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://link.springer.com/book/10.1007/979-8-8688-0413-7) (ISBN 979-8-8688-0412-0)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import statsmodels.formula.api as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import statsmodels.graphics.api as smg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import patsy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['mathtext.fontset'] = 'stix'\n",
    "mpl.rcParams['font.family'] = 'serif'\n",
    "mpl.rcParams['font.sans-serif'] = 'stix'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Statistical models and patsy formula"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(123456789)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x1 = np.array([6, 7, 8, 9, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x2 = np.array([11, 12, 13, 14, 15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X = np.vstack([np.ones(5), x1, x2, x1*x2]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/dw/s8n_0fz93_517nztg9jpvkb80000gn/T/ipykernel_30051/927016155.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",
      "  beta, res, rank, sval = np.linalg.lstsq(X, y)\n"
     ]
    }
   ],
   "source": [
    "beta, res, rank, sval = np.linalg.lstsq(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.55555556e-01,  1.88888889e+00, -8.88888889e-01, -8.88178420e-16])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = {\"y\": y, \"x1\": x1, \"x2\": x2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "y, X = patsy.dmatrices(\"y ~ 1 + x1 + x2 + x1*x2\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "patsy.design_info.DesignMatrix"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_data = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "y, X = patsy.dmatrices(\"y ~ 1 + x1 + x2 + x1:x2\", df_data, return_type=\"dataframe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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>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": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = sm.OLS(y, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -5.555556e-01\n",
       "x1           1.888889e+00\n",
       "x2          -8.888889e-01\n",
       "x1:x2       -8.326673e-16\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"y ~ 1 + x1 + x2 + x1:x2\", df_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept   -5.555556e-01\n",
       "x1           1.888889e+00\n",
       "x2          -8.888889e-01\n",
       "x1:x2       -8.326673e-16\n",
       "dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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:                 5.350e+26\n",
      "Date:                Sun, 03 Nov 2024   Prob (F-statistic):           1.87e-27\n",
      "Time:                        09:17:52   Log-Likelihood:                 145.03\n",
      "No. Observations:                   5   AIC:                            -284.1\n",
      "Df Residuals:                       2   BIC:                            -285.2\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    2.2e-13  -2.52e+12      0.000      -0.556      -0.556\n",
      "x1             1.8889   8.23e-13    2.3e+12      0.000       1.889       1.889\n",
      "x2            -0.8889    2.8e-13  -3.17e+12      0.000      -0.889      -0.889\n",
      "x1:x2      -8.327e-16   2.58e-14     -0.032      0.977   -1.12e-13     1.1e-13\n",
      "==============================================================================\n",
      "Omnibus:                          nan   Durbin-Watson:                   0.002\n",
      "Prob(Omnibus):                    nan   Jarque-Bera (JB):                0.545\n",
      "Skew:                          -0.111   Prob(JB):                        0.762\n",
      "Kurtosis:                       1.398   Cond. No.                     6.86e+17\n",
      "==============================================================================\n",
      "\n",
      "Notes:\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/npbook_py310/lib/python3.10/site-packages/statsmodels/stats/stattools.py:74: ValueWarning: omni_normtest is not valid with less than 8 observations; 5 samples were given.\n",
      "  warn(\"omni_normtest is not valid with less than 8 observations; %i \"\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.55555556e-01,  1.88888889e+00, -8.88888889e-01, -8.88178420e-16])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data = defaultdict(lambda: np.array([1,2,3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ 1 + a + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a', 'b']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ -1 + a + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b', 'a:b']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a * b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b', 'a:b', 'c', 'a:c', 'b:c', 'a:b:c']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a * b * c\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b', 'a:b', 'c', 'a:c', 'b:c']"
      ]
     },
     "execution_count": 44,
     "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": 45,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data = {k: np.array([]) for k in [\"y\", \"a\", \"b\", \"c\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a', 'b']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a + b\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'I(a + b)']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ I(a + b)\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'a']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ a*a\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'I(a ** 2)']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ I(a**2)\", data=data)[1].design_info.term_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'np.log(a)', 'b']"
      ]
     },
     "execution_count": 50,
     "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": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "z = lambda x1, x2: x1+x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept', 'z(a, b)']"
      ]
     },
     "execution_count": 52,
     "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": 53,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data = {\"y\": [1, 2, 3], \"a\": [1, 2, 3]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     a\n",
       "0  1.0\n",
       "1  2.0\n",
       "2  3.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + a\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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)[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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + C(a)\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = {\"y\": [1, 2, 3], \"a\": [\"type A\", \"type B\", \"type C\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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>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": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patsy.dmatrices(\"y ~ - 1 + a\", data=data, return_type=\"dataframe\")[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 58,
     "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": 59,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.random.seed(123456789)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "N = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x1 = np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x2 = np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\"x1\": x1, \"x2\": x2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def y_true(x1, x2):\n",
    "    return 1  + 2 * x1 + 3 * x2 + 4 * x1 * x2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data[\"y_true\"] = y_true(x1, x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "e = np.random.randn(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data[\"y\"] = data[\"y_true\"] + e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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>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": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 70,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 4)"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"y ~ x1 + x2\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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:                Sun, 03 Nov 2024   Prob (F-statistic):           8.36e-11\n",
      "Time:                        09:18:04   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",
      "Notes:\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": 74,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3802538325513255"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.resid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "z, p = stats.normaltest(result.resid.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.6524990253009316e-05"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Intercept    0.986826\n",
       "x1           1.081044\n",
       "x2           3.079284\n",
       "dtype: float64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 80,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"y ~ x1 + x2 + x1*x2\", data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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:                Sun, 03 Nov 2024   Prob (F-statistic):           1.21e-64\n",
      "Time:                        09:18:06   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",
      "Notes:\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": 83,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9553393745884368"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.rsquared"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "z, p = stats.normaltest(result.resid.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.22874710482505045"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 88,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "x = np.linspace(-1, 1, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X1, X2 = np.meshgrid(x, x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "new_data = pd.DataFrame({\"x1\": X1.ravel(), \"x2\": X2.ravel()})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "y_pred = result.predict(new_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2500,)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred = y_pred.values.reshape(50, 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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": 95,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = sm.datasets.get_rdataset(\"Icecream\", \"Ecdat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Ice Cream Consumption'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ".. container::\n",
      "\n",
      "   .. container::\n",
      "\n",
      "      ======== ===============\n",
      "      Icecream R Documentation\n",
      "      ======== ===============\n",
      "\n",
      "      .. rubric:: Ice Cream Consumption\n",
      "         :name: ice-cream-consumption\n",
      "\n",
      "      .. rubric:: Description\n",
      "         :name: description\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",
      "      .. rubric:: Usage\n",
      "         :name: usage\n",
      "\n",
      "      .. code:: R\n",
      "\n",
      "         data(Icecream)\n",
      "\n",
      "      .. rubric:: Format\n",
      "         :name: format\n",
      "\n",
      "      A time series 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",
      "      .. rubric:: Source\n",
      "         :name: source\n",
      "\n",
      "      Hildreth, C. and J. Lu (1960) *Demand relations with\n",
      "      autocorrelated disturbances*, Technical Bulletin No 2765, Michigan\n",
      "      State University.\n",
      "\n",
      "      .. rubric:: References\n",
      "         :name: references\n",
      "\n",
      "      Verbeek, Marno (2004) *A Guide to Modern Econometrics*, John Wiley\n",
      "      and Sons, chapter 4.\n",
      "\n",
      "      .. rubric:: See Also\n",
      "         :name: see-also\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": 98,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   cons    30 non-null     float64\n",
      " 1   income  30 non-null     int64  \n",
      " 2   price   30 non-null     float64\n",
      " 3   temp    30 non-null     int64  \n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 1.1 KB\n"
     ]
    }
   ],
   "source": [
    "dataset.data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = smf.ols(\"cons ~ -1 + price + temp\", data=dataset.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                 OLS Regression Results                                \n",
      "=======================================================================================\n",
      "Dep. Variable:                   cons   R-squared (uncentered):                   0.986\n",
      "Model:                            OLS   Adj. R-squared (uncentered):              0.985\n",
      "Method:                 Least Squares   F-statistic:                              1001.\n",
      "Date:                Sun, 03 Nov 2024   Prob (F-statistic):                    9.03e-27\n",
      "Time:                        09:18:14   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",
      "Notes:\n",
      "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
      "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(result.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "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": 103,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 4))\n",
    "\n",
    "sns.regplot(dataset.data, x=\"price\", y=\"cons\", ax=ax1);\n",
    "sns.regplot(dataset.data, x=\"temp\", y=\"cons\", 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": 104,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df = sm.datasets.get_rdataset(\"iris\").data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   Sepal.Length  150 non-null    float64\n",
      " 1   Sepal.Width   150 non-null    float64\n",
      " 2   Petal.Length  150 non-null    float64\n",
      " 3   Petal.Width   150 non-null    float64\n",
      " 4   Species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Species.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df_subset = df[(df.Species == \"versicolor\") | (df.Species == \"virginica\" )].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_subset = df[df.Species.isin([\"versicolor\", \"virginica\"])].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_subset.Species = df_subset.Species.map({\"versicolor\": 1, \"virginica\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 111,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_subset.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "model = smf.logit(\"Species ~ Sepal_Length + Sepal_Width + Petal_Length + Petal_Width\", data=df_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 114,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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:                Sun, 03 Nov 2024   Pseudo R-squ.:                  0.9142\n",
      "Time:                        09:18:18   Log-Likelihood:                -5.9493\n",
      "converged:                       True   LL-Null:                       -69.315\n",
      "Covariance Type:            nonrobust   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": 115,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 116,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = smf.logit(\"Species ~ Petal_Length + Petal_Width\", data=df_subset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 118,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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:                Sun, 03 Nov 2024   Pseudo R-squ.:                  0.8517\n",
      "Time:                        09:18:18   Log-Likelihood:                -10.282\n",
      "converged:                       True   LL-Null:                       -69.315\n",
      "Covariance Type:            nonrobust   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": 119,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 120,
   "metadata": {
    "tags": []
   },
   "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": 121,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 122,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_new[\"P-Species\"] = result.predict(df_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.995472\n",
       "1    0.799899\n",
       "2    0.000033\n",
       "Name: P-Species, dtype: float64"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new[\"P-Species\"].head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df_new[\"Species\"] = (df_new[\"P-Species\"] > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 127,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "dataset = sm.datasets.get_rdataset(\"discoveries\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df = dataset.data.set_index(\"time\").rename(columns={\"value\": \"discoveries\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 131,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "model = smf.poisson(\"discoveries ~ 1\", data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 133,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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:                Sun, 03 Nov 2024   Pseudo R-squ.:                   0.000\n",
      "Time:                        09:18:26   Log-Likelihood:                -216.85\n",
      "converged:                       True   LL-Null:                       -216.85\n",
      "Covariance Type:            nonrobust   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": 134,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "lmbda = np.exp(result.params) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "X = stats.poisson(lmbda)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>1.020084</td>\n",
       "      <td>1.242721</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  0         1\n",
       "Intercept  1.020084  1.242721"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result.conf_int()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_ci_l = stats.poisson(np.exp(result.conf_int().values)[0, 0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "X_ci_u = stats.poisson(np.exp(result.conf_int().values)[0, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "v, k = np.histogram(df.values, bins=12, range=(0, 12), density=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 141,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": 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": 142,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df_march = df[df.index.month == 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "df_april = df[df.index.month == 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_march.plot(figsize=(12, 4));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x300 with 4 Axes>"
      ]
     },
     "metadata": {},
     "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": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.tsa import ar_model, arima_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.tsa.ar_model.AR"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.tsa.AR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "#help(sm.tsa)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ar_model.AutoReg(df_march.temp, lags=72)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#model = sm.tsa.AR(df_march.temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.9985623006352928"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sm.stats.durbin_watson(result.resid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 154,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "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": 155,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Using ARMA model on daily average temperatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_march = df_march.resample(\"D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df_april = df_april.resample(\"D\").mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.tsa.arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "statsmodels.tsa.arima.model.ARIMA"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "statsmodels.tsa.arima.model.ARIMA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "#model = sm.tsa.ARMA(df_march, (4, 1))\n",
    "model = statsmodels.tsa.arima.model.ARIMA(df_march.temp, order=(4, 0, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "result = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(12, 4))\n",
    "ax.plot(df_march.index.values[-7:], df_march.temp.values[-7:], 's-', label=\"train data\")\n",
    "ax.plot(df_april.index.values[:7], df_april.temp.values[:7], 's-', label=\"actual outcome\")\n",
    "ax.plot(pd.date_range(\"2014-04-01\", \"2014-04-7\").values,\n",
    "        result.predict(\"2014-04-01\", \"2014-04-7\"), 's-', label=\"predicted outcome\")\n",
    "ax.legend()\n",
    "fig.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Versions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%reload_ext version_information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "application/json": {
       "Software versions": [
        {
         "module": "Python",
         "version": "3.10.12 64bit [Clang 14.0.6 ]"
        },
        {
         "module": "IPython",
         "version": "8.12.0"
        },
        {
         "module": "OS",
         "version": "macOS 10.15.7 x86_64 i386 64bit"
        },
        {
         "module": "numpy",
         "version": "1.22.3"
        },
        {
         "module": "matplotlib",
         "version": "3.7.1"
        },
        {
         "module": "pandas",
         "version": "2.0.3"
        },
        {
         "module": "scipy",
         "version": "1.7.3"
        },
        {
         "module": "statsmodels",
         "version": "0.14.0"
        },
        {
         "module": "patsy",
         "version": "0.5.3"
        }
       ]
      },
      "text/html": [
       "<table><tr><th>Software</th><th>Version</th></tr><tr><td>Python</td><td>3.10.12 64bit [Clang 14.0.6 ]</td></tr><tr><td>IPython</td><td>8.12.0</td></tr><tr><td>OS</td><td>macOS 10.15.7 x86\\_64 i386 64bit</td></tr><tr><td>numpy</td><td>1.22.3</td></tr><tr><td>matplotlib</td><td>3.7.1</td></tr><tr><td>pandas</td><td>2.0.3</td></tr><tr><td>scipy</td><td>1.7.3</td></tr><tr><td>statsmodels</td><td>0.14.0</td></tr><tr><td>patsy</td><td>0.5.3</td></tr><tr><td colspan='2'>Sun Nov 03 09:18:38 2024 JST</td></tr></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{|l|l|}\\hline\n",
       "{\\bf Software} & {\\bf Version} \\\\ \\hline\\hline\n",
       "Python & 3.10.12 64bit [Clang 14.0.6 ] \\\\ \\hline\n",
       "IPython & 8.12.0 \\\\ \\hline\n",
       "OS & macOS 10.15.7 x86\\_64 i386 64bit \\\\ \\hline\n",
       "numpy & 1.22.3 \\\\ \\hline\n",
       "matplotlib & 3.7.1 \\\\ \\hline\n",
       "pandas & 2.0.3 \\\\ \\hline\n",
       "scipy & 1.7.3 \\\\ \\hline\n",
       "statsmodels & 0.14.0 \\\\ \\hline\n",
       "patsy & 0.5.3 \\\\ \\hline\n",
       "\\hline \\multicolumn{2}{|l|}{Sun Nov 03 09:18:38 2024 JST} \\\\ \\hline\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "Software versions\n",
       "Python 3.10.12 64bit [Clang 14.0.6 ]\n",
       "IPython 8.12.0\n",
       "OS macOS 10.15.7 x86_64 i386 64bit\n",
       "numpy 1.22.3\n",
       "matplotlib 3.7.1\n",
       "pandas 2.0.3\n",
       "scipy 1.7.3\n",
       "statsmodels 0.14.0\n",
       "patsy 0.5.3\n",
       "Sun Nov 03 09:18:38 2024 JST"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%version_information numpy, matplotlib, pandas, scipy, statsmodels, patsy"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "npbook_py310",
   "language": "python",
   "name": "npbook_py310"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}