales for  TV=199, Radio=32, Newspaper=88 is 15.92"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style>\n",
+       "    .dataframe thead tr:only-child th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: left;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Coef</th>\n",
+       "      <th>Columns</th>\n",
+       "      <th>Int</th>\n",
+       "      <th>Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[0.0836113918268]</td>\n",
+       "      <td>TV</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.003590</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[0.506253594753]</td>\n",
+       "      <td>radio</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.971840</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[0.349023408698]</td>\n",
+       "      <td>newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2.281992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[0.0539244890605, 0.241431961061]</td>\n",
+       "      <td>TV, radio</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.795534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[0.0665655550235, 0.108210290382]</td>\n",
+       "      <td>TV, newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.351303</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[0.383738723667, 0.116026824632]</td>\n",
+       "      <td>radio, newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.883228</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[0.052290638823, 0.22477230256, 0.0233762837797]</td>\n",
+       "      <td>TV, radio, newspaper</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.789118</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                               Coef               Columns  \\\n",
+       "0                                 [0.0836113918268]                    TV   \n",
+       "1                                  [0.506253594753]                 radio   \n",
+       "2                                  [0.349023408698]             newspaper   \n",
+       "3                 [0.0539244890605, 0.241431961061]             TV, radio   \n",
+       "4                 [0.0665655550235, 0.108210290382]         TV, newspaper   \n",
+       "5                  [0.383738723667, 0.116026824632]      radio, newspaper   \n",
+       "6  [0.052290638823, 0.22477230256, 0.0233762837797]  TV, radio, newspaper   \n",
+       "\n",
+       "   Int     Score  \n",
+       "0  0.0  0.003590  \n",
+       "1  0.0 -0.971840  \n",
+       "2  0.0 -2.281992  \n",
+       "3  0.0  0.795534  \n",
+       "4  0.0  0.351303  \n",
+       "5  0.0 -0.883228  \n",
+       "6  0.0  0.789118  "
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rows = []\n",
+    "for i in range(1,11):\n",
+    "    combos = list(combinations(['TV', 'radio', 'newspaper'],i))\n",
+    "    for j,com in enumerate(combos):\n",
+    "        y = df.sales\n",
+    "        X = pd.DataFrame(df, columns=com)\n",
+    "        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)\n",
+    "        model = linear_model.LinearRegression(fit_intercept=False, normalize = True).fit(X_train, y_train)\n",
+    "        score = model.score(X_test, y_test)\n",
+    "        s = ', '.join(com)\n",
+    "        rows.append({'Score':score, 'Columns':s, 'Coef':model.coef_,'Int':model.intercept_})\n",
+    "        # print('score:', score, 'columns:', s)\n",
+    "df1 = pd.DataFrame(rows)\n",
+    "df1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Coef       [0.0539244890605, 0.241431961061]\n",
+       "Columns                            TV, radio\n",
+       "Int                                        0\n",
+       "Score                               0.795534\n",
+       "Name: 3, dtype: object"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.iloc[df1.Score.idxmax()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>Thu, 11 Jan 2018</td> <th>  Prob (F-statistic):</th> <td>1.58e-96</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>08:52:51</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>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",
+       "<tr>\n",
+       "  <th>const</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",
+       "</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>"
+      ],
+      "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:                Thu, 11 Jan 2018   Prob (F-statistic):           1.58e-96\n",
+       "Time:                        08:52:51   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",
+       "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",
+       "const          2.9389      0.312      9.422      0.000       2.324       3.554\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": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Checking using OLS\n",
+    "y = df ['sales']\n",
+    "X = df[['TV','radio', 'newspaper']].astype(float) \n",
+    "X['const'] = 1\n",
+    "model = sm.OLS(y,X).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "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>   0.982</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.982</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   3566.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 11 Jan 2018</td> <th>  Prob (F-statistic):</th> <td>2.43e-171</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>08:54:31</td>     <th>  Log-Likelihood:    </th> <td> -423.54</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   853.1</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   197</td>      <th>  BIC:               </th> <td>   863.0</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>TV</th>        <td>    0.0538</td> <td>    0.001</td> <td>   40.507</td> <td> 0.000</td> <td>    0.051</td> <td>    0.056</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>radio</th>     <td>    0.2222</td> <td>    0.009</td> <td>   23.595</td> <td> 0.000</td> <td>    0.204</td> <td>    0.241</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>newspaper</th> <td>    0.0168</td> <td>    0.007</td> <td>    2.517</td> <td> 0.013</td> <td>    0.004</td> <td>    0.030</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 5.982</td> <th>  Durbin-Watson:     </th> <td>   2.038</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.050</td> <th>  Jarque-Bera (JB):  </th> <td>   7.039</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.232</td> <th>  Prob(JB):          </th> <td>  0.0296</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 3.794</td> <th>  Cond. No.          </th> <td>    12.6</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  sales   R-squared:                       0.982\n",
+       "Model:                            OLS   Adj. R-squared:                  0.982\n",
+       "Method:                 Least Squares   F-statistic:                     3566.\n",
+       "Date:                Thu, 11 Jan 2018   Prob (F-statistic):          2.43e-171\n",
+       "Time:                        08:54:31   Log-Likelihood:                -423.54\n",
+       "No. Observations:                 200   AIC:                             853.1\n",
+       "Df Residuals:                     197   BIC:                             863.0\n",
+       "Df Model:                           3                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "TV             0.0538      0.001     40.507      0.000       0.051       0.056\n",
+       "radio          0.2222      0.009     23.595      0.000       0.204       0.241\n",
+       "newspaper      0.0168      0.007      2.517      0.013       0.004       0.030\n",
+       "==============================================================================\n",
+       "Omnibus:                        5.982   Durbin-Watson:                   2.038\n",
+       "Prob(Omnibus):                  0.050   Jarque-Bera (JB):                7.039\n",
+       "Skew:                          -0.232   Prob(JB):                       0.0296\n",
+       "Kurtosis:                       3.794   Cond. No.                         12.6\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df ['sales']\n",
+    "X = df[['TV', 'radio','newspaper']].astype(float) \n",
+    "#X['const'] = 1\n",
+    "model = sm.OLS(y,X).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "19.295"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_hat=0.0538*199 + 0.2222*32 + 0.0168*88\n",
+    "y_hat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "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>   0.981</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.981</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5206.</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Date:</th>             <td>Thu, 11 Jan 2018</td> <th>  Prob (F-statistic):</th> <td>6.73e-172</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Time:</th>                 <td>08:57:34</td>     <th>  Log-Likelihood:    </th> <td> -426.71</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>No. Observations:</th>      <td>   200</td>      <th>  AIC:               </th> <td>   857.4</td> \n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Df Residuals:</th>          <td>   198</td>      <th>  BIC:               </th> <td>   864.0</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>TV</th>    <td>    0.0548</td> <td>    0.001</td> <td>   42.962</td> <td> 0.000</td> <td>    0.052</td> <td>    0.057</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>radio</th> <td>    0.2356</td> <td>    0.008</td> <td>   29.909</td> <td> 0.000</td> <td>    0.220</td> <td>    0.251</td>\n",
+       "</tr>\n",
+       "</table>\n",
+       "<table class=\"simpletable\">\n",
+       "<tr>\n",
+       "  <th>Omnibus:</th>       <td> 6.047</td> <th>  Durbin-Watson:     </th> <td>   2.080</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Prob(Omnibus):</th> <td> 0.049</td> <th>  Jarque-Bera (JB):  </th> <td>   8.829</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Skew:</th>          <td>-0.112</td> <th>  Prob(JB):          </th> <td>  0.0121</td>\n",
+       "</tr>\n",
+       "<tr>\n",
+       "  <th>Kurtosis:</th>      <td> 4.005</td> <th>  Cond. No.          </th> <td>    9.37</td>\n",
+       "</tr>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<class 'statsmodels.iolib.summary.Summary'>\n",
+       "\"\"\"\n",
+       "                            OLS Regression Results                            \n",
+       "==============================================================================\n",
+       "Dep. Variable:                  sales   R-squared:                       0.981\n",
+       "Model:                            OLS   Adj. R-squared:                  0.981\n",
+       "Method:                 Least Squares   F-statistic:                     5206.\n",
+       "Date:                Thu, 11 Jan 2018   Prob (F-statistic):          6.73e-172\n",
+       "Time:                        08:57:34   Log-Likelihood:                -426.71\n",
+       "No. Observations:                 200   AIC:                             857.4\n",
+       "Df Residuals:                     198   BIC:                             864.0\n",
+       "Df Model:                           2                                         \n",
+       "Covariance Type:            nonrobust                                         \n",
+       "==============================================================================\n",
+       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
+       "------------------------------------------------------------------------------\n",
+       "TV             0.0548      0.001     42.962      0.000       0.052       0.057\n",
+       "radio          0.2356      0.008     29.909      0.000       0.220       0.251\n",
+       "==============================================================================\n",
+       "Omnibus:                        6.047   Durbin-Watson:                   2.080\n",
+       "Prob(Omnibus):                  0.049   Jarque-Bera (JB):                8.829\n",
+       "Skew:                          -0.112   Prob(JB):                       0.0121\n",
+       "Kurtosis:                       4.005   Cond. No.                         9.37\n",
+       "==============================================================================\n",
+       "\n",
+       "Warnings:\n",
+       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
+       "\"\"\""
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y = df ['sales']\n",
+    "X = df[['TV', 'radio']].astype(float) \n",
+    "#X['const'] = 1\n",
+    "model = sm.OLS(y,X).fit()\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
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+ "nbformat": 4,
+ "nbformat_minor": 2
+}