ch14-code-listing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chapter 14: Statistical modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Robert Johansson\n",
    "\n",
    "Source code listings for [Numerical Python - Scientific Computing and Data Science Applications with Numpy, SciPy and Matplotlib](https://www.apress.com/us/book/9781484242452) (ISBN 978-1-484242-45-2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.graphics.api as smg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical models and patsy formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(123456789)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x1 = np.array([6, 7, 8, 9, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x2 = np.array([11, 12, 13, 14, 15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = np.vstack([np.ones(5), x1, x2, x1*x2]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   6.,  11.,  66.],\n",
       "       [  1.,   7.,  12.,  84.],\n",
       "       [  1.,   8.,  13., 104.],\n",
       "       [  1.,   9.,  14., 126.],\n",
       "       [  1.,  10.,  15., 150.]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "beta, res, rank, sval = np.linalg.lstsq(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.55555556e-01,  1.88888889e+00, -8.88888889e-01, -8.88178420e-16])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {\"y\": y, \"x1\": x1, \"x2\": x2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y, X = patsy.dmatrices(\"y ~ 1 + x1 + x2 + x1*x2\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 1)\n",
       "  y\n",
       "  1\n",
       "  2\n",
       "  3\n",
       "  4\n",
       "  5\n",
       "  Terms:\n",
       "    'y' (column 0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DesignMatrix with shape (5, 4)\n",
       "  Intercept  x1  x2  x1:x2\n",
       "          1   6  11     66\n",
       "          1   7  12     84\n",
       "          1   8  13    104\n",
       "          1   9  14    126\n",
       "          1  10  15    150\n",
       "  Terms:\n",
       "    'Intercept' (column 0)\n",
       "    'x1' (column 1)\n",
       "    'x2' (column 2)\n",
       "    'x1:x2' (column 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patsy.design_info.DesignMatrix"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   6.,  11.,  66.],\n",
       "       [  1.,   7.,  12.,  84.],\n",
       "       [  1.,   8.,  13., 104.],\n",
       "       [  1.,   9.,  14., 126.],\n",
       "       [  1.,  10.,  15., 150.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y, X = patsy.dmatrices(\"y ~ 1 + x1 + x2 + x1:x2\", df_data, return_type=\"dataframe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Intercept</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x1:x2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>66.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>104.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>126.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Intercept    x1    x2  x1:x2\n",
       "0        1.0   6.0  11.0   66.0\n",
       "1        1.0   7.0  12.0   84.0\n",
       "2        1.0   8.0  13.0  104.0\n",
       "3        1.0   9.0  14.0  126.0\n",
       "4        1.0  10.0  15.0  150.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -5.555556e-01\n",
       "x1           1.888889e+00\n",
       "x2          -8.888889e-01\n",
       "x1:x2       -6.661338e-16\n",
       "dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"y ~ 1 + x1 + x2 + x1:x2\", df_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -5.555556e-01\n",
       "x1           1.888889e+00\n",
       "x2          -8.888889e-01\n",
       "x1:x2       -6.661338e-16\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       1.000\n",
      "Model:                            OLS   Adj. R-squared:                  1.000\n",
      "Method:                 Least Squares   F-statistic:                 2.807e+27\n",
      "Date:                Mon, 06 May 2019   Prob (F-statistic):           3.56e-28\n",
      "Time:                        15:42:33   Log-Likelihood:                 149.18\n",
      "No. Observations:                   5   AIC:                            -292.4\n",
      "Df Residuals:                       2   BIC:                            -293.5\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept     -0.5556   9.62e-14  -5.77e+12      0.000      -0.556      -0.556\n",
      "x1             1.8889   3.59e-13   5.26e+12      0.000       1.889       1.889\n",
      "x2            -0.8889   1.22e-13  -7.27e+12      0.000      -0.889      -0.889\n",
      "x1:x2      -6.661e-16   1.13e-14     -0.059      0.958   -4.92e-14    4.79e-14\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   0.023\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.391\n",
      "Skew:                           0.250   Prob(JB):                        0.822\n",
      "Kurtosis:                       1.724   Cond. No.                     6.86e+17\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The smallest eigenvalue is 1.31e-31. This might indicate that there are\n",
      "strong multicollinearity problems or that the design matrix is singular.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/statsmodels/stats/stattools.py:72: ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n",
      "  \"samples were given.\" % int(n), ValueWarning)\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.55555556e-01,  1.88888889e+00, -8.88888889e-01, -8.88178420e-16])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = defaultdict(lambda: np.array([1,2,3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ 1 + a + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ -1 + a + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b', 'a:b']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a * b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b', 'a:b', 'c', 'a:c', 'b:c', 'a:b:c']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a * b * c\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b', 'a:b', 'c', 'a:c', 'b:c']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a * b * c - a:b:c\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = {k: np.array([]) for k in [\"y\", \"a\", \"b\", \"c\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'I(a + b)']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ I(a + b)\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a*a\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'I(a ** 2)']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ I(a**2)\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'np.log(a)', 'b']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ np.log(a) + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = lambda x1, x2: x1+x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'z(a, b)']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ z(a, b)\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = {\"y\": [1, 2, 3], \"a\": [1, 2, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>a</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     a\n",
       "0  1.0\n",
       "1  2.0\n",
       "2  3.0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + a\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>C(a)[1]</th>\n",
       "      <th>C(a)[2]</th>\n",
       "      <th>C(a)[3]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C(a)[1]  C(a)[2]  C(a)[3]\n",
       "0      1.0      0.0      0.0\n",
       "1      0.0      1.0      0.0\n",
       "2      0.0      0.0      1.0"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + C(a)\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = {\"y\": [1, 2, 3], \"a\": [\"type A\", \"type B\", \"type C\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "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",
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       "\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>a[type A]</th>\n",
       "      <th>a[type B]</th>\n",
       "      <th>a[type C]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   a[type A]  a[type B]  a[type C]\n",
       "0        1.0        0.0        0.0\n",
       "1        0.0        1.0        0.0\n",
       "2        0.0        0.0        1.0"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + a\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C(a, Poly).Constant</th>\n",
       "      <th>C(a, Poly).Linear</th>\n",
       "      <th>C(a, Poly).Quadratic</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-7.071068e-01</td>\n",
       "      <td>0.408248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.551115e-17</td>\n",
       "      <td>-0.816497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.071068e-01</td>\n",
       "      <td>0.408248</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C(a, Poly).Constant  C(a, Poly).Linear  C(a, Poly).Quadratic\n",
       "0                  1.0      -7.071068e-01              0.408248\n",
       "1                  1.0      -5.551115e-17             -0.816497\n",
       "2                  1.0       7.071068e-01              0.408248"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + C(a, Poly)\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(123456789)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "N = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x1 = np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x2 = np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\"x1\": x1, \"x2\": x2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def y_true(x1, x2):\n",
    "    return 1  + 2 * x1 + 3 * x2 + 4 * x1 * x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data[\"y_true\"] = y_true(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "e = np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data[\"y\"] = data[\"y_true\"] + e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>y_true</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.212902</td>\n",
       "      <td>-0.474588</td>\n",
       "      <td>-0.198823</td>\n",
       "      <td>-1.452775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.128398</td>\n",
       "      <td>-1.524772</td>\n",
       "      <td>-12.298805</td>\n",
       "      <td>-12.560965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.841711</td>\n",
       "      <td>-1.939271</td>\n",
       "      <td>-15.420705</td>\n",
       "      <td>-14.715090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.082382</td>\n",
       "      <td>0.345148</td>\n",
       "      <td>2.313945</td>\n",
       "      <td>1.190283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.858964</td>\n",
       "      <td>-0.621523</td>\n",
       "      <td>-1.282107</td>\n",
       "      <td>0.307772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         x1        x2     y_true          y\n",
       "0  2.212902 -0.474588  -0.198823  -1.452775\n",
       "1  2.128398 -1.524772 -12.298805 -12.560965\n",
       "2  1.841711 -1.939271 -15.420705 -14.715090\n",
       "3  0.082382  0.345148   2.313945   1.190283\n",
       "4  0.858964 -0.621523  -1.282107   0.307772"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "axes[0].scatter(data[\"x1\"], data[\"y\"])\n",
    "axes[1].scatter(data[\"x2\"], data[\"y\"])\n",
    "\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"y ~ x1 + x2\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.380\n",
      "Model:                            OLS   Adj. R-squared:                  0.367\n",
      "Method:                 Least Squares   F-statistic:                     29.76\n",
      "Date:                Mon, 06 May 2019   Prob (F-statistic):           8.36e-11\n",
      "Time:                        15:42:50   Log-Likelihood:                -271.52\n",
      "No. Observations:                 100   AIC:                             549.0\n",
      "Df Residuals:                      97   BIC:                             556.9\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.9868      0.382      2.581      0.011       0.228       1.746\n",
      "x1             1.0810      0.391      2.766      0.007       0.305       1.857\n",
      "x2             3.0793      0.432      7.134      0.000       2.223       3.936\n",
      "==============================================================================\n",
      "Omnibus:                       19.951   Durbin-Watson:                   1.682\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               49.964\n",
      "Skew:                          -0.660   Prob(JB):                     1.41e-11\n",
      "Kurtosis:                       6.201   Cond. No.                         1.32\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3802538325513254"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    -3.370455\n",
       "1   -11.153477\n",
       "2   -11.721319\n",
       "3    -0.948410\n",
       "4     0.306215\n",
       "dtype: float64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.resid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "z, p = stats.normaltest(result.resid.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.6524990253009316e-05"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    0.986826\n",
       "x1           1.081044\n",
       "x2           3.079284\n",
       "dtype: float64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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p882S8jymAeiYSk/MwEByf955u44wiEjuP/3pzvTK7L57srRkJ/etHtNDAwCNZVmiOjgizrR9liRFxI9s1zvtG+gptdeRmZuTrr66s1f6rSxhcR0ZAGivLDM429NTxUOSbB+sZEYH6GmTk7vCTUXe4aZ6lqb6gEkOjgSA9soyg/PHkm6VdKDtGUlvlvTuPIsCOmHTps58DrM0ANB5LWdw0gM336Ek1NwgaSwi7sy3LCB/w8P1x1stwNbOwpx7bnLf6DmzNADQeQ1ncGwfXTP0RHo/bHs4Iu7Nrywgf1NTC3twpGRX0po19bd9V54zCwMA3a/ZEtX/avJaSDqhzbUAHVUJKdVbwgkvAFAOzQ7bPL6ThQCdUHuNm6mpZOkIAFAuLZuMbe8p6TxJP69k5ubrkq6OiB/nXBvQVvW2hU9MJI+ZtQGAcsmyTfw6SYdLukLSlZIOk3R9nkUBeai3LXx+PhkHAJRLloBzaES8JyK+lt4mJL0678KAlaq9SvHcXP33dWq7OACgc7IEnPtsH1t5YvuNkr6RX0nAylWWo6qPXGi0/bvRdnEAQO/KcqG/N0p6l+3Kv3OHJT1s+0FJERFH5FYdkFFt8/Czz9a/SnH16d5Ssi18aqqztQIA8pcl4JycexVARvV2QUmLm4cbqVxRmG3hAFBuLQNORMzZfpmkA6vfz4X+0GmNdkHttdfi2ZpGRkbYFg4A/SDLNvFLlRzT8O9KD9wUF/pDARrtgsoabliOAoD+kWWJ6jckHRwR2/MuBmhmqbudhoakffZhOQoA+lGWgLNB0kslfT/nWoCmhofr99cMDUk/+tHiM6Uuv5xAAwD9Kss28Y8q2Sp+m+2bK7e8C0N/q72GzcxMMgMzOLjwfZUgMz298ATv6WnCDQD0M0f1ntl6b7AfkvQZSQ9K2lkZj4i78i0tu7GxsZidnS26DLRJbTOxlASZ6enkMYdjAkD/sr0uIsZavi9DwLkrIv572yrLAQGnXBpddZgdUACArAEnyxLVOtsftX2c7aMrtzbUCNRdimrUTMyRCgCArLI0GR+V3h9bNcY2caxYo+va7LeftHXr4vdzpAIAIKssF/o7vhOFoP80uq7NXnslPTe1PThcwwYAkFWWJSrZfrvtD9i+uHLLqyDbl9j+D9vr09speX0WitVoyempp9gVBQBYmZYBx/bVks6U9D5JlnSGpJGc6/rziDgyva3N+bPQAfV6bRotOQ0PJ2Fm40Zp587knnADAFiKLDM4b4qId0n6r4j4sKTjlJxLBWRS6bWZm0sOu6z02pxySv3r2rAUBQBYqSwB50fp/bztn5b0vKSD8itJknS+7QdsX5Me9LmI7Qnbs7Znt2zZknM5WIlGvTZr17IUBQDIR5br4PxPSVdIOlHSVUp2UH02Ipbdh2P7dkmvqPPSpKRvSnoy/ZxLJb0yIs5p9v24Dk53GxhIZm5q2ckSFAAAWbXtOjgRcWlEPB0RX1TSe/OalYSb9HueFBGvrXO7KSK+FxE7ImKnpM9KOmYln4XOqNdjU9Gs1wYAgDw0DDi232D7FVXP3yXpRkmX2t4vr4Jsv7Lq6elKDvtEwZoFmEY9NpX3NDpDil4bAEBems3gfEbSdkmy/YuSLpN0naRnJE3nWNOf2X7Q9gOSjpf0ezl+FjJoFWAa9dhMTiaPx8fptQEAdFbDHhzb90fE69PHV0naEhGXpM/XR8SRHauyBXpw8tXqbCh6bAAAndKOHpxVtitXOj5R0t9XvZbliAeURKuzoeixAQB0m2YB5wZJd9m+SclW8a9Lku2fUbJMhT7RKsDQYwMA6DYNA05ETEm6SNK1kn4+dq1lDSi5qjH6RKsAQ48NAKDbtLwOTi+gByd/MzNJ0/CmTcnMzdQUAQYA0HlZe3DopUEm4+MEGgBA78h0mjgAAEAvIeAAAIDSIeAAAIDSIeD0uWZHMAAA0KtoMu5jlSMYKscsVI5gkGgoBgD0NmZw+lirM6QAAOhVBJw+1uoIBgAAehUBp4Sy9tVwhhQAoKwIOCVT6auZm0tO+K701dQLOZwhBQAoKwJOySylr4YzpAAAZcVZVCUzMJDM3NSypZ07O18PAADtlPUsKmZwSoa+GgAACDilQ18NAAAEnNKhrwYAAK5kXErj4wQaAEB/YwYHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUDgEHAACUTiEBx/YZth+yvdP2WM1rf2j7UduP2H5bEfUBAIDeVtRp4hskvUPSZ6oHbR8m6Z2SDpf005Jut/3qiNjR+RIBAECvKmQGJyIejohH6rx0mqQvRMRzEfGYpEclHdPZ6gAAQK/rth6cV0l6vOr55nRsEdsTtmdtz27ZsqUjxQEAgN6Q2xKV7dslvaLOS5MRcVOjL6szFvXeGBHTkqYlaWxsrO57AABAf8ot4ETEScv4ss2SDqx6foCk77anIgAA0C+6bYnqZknvtL2H7YMkHSLpWwXXBAAAekxR28RPt71Z0nGSvmz7NkmKiIck3Sjp25JulfRedlABAIClKmSbeER8SdKXGrw2JWmqsxUBAIAy6bYlKtSYmZFGR6WBgeR+ZqboigAA6H5FXegPGczMSBMT0vx88nxuLnkuSePjxdUFAEC3Ywani01O7go3FfPzyTgAAGiMgNPFNm1a2jgAAEgQcLrY8PDSxgEAQIKAk5N2NAdPTUmDgwvHBgeTcQAA0BgBJweV5uC5OSliV3PwUkPO+Lg0PS2NjEh2cj89TYMxAACtOKL3j3EaGxuL2dnZosv4idHRJNTUGhmRNm7sdDUAAJSH7XURMdbqfczg5IDmYAAAikXAyQHNwQAAFIuAkwOagwEAKBYBJwc0BwMAUCyOasjJ+DiBBgCAojCDAwAASoeAAwAASoeAAwAASoeAAwAASoeAAwAASqcvAk47Dr4EAAC9o/TbxCsHX87PJ88rB19KbOMGAKCsSj+DMzm5K9xUzM8n4wAAoJxKH3A4+BIAgP5T+oDDwZcAAPSf0gccDr4EAKD/lD7gcPAlAAD9p/S7qCQOvgQAoN+UfgYHAAD0HwIOAAAoHQIOAAAoHQIOAAAoHQIOAAAoHQIOAAAoHUdE0TWsmO0tkuaKrmOJVkt6sugi0BR/o+7H36j78Tfqfr32NxqJiP1bvakUAacX2Z6NiLGi60Bj/I26H3+j7sffqPuV9W/EEhUAACgdAg4AACgdAk5xposuAC3xN+p+/I26H3+j7lfKvxE9OAAAoHSYwQEAAKVDwAEAAKVDwCmI7Y/Z/hfbD9j+ku2XFl0TFrJ9hu2HbO+0XbotlL3M9sm2H7H9qO0PFV0PFrN9je3v295QdC1YzPaBtr9m++H0v3MXFl1TuxFwivNVSa+NiCMk/aukPyy4Hiy2QdI7JN1ddCHYxfYqSVdJ+mVJh0k6y/ZhxVaFOq6VdHLRRaChFyRdFBE/K+lYSe8t2/+OCDgFiYivRMQL6dNvSjqgyHqwWEQ8HBGPFF0HFjlG0qMR8Z2I2C7pC5JOK7gm1IiIuyU9VXQdqC8inoiIe9PH2yQ9LOlVxVbVXgSc7nCOpL8rugigR7xK0uNVzzerZP9hBjrJ9qikoyT9c7GVtNduRRdQZrZvl/SKOi9NRsRN6XsmlUwVznSyNiSy/I3QdVxnjOtdAMtgex9JX5T0/oj4QdH1tBMBJ0cRcVKz122vkfQrkk4MLkhUiFZ/I3SlzZIOrHp+gKTvFlQL0LNs764k3MxExP8rup52Y4mqILZPlvRBSadGxHzR9QA95B5Jh9g+yPaLJL1T0s0F1wT0FNuW9DlJD0fEJ4quJw8EnOJcKWlfSV+1vd721UUXhIVsn257s6TjJH3Z9m1F1wQpbc4/X9JtShojb4yIh4qtCrVs3yDpnyQdanuz7fcUXRMWeLOksyWdkP5/0HrbpxRdVDtxVAMAACgdZnAAAEDpEHAAAEDpEHAAAEDpEHAAAEDpEHAAAEDpEHCAPmB7qGor6H/a/o/08dO2v93hWo6s3o5q+9Tlnghue6Pt1XXGX2L7Otv/nt5mbL9sJXU3+PyGP4vtS2z/frs/E0A2BBygD0TE1og4MiKOlHS1pD9PHx8paWe7P892s6ukHynpJ6EgIm6OiMvaXMLnJH0nIg6OiIMlParkdOt268TPAmAZCDgAVtn+rO2HbH/F9l6SZPtg27faXmf767Zfk46P2L7D9gPp/XA6fq3tT9j+mqQ/tb237Wts32P7PtunpVce/hNJZ6YzSGfafrftK9Pv8XLbX7J9f3p7Uzr+N2kdD9meaPbD2P4ZST8n6dKq4T+R9Hrbh9p+i+1bqt5/pe13p48vTuvdYHs6vdqrbN9p+09tf8v2v9r+hVY/S01NjX6XZ6Sfdb/tu5f+pwPQCAEHwCGSroqIwyU9LenX0vFpSe+LiJ+T9PuSPpWOXynpuog4QskhsZ+s+l6vlnRSRFwkaVLS30fEGyQdL+ljknaXdLGkv0pnlP6qppZPSrorIl4v6WhJlSsUn5PWMSbpAttDTX6ewyStj4gdlYH08X2SfrbF7+LKiHhDRLxW0l5Kzoqr2C0ijpH0fkl/HBHbW/ws1Rr9Li+W9Lb05z21RW0AloDDNgE8FhHr08frJI2mJwy/SdJfp5MYkrRHen+cpHekj6+X9GdV3+uvq4LFL0k6taoPZU9Jwy1qOUHSu6SfhJJn0vELbJ+ePj5QSSjb2uB7WPVPF693Cnmt421/QNKgpP2UBKy/TV+rHEa4TtJohu+VfGjz3+U3JF1r+8aq7w+gDQg4AJ6rerxDyczFgKSn0z6dVqrDxA+rHlvSr0XEI9Vvtv3GpRRn+y2STpJ0XETM275TSVhq5CFJR9keiIid6fcYkHSEpHuVhKzq2es90/fsqWRmZSwiHrd9Sc3nVH5PO7S0/3Y2/F1GxO+mv4+3S1pv+8iIaBTcACwBS1QAFomIH0h6zPYZUnLysO3Xpy//o5ITvCVpXNI/NPg2t0l6X1Ufy1Hp+DYlB83Wc4ekc9P3r7L9YkkvkfRfabh5jaRjW9T+qJLlqD+qGv4jSXdExCZJc5IOs72H7ZdIOjF9TyXMPJnOuvx6s8/J8LNU6mn4u7R9cET8c0RcLOlJJbNTANqAgAOgkXFJ77F9v5JZkdPS8Qsk/bbtB5ScRnxhg6+/VEnPzQO2N2hX0+/XlASM9bbPrPmaC5UsEz2oZCnocEm3Stot/bxLJX0zQ+3nSDrE9qO2tygJRb8rSRHxuKQbJT2gpIfovnT8aUmflfSgpL+RdE+Gz2n2s1Rr9Lv8mO0H09/P3ZLuz/CZADLgNHEApWb7UElrlTT5ri26HgCdQcABAAClwxIVAAAoHQIOAAAoHQIOAAAoHQIOAAAoHQIOAAAoHQIOAAAonf8PR6FqYlWr71kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "smg.qqplot(result.resid, ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-qqplot-model-1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"y ~ x1 + x2 + x1*x2\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      y   R-squared:                       0.955\n",
      "Model:                            OLS   Adj. R-squared:                  0.954\n",
      "Method:                 Least Squares   F-statistic:                     684.5\n",
      "Date:                Mon, 06 May 2019   Prob (F-statistic):           1.21e-64\n",
      "Time:                        15:42:53   Log-Likelihood:                -140.01\n",
      "No. Observations:                 100   AIC:                             288.0\n",
      "Df Residuals:                      96   BIC:                             298.4\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.8706      0.103      8.433      0.000       0.666       1.076\n",
      "x1             1.9693      0.108     18.160      0.000       1.754       2.185\n",
      "x2             2.9670      0.117     25.466      0.000       2.736       3.198\n",
      "x1:x2          3.9440      0.112     35.159      0.000       3.721       4.167\n",
      "==============================================================================\n",
      "Omnibus:                        2.950   Durbin-Watson:                   2.072\n",
      "Prob(Omnibus):                  0.229   Jarque-Bera (JB):                2.734\n",
      "Skew:                           0.327   Prob(JB):                        0.255\n",
      "Kurtosis:                       2.521   Cond. No.                         1.38\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    0.870620\n",
       "x1           1.969345\n",
       "x2           2.967004\n",
       "x1:x2        3.943993\n",
       "dtype: float64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9553393745884368"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z, p = stats.normaltest(result.resid.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22874710482505126"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 4))\n",
    "smg.qqplot(result.resid, ax=ax)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-qqplot-model-2.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-1, 1, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X1, X2 = np.meshgrid(x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame({\"x1\": X1.ravel(), \"x2\": X2.ravel()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "y_pred = result.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_pred = y_pred.values.reshape(50, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(12, 5), sharey=True)\n",
    "\n",
    "def plot_y_contour(ax, Y, title):\n",
    "    c = ax.contourf(X1, X2, Y, 15, cmap=plt.cm.RdBu)\n",
    "    ax.set_xlabel(r\"$x_1$\", fontsize=20)\n",
    "    ax.set_ylabel(r\"$x_2$\", fontsize=20)\n",
    "    ax.set_title(title)\n",
    "    cb = fig.colorbar(c, ax=ax)\n",
    "    cb.set_label(r\"$y$\", fontsize=20)\n",
    "\n",
    "plot_y_contour(axes[0], y_true(X1, X2), \"true relation\")\n",
    "plot_y_contour(axes[1], y_pred, \"fitted model\")\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-comparison-model-true.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datasets from R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset = sm.datasets.get_rdataset(\"Icecream\", \"Ecdat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ice Cream Consumption'"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----------+-----------------+\n",
      "| Icecream | R Documentation |\n",
      "+----------+-----------------+\n",
      "\n",
      "Ice Cream Consumption\n",
      "---------------------\n",
      "\n",
      "Description\n",
      "~~~~~~~~~~~\n",
      "\n",
      "four–weekly observations from 1951–03–18 to 1953–07–11\n",
      "\n",
      "*number of observations* : 30\n",
      "\n",
      "*observation* : country\n",
      "\n",
      "*country* : United States\n",
      "\n",
      "Usage\n",
      "~~~~~\n",
      "\n",
      "::\n",
      "\n",
      "   data(Icecream)\n",
      "\n",
      "Format\n",
      "~~~~~~\n",
      "\n",
      "A time serie containing :\n",
      "\n",
      "cons\n",
      "   consumption of ice cream per head (in pints);\n",
      "\n",
      "income\n",
      "   average family income per week (in US Dollars);\n",
      "\n",
      "price\n",
      "   price of ice cream (per pint);\n",
      "\n",
      "temp\n",
      "   average temperature (in Fahrenheit);\n",
      "\n",
      "Source\n",
      "~~~~~~\n",
      "\n",
      "Hildreth, C. and J. Lu (1960) *Demand relations with autocorrelated\n",
      "disturbances*, Technical Bulletin No 2765, Michigan State University.\n",
      "\n",
      "References\n",
      "~~~~~~~~~~\n",
      "\n",
      "Verbeek, Marno (2004) *A Guide to Modern Econometrics*, John Wiley and\n",
      "Sons, chapter 4.\n",
      "\n",
      "See Also\n",
      "~~~~~~~~\n",
      "\n",
      "``Index.Source``, ``Index.Economics``, ``Index.Econometrics``,\n",
      "``Index.Observations``,\n",
      "\n",
      "``Index.Time.Series``\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(dataset.__doc__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 4 columns):\n",
      "cons      30 non-null float64\n",
      "income    30 non-null int64\n",
      "price     30 non-null float64\n",
      "temp      30 non-null int64\n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 1.0 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"cons ~ -1 + price + temp\", data=dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   cons   R-squared:                       0.986\n",
      "Model:                            OLS   Adj. R-squared:                  0.985\n",
      "Method:                 Least Squares   F-statistic:                     1001.\n",
      "Date:                Mon, 06 May 2019   Prob (F-statistic):           9.03e-27\n",
      "Time:                        15:43:04   Log-Likelihood:                 51.903\n",
      "No. Observations:                  30   AIC:                            -99.81\n",
      "Df Residuals:                      28   BIC:                            -97.00\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "price          0.7254      0.093      7.805      0.000       0.535       0.916\n",
      "temp           0.0032      0.000      6.549      0.000       0.002       0.004\n",
      "==============================================================================\n",
      "Omnibus:                        5.350   Durbin-Watson:                   0.637\n",
      "Prob(Omnibus):                  0.069   Jarque-Bera (JB):                3.675\n",
      "Skew:                           0.776   Prob(JB):                        0.159\n",
      "Kurtosis:                       3.729   Cond. No.                         593.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "smg.plot_fit(result, 0, ax=ax1)\n",
    "smg.plot_fit(result, 1, ax=ax2)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-regressionplots.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "sns.regplot(\"price\", \"cons\", dataset.data, ax=ax1);\n",
    "sns.regplot(\"temp\", \"cons\", dataset.data, ax=ax2);\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-regressionplots-seaborn.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discrete regression, logistic regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = sm.datasets.get_rdataset(\"iris\").data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "Sepal.Length    150 non-null float64\n",
      "Sepal.Width     150 non-null float64\n",
      "Petal.Length    150 non-null float64\n",
      "Petal.Width     150 non-null float64\n",
      "Species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_subset = df[(df.Species == \"versicolor\") | (df.Species == \"virginica\" )].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subset = df[df.Species.isin([\"versicolor\", \"virginica\"])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_subset.Species = df_subset.Species.map({\"versicolor\": 1, \"virginica\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_subset.rename(columns={\"Sepal.Length\": \"Sepal_Length\", \"Sepal.Width\": \"Sepal_Width\",\n",
    "                          \"Petal.Length\": \"Petal_Length\", \"Petal.Width\": \"Petal_Width\"}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Sepal_Length  Sepal_Width  Petal_Length  Petal_Width  Species\n",
       "50           7.0          3.2           4.7          1.4        1\n",
       "51           6.4          3.2           4.5          1.5        1\n",
       "52           6.9          3.1           4.9          1.5        1"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = smf.logit(\"Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width\", data=df_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.059493\n",
      "         Iterations 12\n"
     ]
    }
   ],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Species   No. Observations:                  100\n",
      "Model:                          Logit   Df Residuals:                       95\n",
      "Method:                           MLE   Df Model:                            4\n",
      "Date:                Mon, 06 May 2019   Pseudo R-squ.:                  0.9142\n",
      "Time:                        15:43:11   Log-Likelihood:                -5.9493\n",
      "converged:                       True   LL-Null:                       -69.315\n",
      "                                        LLR p-value:                 1.947e-26\n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept       42.6378     25.708      1.659      0.097      -7.748      93.024\n",
      "Sepal_Length     2.4652      2.394      1.030      0.303      -2.228       7.158\n",
      "Sepal_Width      6.6809      4.480      1.491      0.136      -2.099      15.461\n",
      "Petal_Length    -9.4294      4.737     -1.990      0.047     -18.714      -0.145\n",
      "Petal_Width    -18.2861      9.743     -1.877      0.061     -37.381       0.809\n",
      "================================================================================\n",
      "\n",
      "Possibly complete quasi-separation: A fraction 0.60 of observations can be\n",
      "perfectly predicted. This might indicate that there is complete\n",
      "quasi-separation. In this case some parameters will not be identified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Logit Marginal Effects       \n",
      "=====================================\n",
      "Dep. Variable:                Species\n",
      "Method:                          dydx\n",
      "At:                           overall\n",
      "================================================================================\n",
      "                  dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Sepal_Length     0.0445      0.038      1.163      0.245      -0.031       0.120\n",
      "Sepal_Width      0.1207      0.064      1.891      0.059      -0.004       0.246\n",
      "Petal_Length    -0.1703      0.057     -2.965      0.003      -0.283      -0.058\n",
      "Petal_Width     -0.3303      0.110     -2.998      0.003      -0.546      -0.114\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(result.get_margeff().summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Sepal_Length and Sepal_Width do not seem to contribute much to predictiveness of the model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = smf.logit(\"Species ~ Petal_Length + Petal_Width\", data=df_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.102818\n",
      "         Iterations 10\n"
     ]
    }
   ],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                           Logit Regression Results                           \n",
      "==============================================================================\n",
      "Dep. Variable:                Species   No. Observations:                  100\n",
      "Model:                          Logit   Df Residuals:                       97\n",
      "Method:                           MLE   Df Model:                            2\n",
      "Date:                Mon, 06 May 2019   Pseudo R-squ.:                  0.8517\n",
      "Time:                        15:43:13   Log-Likelihood:                -10.282\n",
      "converged:                       True   LL-Null:                       -69.315\n",
      "                                        LLR p-value:                 2.303e-26\n",
      "================================================================================\n",
      "                   coef    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Intercept       45.2723     13.612      3.326      0.001      18.594      71.951\n",
      "Petal_Length    -5.7545      2.306     -2.496      0.013     -10.274      -1.235\n",
      "Petal_Width    -10.4467      3.756     -2.782      0.005     -17.808      -3.086\n",
      "================================================================================\n",
      "\n",
      "Possibly complete quasi-separation: A fraction 0.34 of observations can be\n",
      "perfectly predicted. This might indicate that there is complete\n",
      "quasi-separation. In this case some parameters will not be identified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Logit Marginal Effects       \n",
      "=====================================\n",
      "Dep. Variable:                Species\n",
      "Method:                          dydx\n",
      "At:                           overall\n",
      "================================================================================\n",
      "                  dy/dx    std err          z      P>|z|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------\n",
      "Petal_Length    -0.1736      0.052     -3.347      0.001      -0.275      -0.072\n",
      "Petal_Width     -0.3151      0.068     -4.608      0.000      -0.449      -0.181\n",
      "================================================================================\n"
     ]
    }
   ],
   "source": [
    "print(result.get_margeff().summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = result.params\n",
    "beta0 = -params['Intercept']/params['Petal_Width']\n",
    "beta1 = -params['Petal_Length']/params['Petal_Width']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_new = pd.DataFrame({\"Petal_Length\": np.random.randn(20)*0.5 + 5,\n",
    "                       \"Petal_Width\": np.random.randn(20)*0.5 + 1.7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_new[\"P-Species\"] = result.predict(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.995472\n",
       "1    0.799899\n",
       "2    0.000033\n",
       "Name: P-Species, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[\"P-Species\"].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_new[\"Species\"] = (df_new[\"P-Species\"] > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>P-Species</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4.717684</td>\n",
       "      <td>1.218695</td>\n",
       "      <td>0.995472</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.280952</td>\n",
       "      <td>1.292013</td>\n",
       "      <td>0.799899</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.610778</td>\n",
       "      <td>2.230056</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.458715</td>\n",
       "      <td>1.907844</td>\n",
       "      <td>0.421614</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.822227</td>\n",
       "      <td>1.938929</td>\n",
       "      <td>0.061070</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Petal_Length  Petal_Width  P-Species  Species\n",
       "0      4.717684     1.218695   0.995472        1\n",
       "1      5.280952     1.292013   0.799899        1\n",
       "2      5.610778     2.230056   0.000033        0\n",
       "3      4.458715     1.907844   0.421614        0\n",
       "4      4.822227     1.938929   0.061070        0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 4))\n",
    "\n",
    "ax.plot(df_subset[df_subset.Species == 0].Petal_Length.values,\n",
    "        df_subset[df_subset.Species == 0].Petal_Width.values, 's', label='virginica')\n",
    "ax.plot(df_new[df_new.Species == 0].Petal_Length.values,\n",
    "        df_new[df_new.Species == 0].Petal_Width.values,\n",
    "        'o', markersize=10, color=\"steelblue\", label='virginica (pred.)')\n",
    "\n",
    "ax.plot(df_subset[df_subset.Species == 1].Petal_Length.values,\n",
    "        df_subset[df_subset.Species == 1].Petal_Width.values, 's', label='versicolor')\n",
    "ax.plot(df_new[df_new.Species == 1].Petal_Length.values,\n",
    "        df_new[df_new.Species == 1].Petal_Width.values,\n",
    "        'o', markersize=10, color=\"green\", label='versicolor (pred.)')\n",
    "\n",
    "_x = np.array([4.0, 6.1])\n",
    "ax.plot(_x, beta0 + beta1 * _x, 'k')\n",
    "\n",
    "ax.set_xlabel('Petal length')\n",
    "ax.set_ylabel('Petal width')\n",
    "ax.legend(loc=2)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-logit.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poisson distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "dataset = sm.datasets.get_rdataset(\"discoveries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = dataset.data.set_index(\"time\").rename(columns={\"value\": \"discoveries\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>time</th>\n",
       "      <th>1860</th>\n",
       "      <th>1861</th>\n",
       "      <th>1862</th>\n",
       "      <th>1863</th>\n",
       "      <th>1864</th>\n",
       "      <th>1865</th>\n",
       "      <th>1866</th>\n",
       "      <th>1867</th>\n",
       "      <th>1868</th>\n",
       "      <th>1869</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>discoveries</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "time         1860  1861  1862  1863  1864  1865  1866  1867  1868  1869\n",
       "discoveries     5     3     0     2     0     3     2     3     6     1"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(16, 4))\n",
    "df.plot(kind='bar', ax=ax)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-discoveries.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = smf.poisson(\"discoveries ~ 1\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 2.168457\n",
      "         Iterations 1\n"
     ]
    }
   ],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          Poisson Regression Results                          \n",
      "==============================================================================\n",
      "Dep. Variable:            discoveries   No. Observations:                  100\n",
      "Model:                        Poisson   Df Residuals:                       99\n",
      "Method:                           MLE   Df Model:                            0\n",
      "Date:                Mon, 06 May 2019   Pseudo R-squ.:                   0.000\n",
      "Time:                        15:43:21   Log-Likelihood:                -216.85\n",
      "converged:                       True   LL-Null:                       -216.85\n",
      "                                        LLR p-value:                       nan\n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.1314      0.057     19.920      0.000       1.020       1.243\n",
      "==============================================================================\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lmbda = np.exp(result.params) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "X = stats.poisson(lmbda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <th>Intercept</th>\n",
       "      <td>1.020084</td>\n",
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      "text/plain": [
       "                  0         1\n",
       "Intercept  1.020084  1.242721"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_ci_l = stats.poisson(np.exp(result.conf_int().values)[0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_ci_u = stats.poisson(np.exp(result.conf_int().values)[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: VisibleDeprecationWarning: Passing `normed=True` on non-uniform bins has always been broken, and computes neither the probability density function nor the probability mass function. The result is only correct if the bins are uniform, when density=True will produce the same result anyway. The argument will be removed in a future version of numpy.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "v, k = np.histogram(df.values, bins=12, range=(0, 12), normed=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "ax.bar(k[:-1], v, color=\"steelblue\",  align='center', label='Dicoveries per year') \n",
    "ax.bar(k-0.125, X_ci_l.pmf(k), color=\"red\", alpha=0.5, align='center', width=0.25, label='Poisson fit (CI, lower)')\n",
    "ax.bar(k, X.pmf(k), color=\"green\",  align='center', width=0.5, label='Poisson fit')\n",
    "ax.bar(k+0.125, X_ci_u.pmf(k), color=\"red\",  alpha=0.5, align='center', width=0.25, label='Poisson fit (CI, upper)')\n",
    "\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-discoveries-per-year.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"temperature_outdoor_2014.tsv\", header=None, delimiter=\"\\t\", names=[\"time\", \"temp\"])\n",
    "df.time = pd.to_datetime(df.time, unit=\"s\")\n",
    "df = df.set_index(\"time\").resample(\"H\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_march = df[df.index.month == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_april = df[df.index.month == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_march.plot(figsize=(12, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 4, figsize=(12, 3))\n",
    "smg.tsa.plot_acf(df_march.temp, lags=72, ax=axes[0])\n",
    "smg.tsa.plot_acf(df_march.temp.diff().dropna(), lags=72, ax=axes[1])\n",
    "smg.tsa.plot_acf(df_march.temp.diff().diff().dropna(), lags=72, ax=axes[2])\n",
    "smg.tsa.plot_acf(df_march.temp.diff().diff().diff().dropna(), lags=72, ax=axes[3])\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-timeseries-autocorrelation.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "model = sm.tsa.AR(df_march.temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "result = model.fit(72)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9985623006352906"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.durbin_watson(result.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(8, 3))\n",
    "smg.tsa.plot_acf(result.resid, lags=72, ax=ax)\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-timeseries-resid-acf.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rob/miniconda3/envs/py3.6/lib/python3.6/site-packages/statsmodels/tsa/base/tsa_model.py:336: FutureWarning: Creating a DatetimeIndex by passing range endpoints is deprecated.  Use `pandas.date_range` instead.\n",
      "  freq=base_index.freq)\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "ax.plot(df_march.index.values[-72:], df_march.temp.values[-72:], label=\"train data\")\n",
    "ax.plot(df_april.index.values[:72], df_april.temp.values[:72], label=\"actual outcome\")\n",
    "ax.plot(pd.date_range(\"2014-04-01\", \"2014-04-4\", freq=\"H\").values,\n",
    "        result.predict(\"2014-04-01\", \"2014-04-4\"), label=\"predicted outcome\")\n",
    "\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "fig.savefig(\"ch14-timeseries-prediction.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using ARMA model on daily average temperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_march = df_march.resample(\"D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_april = df_april.resample(\"D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "model = sm.tsa.ARMA(df_march, (4, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "ax.plot(df_march.index.values[-3:], df_march.temp.values[-3:], 's-', label=\"train data\")\n",
    "ax.plot(df_april.index.values[:3], df_april.temp.values[:3], 's-', label=\"actual outcome\")\n",
    "ax.plot(pd.date_range(\"2014-04-01\", \"2014-04-3\").values,\n",
    "        result.predict(\"2014-04-01\", \"2014-04-3\"), 's-', label=\"predicted outcome\")\n",
    "ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.6.8 64bit [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>IPython</td><td>7.5.0</td></tr><tr><td>OS</td><td>Darwin 18.2.0 x86_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.16.3</td></tr><tr><td>matplotlib</td><td>3.0.3</td></tr><tr><td>pandas</td><td>0.24.2</td></tr><tr><td>scipy</td><td>1.2.1</td></tr><tr><td>statsmodels</td><td>0.9.0</td></tr><tr><td>patsy</td><td>0.5.1</td></tr><tr><td colspan='2'>Mon May 06 15:43:34 2019 JST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.6.8 64bit [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE\\_401/final)] \\\\ \\hline\n",
       "IPython & 7.5.0 \\\\ \\hline\n",
       "OS & Darwin 18.2.0 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.16.3 \\\\ \\hline\n",
       "matplotlib & 3.0.3 \\\\ \\hline\n",
       "pandas & 0.24.2 \\\\ \\hline\n",
       "scipy & 1.2.1 \\\\ \\hline\n",
       "statsmodels & 0.9.0 \\\\ \\hline\n",
       "patsy & 0.5.1 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Mon May 06 15:43:34 2019 JST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.6.8 64bit [GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]\n",
       "IPython 7.5.0\n",
       "OS Darwin 18.2.0 x86_64 i386 64bit\n",
       "numpy 1.16.3\n",
       "matplotlib 3.0.3\n",
       "pandas 0.24.2\n",
       "scipy 1.2.1\n",
       "statsmodels 0.9.0\n",
       "patsy 0.5.1\n",
       "Mon May 06 15:43:34 2019 JST"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information numpy, matplotlib, pandas, scipy, statsmodels, patsy"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py3.6",
   "language": "python",
   "name": "py3.6"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}