{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Multiple Linear Regression\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preliminaries\n",
    "As before, we need to start by:\n",
    "1. Loading the Pandas and Statsmodels libraries\n",
    "2. Reading the data from a CSV file\n",
    "3. Fixing the column names using Panda's `rename()` method\n",
    "4. Converting the \"AirEntrain\" column to a categorical variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>No</th>\n",
       "      <th>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>SP</th>\n",
       "      <th>CoarseAgg</th>\n",
       "      <th>FineAgg</th>\n",
       "      <th>AirEntrain</th>\n",
       "      <th>Strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>No</td>\n",
       "      <td>34.990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>163.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>32.272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>162.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>35.450</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  Cement   Slag  FlyAsh  Water    SP  CoarseAgg  FineAgg AirEntrain  \\\n",
       "0   1   273.0   82.0   105.0  210.0   9.0      904.0    680.0         No   \n",
       "1   2   163.0  149.0   191.0  180.0  12.0      843.0    746.0        Yes   \n",
       "2   3   162.0  148.0   191.0  179.0  16.0      840.0    743.0        Yes   \n",
       "\n",
       "   Strength  \n",
       "0    34.990  \n",
       "1    32.272  \n",
       "2    35.450  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "\n",
    "con = pd.read_csv('Data/ConcreteStrength.csv')\n",
    "con.rename(columns={'Fly ash': 'FlyAsh', 'Coarse Aggr.': \"CoarseAgg\",\n",
    "                    'Fine Aggr.': 'FineAgg', 'Air Entrainment': 'AirEntrain', \n",
    "                    'Compressive Strength (28-day)(Mpa)': 'Strength'}, inplace=True)\n",
    "con['AirEntrain'] = con['AirEntrain'].astype('category')\n",
    "con.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I then define to separate data frames:\n",
    "1. _Y_ to hold my response variable (the single column \"Strength\")\n",
    "2. _X_ to hold my explanatory variables\n",
    "\n",
    "Note that I have excluded \"AirEntrain\" at this point because it is categorical.  As we have seen in Excel, SAS Enterprise Guide, and R, including categorical variables in a linear regression requires some additional work.\n",
    "\n",
    "I complete my _X_ matrix by running the Statsmodels `add_constant()` method, as explained in the tutorial on [linear regression](regression.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\tutorials\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    }
   ],
   "source": [
    "Y = con['Strength']\n",
    "X = con[['No',\n",
    " 'Cement',\n",
    " 'Slag',\n",
    " 'FlyAsh',\n",
    " 'Water',\n",
    " 'SP',\n",
    " 'CoarseAgg',\n",
    " 'FineAgg']]\n",
    "X = sm.add_constant(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Kitchen sink model\n",
    "Our tradition is to start with a \"kitchen sink\" model, which includes all our (numerical) explanatory variables. The Statsmodels OLS output gives us some warnings at the bottom of the output.  We can ignore these at this early stage of the modeling process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Strength</td>     <th>  R-squared:         </th> <td>   0.827</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.812</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   56.21</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>1.68e-32</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:11:31</td>     <th>  Log-Likelihood:    </th> <td> -284.49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   103</td>      <th>  AIC:               </th> <td>   587.0</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    94</td>      <th>  BIC:               </th> <td>   610.7</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>     <td>  115.2834</td> <td>  142.786</td> <td>    0.807</td> <td> 0.421</td> <td> -168.222</td> <td>  398.789</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No</th>        <td>   -0.0077</td> <td>    0.021</td> <td>   -0.372</td> <td> 0.711</td> <td>   -0.049</td> <td>    0.033</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cement</th>    <td>    0.0826</td> <td>    0.047</td> <td>    1.758</td> <td> 0.082</td> <td>   -0.011</td> <td>    0.176</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Slag</th>      <td>   -0.0225</td> <td>    0.065</td> <td>   -0.346</td> <td> 0.730</td> <td>   -0.152</td> <td>    0.107</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FlyAsh</th>    <td>    0.0668</td> <td>    0.048</td> <td>    1.380</td> <td> 0.171</td> <td>   -0.029</td> <td>    0.163</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Water</th>     <td>   -0.2165</td> <td>    0.142</td> <td>   -1.520</td> <td> 0.132</td> <td>   -0.499</td> <td>    0.066</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>        <td>    0.2518</td> <td>    0.213</td> <td>    1.181</td> <td> 0.241</td> <td>   -0.172</td> <td>    0.675</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CoarseAgg</th> <td>   -0.0479</td> <td>    0.056</td> <td>   -0.857</td> <td> 0.393</td> <td>   -0.159</td> <td>    0.063</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FineAgg</th>   <td>   -0.0356</td> <td>    0.057</td> <td>   -0.622</td> <td> 0.536</td> <td>   -0.149</td> <td>    0.078</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 2.168</td> <th>  Durbin-Watson:     </th> <td>   1.715</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.338</td> <th>  Jarque-Bera (JB):  </th> <td>   2.183</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.309</td> <th>  Prob(JB):          </th> <td>   0.336</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.644</td> <th>  Cond. No.          </th> <td>4.36e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.36e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Strength   R-squared:                       0.827\n",
       "Model:                            OLS   Adj. R-squared:                  0.812\n",
       "Method:                 Least Squares   F-statistic:                     56.21\n",
       "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           1.68e-32\n",
       "Time:                        07:11:31   Log-Likelihood:                -284.49\n",
       "No. Observations:                 103   AIC:                             587.0\n",
       "Df Residuals:                      94   BIC:                             610.7\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "const        115.2834    142.786      0.807      0.421    -168.222     398.789\n",
       "No            -0.0077      0.021     -0.372      0.711      -0.049       0.033\n",
       "Cement         0.0826      0.047      1.758      0.082      -0.011       0.176\n",
       "Slag          -0.0225      0.065     -0.346      0.730      -0.152       0.107\n",
       "FlyAsh         0.0668      0.048      1.380      0.171      -0.029       0.163\n",
       "Water         -0.2165      0.142     -1.520      0.132      -0.499       0.066\n",
       "SP             0.2518      0.213      1.181      0.241      -0.172       0.675\n",
       "CoarseAgg     -0.0479      0.056     -0.857      0.393      -0.159       0.063\n",
       "FineAgg       -0.0356      0.057     -0.622      0.536      -0.149       0.078\n",
       "==============================================================================\n",
       "Omnibus:                        2.168   Durbin-Watson:                   1.715\n",
       "Prob(Omnibus):                  0.338   Jarque-Bera (JB):                2.183\n",
       "Skew:                          -0.309   Prob(JB):                        0.336\n",
       "Kurtosis:                       2.644   Cond. No.                     4.36e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.36e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks = sm.OLS(Y, X)\n",
    "ks_res =ks.fit()\n",
    "ks_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical explanatory variables\n",
    "Recall how we have dealt with categorical explanatory variables to this point:\n",
    "1. **Excel**: We used IF statements and other tricks to create _n_-1 new columns in the spreadsheet (where _n_ is the number of values in the categorical variable)\n",
    "2. **SAS Enterprise Guide**: We used the recoding functionality in the query builder to add _n_-1 new columns to the data set\n",
    "3. **R**: We converted the variable to a _factor_ data type and let R construct the _n_-1 dummy columns behind the scenes\n",
    "\n",
    "In Python, we can use either the manual approach (create a matrix of dummy variables ourselves) or the automatic approach (let the algorithm sort it out behind the scenes).  I am partial to the manual approach because dealing intelligently with categorical variables in real-world data _almost always_ involves significant work.  Specifically: we typically need to change the granularity of the variable to provide more generalizable results."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a matrix of dummy variables\n",
    "When I say the \"manual\" approach in Python, I actually mean \"quite a bit less manual\" than Excel.  Many different libraries in Python provide many different routines for encoding categorical variables.  All of these routines bypass the drudgery of writing IF statements to map from categorical values to (0, 1) values.  Here we will use Pandas's aptly-named `get_dummies()` method.\n",
    "\n",
    "In this approach, we pass `get_dummies()` a column in a data frame and it creates a full matrix of zero-one values.  In other words, it gives us a matrix with 103 rows (because we have 103 rows in the \"Concrete Strength\" data set and two columns (because the \"AirEntrain\" variable has two values: Yes and No)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     No  Yes\n",
       "0     1    0\n",
       "1     0    1\n",
       "2     0    1\n",
       "3     1    0\n",
       "4     1    0\n",
       "..   ..  ...\n",
       "98    1    0\n",
       "99    1    0\n",
       "100   0    1\n",
       "101   0    1\n",
       "102   1    0\n",
       "\n",
       "[103 rows x 2 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AirEntrain_d = pd.get_dummies(con['AirEntrain'])\n",
    "AirEntrain_d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"Yes\" and \"No\" column headings are problematic, especially if we have to convert many categorical variables with Yes/No values.  Accordingly, I need to make some changes to the default dummy matrix:\n",
    "1. I want to use AirEntrain=No as my baseline for the dummy variable.  As such, I need to drop the \"No\" column from the matrix before passing it to the regression.\n",
    "2. I like to embed my choice of baseline into the the dummy column names. This makes it easier to interpret the regression coefficients\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AirEntrain_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AirEntrain_Yes\n",
       "0               0\n",
       "1               1\n",
       "2               1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AirEntrain_d.drop(columns='No', inplace=True)\n",
    "AirEntrain_d.rename(columns={'Yes': 'AirEntrain_Yes'}, inplace=True)\n",
    "AirEntrain_d.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A slightly more automated version\n",
    "Not surprisingly, we can cut down the number of steps in this process by passing `get_dummies()` additional arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>AirEntrain_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AirEntrain_Yes\n",
       "0               0\n",
       "1               1\n",
       "2               1"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AirEntrain_d = pd.get_dummies(con['AirEntrain'], prefix='AirEntrain', drop_first=True)\n",
    "AirEntrain_d.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding the dummy columns to the existing _X_ matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>const</th>\n",
       "      <th>No</th>\n",
       "      <th>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>SP</th>\n",
       "      <th>CoarseAgg</th>\n",
       "      <th>FineAgg</th>\n",
       "      <th>AirEntrain_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>163.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>162.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4</td>\n",
       "      <td>162.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>838.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5</td>\n",
       "      <td>154.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>658.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   const  No  Cement   Slag  FlyAsh  Water    SP  CoarseAgg  FineAgg  \\\n",
       "0    1.0   1   273.0   82.0   105.0  210.0   9.0      904.0    680.0   \n",
       "1    1.0   2   163.0  149.0   191.0  180.0  12.0      843.0    746.0   \n",
       "2    1.0   3   162.0  148.0   191.0  179.0  16.0      840.0    743.0   \n",
       "3    1.0   4   162.0  148.0   190.0  179.0  19.0      838.0    741.0   \n",
       "4    1.0   5   154.0  112.0   144.0  220.0  10.0      923.0    658.0   \n",
       "\n",
       "   AirEntrain_Yes  \n",
       "0               0  \n",
       "1               1  \n",
       "2               1  \n",
       "3               0  \n",
       "4               0  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fullX = pd.concat([X, AirEntrain_d['AirEntrain_Yes']], axis=1)\n",
    "fullX.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the full regression"
   ]
  },
  {
   "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>Strength</td>     <th>  R-squared:         </th> <td>   0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   125.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>5.83e-48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:11:31</td>     <th>  Log-Likelihood:    </th> <td> -242.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   103</td>      <th>  AIC:               </th> <td>   504.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    93</td>      <th>  BIC:               </th> <td>   531.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>   41.5005</td> <td>   95.617</td> <td>    0.434</td> <td> 0.665</td> <td> -148.375</td> <td>  231.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No</th>             <td>   -0.0173</td> <td>    0.014</td> <td>   -1.251</td> <td> 0.214</td> <td>   -0.045</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cement</th>         <td>    0.0962</td> <td>    0.031</td> <td>    3.063</td> <td> 0.003</td> <td>    0.034</td> <td>    0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Slag</th>           <td>    0.0157</td> <td>    0.044</td> <td>    0.359</td> <td> 0.720</td> <td>   -0.071</td> <td>    0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FlyAsh</th>         <td>    0.0869</td> <td>    0.032</td> <td>    2.684</td> <td> 0.009</td> <td>    0.023</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Water</th>          <td>   -0.1380</td> <td>    0.095</td> <td>   -1.446</td> <td> 0.151</td> <td>   -0.328</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>             <td>    0.1902</td> <td>    0.143</td> <td>    1.334</td> <td> 0.186</td> <td>   -0.093</td> <td>    0.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CoarseAgg</th>      <td>   -0.0160</td> <td>    0.037</td> <td>   -0.428</td> <td> 0.669</td> <td>   -0.090</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FineAgg</th>        <td>   -0.0021</td> <td>    0.038</td> <td>   -0.053</td> <td> 0.957</td> <td>   -0.078</td> <td>    0.074</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AirEntrain_Yes</th> <td>   -6.0683</td> <td>    0.559</td> <td>  -10.848</td> <td> 0.000</td> <td>   -7.179</td> <td>   -4.957</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.217</td> <th>  Durbin-Watson:     </th> <td>   1.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.121</td> <th>  Jarque-Bera (JB):  </th> <td>   3.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.351</td> <th>  Prob(JB):          </th> <td>   0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.594</td> <th>  Cond. No.          </th> <td>4.37e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.37e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Strength   R-squared:                       0.924\n",
       "Model:                            OLS   Adj. R-squared:                  0.916\n",
       "Method:                 Least Squares   F-statistic:                     125.1\n",
       "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           5.83e-48\n",
       "Time:                        07:11:31   Log-Likelihood:                -242.38\n",
       "No. Observations:                 103   AIC:                             504.8\n",
       "Df Residuals:                      93   BIC:                             531.1\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const             41.5005     95.617      0.434      0.665    -148.375     231.376\n",
       "No                -0.0173      0.014     -1.251      0.214      -0.045       0.010\n",
       "Cement             0.0962      0.031      3.063      0.003       0.034       0.159\n",
       "Slag               0.0157      0.044      0.359      0.720      -0.071       0.102\n",
       "FlyAsh             0.0869      0.032      2.684      0.009       0.023       0.151\n",
       "Water             -0.1380      0.095     -1.446      0.151      -0.328       0.051\n",
       "SP                 0.1902      0.143      1.334      0.186      -0.093       0.473\n",
       "CoarseAgg         -0.0160      0.037     -0.428      0.669      -0.090       0.058\n",
       "FineAgg           -0.0021      0.038     -0.053      0.957      -0.078       0.074\n",
       "AirEntrain_Yes    -6.0683      0.559    -10.848      0.000      -7.179      -4.957\n",
       "==============================================================================\n",
       "Omnibus:                        4.217   Durbin-Watson:                   1.637\n",
       "Prob(Omnibus):                  0.121   Jarque-Bera (JB):                3.635\n",
       "Skew:                           0.351   Prob(JB):                        0.162\n",
       "Kurtosis:                       3.594   Cond. No.                     4.37e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.37e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ks2 = sm.OLS(Y, fullX)\n",
    "ks2_res = ks2.fit()\n",
    "ks2_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using R-like formulas\n",
    "As I mentioned previously, R was used in statistics long before Python was popular.  As a consequence, some of the data science libraries for Python mimic the R way of doing things.  This makes it much easier for people who know R to transition to Python.  If, however, you do not know R, it just adds a lot of confusion.\n",
    "\n",
    "Having said this, formula notation in R turns out to be pretty handy.  Instead of defining separate _Y_ and _X_ matrices, you simply pass R a formula of the form \"Y ~ X1, X2, ... Xn\" and it takes care of the rest.  It turns out that Statsmodels includes a whole library for doing things the R way.  Two things to know:\n",
    "1. You have to import the statsmodels.formula.api library instead of (or, more typically, in addition to) the statsmodels.api library\n",
    "2. The method names in the \"formula\" api are lowercase (e.g., `ols()` instead of `OLS()`\n",
    "\n",
    "Yes, this is confusing, but you can do things like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Strength</td>     <th>  R-squared:         </th> <td>   0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.916</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   125.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>5.83e-48</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:11:31</td>     <th>  Log-Likelihood:    </th> <td> -242.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   103</td>      <th>  AIC:               </th> <td>   504.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    93</td>      <th>  BIC:               </th> <td>   531.1</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     9</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>   41.5005</td> <td>   95.617</td> <td>    0.434</td> <td> 0.665</td> <td> -148.375</td> <td>  231.376</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AirEntrain[T.Yes]</th> <td>   -6.0683</td> <td>    0.559</td> <td>  -10.848</td> <td> 0.000</td> <td>   -7.179</td> <td>   -4.957</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No</th>                <td>   -0.0173</td> <td>    0.014</td> <td>   -1.251</td> <td> 0.214</td> <td>   -0.045</td> <td>    0.010</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cement</th>            <td>    0.0962</td> <td>    0.031</td> <td>    3.063</td> <td> 0.003</td> <td>    0.034</td> <td>    0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Slag</th>              <td>    0.0157</td> <td>    0.044</td> <td>    0.359</td> <td> 0.720</td> <td>   -0.071</td> <td>    0.102</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Water</th>             <td>   -0.1380</td> <td>    0.095</td> <td>   -1.446</td> <td> 0.151</td> <td>   -0.328</td> <td>    0.051</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CoarseAgg</th>         <td>   -0.0160</td> <td>    0.037</td> <td>   -0.428</td> <td> 0.669</td> <td>   -0.090</td> <td>    0.058</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FlyAsh</th>            <td>    0.0869</td> <td>    0.032</td> <td>    2.684</td> <td> 0.009</td> <td>    0.023</td> <td>    0.151</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>                <td>    0.1902</td> <td>    0.143</td> <td>    1.334</td> <td> 0.186</td> <td>   -0.093</td> <td>    0.473</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FineAgg</th>           <td>   -0.0021</td> <td>    0.038</td> <td>   -0.053</td> <td> 0.957</td> <td>   -0.078</td> <td>    0.074</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.217</td> <th>  Durbin-Watson:     </th> <td>   1.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.121</td> <th>  Jarque-Bera (JB):  </th> <td>   3.635</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.351</td> <th>  Prob(JB):          </th> <td>   0.162</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.594</td> <th>  Cond. No.          </th> <td>4.37e+05</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 4.37e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Strength   R-squared:                       0.924\n",
       "Model:                            OLS   Adj. R-squared:                  0.916\n",
       "Method:                 Least Squares   F-statistic:                     125.1\n",
       "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           5.83e-48\n",
       "Time:                        07:11:31   Log-Likelihood:                -242.38\n",
       "No. Observations:                 103   AIC:                             504.8\n",
       "Df Residuals:                      93   BIC:                             531.1\n",
       "Df Model:                           9                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            41.5005     95.617      0.434      0.665    -148.375     231.376\n",
       "AirEntrain[T.Yes]    -6.0683      0.559    -10.848      0.000      -7.179      -4.957\n",
       "No                   -0.0173      0.014     -1.251      0.214      -0.045       0.010\n",
       "Cement                0.0962      0.031      3.063      0.003       0.034       0.159\n",
       "Slag                  0.0157      0.044      0.359      0.720      -0.071       0.102\n",
       "Water                -0.1380      0.095     -1.446      0.151      -0.328       0.051\n",
       "CoarseAgg            -0.0160      0.037     -0.428      0.669      -0.090       0.058\n",
       "FlyAsh                0.0869      0.032      2.684      0.009       0.023       0.151\n",
       "SP                    0.1902      0.143      1.334      0.186      -0.093       0.473\n",
       "FineAgg              -0.0021      0.038     -0.053      0.957      -0.078       0.074\n",
       "==============================================================================\n",
       "Omnibus:                        4.217   Durbin-Watson:                   1.637\n",
       "Prob(Omnibus):                  0.121   Jarque-Bera (JB):                3.635\n",
       "Skew:                           0.351   Prob(JB):                        0.162\n",
       "Kurtosis:                       3.594   Cond. No.                     4.37e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 4.37e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.formula.api as smf\n",
    "ksf =  smf.ols(' Strength ~ No + Cement + Slag + Water + CoarseAgg + FlyAsh + SP + FineAgg + AirEntrain', data=con)\n",
    "ksf_res = ksf.fit()\n",
    "ksf_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is exactly the same formula we used in R for the kitchen sink model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking for colinearity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatterplot matrix\n",
    "We can run a scatterplot matrix on our original _X_ matrix using Seaborn's handy `pairplot()` method. A nice feature of this presentation is a histogram for each variable. **Note** that this may take a few seconds to generate so you  have to be patient.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1620x1620 with 90 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.pairplot(X);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Restricting variables in the scatterplot matrix\n",
    "With wide data sets (many columns), the scatterplots become unreadable.  Thus, it is often better to restrict the variables in the scatterplot matrix to a named set in order to maximize readability.  Here I have excluded the constant, response variable, and all dummy columns.\n",
    "\n",
    "A few things that catch my eye in the scatterplot matrix:\n",
    "1. The \"No\" variable (experiment number) does not appear to be correlated with any other variable.  That is good news&mdash;we should not expect it to in a well-run experiment.\n",
    "2. There is some linearity and other weirdness in the relationships between \"FlyAsh\", \"Slag\", and \"Cement\". This suggests problems with the experimental design. Unfortunately, these problems cannot be fixed in the data analysis stage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 72 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(X[['No',\n",
    " 'Cement',\n",
    " 'Slag',\n",
    " 'FlyAsh',\n",
    " 'Water',\n",
    " 'SP',\n",
    " 'CoarseAgg',\n",
    " 'FineAgg']]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation matrix\n",
    "If the scatterplot matrix remains too hard to read, you can always revert to a simple correlation matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>No</th>\n",
       "      <th>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>SP</th>\n",
       "      <th>CoarseAgg</th>\n",
       "      <th>FineAgg</th>\n",
       "      <th>Strength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.08</td>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cement</th>\n",
       "      <td>-0.03</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slag</th>\n",
       "      <td>-0.08</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlyAsh</th>\n",
       "      <td>0.34</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.32</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water</th>\n",
       "      <td>-0.14</td>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>-0.24</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>0.11</td>\n",
       "      <td>-0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>-0.33</td>\n",
       "      <td>-0.11</td>\n",
       "      <td>0.31</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>-0.16</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoarseAgg</th>\n",
       "      <td>0.22</td>\n",
       "      <td>-0.31</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>0.17</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>-0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FineAgg</th>\n",
       "      <td>-0.31</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>-0.28</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.06</td>\n",
       "      <td>-0.49</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Strength</th>\n",
       "      <td>0.19</td>\n",
       "      <td>0.46</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>0.41</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>-0.02</td>\n",
       "      <td>-0.15</td>\n",
       "      <td>-0.17</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             No  Cement  Slag  FlyAsh  Water    SP  CoarseAgg  FineAgg  \\\n",
       "No         1.00   -0.03 -0.08    0.34  -0.14 -0.33       0.22    -0.31   \n",
       "Cement    -0.03    1.00 -0.24   -0.49   0.22 -0.11      -0.31     0.06   \n",
       "Slag      -0.08   -0.24  1.00   -0.32  -0.03  0.31      -0.22    -0.18   \n",
       "FlyAsh     0.34   -0.49 -0.32    1.00  -0.24 -0.14       0.17    -0.28   \n",
       "Water     -0.14    0.22 -0.03   -0.24   1.00 -0.16      -0.60     0.11   \n",
       "SP        -0.33   -0.11  0.31   -0.14  -0.16  1.00      -0.10     0.06   \n",
       "CoarseAgg  0.22   -0.31 -0.22    0.17  -0.60 -0.10       1.00    -0.49   \n",
       "FineAgg   -0.31    0.06 -0.18   -0.28   0.11  0.06      -0.49     1.00   \n",
       "Strength   0.19    0.46 -0.33    0.41  -0.22 -0.02      -0.15    -0.17   \n",
       "\n",
       "           Strength  \n",
       "No             0.19  \n",
       "Cement         0.46  \n",
       "Slag          -0.33  \n",
       "FlyAsh         0.41  \n",
       "Water         -0.22  \n",
       "SP            -0.02  \n",
       "CoarseAgg     -0.15  \n",
       "FineAgg       -0.17  \n",
       "Strength       1.00  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(con.corr(),2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model refinement\n",
    "The kitchen sink model is unlikely to be the best model.  At the very least, we need to remove variables that should not be in the model for **methodological** reasons, such as collinearity.  Then, depending on our philosophical view on such things, we can go into data mining mode and attempt to generate the \"best\" model by removing or adding explanatory variables. Two clarifications:\n",
    "1. The **best** model is typically defined in terms of the trade-off between goodness of fit (e.g., $R^2$) and model complexity (the number of explanatory variables).  This trade-off provides the rationale for the _adjusted_ $R^2$ measure. Given two models with similar explanatory power, the one with the fewest explanatory variables is deemed better.  \n",
    "2. **Data mining mode** means we suspend our knowledge about the underlying domain and instead focus on technical measures of explanatory power. In this mode, we keep our theories about cause and effect to ourselves: If the measure indicates a variable has explanatory power, we leave it in the model; if the measure indicates the variable has low explanatory power, we take it out of the model. Many different heuristic measures of explanatory power exist, including the _p_-value of the coefficient and the more sophistical measures (AIC, Mallows Cp) used by SAS and R."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual stepwise refinement\n",
    "When we did manual stepwise refinement in Excel, our heuristic was to start with the kitchen sink model and remove the variable with the highest _p_-value (probability of zero slope).\n",
    "\n",
    "If we scroll up to the results of the kitchen sink model, we see that the variable with the highest _p_-value is \"FineAgg\". If we are using the matrix version of the `OLS()` method, we can drop the column from the _X_ matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Strength</td>     <th>  R-squared:         </th> <td>   0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   142.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>4.73e-49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:12:11</td>     <th>  Log-Likelihood:    </th> <td> -242.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   103</td>      <th>  AIC:               </th> <td>   502.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    94</td>      <th>  BIC:               </th> <td>   526.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td>   36.4097</td> <td>    8.674</td> <td>    4.197</td> <td> 0.000</td> <td>   19.186</td> <td>   53.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No</th>             <td>   -0.0178</td> <td>    0.011</td> <td>   -1.674</td> <td> 0.097</td> <td>   -0.039</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cement</th>         <td>    0.0978</td> <td>    0.005</td> <td>   18.070</td> <td> 0.000</td> <td>    0.087</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Slag</th>           <td>    0.0180</td> <td>    0.006</td> <td>    2.819</td> <td> 0.006</td> <td>    0.005</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FlyAsh</th>         <td>    0.0887</td> <td>    0.005</td> <td>   17.367</td> <td> 0.000</td> <td>    0.079</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Water</th>          <td>   -0.1330</td> <td>    0.019</td> <td>   -7.131</td> <td> 0.000</td> <td>   -0.170</td> <td>   -0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>             <td>    0.1950</td> <td>    0.109</td> <td>    1.791</td> <td> 0.077</td> <td>   -0.021</td> <td>    0.411</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CoarseAgg</th>      <td>   -0.0141</td> <td>    0.005</td> <td>   -2.964</td> <td> 0.004</td> <td>   -0.023</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AirEntrain_Yes</th> <td>   -6.0707</td> <td>    0.555</td> <td>  -10.946</td> <td> 0.000</td> <td>   -7.172</td> <td>   -4.970</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.255</td> <th>  Durbin-Watson:     </th> <td>   1.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.119</td> <th>  Jarque-Bera (JB):  </th> <td>   3.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.352</td> <th>  Prob(JB):          </th> <td>   0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.601</td> <th>  Cond. No.          </th> <td>3.15e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.15e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Strength   R-squared:                       0.924\n",
       "Model:                            OLS   Adj. R-squared:                  0.917\n",
       "Method:                 Least Squares   F-statistic:                     142.2\n",
       "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           4.73e-49\n",
       "Time:                        07:12:11   Log-Likelihood:                -242.38\n",
       "No. Observations:                 103   AIC:                             502.8\n",
       "Df Residuals:                      94   BIC:                             526.5\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const             36.4097      8.674      4.197      0.000      19.186      53.633\n",
       "No                -0.0178      0.011     -1.674      0.097      -0.039       0.003\n",
       "Cement             0.0978      0.005     18.070      0.000       0.087       0.109\n",
       "Slag               0.0180      0.006      2.819      0.006       0.005       0.031\n",
       "FlyAsh             0.0887      0.005     17.367      0.000       0.079       0.099\n",
       "Water             -0.1330      0.019     -7.131      0.000      -0.170      -0.096\n",
       "SP                 0.1950      0.109      1.791      0.077      -0.021       0.411\n",
       "CoarseAgg         -0.0141      0.005     -2.964      0.004      -0.023      -0.005\n",
       "AirEntrain_Yes    -6.0707      0.555    -10.946      0.000      -7.172      -4.970\n",
       "==============================================================================\n",
       "Omnibus:                        4.255   Durbin-Watson:                   1.637\n",
       "Prob(Omnibus):                  0.119   Jarque-Bera (JB):                3.680\n",
       "Skew:                           0.352   Prob(JB):                        0.159\n",
       "Kurtosis:                       3.601   Cond. No.                     3.15e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.15e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1 = fullX.drop(columns='FineAgg', inplace=False)\n",
    "mod1 = sm.OLS(Y, X1)\n",
    "mod1_res = mod1.fit()\n",
    "mod1_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can continue this refinement until all the explanatory variables are significant at some level.  This backstep approach is the simplest method of model refinement."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Manual refinement in formula mode\n",
    "Dropping variables in formula mode is a simple matter of removing them from the R-like formula.  Here I have removed \"FineAgg\":"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Strength</td>     <th>  R-squared:         </th> <td>   0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   142.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>4.73e-49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:12:11</td>     <th>  Log-Likelihood:    </th> <td> -242.38</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   103</td>      <th>  AIC:               </th> <td>   502.8</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    94</td>      <th>  BIC:               </th> <td>   526.5</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>         <td>   36.4097</td> <td>    8.674</td> <td>    4.197</td> <td> 0.000</td> <td>   19.186</td> <td>   53.633</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AirEntrain[T.Yes]</th> <td>   -6.0707</td> <td>    0.555</td> <td>  -10.946</td> <td> 0.000</td> <td>   -7.172</td> <td>   -4.970</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No</th>                <td>   -0.0178</td> <td>    0.011</td> <td>   -1.674</td> <td> 0.097</td> <td>   -0.039</td> <td>    0.003</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cement</th>            <td>    0.0978</td> <td>    0.005</td> <td>   18.070</td> <td> 0.000</td> <td>    0.087</td> <td>    0.109</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Slag</th>              <td>    0.0180</td> <td>    0.006</td> <td>    2.819</td> <td> 0.006</td> <td>    0.005</td> <td>    0.031</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Water</th>             <td>   -0.1330</td> <td>    0.019</td> <td>   -7.131</td> <td> 0.000</td> <td>   -0.170</td> <td>   -0.096</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CoarseAgg</th>         <td>   -0.0141</td> <td>    0.005</td> <td>   -2.964</td> <td> 0.004</td> <td>   -0.023</td> <td>   -0.005</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FlyAsh</th>            <td>    0.0887</td> <td>    0.005</td> <td>   17.367</td> <td> 0.000</td> <td>    0.079</td> <td>    0.099</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>                <td>    0.1950</td> <td>    0.109</td> <td>    1.791</td> <td> 0.077</td> <td>   -0.021</td> <td>    0.411</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.255</td> <th>  Durbin-Watson:     </th> <td>   1.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.119</td> <th>  Jarque-Bera (JB):  </th> <td>   3.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.352</td> <th>  Prob(JB):          </th> <td>   0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.601</td> <th>  Cond. No.          </th> <td>3.15e+04</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 3.15e+04. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Strength   R-squared:                       0.924\n",
       "Model:                            OLS   Adj. R-squared:                  0.917\n",
       "Method:                 Least Squares   F-statistic:                     142.2\n",
       "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           4.73e-49\n",
       "Time:                        07:12:11   Log-Likelihood:                -242.38\n",
       "No. Observations:                 103   AIC:                             502.8\n",
       "Df Residuals:                      94   BIC:                             526.5\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "Intercept            36.4097      8.674      4.197      0.000      19.186      53.633\n",
       "AirEntrain[T.Yes]    -6.0707      0.555    -10.946      0.000      -7.172      -4.970\n",
       "No                   -0.0178      0.011     -1.674      0.097      -0.039       0.003\n",
       "Cement                0.0978      0.005     18.070      0.000       0.087       0.109\n",
       "Slag                  0.0180      0.006      2.819      0.006       0.005       0.031\n",
       "Water                -0.1330      0.019     -7.131      0.000      -0.170      -0.096\n",
       "CoarseAgg            -0.0141      0.005     -2.964      0.004      -0.023      -0.005\n",
       "FlyAsh                0.0887      0.005     17.367      0.000       0.079       0.099\n",
       "SP                    0.1950      0.109      1.791      0.077      -0.021       0.411\n",
       "==============================================================================\n",
       "Omnibus:                        4.255   Durbin-Watson:                   1.637\n",
       "Prob(Omnibus):                  0.119   Jarque-Bera (JB):                3.680\n",
       "Skew:                           0.352   Prob(JB):                        0.159\n",
       "Kurtosis:                       3.601   Cond. No.                     3.15e+04\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 3.15e+04. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod1f =  smf.ols(' Strength ~ No + Cement + Slag + Water + CoarseAgg + FlyAsh + SP + AirEntrain', data=con)\n",
    "mod1f_res = mod1f.fit()\n",
    "mod1f_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Automated stepwise refinement\n",
    "What we are attempting to replicate in automated stepwise refinement is the \"model selection method\" feature of SAS Enterprise Guide.  Recall that SAS provides several algorithms for determining which explanatory variables to include in the final regression model and which to exclude.\n",
    "\n",
    "Unfortunately for us, model refinement is conspicuously absent from the popular Python libraries.  My own theory is this:\n",
    "1. Hardcore statisticians do not believe in iterative model refinement.  Instead, they believe model selection should be guided by a solid understanding of the underlying domain.  In this view, a variable should only be included in a model if there is a _reason_ to include it (if a plausible story can be told about _why_ it should be in the model).\n",
    "2. The machine learning community is understandably more sympathetic to the \"throw everything into the model and let the algorithm sort it out\" approach.  However, this community has no special affinity for ordinary least squares regression&mdash;there are so many other techniques out there that are deemed better/more stable.\n",
    "\n",
    "The bottom line (in my theory) is that neither the hardcore statistics community nor the machine learning community see iterative refinement of OLS models as worthwhile for real-word modeling.  Accordingly, you should adopt one of the two perspectives above:\n",
    "1. Use manual model refinement guided by domain knowledge to create a linear regression model that makes sense\n",
    "2. Build on your new foundation of Python to learn more sophisticated machine learning techniques and forget about stepwise refinement of linear regression.\n",
    "\n",
    "Given this, I have moved the section on stepwise refinement to the end of the lesson."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression diagnostics\n",
    "As we did in the lesson on [simple regression](regression.html), we can generate our favorite diagnostic plots to determine whether the resulting regression model is valid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\tutorials\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\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": [
    "from scipy import stats\n",
    "sns.distplot(mod1_res.resid, fit=stats.norm);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\tutorials\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\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": [
    "sns.boxplot(mod1_res.resid, showmeans=True);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\tutorials\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\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": [
    "sm.qqplot(mod1_res.resid, line='s');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\tutorials\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\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": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "Y_max = Y.max()\n",
    "Y_min = Y.min()\n",
    "\n",
    "ax = sns.scatterplot(mod1_res.fittedvalues, Y)\n",
    "ax.set(ylim=(Y_min, Y_max))\n",
    "ax.set(xlim=(Y_min, Y_max))\n",
    "ax.set_xlabel(\"Predicted value of Strength\")\n",
    "ax.set_ylabel(\"Observed value of Strength\")\n",
    "\n",
    "X_ref = Y_ref = np.linspace(Y_min, Y_max, 100)\n",
    "plt.plot(X_ref, Y_ref, color='red', linewidth=1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standardized regression coefficients\n",
    "As discussed in the [R tutorials](http://www.sfu.ca/~mjbrydon/tutorials/BAinR/multiregression.html#standardized-regression-coefficients), standardized regression coefficients provide an easy way to estimate effect size that is independent of units.\n",
    "\n",
    "Although extracting standardized coefficients is farily easy in R, we have to be a bit more explicit in Python:\n",
    "1. Transform the _Y_ and each column of the _X_ matrices into standardize values (_z_-scores) with mean = 0 and standard deviation = 1.0.\n",
    "2. Run the regression with the standardized inputs.  This provides standardized regression coefficients\n",
    "3. Extract and display the standardized coefficient\n",
    "\n",
    "### Creating standardized input matrices\n",
    "Here, I use the `zscore()` method from Scipy.  The only trick is that `zscore()` returns an array and I prefer to work with Pandas data frames (or series, for single-column data frames).  To get around this, I wrap the `zscore()` call inside the `Series()` constructor.  I pass the constructor the name of the original _Y_ series to keep everything the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.233336\n",
       "1   -0.061669\n",
       "2    0.283264\n",
       "Name: Strength, dtype: float64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "Y_norm = pd.Series(stats.zscore(Y), name=Y.name)\n",
    "Y_norm.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The _X_ matrix is a bit trickier because the first column (the \"const\" column we created above) has zero variance&mdash;recall that it is just a column of 1s.  The definition of _z_-score is $$z = \\frac{x - \\bar{x}}{S}$$.  If there is no variance, the _z_-score is undefined and everything breaks.  To get around this, I do the following:\n",
    "1. I create a new data frame called \"X1_norm by using the Pandas `loc[]` function to select just a subset of columns. In the first line, I select all rows (:) and all columns where the column name is not equal to \"const.\n",
    "2. I apply the `zscore()` method to the entire \"X1_norm\" data frame.\n",
    "3. Since I stripped the constant in the first line, I add it back by recalling the `add_constant()` method\n",
    "4. I apply the column names from my original \"X1\" data frame to the new \"X1_norm\" data frame\n",
    "5. I perform a quick check to confirm the values for all explanatory variables are normalized with mean = 0 and (population) standard deviation = 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\envs\\tutorials\\lib\\site-packages\\statsmodels\\tsa\\tsatools.py:142: FutureWarning: In a future version of pandas all arguments of concat except for the argument 'objs' will be keyword-only\n",
      "  x = pd.concat(x[::order], 1)\n"
     ]
    },
    {
     "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>mean</th>\n",
       "      <th>std dev</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>const</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>No</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Cement</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Slag</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FlyAsh</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Water</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SP</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CoarseAgg</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AirEntrain_Yes</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean  std dev\n",
       "const            1.0      0.0\n",
       "No              -0.0      1.0\n",
       "Cement           0.0      1.0\n",
       "Slag             0.0      1.0\n",
       "FlyAsh          -0.0      1.0\n",
       "Water           -0.0      1.0\n",
       "SP               0.0      1.0\n",
       "CoarseAgg        0.0      1.0\n",
       "AirEntrain_Yes  -0.0      1.0"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X1_norm = X1.loc[:, X1.columns != \"const\"]\n",
    "X1_norm = pd.DataFrame(stats.zscore(X1_norm))\n",
    "X1_norm = sm.add_constant(X1_norm)\n",
    "X1_norm.columns = X1.columns\n",
    "check = pd.concat([round(X1_norm.mean(axis=0), 5), round(X1_norm.std(axis=0, ddof=0), 5)], axis=1)\n",
    "check.columns=[\"mean\", \"std dev\"]\n",
    "check"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the standardized regression\n",
    "Once the standardized input matrices are in place, running a standardized regression is no different from running any other regression.  The difference is that we know the coefficients are now expressed  in terms of the number of standard deviations rather than kilograms, megapascals, and so on."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>Strength</td>     <th>  R-squared:         </th> <td>   0.924</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.917</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   142.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 12 Nov 2021</td> <th>  Prob (F-statistic):</th> <td>4.73e-49</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>07:12:12</td>     <th>  Log-Likelihood:    </th> <td> -13.650</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   103</td>      <th>  AIC:               </th> <td>   45.30</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    94</td>      <th>  BIC:               </th> <td>   69.01</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     8</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>           <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>          <td> 1.665e-16</td> <td>    0.028</td> <td> 5.84e-15</td> <td> 1.000</td> <td>   -0.057</td> <td>    0.057</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No</th>             <td>   -0.0575</td> <td>    0.034</td> <td>   -1.674</td> <td> 0.097</td> <td>   -0.126</td> <td>    0.011</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Cement</th>         <td>    0.8336</td> <td>    0.046</td> <td>   18.070</td> <td> 0.000</td> <td>    0.742</td> <td>    0.925</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Slag</th>           <td>    0.1175</td> <td>    0.042</td> <td>    2.819</td> <td> 0.006</td> <td>    0.035</td> <td>    0.200</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>FlyAsh</th>         <td>    0.8180</td> <td>    0.047</td> <td>   17.367</td> <td> 0.000</td> <td>    0.724</td> <td>    0.911</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Water</th>          <td>   -0.2903</td> <td>    0.041</td> <td>   -7.131</td> <td> 0.000</td> <td>   -0.371</td> <td>   -0.209</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>SP</th>             <td>    0.0591</td> <td>    0.033</td> <td>    1.791</td> <td> 0.077</td> <td>   -0.006</td> <td>    0.125</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CoarseAgg</th>      <td>   -0.1342</td> <td>    0.045</td> <td>   -2.964</td> <td> 0.004</td> <td>   -0.224</td> <td>   -0.044</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>AirEntrain_Yes</th> <td>   -0.3282</td> <td>    0.030</td> <td>  -10.946</td> <td> 0.000</td> <td>   -0.388</td> <td>   -0.269</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 4.255</td> <th>  Durbin-Watson:     </th> <td>   1.637</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.119</td> <th>  Jarque-Bera (JB):  </th> <td>   3.680</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.352</td> <th>  Prob(JB):          </th> <td>   0.159</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 3.601</td> <th>  Cond. No.          </th> <td>    4.11</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:               Strength   R-squared:                       0.924\n",
       "Model:                            OLS   Adj. R-squared:                  0.917\n",
       "Method:                 Least Squares   F-statistic:                     142.2\n",
       "Date:                Fri, 12 Nov 2021   Prob (F-statistic):           4.73e-49\n",
       "Time:                        07:12:12   Log-Likelihood:                -13.650\n",
       "No. Observations:                 103   AIC:                             45.30\n",
       "Df Residuals:                      94   BIC:                             69.01\n",
       "Df Model:                           8                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================\n",
       "                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "----------------------------------------------------------------------------------\n",
       "const           1.665e-16      0.028   5.84e-15      1.000      -0.057       0.057\n",
       "No                -0.0575      0.034     -1.674      0.097      -0.126       0.011\n",
       "Cement             0.8336      0.046     18.070      0.000       0.742       0.925\n",
       "Slag               0.1175      0.042      2.819      0.006       0.035       0.200\n",
       "FlyAsh             0.8180      0.047     17.367      0.000       0.724       0.911\n",
       "Water             -0.2903      0.041     -7.131      0.000      -0.371      -0.209\n",
       "SP                 0.0591      0.033      1.791      0.077      -0.006       0.125\n",
       "CoarseAgg         -0.1342      0.045     -2.964      0.004      -0.224      -0.044\n",
       "AirEntrain_Yes    -0.3282      0.030    -10.946      0.000      -0.388      -0.269\n",
       "==============================================================================\n",
       "Omnibus:                        4.255   Durbin-Watson:                   1.637\n",
       "Prob(Omnibus):                  0.119   Jarque-Bera (JB):                3.680\n",
       "Skew:                           0.352   Prob(JB):                        0.159\n",
       "Kurtosis:                       3.601   Cond. No.                         4.11\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modstd = sm.OLS(Y_norm, X1_norm)\n",
    "modstd_res = modstd.fit()\n",
    "modstd_res.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tornado diagram\n",
    "Once we have the regression results, we can extract the coefficients using the `params` property and graph the standardized coefficients.  The only trick to getting a tornado diagram is that the coefficients have to be sorted in descending order by the _absolute value_ of the coefficient.  I resort to a bit of Python trickery to get the items in the desired order.  As before, we see that \"Cement\" and \"FlyAsh\" are the most important drivers of concrete strength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DNY7dc0C7lax6P+sV27sWy2cCvUWz14FP2H5d0vZU3ut6epDaIiKiQfI8q4o/l/T3VL4eTwPHF9s3BX4maUMq71/9le3ft6bEiIjOlbACbF8HXFdj+ytAfq8qIqLF8tmAERFRegmriIgovUwDRsOcdP5HWl1CRLSJjKwiIqL0ElYREVF6CauIiCi9hFVERJRebrCIjnDOJ6a37Npnf+P6ll07ol1kZBUREaWXsIqIiNJLWEVEROklrCIiovQSVhERUXodE1aSzpa0VNJiSQsl7SFprqR8qnpERMl1xK3rkqYBhwBTbC+XNA7YqMVlRUREnTplZDUeWGZ7OVSeRlw8vfh/SfqKpN5i9PVPVdsPlvSIpDslXSTpB02uPSKi43VKWN0CbCvpMUmXStqvRpuzbfcAuwL7SdpV0sbAV4EP294H6BrsApJmFmHX29fX15BORER0qo4IK9u/A6YCM4E+4DpJxw9o9nFJ9wMPAO8FJgETgSdtP1W0+dYarjHbdo/tnq6uQTMtIiLWQke8ZwVgeyUwF5graQlwXP8+Se8CTgN2t/2ipCuBjQG1oNSIiBigI0ZWknaUtEPVpsnA01XrWwCvAi9J2hr4cLH9EeDdkrqL9SMbXGpERNTQKSOrMcDFksYCK4DHqUwJXg9ge5GkB4ClwJPAXcX21yR9FrhZ0jLg5y2oPSKi43VEWNleAOxVY9f+VW2OH+Twn9meKEnAl4HeES8wIiLWqCOmAdfRpyUtpDLq2pLK3YEREdFEHTGyWhe2LwAuaHUdERGdLCOriIgovYRVRESUXqYBoyPk0fIRo1tGVhERUXoJq4iIKL2EVURElF7CKiIiSi83WERHePic29bp+J3OPnCEKomItZGRVURElF7CKiIiSi9hFRERpZewioiI0ktYRURE6SWsqkg6W9JSSYslLZS0h6S5kh6VtEjSXZJ2bHWdERGdJreuFyRNAw4BptheLmkcsFGxe4btXkkzgfOAQ1tVZ0REJ8rIapXxwDLbywFsL7P97IA284A/bnplEREdLmG1yi3AtpIek3SppP1qtPkIsKTWwZJmSuqV1NvX19fQQiMiOk3CqmD7d8BUYCbQB1wn6fhi95zi0fZ7A6cNcvxs2z22e7q6uppQcURE58h7VlVsrwTmAnMlLQGOK3bNsN3bssIiIjpcRlYFSTtK2qFq02Tg6RaVExERVTKyWmUMcLGkscAK4HEqU4J5xGxERIslrAq2FwB71di1f5NLiYiIATINGBERpZewioiI0ktYRURE6SWsIiKi9HKDRXSEPJY+YnTLyCoiIkovYRUREaWXsIqIiNJLWEVEROnlBosYNWbNmtWSYyOi9TKyioiI0ktYRURE6SWsIiKi9BJWERFRegmriIgovYRVQZIlnV+1fpqkWS0sKSIiCgmrVZYDfyFpXKsLiYiIP5SwWmUFMBv4m4E7JG0n6VZJi4s/JzS/vIiIzpWw+kNfBmZI2nLA9kuAq23vCswBLmp6ZRERHSxhVcX2y8DVwMkDdk0DvlksXwPsM/BYSTMl9Urq7evra2yhEREdJmG1uguBE4HN1tDGq22wZ9vusd3T1dXVqNoiIjpSwmoA2y8A36YSWP3mA0cVyzOAO5tdV0REJ0tY1XY+UH1X4MnACZIWA58ETmlJVRERHSqful6wPaZq+Xlg06r1XwJ5LnpERItkZBUREaWXsIqIiNJLWEVEROklrCIiovRyg0WMGnk0fUTnysgqIiJKL2EVERGll7CKiIjSS1hFRETpJawiIqL0ElYREVF6CauIiCi9hFVERJRewioiIkovYRUREaWXsIqIiNJLWA2DpLNaXUNERCdKWA1PwioiogXaMqwkHStpsaRFkq6RtJ2kW4ttt0qaULS7UtJFkuZLelLS9GL7eEnzJC2U9KCkD0g6F9ik2DanpR2MiOgwbfeIEEnvBc4G9ra9TNIfAVcBV9u+StKngIuAw4tDxgP7ABOBm4DrgWOAH9s+R9L6wKa275B0ku3Jg1x3JjATYMKECQ3rX0REJ2rHkdWBwPW2lwHYfgGYBnyz2H8NlXDq9z3bb9p+CNi62HYfcIKkWcAutl8Z6qK2Z9vusd3T1dU1Ql2JiAhoz7AS4CHaVO9fPuBYbM8D9gWeAa6RdOyIVhgREcPSjmF1K/BxSW8FKKYB5wNHFftnAHeu6QSStgN+a/trwH8AU4pdb0jasCFVR0TEoNruPSvbSyWdA9wuaSXwAHAycIWk04E+4IQhTrM/cLqkN4DfAf0jq9nAYkn3257RkA5ERMRqZA81YxbD1dPT497e3laXERExqkhaYLun1r52nAaMiIg2k7CKiIjSS1hFRETpJawiIqL0ElYREVF6CauIiCi93LreAJL6gKdbXcc6Ggcsa3URI6jd+gPt16f0p/wa3aftbNf8vLqEVdQkqXew33cYjdqtP9B+fUp/yq+Vfco0YERElF7CKiIiSi9hFYOZ3eoCRli79Qfar0/pT/m1rE95zyoiIkovI6uIiCi9hFVERJRewiqAykMqJf1E0i+KP7eq0WZbST+T9LCkpZJOaUWtayLpQ5IelfS4pDNr7Jeki4r9iyVNqXWesqijPzOKfiyWNF/Sbq2ocziG6lNVu90lrZQ0vZn1DVc9/ZG0v6SFxb+b25td43DU8T23paTvS1pU9Geo5wOODNt55QXwb8CZxfKZwBdrtBkPTCmWNwceAya1uvaq+tYHngDeDWwELBpYH3Aw8CNAwJ7Ava2uex37sxewVbH84TL3p94+VbW7DfghML3Vda/j39FY4CFgQrH+tlbXvY79Oav//wegC3gB2KjRtWVkFf0OA64qlq8CDh/YwPZztu8vll8BHga2aVaBdXg/8LjtJ23/HriWSr+qHQZc7Yp7gLGSxje70DoN2R/b822/WKzeA7yzyTUOVz1/RwCfA24AftvM4tZCPf05BrjR9q8AbJe5T/X0x8DmkgSMoRJWKxpdWMIq+m1t+zmohBLwtjU1ltQNvA+4t/Gl1W0b4NdV679h9TCtp01ZDLfWE6mMGstsyD5J2gY4ArisiXWtrXr+jt4DbCVprqQFko5tWnXDV09/LgF2Ap4FlgCn2H6z0YVt0OgLRHlI+inw9hq7zh7mecZQ+an3VNsvj0RtI0Q1tg383Yx62pRF3bVKOoBKWO3T0IrWXT19uhD4O9srKz+8l1o9/dkAmAr8CbAJcLeke2w/1uji1kI9/fkzYCFwILA98BNJdzT6/4KEVQexfdBg+yQ9L2m87eeKabGaUxWSNqQSVHNs39igUtfWb4Btq9bfSeWnv+G2KYu6apW0K3A58GHb/69Jta2tevrUA1xbBNU44GBJK2x/rykVDk+933PLbL8KvCppHrAblfd8y6ae/pwAnOvKm1aPS3oKmAj8vJGFZRow+t0EHFcsHwf858AGxRz1fwAP2/5SE2ur133ADpLeJWkj4Cgq/ap2E3BscVfgnsBL/dOfJTRkfyRNAG4EPlnSn9QHGrJPtt9lu9t2N3A98NmSBhXU9z33n8AHJG0gaVNgDyrv95ZRPf35FZVRIpK2BnYEnmx0YRlZRb9zgW9LOpHKN+PHACS9A7jc9sHA3sAngSWSFhbHnWX7hy2odzW2V0g6CfgxlbuarrC9VNJniv2XUbm77GDgceD/U/kpsZTq7M8/Am8FLi1GIitc4k/6rrNPo0Y9/bH9sKSbgcXAm1T+PT3YuqoHV+ffz78AV0paQmXa8O9sN/xRKPm4pYiIKL1MA0ZEROklrCIiovQSVhERUXoJq4iIKL2EVURElF7CKiIiSi9hFRERpfc/ST7SgHumTscAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "coeff = modstd_res.params\n",
    "coeff = coeff.iloc[(coeff.abs()*-1.0).argsort()]\n",
    "sns.barplot(x=coeff.values, y=coeff.index, orient='h');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stepwise refinement: an aside\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is certainly possible to code your own stepwise refinement procedure in Python: if you Google hard enough, you will find lots of these.  Finding an easy-to-use library version of R's `step()` function is more challenging.  The one I did find belongs to the MLxtend library. It is not a standard install you might have to download it before you can run the `import` commands.  Installation instructions are [here](http://rasbt.github.io/mlxtend/installation/) if you really must try it out.\n",
    "\n",
    "### Create a sklearn kitchen sink model\n",
    "The MLxtend library wraps around the sklearn `LinearRegression` function rather than the Statsmodels version we have been using so far.  Thus, we have to create a new linear regression object.  Unlike Statsmodel, sklearn's version does not require a column of 1s for the constant.  As such, have to create a new explanatory variable matrix without the \"const\" column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>No</th>\n",
       "      <th>Cement</th>\n",
       "      <th>Slag</th>\n",
       "      <th>FlyAsh</th>\n",
       "      <th>Water</th>\n",
       "      <th>SP</th>\n",
       "      <th>CoarseAgg</th>\n",
       "      <th>FineAgg</th>\n",
       "      <th>AirEntrain_Yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>273.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>904.0</td>\n",
       "      <td>680.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>163.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>843.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>162.0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>191.0</td>\n",
       "      <td>179.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>840.0</td>\n",
       "      <td>743.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No  Cement   Slag  FlyAsh  Water    SP  CoarseAgg  FineAgg  AirEntrain_Yes\n",
       "0   1   273.0   82.0   105.0  210.0   9.0      904.0    680.0               0\n",
       "1   2   163.0  149.0   191.0  180.0  12.0      843.0    746.0               1\n",
       "2   3   162.0  148.0   191.0  179.0  16.0      840.0    743.0               1"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X2 = fullX\n",
    "X2.drop(\"const\", axis=1, inplace=True)\n",
    "X2.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we load the required libraries.  I had to install the mlxtend package first.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from mlxtend.feature_selection import SequentialFeatureSelector as SFS\n",
    "from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now create an sklearn regression model and confirm it works"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mod = LinearRegression()\n",
    "mod.fit(X2,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting model is identical to our kitchen sink model from above.  We can verify by manually patching together a table of results (Sklearn's regression routine does not have the tidy summary provided by the Statsmodels version)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>variable</th>\n",
       "      <th>coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>No</td>\n",
       "      <td>-0.017346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Cement</td>\n",
       "      <td>0.096195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Slag</td>\n",
       "      <td>0.015681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FlyAsh</td>\n",
       "      <td>0.086950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Water</td>\n",
       "      <td>-0.138011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SP</td>\n",
       "      <td>0.190158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CoarseAgg</td>\n",
       "      <td>-0.016038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>FineAgg</td>\n",
       "      <td>-0.002053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>AirEntrain_Yes</td>\n",
       "      <td>-6.068252</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         variable  coefficient\n",
       "0              No    -0.017346\n",
       "1          Cement     0.096195\n",
       "2            Slag     0.015681\n",
       "3          FlyAsh     0.086950\n",
       "4           Water    -0.138011\n",
       "5              SP     0.190158\n",
       "6       CoarseAgg    -0.016038\n",
       "7         FineAgg    -0.002053\n",
       "8  AirEntrain_Yes    -6.068252"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coef = pd.concat([pd.Series(X2.columns), pd.Series(mod.coef_)], axis=1)\n",
    "coef.columns=[\"variable\", \"coefficient\"]\n",
    "coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the refinement algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The method we are using from the mlxtend library is called \"sequential feature selector\" (SFS).  We set up an SFS object and pass it a link to our regression model \"mod\" and some other parameters.  Running `fit()` creates a sequence of model versions created using standard backstep refinement (other approaches are possible by setting the `forward` and `floating` parameters.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "sfs = SFS(mod,\n",
    "         k_features='best',\n",
    "         forward=False,\n",
    "         floating=False,\n",
    "         scoring='r2',\n",
    "         cv=30)\n",
    "\n",
    "sfs = sfs.fit(X2,Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing the iterations\n",
    "We can wrap the output from the SFS object in a Pandas data frame and print the result.  Note that the data frame is sorted by the number of variables in the model, not the backstep sequence.  The model with eight variables has the highest \"avg_score\" and lowest \"std_err\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>feature_idx</th>\n",
       "      <th>cv_scores</th>\n",
       "      <th>avg_score</th>\n",
       "      <th>feature_names</th>\n",
       "      <th>ci_bound</th>\n",
       "      <th>std_dev</th>\n",
       "      <th>std_err</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(0, 1, 2, 3, 4, 5, 6, 7, 8)</td>\n",
       "      <td>[0.5998230505243064, 0.8609825359671671, 0.776...</td>\n",
       "      <td>0.482171</td>\n",
       "      <td>(No, Cement, Slag, FlyAsh, Water, SP, CoarseAg...</td>\n",
       "      <td>0.249123</td>\n",
       "      <td>0.656901</td>\n",
       "      <td>0.121983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(0, 1, 2, 3, 5, 6, 7, 8)</td>\n",
       "      <td>[0.7198251274370655, 0.8714981991943249, 0.754...</td>\n",
       "      <td>0.538054</td>\n",
       "      <td>(No, Cement, Slag, FlyAsh, SP, CoarseAgg, Fine...</td>\n",
       "      <td>0.174763</td>\n",
       "      <td>0.460825</td>\n",
       "      <td>0.085573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(1, 2, 3, 5, 6, 7, 8)</td>\n",
       "      <td>[0.36909818696153174, 0.8029635284896339, 0.88...</td>\n",
       "      <td>0.489133</td>\n",
       "      <td>(Cement, Slag, FlyAsh, SP, CoarseAgg, FineAgg,...</td>\n",
       "      <td>0.193566</td>\n",
       "      <td>0.510403</td>\n",
       "      <td>0.09478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(1, 2, 3, 6, 7, 8)</td>\n",
       "      <td>[-0.8836565141299193, 0.706855501290562, 0.793...</td>\n",
       "      <td>0.383902</td>\n",
       "      <td>(Cement, Slag, FlyAsh, CoarseAgg, FineAgg, Air...</td>\n",
       "      <td>0.234757</td>\n",
       "      <td>0.619018</td>\n",
       "      <td>0.114949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(1, 2, 3, 7, 8)</td>\n",
       "      <td>[-1.069902143989971, 0.7714511447380333, 0.721...</td>\n",
       "      <td>0.203047</td>\n",
       "      <td>(Cement, Slag, FlyAsh, FineAgg, AirEntrain_Yes)</td>\n",
       "      <td>0.312601</td>\n",
       "      <td>0.824281</td>\n",
       "      <td>0.153065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(1, 2, 3, 8)</td>\n",
       "      <td>[-1.4776700426094087, 0.7791381638911108, 0.97...</td>\n",
       "      <td>0.1767</td>\n",
       "      <td>(Cement, Slag, FlyAsh, AirEntrain_Yes)</td>\n",
       "      <td>0.364705</td>\n",
       "      <td>0.961673</td>\n",
       "      <td>0.178578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(1, 3, 8)</td>\n",
       "      <td>[-2.8515869804653056, 0.7005985979007634, 0.85...</td>\n",
       "      <td>-0.118087</td>\n",
       "      <td>(Cement, FlyAsh, AirEntrain_Yes)</td>\n",
       "      <td>0.610482</td>\n",
       "      <td>1.609749</td>\n",
       "      <td>0.298923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(1, 3)</td>\n",
       "      <td>[-3.062125555844749, 0.2731746363441382, 0.902...</td>\n",
       "      <td>-0.732105</td>\n",
       "      <td>(Cement, FlyAsh)</td>\n",
       "      <td>0.797207</td>\n",
       "      <td>2.102115</td>\n",
       "      <td>0.390353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(1,)</td>\n",
       "      <td>[-3.449038765590842, 0.011736536060135583, 0.5...</td>\n",
       "      <td>-3.406733</td>\n",
       "      <td>(Cement,)</td>\n",
       "      <td>2.042631</td>\n",
       "      <td>5.386109</td>\n",
       "      <td>1.000175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   feature_idx  \\\n",
       "9  (0, 1, 2, 3, 4, 5, 6, 7, 8)   \n",
       "8     (0, 1, 2, 3, 5, 6, 7, 8)   \n",
       "7        (1, 2, 3, 5, 6, 7, 8)   \n",
       "6           (1, 2, 3, 6, 7, 8)   \n",
       "5              (1, 2, 3, 7, 8)   \n",
       "4                 (1, 2, 3, 8)   \n",
       "3                    (1, 3, 8)   \n",
       "2                       (1, 3)   \n",
       "1                         (1,)   \n",
       "\n",
       "                                           cv_scores avg_score  \\\n",
       "9  [0.5998230505243064, 0.8609825359671671, 0.776...  0.482171   \n",
       "8  [0.7198251274370655, 0.8714981991943249, 0.754...  0.538054   \n",
       "7  [0.36909818696153174, 0.8029635284896339, 0.88...  0.489133   \n",
       "6  [-0.8836565141299193, 0.706855501290562, 0.793...  0.383902   \n",
       "5  [-1.069902143989971, 0.7714511447380333, 0.721...  0.203047   \n",
       "4  [-1.4776700426094087, 0.7791381638911108, 0.97...    0.1767   \n",
       "3  [-2.8515869804653056, 0.7005985979007634, 0.85... -0.118087   \n",
       "2  [-3.062125555844749, 0.2731746363441382, 0.902... -0.732105   \n",
       "1  [-3.449038765590842, 0.011736536060135583, 0.5... -3.406733   \n",
       "\n",
       "                                       feature_names  ci_bound   std_dev  \\\n",
       "9  (No, Cement, Slag, FlyAsh, Water, SP, CoarseAg...  0.249123  0.656901   \n",
       "8  (No, Cement, Slag, FlyAsh, SP, CoarseAgg, Fine...  0.174763  0.460825   \n",
       "7  (Cement, Slag, FlyAsh, SP, CoarseAgg, FineAgg,...  0.193566  0.510403   \n",
       "6  (Cement, Slag, FlyAsh, CoarseAgg, FineAgg, Air...  0.234757  0.619018   \n",
       "5    (Cement, Slag, FlyAsh, FineAgg, AirEntrain_Yes)  0.312601  0.824281   \n",
       "4             (Cement, Slag, FlyAsh, AirEntrain_Yes)  0.364705  0.961673   \n",
       "3                   (Cement, FlyAsh, AirEntrain_Yes)  0.610482  1.609749   \n",
       "2                                   (Cement, FlyAsh)  0.797207  2.102115   \n",
       "1                                          (Cement,)  2.042631  5.386109   \n",
       "\n",
       "    std_err  \n",
       "9  0.121983  \n",
       "8  0.085573  \n",
       "7   0.09478  \n",
       "6  0.114949  \n",
       "5  0.153065  \n",
       "4  0.178578  \n",
       "3  0.298923  \n",
       "2  0.390353  \n",
       "1  1.000175  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(sfs.get_metric_dict(confidence_interval=0.95)).T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Picking the best model visually\n",
    "MLxtend includes a handy plot for visualizing the best model. As in SAS Enterprise Guide, we are looking for the model with the highest score.  Again, it appears the model with 8 variables is slightly better than some of the other models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "plot_sfs(sfs.get_metric_dict(), kind='std_err')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Viewing the best model\n",
    "We can access the model with 8 variables using 8 as an index (as in the data frame presentation above). We can further narrow down on variable names in the best model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('No',\n",
       " 'Cement',\n",
       " 'Slag',\n",
       " 'FlyAsh',\n",
       " 'SP',\n",
       " 'CoarseAgg',\n",
       " 'FineAgg',\n",
       " 'AirEntrain_Yes')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sfs.subsets_[8].get('feature_names')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interestingly, this backstep algorithm dropped the variable \"Water\" whereas we dropped \"FineAgg\" in the manual approach.  R's `step()` function also dropped \"FineAgg\" first.  Divergent results such as this are part of the reason some in the machine learning community do not like stepwise refinement of linear regression models.  However, they remain interesting for teaching purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}