{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interactions and ANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: This script is based heavily on Jonathan Taylor's class notes https://web.stanford.edu/class/stats191/notebooks/Interactions.html\n",
    "\n",
    "Download and format data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:02.730816Z",
     "iopub.status.busy": "2022-11-02T17:11:02.728125Z",
     "iopub.status.idle": "2022-11-02T17:11:03.213901Z",
     "shell.execute_reply": "2022-11-02T17:11:03.213232Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:03.219460Z",
     "iopub.status.busy": "2022-11-02T17:11:03.218233Z",
     "iopub.status.idle": "2022-11-02T17:11:04.397276Z",
     "shell.execute_reply": "2022-11-02T17:11:04.396582Z"
    }
   },
   "outputs": [],
   "source": [
    "from urllib.request import urlopen\n",
    "import numpy as np\n",
    "\n",
    "np.set_printoptions(precision=4, suppress=True)\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option(\"display.width\", 100)\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.graphics.api import interaction_plot, abline_plot\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "\n",
    "try:\n",
    "    salary_table = pd.read_csv(\"salary.table\")\n",
    "except:  # recent pandas can read URL without urlopen\n",
    "    url = \"http://stats191.stanford.edu/data/salary.table\"\n",
    "    fh = urlopen(url)\n",
    "    salary_table = pd.read_table(fh)\n",
    "    salary_table.to_csv(\"salary.table\")\n",
    "\n",
    "E = salary_table.E\n",
    "M = salary_table.M\n",
    "X = salary_table.X\n",
    "S = salary_table.S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.402842Z",
     "iopub.status.busy": "2022-11-02T17:11:04.401640Z",
     "iopub.status.idle": "2022-11-02T17:11:04.649647Z",
     "shell.execute_reply": "2022-11-02T17:11:04.649023Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "symbols = [\"D\", \"^\"]\n",
    "colors = [\"r\", \"g\", \"blue\"]\n",
    "factor_groups = salary_table.groupby([\"E\", \"M\"])\n",
    "for values, group in factor_groups:\n",
    "    i, j = values\n",
    "    plt.scatter(group[\"X\"], group[\"S\"], marker=symbols[j], color=colors[i - 1], s=144)\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fit a linear model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.654690Z",
     "iopub.status.busy": "2022-11-02T17:11:04.653545Z",
     "iopub.status.idle": "2022-11-02T17:11:04.674058Z",
     "shell.execute_reply": "2022-11-02T17:11:04.673462Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.957\n",
      "Model:                            OLS   Adj. R-squared:                  0.953\n",
      "Method:                 Least Squares   F-statistic:                     226.8\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           2.23e-27\n",
      "Time:                        17:11:04   Log-Likelihood:                -381.63\n",
      "No. Observations:                  46   AIC:                             773.3\n",
      "Df Residuals:                      41   BIC:                             782.4\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8035.5976    386.689     20.781      0.000    7254.663    8816.532\n",
      "C(E)[T.2]   3144.0352    361.968      8.686      0.000    2413.025    3875.045\n",
      "C(E)[T.3]   2996.2103    411.753      7.277      0.000    2164.659    3827.762\n",
      "C(M)[T.1]   6883.5310    313.919     21.928      0.000    6249.559    7517.503\n",
      "X            546.1840     30.519     17.896      0.000     484.549     607.819\n",
      "==============================================================================\n",
      "Omnibus:                        2.293   Durbin-Watson:                   2.237\n",
      "Prob(Omnibus):                  0.318   Jarque-Bera (JB):                1.362\n",
      "Skew:                          -0.077   Prob(JB):                        0.506\n",
      "Kurtosis:                       2.171   Cond. No.                         33.5\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "formula = \"S ~ C(E) + C(M) + X\"\n",
    "lm = ols(formula, salary_table).fit()\n",
    "print(lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Have a look at the created design matrix: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.678794Z",
     "iopub.status.busy": "2022-11-02T17:11:04.677666Z",
     "iopub.status.idle": "2022-11-02T17:11:04.684865Z",
     "shell.execute_reply": "2022-11-02T17:11:04.684321Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 1., 1.],\n",
       "       [1., 0., 1., 0., 1.],\n",
       "       [1., 0., 1., 1., 1.],\n",
       "       [1., 1., 0., 0., 1.],\n",
       "       [1., 0., 1., 0., 1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.model.exog[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or since we initially passed in a DataFrame, we have a DataFrame available in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.689984Z",
     "iopub.status.busy": "2022-11-02T17:11:04.688855Z",
     "iopub.status.idle": "2022-11-02T17:11:04.702439Z",
     "shell.execute_reply": "2022-11-02T17:11:04.701846Z"
    }
   },
   "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>C(E)[T.2]</th>\n",
       "      <th>C(E)[T.3]</th>\n",
       "      <th>C(M)[T.1]</th>\n",
       "      <th>X</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",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]    X\n",
       "0        1.0        0.0        0.0        1.0  1.0\n",
       "1        1.0        0.0        1.0        0.0  1.0\n",
       "2        1.0        0.0        1.0        1.0  1.0\n",
       "3        1.0        1.0        0.0        0.0  1.0\n",
       "4        1.0        0.0        1.0        0.0  1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.model.data.orig_exog[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We keep a reference to the original untouched data in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.706900Z",
     "iopub.status.busy": "2022-11-02T17:11:04.705769Z",
     "iopub.status.idle": "2022-11-02T17:11:04.714803Z",
     "shell.execute_reply": "2022-11-02T17:11:04.714258Z"
    }
   },
   "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>S</th>\n",
       "      <th>X</th>\n",
       "      <th>E</th>\n",
       "      <th>M</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13876</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11608</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18701</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11283</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11767</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       S  X  E  M\n",
       "0  13876  1  1  1\n",
       "1  11608  1  3  0\n",
       "2  18701  1  3  1\n",
       "3  11283  1  2  0\n",
       "4  11767  1  3  0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lm.model.data.frame[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Influence statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.719267Z",
     "iopub.status.busy": "2022-11-02T17:11:04.718162Z",
     "iopub.status.idle": "2022-11-02T17:11:04.758832Z",
     "shell.execute_reply": "2022-11-02T17:11:04.758188Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================================================================================\n",
      "       obs      endog     fitted     Cook's   student.   hat diag    dffits   ext.stud.     dffits\n",
      "                           value          d   residual              internal   residual           \n",
      "--------------------------------------------------------------------------------------------------\n",
      "         0  13876.000  15465.313      0.104     -1.683      0.155     -0.722     -1.723     -0.739\n",
      "         1  11608.000  11577.992      0.000      0.031      0.130      0.012      0.031      0.012\n",
      "         2  18701.000  18461.523      0.001      0.247      0.109      0.086      0.244      0.085\n",
      "         3  11283.000  11725.817      0.005     -0.458      0.113     -0.163     -0.453     -0.162\n",
      "         4  11767.000  11577.992      0.001      0.197      0.130      0.076      0.195      0.075\n",
      "         5  20872.000  19155.532      0.092      1.787      0.126      0.678      1.838      0.698\n",
      "         6  11772.000  12272.001      0.006     -0.513      0.101     -0.172     -0.509     -0.170\n",
      "         7  10535.000   9127.966      0.056      1.457      0.116      0.529      1.478      0.537\n",
      "         8  12195.000  12124.176      0.000      0.074      0.123      0.028      0.073      0.027\n",
      "         9  12313.000  12818.185      0.005     -0.516      0.091     -0.163     -0.511     -0.161\n",
      "        10  14975.000  16557.681      0.084     -1.655      0.134     -0.650     -1.692     -0.664\n",
      "        11  21371.000  19701.716      0.078      1.728      0.116      0.624      1.772      0.640\n",
      "        12  19800.000  19553.891      0.001      0.252      0.096      0.082      0.249      0.081\n",
      "        13  11417.000  10220.334      0.033      1.227      0.098      0.405      1.234      0.408\n",
      "        14  20263.000  20100.075      0.001      0.166      0.093      0.053      0.165      0.053\n",
      "        15  13231.000  13216.544      0.000      0.015      0.114      0.005      0.015      0.005\n",
      "        16  12884.000  13364.369      0.004     -0.488      0.082     -0.146     -0.483     -0.145\n",
      "        17  13245.000  13910.553      0.007     -0.674      0.075     -0.192     -0.669     -0.191\n",
      "        18  13677.000  13762.728      0.000     -0.089      0.113     -0.032     -0.087     -0.031\n",
      "        19  15965.000  17650.049      0.082     -1.747      0.119     -0.642     -1.794     -0.659\n",
      "        20  12336.000  11312.702      0.021      1.043      0.087      0.323      1.044      0.323\n",
      "        21  21352.000  21192.443      0.001      0.163      0.091      0.052      0.161      0.051\n",
      "        22  13839.000  14456.737      0.006     -0.624      0.070     -0.171     -0.619     -0.170\n",
      "        23  22884.000  21340.268      0.052      1.579      0.095      0.511      1.610      0.521\n",
      "        24  16978.000  18742.417      0.083     -1.822      0.111     -0.644     -1.877     -0.664\n",
      "        25  14803.000  15549.105      0.008     -0.751      0.065     -0.199     -0.747     -0.198\n",
      "        26  17404.000  19288.601      0.093     -1.944      0.110     -0.684     -2.016     -0.709\n",
      "        27  22184.000  22284.811      0.000     -0.103      0.096     -0.034     -0.102     -0.033\n",
      "        28  13548.000  12405.070      0.025      1.162      0.083      0.350      1.167      0.352\n",
      "        29  14467.000  13497.438      0.018      0.987      0.086      0.304      0.987      0.304\n",
      "        30  15942.000  16641.473      0.007     -0.705      0.068     -0.190     -0.701     -0.189\n",
      "        31  23174.000  23377.179      0.001     -0.209      0.108     -0.073     -0.207     -0.072\n",
      "        32  23780.000  23525.004      0.001      0.260      0.092      0.083      0.257      0.082\n",
      "        33  25410.000  24071.188      0.040      1.370      0.096      0.446      1.386      0.451\n",
      "        34  14861.000  14043.622      0.014      0.834      0.091      0.263      0.831      0.262\n",
      "        35  16882.000  17733.841      0.012     -0.863      0.077     -0.249     -0.860     -0.249\n",
      "        36  24170.000  24469.547      0.003     -0.312      0.127     -0.119     -0.309     -0.118\n",
      "        37  15990.000  15135.990      0.018      0.878      0.104      0.300      0.876      0.299\n",
      "        38  26330.000  25163.556      0.035      1.202      0.109      0.420      1.209      0.422\n",
      "        39  17949.000  18826.209      0.017     -0.897      0.093     -0.288     -0.895     -0.287\n",
      "        40  25685.000  26108.099      0.008     -0.452      0.169     -0.204     -0.447     -0.202\n",
      "        41  27837.000  26802.108      0.039      1.087      0.141      0.440      1.089      0.441\n",
      "        42  18838.000  19918.577      0.033     -1.119      0.117     -0.407     -1.123     -0.408\n",
      "        43  17483.000  16774.542      0.018      0.743      0.138      0.297      0.739      0.295\n",
      "        44  19207.000  20464.761      0.052     -1.313      0.131     -0.511     -1.325     -0.515\n",
      "        45  19346.000  18959.278      0.009      0.423      0.208      0.216      0.419      0.214\n",
      "==================================================================================================\n"
     ]
    }
   ],
   "source": [
    "infl = lm.get_influence()\n",
    "print(infl.summary_table())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "or get a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.763815Z",
     "iopub.status.busy": "2022-11-02T17:11:04.762683Z",
     "iopub.status.idle": "2022-11-02T17:11:04.769260Z",
     "shell.execute_reply": "2022-11-02T17:11:04.768698Z"
    }
   },
   "outputs": [],
   "source": [
    "df_infl = infl.summary_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.773753Z",
     "iopub.status.busy": "2022-11-02T17:11:04.772647Z",
     "iopub.status.idle": "2022-11-02T17:11:04.787247Z",
     "shell.execute_reply": "2022-11-02T17:11:04.786691Z"
    }
   },
   "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>dfb_Intercept</th>\n",
       "      <th>dfb_C(E)[T.2]</th>\n",
       "      <th>dfb_C(E)[T.3]</th>\n",
       "      <th>dfb_C(M)[T.1]</th>\n",
       "      <th>dfb_X</th>\n",
       "      <th>cooks_d</th>\n",
       "      <th>standard_resid</th>\n",
       "      <th>hat_diag</th>\n",
       "      <th>dffits_internal</th>\n",
       "      <th>student_resid</th>\n",
       "      <th>dffits</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.505123</td>\n",
       "      <td>0.376134</td>\n",
       "      <td>0.483977</td>\n",
       "      <td>-0.369677</td>\n",
       "      <td>0.399111</td>\n",
       "      <td>0.104186</td>\n",
       "      <td>-1.683099</td>\n",
       "      <td>0.155327</td>\n",
       "      <td>-0.721753</td>\n",
       "      <td>-1.723037</td>\n",
       "      <td>-0.738880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.004663</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.006733</td>\n",
       "      <td>-0.006220</td>\n",
       "      <td>-0.004449</td>\n",
       "      <td>0.000029</td>\n",
       "      <td>0.031318</td>\n",
       "      <td>0.130266</td>\n",
       "      <td>0.012120</td>\n",
       "      <td>0.030934</td>\n",
       "      <td>0.011972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.013627</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.036876</td>\n",
       "      <td>0.030514</td>\n",
       "      <td>-0.034970</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.246931</td>\n",
       "      <td>0.109021</td>\n",
       "      <td>0.086377</td>\n",
       "      <td>0.244082</td>\n",
       "      <td>0.085380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.083152</td>\n",
       "      <td>-0.074411</td>\n",
       "      <td>0.009704</td>\n",
       "      <td>0.053783</td>\n",
       "      <td>0.105122</td>\n",
       "      <td>0.005338</td>\n",
       "      <td>-0.457630</td>\n",
       "      <td>0.113030</td>\n",
       "      <td>-0.163364</td>\n",
       "      <td>-0.453173</td>\n",
       "      <td>-0.161773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.029382</td>\n",
       "      <td>0.000917</td>\n",
       "      <td>0.042425</td>\n",
       "      <td>-0.039198</td>\n",
       "      <td>-0.028036</td>\n",
       "      <td>0.001166</td>\n",
       "      <td>0.197257</td>\n",
       "      <td>0.130266</td>\n",
       "      <td>0.076340</td>\n",
       "      <td>0.194929</td>\n",
       "      <td>0.075439</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dfb_Intercept  dfb_C(E)[T.2]  dfb_C(E)[T.3]  dfb_C(M)[T.1]     dfb_X   cooks_d  standard_resid  \\\n",
       "0      -0.505123       0.376134       0.483977      -0.369677  0.399111  0.104186       -1.683099   \n",
       "1       0.004663       0.000145       0.006733      -0.006220 -0.004449  0.000029        0.031318   \n",
       "2       0.013627       0.000367       0.036876       0.030514 -0.034970  0.001492        0.246931   \n",
       "3      -0.083152      -0.074411       0.009704       0.053783  0.105122  0.005338       -0.457630   \n",
       "4       0.029382       0.000917       0.042425      -0.039198 -0.028036  0.001166        0.197257   \n",
       "\n",
       "   hat_diag  dffits_internal  student_resid    dffits  \n",
       "0  0.155327        -0.721753      -1.723037 -0.738880  \n",
       "1  0.130266         0.012120       0.030934  0.011972  \n",
       "2  0.109021         0.086377       0.244082  0.085380  \n",
       "3  0.113030        -0.163364      -0.453173 -0.161773  \n",
       "4  0.130266         0.076340       0.194929  0.075439  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_infl[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now plot the residuals within the groups separately:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.791900Z",
     "iopub.status.busy": "2022-11-02T17:11:04.790801Z",
     "iopub.status.idle": "2022-11-02T17:11:04.994597Z",
     "shell.execute_reply": "2022-11-02T17:11:04.993960Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Residuals')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid = lm.resid\n",
    "plt.figure(figsize=(6, 6))\n",
    "for values, group in factor_groups:\n",
    "    i, j = values\n",
    "    group_num = i * 2 + j - 1  # for plotting purposes\n",
    "    x = [group_num] * len(group)\n",
    "    plt.scatter(\n",
    "        x,\n",
    "        resid[group.index],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\n",
    "plt.xlabel(\"Group\")\n",
    "plt.ylabel(\"Residuals\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will test some interactions using anova or f_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:04.999618Z",
     "iopub.status.busy": "2022-11-02T17:11:04.998491Z",
     "iopub.status.idle": "2022-11-02T17:11:05.018792Z",
     "shell.execute_reply": "2022-11-02T17:11:05.018231Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.961\n",
      "Model:                            OLS   Adj. R-squared:                  0.955\n",
      "Method:                 Least Squares   F-statistic:                     158.6\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           8.23e-26\n",
      "Time:                        17:11:05   Log-Likelihood:                -379.47\n",
      "No. Observations:                  46   AIC:                             772.9\n",
      "Df Residuals:                      39   BIC:                             785.7\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept    7256.2800    549.494     13.205      0.000    6144.824    8367.736\n",
      "C(E)[T.2]    4172.5045    674.966      6.182      0.000    2807.256    5537.753\n",
      "C(E)[T.3]    3946.3649    686.693      5.747      0.000    2557.396    5335.333\n",
      "C(M)[T.1]    7102.4539    333.442     21.300      0.000    6428.005    7776.903\n",
      "X             632.2878     53.185     11.888      0.000     524.710     739.865\n",
      "C(E)[T.2]:X  -125.5147     69.863     -1.797      0.080    -266.826      15.796\n",
      "C(E)[T.3]:X  -141.2741     89.281     -1.582      0.122    -321.861      39.313\n",
      "==============================================================================\n",
      "Omnibus:                        0.432   Durbin-Watson:                   2.179\n",
      "Prob(Omnibus):                  0.806   Jarque-Bera (JB):                0.590\n",
      "Skew:                           0.144   Prob(JB):                        0.744\n",
      "Kurtosis:                       2.526   Cond. No.                         69.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "interX_lm = ols(\"S ~ C(E) * X + C(M)\", salary_table).fit()\n",
    "print(interX_lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do an ANOVA check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.023339Z",
     "iopub.status.busy": "2022-11-02T17:11:05.022240Z",
     "iopub.status.idle": "2022-11-02T17:11:05.067378Z",
     "shell.execute_reply": "2022-11-02T17:11:05.066768Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
      "0      41.0  4.328072e+07      0.0           NaN       NaN       NaN\n",
      "1      39.0  3.941068e+07      2.0  3.870040e+06  1.914856  0.160964\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.999\n",
      "Model:                            OLS   Adj. R-squared:                  0.999\n",
      "Method:                 Least Squares   F-statistic:                     5517.\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           1.67e-55\n",
      "Time:                        17:11:05   Log-Likelihood:                -298.74\n",
      "No. Observations:                  46   AIC:                             611.5\n",
      "Df Residuals:                      39   BIC:                             624.3\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=======================================================================================\n",
      "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------------\n",
      "Intercept            9472.6854     80.344    117.902      0.000    9310.175    9635.196\n",
      "C(E)[T.2]            1381.6706     77.319     17.870      0.000    1225.279    1538.063\n",
      "C(E)[T.3]            1730.7483    105.334     16.431      0.000    1517.690    1943.806\n",
      "C(M)[T.1]            3981.3769    101.175     39.351      0.000    3776.732    4186.022\n",
      "C(E)[T.2]:C(M)[T.1]  4902.5231    131.359     37.322      0.000    4636.825    5168.222\n",
      "C(E)[T.3]:C(M)[T.1]  3066.0351    149.330     20.532      0.000    2763.986    3368.084\n",
      "X                     496.9870      5.566     89.283      0.000     485.728     508.246\n",
      "==============================================================================\n",
      "Omnibus:                       74.761   Durbin-Watson:                   2.244\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             1037.873\n",
      "Skew:                          -4.103   Prob(JB):                    4.25e-226\n",
      "Kurtosis:                      24.776   Cond. No.                         79.0\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "   df_resid           ssr  df_diff       ss_diff           F        Pr(>F)\n",
      "0      41.0  4.328072e+07      0.0           NaN         NaN           NaN\n",
      "1      39.0  1.178168e+06      2.0  4.210255e+07  696.844466  3.025504e-31\n"
     ]
    }
   ],
   "source": [
    "from statsmodels.stats.api import anova_lm\n",
    "\n",
    "table1 = anova_lm(lm, interX_lm)\n",
    "print(table1)\n",
    "\n",
    "interM_lm = ols(\"S ~ X + C(E)*C(M)\", data=salary_table).fit()\n",
    "print(interM_lm.summary())\n",
    "\n",
    "table2 = anova_lm(lm, interM_lm)\n",
    "print(table2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The design matrix as a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.072282Z",
     "iopub.status.busy": "2022-11-02T17:11:05.071152Z",
     "iopub.status.idle": "2022-11-02T17:11:05.084950Z",
     "shell.execute_reply": "2022-11-02T17:11:05.084396Z"
    }
   },
   "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>C(E)[T.2]</th>\n",
       "      <th>C(E)[T.3]</th>\n",
       "      <th>C(M)[T.1]</th>\n",
       "      <th>C(E)[T.2]:C(M)[T.1]</th>\n",
       "      <th>C(E)[T.3]:C(M)[T.1]</th>\n",
       "      <th>X</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",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\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": [
       "   Intercept  C(E)[T.2]  C(E)[T.3]  C(M)[T.1]  C(E)[T.2]:C(M)[T.1]  C(E)[T.3]:C(M)[T.1]    X\n",
       "0        1.0        0.0        0.0        1.0                  0.0                  0.0  1.0\n",
       "1        1.0        0.0        1.0        0.0                  0.0                  0.0  1.0\n",
       "2        1.0        0.0        1.0        1.0                  0.0                  1.0  1.0\n",
       "3        1.0        1.0        0.0        0.0                  0.0                  0.0  1.0\n",
       "4        1.0        0.0        1.0        0.0                  0.0                  0.0  1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interM_lm.model.data.orig_exog[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The design matrix as an ndarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.089571Z",
     "iopub.status.busy": "2022-11-02T17:11:05.088454Z",
     "iopub.status.idle": "2022-11-02T17:11:05.095118Z",
     "shell.execute_reply": "2022-11-02T17:11:05.094579Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Intercept',\n",
       " 'C(E)[T.2]',\n",
       " 'C(E)[T.3]',\n",
       " 'C(M)[T.1]',\n",
       " 'C(E)[T.2]:C(M)[T.1]',\n",
       " 'C(E)[T.3]:C(M)[T.1]',\n",
       " 'X']"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interM_lm.model.exog\n",
    "interM_lm.model.exog_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.099519Z",
     "iopub.status.busy": "2022-11-02T17:11:05.098431Z",
     "iopub.status.idle": "2022-11-02T17:11:05.289478Z",
     "shell.execute_reply": "2022-11-02T17:11:05.288841Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'standardized resids')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infl = interM_lm.get_influence()\n",
    "resid = infl.resid_studentized_internal\n",
    "plt.figure(figsize=(6, 6))\n",
    "for values, group in factor_groups:\n",
    "    i, j = values\n",
    "    idx = group.index\n",
    "    plt.scatter(\n",
    "        X[idx],\n",
    "        resid[idx],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"standardized resids\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looks like one observation is an outlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.294627Z",
     "iopub.status.busy": "2022-11-02T17:11:05.293465Z",
     "iopub.status.idle": "2022-11-02T17:11:05.347719Z",
     "shell.execute_reply": "2022-11-02T17:11:05.347089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.955\n",
      "Model:                            OLS   Adj. R-squared:                  0.950\n",
      "Method:                 Least Squares   F-statistic:                     211.7\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           2.45e-26\n",
      "Time:                        17:11:05   Log-Likelihood:                -373.79\n",
      "No. Observations:                  45   AIC:                             757.6\n",
      "Df Residuals:                      40   BIC:                             766.6\n",
      "Df Model:                           4                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   8044.7518    392.781     20.482      0.000    7250.911    8838.592\n",
      "C(E)[T.2]   3129.5286    370.470      8.447      0.000    2380.780    3878.277\n",
      "C(E)[T.3]   2999.4451    416.712      7.198      0.000    2157.238    3841.652\n",
      "C(M)[T.1]   6866.9856    323.991     21.195      0.000    6212.175    7521.796\n",
      "X            545.7855     30.912     17.656      0.000     483.311     608.260\n",
      "==============================================================================\n",
      "Omnibus:                        2.511   Durbin-Watson:                   2.265\n",
      "Prob(Omnibus):                  0.285   Jarque-Bera (JB):                1.400\n",
      "Skew:                          -0.044   Prob(JB):                        0.496\n",
      "Kurtosis:                       2.140   Cond. No.                         33.1\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                      S   R-squared:                       0.959\n",
      "Model:                            OLS   Adj. R-squared:                  0.952\n",
      "Method:                 Least Squares   F-statistic:                     147.7\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           8.97e-25\n",
      "Time:                        17:11:05   Log-Likelihood:                -371.70\n",
      "No. Observations:                  45   AIC:                             757.4\n",
      "Df Residuals:                      38   BIC:                             770.0\n",
      "Df Model:                           6                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===============================================================================\n",
      "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-------------------------------------------------------------------------------\n",
      "Intercept    7266.0887    558.872     13.001      0.000    6134.711    8397.466\n",
      "C(E)[T.2]    4162.0846    685.728      6.070      0.000    2773.900    5550.269\n",
      "C(E)[T.3]    3940.4359    696.067      5.661      0.000    2531.322    5349.549\n",
      "C(M)[T.1]    7088.6387    345.587     20.512      0.000    6389.035    7788.243\n",
      "X             631.6892     53.950     11.709      0.000     522.473     740.905\n",
      "C(E)[T.2]:X  -125.5009     70.744     -1.774      0.084    -268.714      17.712\n",
      "C(E)[T.3]:X  -139.8410     90.728     -1.541      0.132    -323.511      43.829\n",
      "==============================================================================\n",
      "Omnibus:                        0.617   Durbin-Watson:                   2.194\n",
      "Prob(Omnibus):                  0.734   Jarque-Bera (JB):                0.728\n",
      "Skew:                           0.162   Prob(JB):                        0.695\n",
      "Kurtosis:                       2.468   Cond. No.                         68.7\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "\n",
      "\n",
      "   df_resid           ssr  df_diff       ss_diff         F    Pr(>F)\n",
      "0      40.0  4.320910e+07      0.0           NaN       NaN       NaN\n",
      "1      38.0  3.937424e+07      2.0  3.834859e+06  1.850508  0.171042\n",
      "\n",
      "\n",
      "   df_resid           ssr  df_diff       ss_diff            F        Pr(>F)\n",
      "0      40.0  4.320910e+07      0.0           NaN          NaN           NaN\n",
      "1      38.0  1.711881e+05      2.0  4.303791e+07  4776.734853  2.291239e-46\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "drop_idx = abs(resid).argmax()\n",
    "print(drop_idx)  # zero-based index\n",
    "idx = salary_table.index.drop(drop_idx)\n",
    "\n",
    "lm32 = ols(\"S ~ C(E) + X + C(M)\", data=salary_table, subset=idx).fit()\n",
    "\n",
    "print(lm32.summary())\n",
    "print(\"\\n\")\n",
    "\n",
    "interX_lm32 = ols(\"S ~ C(E) * X + C(M)\", data=salary_table, subset=idx).fit()\n",
    "\n",
    "print(interX_lm32.summary())\n",
    "print(\"\\n\")\n",
    "\n",
    "\n",
    "table3 = anova_lm(lm32, interX_lm32)\n",
    "print(table3)\n",
    "print(\"\\n\")\n",
    "\n",
    "\n",
    "interM_lm32 = ols(\"S ~ X + C(E) * C(M)\", data=salary_table, subset=idx).fit()\n",
    "\n",
    "table4 = anova_lm(lm32, interM_lm32)\n",
    "print(table4)\n",
    "print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Replot the residuals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.353100Z",
     "iopub.status.busy": "2022-11-02T17:11:05.351973Z",
     "iopub.status.idle": "2022-11-02T17:11:05.595988Z",
     "shell.execute_reply": "2022-11-02T17:11:05.595365Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'standardized resids')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "resid = interM_lm32.get_influence().summary_frame()[\"standard_resid\"]\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "resid = resid.reindex(X.index)\n",
    "for values, group in factor_groups:\n",
    "    i, j = values\n",
    "    idx = group.index\n",
    "    plt.scatter(\n",
    "        X.loc[idx],\n",
    "        resid.loc[idx],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\n",
    "plt.xlabel(\"X[~[32]]\")\n",
    "plt.ylabel(\"standardized resids\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Plot the fitted values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.601021Z",
     "iopub.status.busy": "2022-11-02T17:11:05.599888Z",
     "iopub.status.idle": "2022-11-02T17:11:05.850411Z",
     "shell.execute_reply": "2022-11-02T17:11:05.849784Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Salary')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lm_final = ols(\"S ~ X + C(E)*C(M)\", data=salary_table.drop([drop_idx])).fit()\n",
    "mf = lm_final.model.data.orig_exog\n",
    "lstyle = [\"-\", \"--\"]\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "for values, group in factor_groups:\n",
    "    i, j = values\n",
    "    idx = group.index\n",
    "    plt.scatter(\n",
    "        X[idx],\n",
    "        S[idx],\n",
    "        marker=symbols[j],\n",
    "        color=colors[i - 1],\n",
    "        s=144,\n",
    "        edgecolors=\"black\",\n",
    "    )\n",
    "    # drop NA because there is no idx 32 in the final model\n",
    "    fv = lm_final.fittedvalues.reindex(idx).dropna()\n",
    "    x = mf.X.reindex(idx).dropna()\n",
    "    plt.plot(x, fv, ls=lstyle[j], color=colors[i - 1])\n",
    "plt.xlabel(\"Experience\")\n",
    "plt.ylabel(\"Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From our first look at the data, the difference between Master's and PhD in the management group is different than in the non-management group. This is an interaction between the two qualitative variables management,M and education,E. We can visualize this by first removing the effect of experience, then plotting the means within each of the 6 groups using interaction.plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:05.857968Z",
     "iopub.status.busy": "2022-11-02T17:11:05.854299Z",
     "iopub.status.idle": "2022-11-02T17:11:06.154478Z",
     "shell.execute_reply": "2022-11-02T17:11:06.153751Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi4AAAINCAYAAAAZcvRHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABa4UlEQVR4nO3deViU5f4G8HvYRWVxA1FU3HBDXCPcTRILF8rKzMzUdi2XjrmUltn5udTJkx3TOqe0TrZZijuKC1pKLgguiFviLqAiDCL7PL8/njMzjKAyMPDOO3N/ruu9nJn3mZnvSDh3z/ssGiGEABEREZEKOChdABEREVF5MbgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajgpXYCt0Ol0uHr1KmrXrg2NRqN0OURERKohhEB2djb8/Pzg4HD/PhUGFwu5evUq/P39lS6DiIhItS5duoTGjRvftw2Di4XUrl0bgPxL9/DwULgaIiIi9dBqtfD39zd8l94Pg4uF6C8PeXh4MLgQERFVQHmGWnBwLhEREakGgwsRERGpBoMLERERqQbHuBARESlMCIGioiIUFxcrXUqVcXZ2hqOjY6Vfh8GFiIhIQQUFBbh27Rru3LmjdClVSqPRoHHjxqhVq1alXofBhYiISCE6nQ4pKSlwdHSEn58fXFxcbHIRUyEErl+/jsuXL6NVq1aV6nlhcCEiIlJIQUEBdDod/P394e7urnQ5Vap+/fo4f/48CgsLKxVcODiXiIhIYQ9a5t4WWKonyfb/poiIiMhmMLgQERGRajC4EBERkWowuBAREdm4F198ERqNBq+99lqpcxMmTIBGo8GLL75Y/YVVAIMLERGRHfD398dPP/2E3Nxcw2N5eXn44Ycf0KRJEwUrMw+DCxERkR3o0qUL/P39sWbNGsNja9asQZMmTdC5c2cFKzMPgwsREZGdGDduHFasWGG4/80332Ds2LEKVmQ+BhciIiI78fzzz+OPP/7AhQsXcOHCBezduxfPP/+80mWZhSvnEhER2Yn69esjIiICK1euhBACERERqFevntJlmYXBhYiIyI6MGzcOEydOBAAsXbpU4WrMx+BCRERkRwYNGoSCggJoNBqEh4crXY7ZGFyIiIjsiKOjI5KTkw231YbBhYiIyM54eHgoXUKFMbgQERHZuJUrV973fFRUVLXUYQmcDk1ERESqweBCREREqsHgQkRERKrB4EJEdmHePMDBQf5JROrFwblEZPPmzQPmzJG39X/Onq1cPURUcexxISKbVjK06M2Zw54XIrVicCEim1VWaNFjeCFSJwYXIrJJ9wstegwvZHO2bwfatZN/2igGFyKyOeUJLXoML2QzhABmzQKSk+WfQihdUZVgcCEim2JOaNFjeCGbsG0bcPCgvH3woLxfDZYuXYpmzZrBzc0NISEhOHDgQJW+H4MLEdmMioQWPYYXUjUh5FQ5/aaJjo7yfhX3uvz888+YOnUq3n//fRw+fBjBwcEIDw9Henp6lb0ngwsR2YTKhBY9hhdSLX1vS3GxvF9cXC29Lp9++ilefvlljB07Fu3atcPy5cvh7u6Ob775psrek+u4EJHqWSK06HGdF1KcEMCdO+a1nzVL9rLogwsg78+aBfTsCWg05Xstd/dyty0oKEB8fDxmzpxpeMzBwQFhYWGIi4srf/1mYnAhItV7/33Lvx6DCynmzh2gVq3Kv05xMXD4MFC7dvmfc/s2ULNmuZreuHEDxcXF8PHxMXncx8cHJ0+eNKdSs/BSERGp3ty5ln29118HdDrLviYRWQZ7XIhI9fS9I5a6XPTFF8CPPwJ9+gD9+gF9+wIdOxrHPRJVKXd32fNRHtu3A5GRD24XFQWEhZXvvcupXr16cHR0RFpamsnjaWlp8PX1LffrmIvBhYhsgiXCy6OPynDyxx/ArVvAunXyAAAvLxlk+vaVYSY4mEGGqohGU77LNUIAf/976bEtd3N0lO2GDi3/WJdycHFxQdeuXbFjxw5E/i886XQ67NixAxMnTrTY+9yNwYWIbEZlwsuHHxqfX1QkhwbExgK7dwO//w5kZgLr18sDADw9gd69ZYjp1w/o1IlBhqpZyXVb7qfkDKPwcIuWMHXqVIwZMwbdunXDQw89hH/+85/IycnB2LFjLfo+JTG4EJFNqVHD/OeUDC0A4OQEPPSQPN55RwaZhAQZYmJjZZDJygI2bpQHAHh4lA4yTvwXlqpKyXVb7tfboqdf12XgQIv2uowYMQLXr1/HnDlzkJqaik6dOiE6OrrUgF1L0ghho2sCVzOtVgtPT09kZWXBw8ND6XKI7NK33wIvvihvDxxYviUs7g4t5VFcDCQmyhBTMsiUVLu2Mcj07Qt06cIgQ6Xl5eUhJSUFAQEBcHNzK/8Tt24FBg0y/w2joy3e61Je9/us5nyHMrhYCIMLkbI2bpRjFIuLgbffBj755MHru1QktJSluBg4csQ0yGRmmrapXRvo1cs4RqZLF8DZufLvTepWoeAiBBASAsTHmzf9zcEB6NoV2L/for0u5WWp4ML8T0Sqt3cv8PTTMkC88AKwaJF8/H5jXiwVWgDZC9+lizymTpV1HD1qHCOze7cMMlu2yAOQy3T07Gm8tNS1K4MMlVNBAXDxovlz9nU64NIl+XxX16qprRowuBCRqh0/DgweDOTlARERwH/+I//HUq+s8GLJ0FIWR0egc2d5TJkig8yxY8YxMrt3y1lLW7fKA5CTSEoGmW7dGGToHlxd5WDb69fNf26DBqoOLQAvFVkMLxURVb8LF4AePYCrV+WfMTH3XoZi3jy5Iu7cucqviqvTlQ4yGRmmbdzdjUGmb1+ge3fAxUWJaqkqVXiMiwpxjIuVYXAhql7Xr8sxI6dPA+3bA3v2AHXqKF1Vxeh0QFKScYzM7t3AzZumbWrUkEFGP0ame3fV/48zgcFFz5zvUC75T0Sqk50NPP64DC1NmsjLLWoNLYC8tBUUBLz5JvDbb0B6uuyR+fxzYPhwoF49IDdXLpI6e7acreTtLRdCnTdPDgbOz1f6U5A1mDdP/vdky7ucc4wLEalKfj7w5JPAoUPyC33bNqBRI6WrsiwHB6BDB3lMnCgnkZw4YeyNiY2VPU47dsgDANzcgNBQ4xiZkBD2yNibkrPobHmXcwYXIlKN4mJgzBjZ81CzJrB5MxAYqHRVVU+jkZfD2rcHJkyQQSY52RhiYmNlL82uXfIAZJB5+GHTIGPjVyLsWllT/201vDC4EJEqCAFMmgT8/LOcbbN2rRznYY80GqBdO3m8/rr8uzl1yhhiYmOBtDTjbUD2vuiDTN++8nZFVhkm63O/9YpsMbwwuBCRKnz0EbB0qfzS/u9/5YaIJGk0QJs28njtNRlkTp82Hex77ZpxTRlAzlB6+GHjYN/QUAYZNXrQIouA7YUXRQfn7tmzB0OGDIGfnx80Gg2ioqJMzt++fRsTJ05E48aNUaNGDbRr1w7Lly83aZOXl4cJEyagbt26qFWrFoYPH15qi+2LFy8iIiIC7u7uaNCgAaZNm4aioiKTNrGxsejSpQtcXV3RsmVLrFy5sio+MhFVwPLlxn98lywBRoxQth5rp9HIS2ivvgr8+CNw5YrskfnyS+C55wA/P7kG2Z498otvwADj7tezZ8txM3fuKP0p6EHKE1r05syx/IDdB32HVxVFg0tOTg6Cg4OxdOnSMs9PnToV0dHR+P7775GcnIzJkydj4sSJWK/fnhXAlClTsGHDBqxevRq7d+/G1atX8eSTTxrOFxcXIyIiAgUFBdi3bx++/fZbrFy5EnNK/LRTUlIQERGB/v37IzExEZMnT8ZLL72ErfqVoYhIMb/+Crzxhrw9e7YcrErm0WiA1q2BV14BVq0CLl+WPTL//jcwapQc3FxQIGcnffSRnK3k5SWnm7/3nhxTlJOj9KegkswJLXqWDi8P+g6vMsJKABBr1641eax9+/biww8/NHmsS5cu4t133xVCCJGZmSmcnZ3F6tWrDeeTk5MFABEXFyeEEGLz5s3CwcFBpKamGtosW7ZMeHh4iPz8fCGEEO+8845o3769yfuMGDFChIeHl7v+rKwsAUBkZWWV+zlEdH87dgjh4iIEIMSrrwqh0yldkW3S6YQ4e1aI//xHiOefF6JxY/l3XvJwchKiRw8hZs0SYutWIbKzla7aNuTm5ooTJ06I3Nzccj/nww9L/3zMOe76WrWIsr7D73a/z2rOd6hVr+PSo0cPrF+/HleuXIEQArt27cLp06cxcOBAAEB8fDwKCwsRFhZmeE6bNm3QpEkTxMXFAQDi4uIQFBRkssV2eHg4tFotkpKSDG1Kvoa+jf41ypKfnw+tVmtyEJHlHD4MDBsmewKGDzeObyHL02iAFi2A8ePl+KGLF4G//gK+/hoYPRrw9weKioB9+4D/+z+5ubC3t1yteOZMuY7O7dtKfwrbIYTs4SrrmD3b/J6Wu82ZI1+nrNdXw5K0Vj049/PPP8crr7yCxo0bw8nJCQ4ODvj3v/+NPn36AABSU1Ph4uICLy8vk+f5+PggNTXV0KZkaNGf15+7XxutVovc3FzUKGPE2vz58zF37lyLfE4iMnXmDDBokPwy7N8f+P57uf8PVQ+NBmjeXB7jxskvs/PnjQN9d+2S4SYuTh4LFsifT/fuxsG+PXvKHbHJfHfuyE04q9JHH8njbrdvy6UGrJnVB5c///wT69evR9OmTbFnzx5MmDABfn5+pXpIqtvMmTMxdepUw32tVgt/f38FKyKyDVevAgMHygXWOncGoqK4/ojSNBogIEAeY8fKx86fN11H5vx54M8/5bFwoQwyXbsa15Hp2RPgbihkCVYbXHJzczFr1iysXbsWERERAICOHTsiMTERn3zyCcLCwuDr64uCggJkZmaa9LqkpaXB19cXAODr64sDBw6YvLZ+1lHJNnfPREpLS4OHh0eZvS0A4OrqClcuS0lkUZmZsqfl/Hl56WLLFn7ZWatmzeQxZoy8f+GCaZBJSQEOHJDHokVyNWB9kOnbVw789fRUrHyr5u5e9qW3BQvK7iWpqPfeA2bMKP3e1s5qx7gUFhaisLAQDg6mJTo6OkKn0wEAunbtCmdnZ+zQr3kN4NSpU7h48SJCQ0MBAKGhoTh27BjS09MNbWJiYuDh4YF27doZ2pR8DX0b/WsQUdXLzQWGDpV79Pj6yqX877qCS1asaVPghReAb74Bzp2TQea77+SlphYt5EaSBw8CH38MDB4s95bq3h2YNg3YuBHIylL6E1gPjUZerrn7mDcP+PBDy7zHhx/K17v7PVQxjqyCA4gtIjs7WyQkJIiEhAQBQHz66aciISFBXLhwQQghRN++fUX79u3Frl27xLlz58SKFSuEm5ub+OKLLwyv8dprr4kmTZqInTt3ikOHDonQ0FARGhpqOF9UVCQ6dOggBg4cKBITE0V0dLSoX7++mDlzpqHNuXPnhLu7u5g2bZpITk4WS5cuFY6OjiI6Orrcn4WziogqrrBQiKFD5YwHDw8hEhOVrogs7eJFIf77XyFeekmIli1Lz3RxcBCia1chpk4VYv16IW7dUrri6qHmWUUP+g4357Oa8x2qaHDZtWuXAFDqGDNmjBBCiGvXrokXX3xR+Pn5CTc3NxEYGCj+8Y9/CF2JOZG5ubnijTfeEN7e3sLd3V088cQT4tq1aybvc/78efHYY4+JGjVqiHr16om3335bFBYWlqqlU6dOwsXFRTRv3lysWLHCrM/C4EJUMTqdEOPGyX9QXV2F2L1b6YqoOly+LMSqVUK8/LIQrVqV/nLVaITo3FmIKVOEWLdOiIwMpSuuGhUJLkJUPLxYcir0g77D72ap4KIRQg2Tn6yfVquFp6cnsrKy4MGL8kTlNnOmvHbv4ACsWSOnQJP9uXrVdIzM6dOm5zUaIDjYONi3d295uUnt8vLykJKSgoCAALiZOQrd3EXoPvxQ2WX/7/dZzfkOtdrBuURk+xYvlqEFAL76iqHFnvn5ASNHygMw7q2kn4J98iSQmCiPf/5TBpmOHY2Dffv0AerWVax8RehDSHnCi9KhxZIYXIhIEd9/D+hXFJg/Xy5+RqTXsCHw7LPyAIDUVOMmkbGxQHIycOSIPD77TLa5O8jUq6dU9dWnPOHFlkILwOBCRArYssW4HsjkycD06YqWQyrg6ys319RvsJmWJjeJ1F9aOnECOHpUHkuWyDZBQcYF8fr0AerXV6j4Kna/8GJroQUAOMbFQjjGhah84uLkbsS5uXKDv+++k+NbiCojPd00yPxvRxcT7dsbx8j06QM0aFC9NZalMmNc7nb3mBdrCy0c40JEqpOUBEREyNAyaBCwYgVDC1lGgwbAU0/JA5ArL+uDzO7dcn2gpCR56DczbtfONMiofd0gfUh5/31g7lzrCi2WxB4XC2GPC9H9XbwoN+W7cgUICQF27LD+PVHIdty4IYOMfozM0aOl27Rtaxwj07evvDxV1fS9EM2aNbvnSu22Ijc3F+fPn690jwuDi4UwuBDd240bcvrqyZPyy+H33+1vBghZl5s3SweZu78N27QxjpHp21cOGLa04uJinD59Gg0aNEBdG/+lyMrKwtWrV9GyZUs4OzubnGNwUQCDC1HZbt+WY1oOHAAaNwb27QO4HylZm4wMGaj1Y2SOHCkdZFq3Nl5a6ttXTuG2hGvXriEzMxMNGjSAu7s7NKpYd988Op0OV69ehbOzM5o0aVLqMzK4KIDBhai0ggK5/9DWrXKxsD/+kD0uRNbu1i1jkNm9G0hIKB1kWrUyDTKNGlXsvYQQSE1NRWZmZuWKtnIODg4ICAiAi4tLqXMMLgpgcCEypdMBzz8P/Pij3HF25045toVIjTIzZZDRX1pKSJD/jZfUsqUxxPTrJ3sYzVFcXIzCwkLLFGyFXFxcSm2crMfgogAGFyIjIeT6LEuWAE5OwIYNchYRka3IzJQ9iPogc/hw6SDTooUxxPTrZx2XSOfNs85ZRwwuCmBwITL6v/8D3n1X3l61CnjuOWXrIapqWVnA3r3GMTLx8aWDTECA6aWlpk2rt0ZrXueFwUUBDC5E0r//Dbzyirz9z38CkyYpWg6RIrRaY5DZvRs4dAgoLjZt06yZaZBp1qzq6rnXhozWEl4YXBTA4EIErF0rFwDT6YBZs4C//13pioisQ3a2DDL6S0sHD5YOMk2bmo6RadZMbiZZWQ/aRdoawguDiwIYXMjexcbKcSz5+cBLL8ndnm1wVieRRdy+XTrIFBWZtmnSxDTIBASY/zv1oNCip3R4YXBRAIML2bPERPmPq1YLREYCq1fLQblEVD45OXKNI/0YmQMHSgcZf3/Twb7Nm98/yJQ3tOgpGV4YXBTA4EL26q+/gJ495W69ffsC0dFAJfeKI7J7OTlyQ1L9GJn9+4G7Z0o3amQMMf36yVlM+iBjbmjRUyq8MLgogMGF7FFqqgwt584BwcHyH1hPT6WrIrI9d+6YBpk//ywdZPz8ZIC5fRtYv77i76VEeGFwUQCDC9mbrCz5j2Riouyy3ru3ejalIyIZZP780zhG5s8/5UrVllLd4cWc71BehSYis+XlAcOGydDi4wNs28bQQlSd3N2BRx6RBwDk5gITJgArVljm9fWXmZSebVQW9rhYCHtcyF4UFwNPPy2nPteuLf+Pr3NnpasiIgeH0vspVYZGU3oRvapizndo2ZsGEBGVQQjg9ddlaHFxkdfRGVqIrMPcudb9epbC4EJE5TZ7tlwZ18FBbp7Yr5/SFRGR3uzZcmyKJSi9rsv9MLgQUbksWWJcCXfZMuDJJ5Wth4hKs0R4sebQAjC4EFE5/PCDcc+hjz4y7kVERNanMuHF2kMLwOBCRA+wdSswZoy8/eabcg8iIrJuFQkvaggtAIMLEd3H/v3A8OFy6fGRI+Vuz9x/iEgdzAkvagktAIMLEd3DyZNARIRcenzgQGDlSjkol4jUozzhRU2hBWBwIaIyXL4sw8rNm8BDDwG//SanPxOR+twvvKgttAAMLkR0l5s3ZWi5dAkIDAQ2bQJq1VK6KiKqjLLCixpDC8DgQkQl5OQAgwcDycly59lt24B69ZSuiogsQR9eNBr1hhaAS/5bDJf8J7UrLJT7D23ZAnh7A7//DrRvr3RVRGQPuOQ/EZlFpwPGjZOhpUYNeXmIoYWIrBGDC5GdEwL429+A778HHB2BX38FQkOVroqIqGwMLkR2btEiYPFieXvFCuDxx5Wth4jofhhciOzYN98AM2bI2//4BzB6tLL1EBE9CIMLkZ1avx54+WV5e/p0YOpUZeshIioPBhciO/T778CIEXJQ7tixwPz5SldERFQ+DC5EduboUWDIECAvDxg6FPjqK+4/RETqweBCZEdSUoDwcCArC+jVC/jpJ8DJSemqiIjKj8GFyE6kp8ul/FNTgaAgYMMGuWYLEZGaMLgQ2QGtFnjsMeDsWaBZMyA6GvDyUroqIiLzMbgQ2bj8fOCJJ4DDh4H69eX+Q35+SldFRFQxDC5ENqy4GHj+eWDnTrnD85YtQKtWSldFRFRxDC5ENkoIYMIEuYS/iwuwbh3QtavSVRERVQ6DC5GN+uAD4Msv5VTnVauARx5RuiIiospjcCGyQUuXAh9+aLz91FPK1kNEZCkMLkQ25pdfgDfflLc/+AB4/XVFyyEisigGFyIbsn27HIwrBPDGG8CcOUpXRERkWQwuRDbi4EEgMhIoLASeeQZYsoRL+ROR7WFwIbIBp04Bjz8O5OQAAwYA330HODoqXRURkeUxuBCp3JUrcv+hGzfkdOe1awFXV6WrIiKqGgwuRCp26xYwaBBw4YJcWG7LFqB2baWrIiKqOgwuRCp15w4wZAhw/DjQsKFcyr9+faWrIiKqWgwuRCqkH4C7d6/cLHHrVrl5IhGRrWNwIVIZIYCXXwY2bQLc3IANG4CgIKWrIiKqHgwuRCozfTrw7bdy1tAvvwC9eildERFR9WFwIVKRTz4BPv5Y3v7Pf+QYFyIie8LgQqQS334LTJsmby9aBLz4oqLlEBEpgsGFSAU2bgTGj5e3//Y3Y4AhIrI3DC5EVm7vXuDpp4HiYuCFF4CFC5WuiIhIOQwuRFbs+HFg8GAgLw+IiJDjWhz4W0tEdoz/BBJZqfPn5VL+mZlAjx5yBpGzs9JVEREpS9HgsmfPHgwZMgR+fn7QaDSIiooq1SY5ORlDhw6Fp6cnatasie7du+PixYuG83l5eZgwYQLq1q2LWrVqYfjw4UhLSzN5jYsXLyIiIgLu7u5o0KABpk2bhqKiIpM2sbGx6NKlC1xdXdGyZUusXLmyKj4yUblcvy5Dy9WrQPv2coyLu7vSVRERKU/R4JKTk4Pg4GAsXbq0zPN//fUXevXqhTZt2iA2NhZHjx7F7Nmz4ebmZmgzZcoUbNiwAatXr8bu3btx9epVPPnkk4bzxcXFiIiIQEFBAfbt24dvv/0WK1euxJw5cwxtUlJSEBERgf79+yMxMRGTJ0/GSy+9hK1bt1bdhye6h+xsudPz6dNAkyZyVVxvb6WrIiKyEsJKABBr1641eWzEiBHi+eefv+dzMjMzhbOzs1i9erXhseTkZAFAxMXFCSGE2Lx5s3BwcBCpqamGNsuWLRMeHh4iPz9fCCHEO++8I9q3b1/qvcPDw8tdf1ZWlgAgsrKyyv0corvl5QkRFiYEIES9ekKcPKl0RUREVc+c71CrHeOi0+mwadMmtG7dGuHh4WjQoAFCQkJMLifFx8ejsLAQYWFhhsfatGmDJk2aIC4uDgAQFxeHoKAg+Pj4GNqEh4dDq9UiKSnJ0Kbka+jb6F+jLPn5+dBqtSYHUWUUFwNjxgDbtwM1awKbNwOBgUpXRURkXaw2uKSnp+P27dtYsGABBg0ahG3btuGJJ57Ak08+id27dwMAUlNT4eLiAi8vL5Pn+vj4IDU11dCmZGjRn9efu18brVaL3NzcMuubP38+PD09DYe/v3+lPzPZLyGASZOAn3+WA3DXrgW6d1e6KiIi62O1wUWn0wEAhg0bhilTpqBTp06YMWMGBg8ejOXLlytcHTBz5kxkZWUZjkuXLildEqnYvHnA0qWARgP897/Ao48qXRERkXWy2uBSr149ODk5oV27diaPt23b1jCryNfXFwUFBcjMzDRpk5aWBl9fX0Obu2cZ6e8/qI2Hhwdq1KhRZn2urq7w8PAwOYgqYvly4P335e0lS4ARI5Sth4jImlltcHFxcUH37t1x6tQpk8dPnz6Npk2bAgC6du0KZ2dn7Nixw3D+1KlTuHjxIkJDQwEAoaGhOHbsGNLT0w1tYmJi4OHhYQhFoaGhJq+hb6N/DaKq8uuvwBtvyNuzZwMTJypbDxGRtXNS8s1v376Ns2fPGu6npKQgMTERderUQZMmTTBt2jSMGDECffr0Qf/+/REdHY0NGzYgNjYWAODp6Ynx48dj6tSpqFOnDjw8PPDmm28iNDQUDz/8MABg4MCBaNeuHUaPHo1FixYhNTUV7733HiZMmABXV1cAwGuvvYZ//etfeOeddzBu3Djs3LkTv/zyCzZt2lTtfydkP3buBEaNkuNbXn0VmDtX6YqIiFSgGmY53dOuXbsEgFLHmDFjDG2+/vpr0bJlS+Hm5iaCg4NFVFSUyWvk5uaKN954Q3h7ewt3d3fxxBNPiGvXrpm0OX/+vHjsscdEjRo1RL169cTbb78tCgsLS9XSqVMn4eLiIpo3by5WrFhh1mfhdGgyx6FDQtSqJac9Dx8uRFGR0hURESnHnO9QjRBCKJibbIZWq4WnpyeysrI43oXu68wZoGdPuTpu//7Ali3A/zr/iIjskjnfoVY7xoXIFl29CgwcKENL585AVBRDCxGRORhciKpJZiYwaJDcPLFFC9nTws45IiLzMLgQVYPcXGDoUODYMcDXF9i2DbhrzUMiIioHBheiKlZUBDz7LPD774Cnp9w0sXlzpasiIlInBheiKqSf6rx+vRzLsn490LGj0lUREakXgwtRFZo1C/jmG8DBQe5D1KeP0hUREakbgwtRFVm8GFiwQN7+6itg2DBl6yEisgUMLkRV4PvvgalT5e3584Hx45Wth4jIVjC4EFnY5s3A2LHy9pQpwPTpytZDRGRLGFyILCguDnjqKTmT6PnngU8+ATQapasiIrIdDC5EFpKUBEREyDVbHnvMOCiXiIgsh/+sElnAxYtAeDhw6xbw8MPA6tWAs7PSVRER2R4GF6JKunFDhpYrV4C2bYGNG4GaNZWuiojINjG4EFXC7dvy8tDJk4C/v1wVt25dpasiIrJdDC5EFVRQAAwfDhw4ANSpI0OLv7/SVRER2TYGF6IK0OmAF1+UmyW6u8sp0G3bKl0VEZHtY3AhMpMQcn2WH38EnJyANWuAkBClqyIisg8MLkRmmj8fWLJE3v72Wzkwl4iIqgeDC5EZ/v1v4N135e3PPgOee07ZeoiI7A2DC1E5rVkDvPaavD1rFvDWW8rWQ0RkjxhciMohNlb2ruh0wEsvAR99pHRFRET2icGF6AESE4Fhw4D8fCAyEli2jPsPEREphcGF6D7++gsYNAjQaoG+fY0ziYiISBkMLkT3kJoKDBwIpKUBwcHAunWAm5vSVRER2TcGF6IyZGXJnpZz54DmzYHoaMDTU+mqiIiIwYXoLnl5ckzLkSOAj49cHdfXV+mqiIgIYHAhMlFcLGcP7d4NeHgAW7YALVooXRUREekxuBD9jxDA668Da9cCrq5yTEvnzkpXRUREJTG4EP3P7NlyZVwHB+CHH4B+/ZSuiIiI7sbgQgS599Df/y5vL18OPPmksvUQEVHZGFzI7v3wAzBpkrz90UfAyy8rWw8REd0bgwvZta1bgTFj5O233pJ7EBERkfVicCG7tX8/MHw4UFQEjBwJLF7MpfyJiKwdgwvZpeRkICICyMmRq+OuXCkH5RIRkXXjP9Vkdy5dAsLDgZs3gYceAn77DXBxUboqIiIqDwYXsis3b8rQcukSEBgIbNoE1KqldFVERFReDC5kN3JygMGD5WWiRo3kUv716ildFRERmYPBhexCYSHw9NPAn38C3t4ytDRponRVRERkLgYXsnk6HTBunNx3qEYNeXmoXTulqyIioopgcCGbJgTwt78B338PODoCv/4KhIYqXRUREVUUgwvZtEWL5PosALBiBfD448rWQ0RElcPgQjbrm2+AGTPk7U8/BUaPVrYeIiKqPAYXsknr1xv3HJo+HZgyRdl6iIjIMioVXPLy8ixVB5HF7NkDjBhhHJQ7f77SFRERkaWYHVx0Oh3mzZuHRo0aoVatWjh37hwAYPbs2fj6668tXiCROY4eBYYOBfLy5J9ffsn9h4iIbInZweWjjz7CypUrsWjRIriUWCe9Q4cO+M9//mPR4ojMkZIiV8XNygJ69QJ++glwclK6KiIisiSzg8t3332Hr776CqNGjYKjo6Ph8eDgYJw8edKixRGVV3q63CwxNRUICgI2bJBrthARkW0xO7hcuXIFLVu2LPW4TqdDYWGhRYoiModWCzz2GHD2LNCsGbB1K+DlpXRVRERUFcwOLu3atcPvv/9e6vFff/0VnTt3tkhRROWVlwdERgKHDwP168ul/Bs2VLoqIiKqKmaPAJgzZw7GjBmDK1euQKfTYc2aNTh16hS+++47bNy4sSpqJCpTcTHw/PPArl1yh+ctW4BWrZSuioiIqpLZPS7Dhg3Dhg0bsH37dtSsWRNz5sxBcnIyNmzYgEcffbQqaiQqRQhgwgTgt98AFxdg3Tqga1elqyIioqpWoTkXvXv3RkxMjKVrISq3Dz4wTnVetQp45BGlKyIioupQ4cmiBQUFSE9Ph06nM3m8SZMmlS6K6H6WLgU+/FDe/uIL4KmnlK2HiIiqj9nB5cyZMxg3bhz27dtn8rgQAhqNBsXFxRYrjuhuP/8MvPmmvD13LvDaa8rWQ0RE1cvs4PLiiy/CyckJGzduRMOGDaHhsqRUTWJi5EaJ+vEts2crXREREVU3s4NLYmIi4uPj0aZNm6qoh6hMBw8CTzwBFBYCzzwDfPYZl/InIrJHFVrH5caNG1VRC1GZTp0CHn8cyMkBwsKA774DSizaTEREdsTs4LJw4UK88847iI2Nxc2bN6HVak0OIku6ckXuP3TjBtCtG7BmDeDqqnRVRESkFI0QQpjzBAcHmXXuHtti74NztVotPD09kZWVBQ8PD6XLsQkZGUCfPkBSEtC6NfDHH3J1XCIisi3mfIeaPcZl165dFS6MqLzu3AGGDJGhxc9P7j/E0EJERGYHl759+1ZFHUQG+gG4+/bJzRKjo+XmiURERBVagC4zMxNff/01kpOTAQDt27fHuHHj4OnpadHiyP4IAbz8MrBpE+DmBmzcCAQFKV0VERFZC7MH5x46dAgtWrTA4sWLkZGRgYyMDHz66ado0aIFDh8+bNZr7dmzB0OGDIGfnx80Gg2ioqLu2fa1116DRqPBP//5T5PHMzIyMGrUKHh4eMDLywvjx4/H7du3TdocPXoUvXv3hpubG/z9/bFo0aJSr7969Wq0adMGbm5uCAoKwubNm836LGQZ06cD334rZw2tXg307Kl0RUREZE3MDi5TpkzB0KFDcf78eaxZswZr1qxBSkoKBg8ejMmTJ5v1Wjk5OQgODsbSpUvv227t2rX4888/4efnV+rcqFGjkJSUhJiYGGzcuBF79uzBK6+8Yjiv1WoxcOBANG3aFPHx8fj444/xwQcf4KuvvjK02bdvH0aOHInx48cjISEBkZGRiIyMxPHjx836PFQ5H38sDwD4+mtg8GBl6yEiIiskzOTm5iaSk5NLPZ6UlCRq1Khh7ssZABBr164t9fjly5dFo0aNxPHjx0XTpk3F4sWLDedOnDghAIiDBw8aHtuyZYvQaDTiypUrQgghvvjiC+Ht7S3y8/MNbaZPny4CAwMN95955hkRERFh8r4hISHi1VdfLXf9WVlZAoDIysoq93PIaOVKIeSFIiE+/ljpaoiIqDqZ8x1qdo+Lh4cHLl68WOrxS5cuoXbt2pUOUiXpdDqMHj0a06ZNQ/v27Uudj4uLg5eXF7p162Z4LCwsDA4ODti/f7+hTZ8+feDi4mJoEx4ejlOnTuHWrVuGNmFhYSavHR4ejri4OIt+Hirbxo3A+PHy9t/+Jg8iIqKymB1cRowYgfHjx+Pnn3/GpUuXcOnSJfz000946aWXMHLkSIsWt3DhQjg5OeGtt94q83xqaioaNGhg8piTkxPq1KmD1NRUQxsfHx+TNvr7D2qjP1+W/Px8Lr5nAXv3Ak8/DRQXA2PGAAsXKl0RERFZM7NnFX3yySfQaDR44YUXUFRUBABwdnbG66+/jgULFlissPj4eHz22Wc4fPiwVW7kOH/+fMydO1fpMlTt+HE5jiUvD4iIAP79b8DB7ChNRET2xOyvCRcXF3z22We4desWEhMTkZiYiIyMDCxevBiuFlyL/ffff0d6ejqaNGkCJycnODk54cKFC3j77bfR7H+Levj6+iI9Pd3keUVFRcjIyICvr6+hTVpamkkb/f0HtdGfL8vMmTORlZVlOC5dulSpz2tvzp+XS/lnZgI9egC//AI4OytdFRERWbsK//+tu7s7goKCEBQUBHd3d0vWBAAYPXo0jh49aghHiYmJ8PPzw7Rp07B161YAQGhoKDIzMxEfH2943s6dO6HT6RASEmJos2fPHhQWFhraxMTEIDAwEN7e3oY2O3bsMHn/mJgYhIaG3rM+V1dXeHh4mBxUPtevy9By9SrQoYMc41IF/wkREZENKvelonHjxj2wjUajwddff13uN799+zbOnj1ruJ+SkoLExETUqVMHTZo0Qd26dU3aOzs7w9fXF4GBgQCAtm3bYtCgQXj55ZexfPlyFBYWYuLEiXj22WcNU6efe+45zJ07F+PHj8f06dNx/PhxfPbZZ1i8eLHhdSdNmoS+ffviH//4ByIiIvDTTz/h0KFDJlOmyTKys+VOz6dPA02bylVx/5cfiYiIHqjcwUU/A6csxcXF2L59O/Lz880KLocOHUL//v0N96dOnQoAGDNmDFauXFmu11i1ahUmTpyIAQMGwMHBAcOHD8eSJUsM5z09PbFt2zZMmDABXbt2Rb169TBnzhyTtV569OiBH374Ae+99x5mzZqFVq1aISoqCh06dCj3Z6EHy88HnnwSOHQIqFdP7j/UqJHSVRERkZqYvTv03datW4dZs2bh6tWrmD59OmbMmGGp2lSFu0PfX3ExMGoU8PPPQM2awK5dQPfuSldFRETWwJzv0AqPcdm7dy969+6N5557DoMHD8a5c+fsNrTQ/QkBTJokQ4uzM7B2LUMLERFVjNnB5cSJExgyZAj69euH1q1b49SpU1i4cKFhoCvR3ebNA5YuBTQa4PvvgUcfVboiIiJSq3IHl0uXLmHs2LEIDg6Gk5MTjh49iq+//hqNGzeuyvpI5ZYvB95/X97+/HPgmWeUrYeIiNSt3INzAwMDodFoMHXqVPTs2RNnzpzBmTNnSrUbOnSoRQsk9fr1V+CNN+TtOXOACROUrYeIiNSv3INzHcqxpKlGo0FxcXGli1IjDs41tXMn8NhjQEEB8OqrwLJl8lIRERHR3cz5Di13j4tOp6t0YWQf4uOBYcNkaBk+3Di+hYiIqLK4MwxZ1Jkzsqfl9m3gkUeAVasAR0elqyIiIlvB4EIWc/UqMHCgXNK/Sxc57dmC21cRERExuJBlZGYCgwbJzRNbtgQ2bwY41IeIiCyNwYUqLTcXGDoUOHYM8PUFtm0DfHyUroqIiGxRuYLLkiVLkJeXBwC4ePEiKrlLANmQoiLg2WeB338HPD3l/kMBAUpXRUREtqpcwWXq1KnQarUAgICAAFy/fr1KiyJ1EEJOdV6/HnBzAzZsADp2VLoqIiKyZeWaDu3n54fffvsNjz/+OIQQuHz5sqEH5m5NmjSxaIFkvWbNAr75BnBwkPsQ9e6tdEVERGTryrUA3VdffYU333wTRUVF92wjhOACdHa0AN3ixcDUqfL2118D48YpWw8REamXOd+h5V45Nzs7GxcuXEDHjh2xfft21K1bt8x2wcHB5ldsA+wpuHz/PTB6tLw9fz7ATcGJiKgyqmTl3Nq1a6NDhw5YsWIFevbsCVcu0GGXNm8Gxo6Vt6dMAaZPV7YeIiKyL+UOLnpjxowBAMTHxyM5ORkA0K5dO3Tp0sWylZHViYsDnnpKziR6/nngk0+4lD8REVUvs4NLeno6nn32WcTGxsLLywsAkJmZif79++Onn35C/fr1LV0jWYGkJCAiQq7Z8thjxkG5RERE1cnsr54333wT2dnZSEpKQkZGBjIyMnD8+HFotVq89dZbVVEjKeziRSA8HLh1C3j4YWD1asDZWemqiIjIHpV7cK6ep6cntm/fju7du5s8fuDAAQwcOBCZmZmWrE81bHVw7o0bcprzyZNA27Zyobl7jMsmIiKqEHO+Q83ucdHpdHAu43+3nZ2dodPpzH05smK3b8vLQydPAv7+clVchhYiIlKS2cHlkUcewaRJk3D16lXDY1euXMGUKVMwYMAAixZHyikoAIYPBw4ckGFl2zYZXoiIiJRkdnD517/+Ba1Wi2bNmqFFixZo0aIFAgICoNVq8fnnn1dFjVTNdDrgxRdlWHF3BzZtAtq0UboqIiKiCswq8vf3x+HDh7F9+3acPHkSANC2bVuEhYVZvDiqfkIAkycDP/4IODkBa9YAISFKV0VERCSZPTiXymYrg3P//nfgvffk7VWrgOeeU7YeIiKyfVU6OJds17//bQwtn33G0EJERNaHwYUAyEtCr70mb7/7LsAleYiIyBoxuBBiY2Xvik4HvPwyMG+e0hURERGVjcHFDsybJ5fnLyuQJCQAQ4cC+fnAE08AX3zB/YeIiMh6mT2rCJCL0J09exbp6emlFp3r06ePRQojy5g3D5gzR97W/zl7tvzzr7/kvkPZ2UDfvsAPP8iZRERERNbK7K+pP//8E8899xwuXLiAuyckaTQaFBcXW6w4qpySoUVPf//ll4GBA4G0NCA4GFi3DnBzq/4aiYiIzGH2dOhOnTqhdevWmDt3Lho2bAjNXdcVPD09LVqgWljbdOiyQktJvr5AairQvDmwd6+8T0REpARzvkPN7nE5c+YMfv31V7Rs2bLCBVLVelBoAWRoqVlTro7L0EJERGph9uDckJAQnD17tipqIQsoT2jRy8mR41qIiIjUwuwelzfffBNvv/02UlNTERQUVGqn6I4dO1qsODKPOaFF7+4Bu0RERNbM7DEuDg6lO2k0Gg2EEHY9OFfpMS4VCS0lffghwwsRESmjSse4pKSkVLgwqhqVDS0Ae16IiEgdzA4uTZs2rYo6qIIsEVr0GF6IiMjaVXh36BMnTuDixYsoKCgweXzo0KEWKUxtlLpU5OAAWHJ/b41GLv1PRERUXar0UtG5c+fwxBNP4NixY4axLQAM67nY6xgXpcyda7keF/3rERERWSuzp0NPmjQJAQEBSE9Ph7u7O5KSkrBnzx5069YNsbGxVVAi3c/s2XJgrSVwgC4REVk7s3tc4uLisHPnTtSrVw8ODg5wcHBAr169MH/+fLz11ltISEioijrpPvRhg7OKiIjI1pnd41JcXIzatWsDAOrVq4erV68CkIN2T506ZdnqqNwq0/PC0EJERGphdo9Lhw4dcOTIEQQEBCAkJASLFi2Ci4sLvvrqKzRv3rwqaqRyqkjPC0MLERGpidnB5b333kNOTg4A4MMPP8TgwYPRu3dv1K1bFz///LPFCyTzmBNeGFqIiEhtKjwduqSMjAx4e3uX2inanii9cu7dHrS+C0MLERFZC3O+Q80e46J39uxZbN26Fbm5uahTp05FX4aqyP3GvDC0EBGRWpkdXG7evIkBAwagdevWePzxx3Ht2jUAwPjx4/H2229bvECquLLCC0MLERGpmdnBZcqUKXB2dsbFixfh7u5ueHzEiBGIjo62aHFUefrwotEwtBARkfqZPTh327Zt2Lp1Kxo3bmzyeKtWrXDhwgWLFUaWM3s2AwsREdkGs3tccnJyTHpa9DIyMuDq6mqRooiIiIjKYnZw6d27N7777jvDfY1GA51Oh0WLFqF///4WLY6IiIioJLMvFS1atAgDBgzAoUOHUFBQgHfeeQdJSUnIyMjA3r17q6JGIiIiIgAV6HHp0KEDTp8+jV69emHYsGHIycnBk08+iYSEBLRo0aIqaiQiIiICYKEF6Mj6FqAjIiJSC3O+Q82+VAQAeXl5OHr0KNLT06HT6UzODR06tCIvSURERPRAZgeX6OhovPDCC7hx40apcxqNBsXFxRYpjIiIiOhuZo9xefPNN/H000/j2rVr0Ol0JgdDCxEREVUls4NLWloapk6dCh8fn6qoh4iIiOiezA4uTz31FGJjY6ugFCIiIqL7M3tW0Z07d/D000+jfv36CAoKgrOzs8n5t956y6IFqgVnFREREVVMlc4q+vHHH7Ft2za4ubkhNjYWGo3GcE6j0dhtcCEiIqKqZ3ZweffddzF37lzMmDEDDg5mX2kiIiIiqjCzk0dBQQFGjBhhkdCyZ88eDBkyBH5+ftBoNIiKijKcKywsxPTp0xEUFISaNWvCz88PL7zwAq5evWryGhkZGRg1ahQ8PDzg5eWF8ePH4/bt2yZtjh49it69e8PNzQ3+/v5YtGhRqVpWr16NNm3awM3NDUFBQdi8eXOlPx8RERFZltnpY8yYMfj5558t8uY5OTkIDg7G0qVLS527c+cODh8+jNmzZ+Pw4cNYs2YNTp06VWqBu1GjRiEpKQkxMTHYuHEj9uzZg1deecVwXqvVYuDAgWjatCni4+Px8ccf44MPPsBXX31laLNv3z6MHDkS48ePR0JCAiIjIxEZGYnjx49b5HMSERGRZZg9OPett97Cd999h+DgYHTs2LHU4NxPP/20YoVoNFi7di0iIyPv2ebgwYN46KGHcOHCBTRp0gTJyclo164dDh48iG7dugGQC+Q9/vjjuHz5Mvz8/LBs2TK8++67SE1NhYuLCwBgxowZiIqKwsmTJwEAI0aMQE5ODjZu3Gh4r4cffhidOnXC8uXLy1U/B+cSERFVjDnfoWb3uBw7dgydO3eGg4MDjh8/joSEBMORmJhY0ZrLJSsrCxqNBl5eXgCAuLg4eHl5GUILAISFhcHBwQH79+83tOnTp48htABAeHg4Tp06hVu3bhnahIWFmbxXeHg44uLi7llLfn4+tFqtyUFERERVy+zBubt27aqKOh4oLy8P06dPx8iRIw1pLDU1FQ0aNDBp5+TkhDp16iA1NdXQJiAgwKSNfvG81NRUeHt7IzU1tdSCej4+PobXKMv8+fMxd+7cSn8uIiIiKj9VTAsqLCzEM888AyEEli1bpnQ5AICZM2ciKyvLcFy6dEnpkoiIiGxehXaHrk760HLhwgXs3LnT5NqXr68v0tPTTdoXFRUhIyMDvr6+hjZpaWkmbfT3H9RGf74srq6ucHV1rfgHIyIiIrNZdY+LPrScOXMG27dvR926dU3Oh4aGIjMzE/Hx8YbHdu7cCZ1Oh5CQEEObPXv2oLCw0NAmJiYGgYGB8Pb2NrTZsWOHyWvHxMQgNDS0qj4aERERVYCiweX27dtITEw0DOpNSUlBYmIiLl68iMLCQjz11FM4dOgQVq1aheLiYqSmpiI1NRUFBQUAgLZt22LQoEF4+eWXceDAAezduxcTJ07Es88+Cz8/PwDAc889BxcXF4wfPx5JSUn4+eef8dlnn2Hq1KmGOiZNmoTo6Gj84x//wMmTJ/HBBx/g0KFDmDhxYrX/nRAREdF9CAXt2rVLACh1jBkzRqSkpJR5DoDYtWuX4TVu3rwpRo4cKWrVqiU8PDzE2LFjRXZ2tsn7HDlyRPTq1Uu4urqKRo0aiQULFpSq5ZdffhGtW7cWLi4uon379mLTpk1mfZasrCwBQGRlZVXo74KIiMhemfMdavY6LlQ2ruNCRERUMVW6jgsRERGRUhhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiOzF9u1Au3byT5VicCEiIrIHQgCzZgHJyfJPIZSuqEIYXIiIiOzBtm3AwYPy9sGD8r4KMbgQERHZOiGA2bMBR0d539FR3ldhrwuDCxERka3T97YUF8v7xcWq7XVhcCEiIrJlQgDvvgs43PWVr9JeFyelCyAiIiILKioCTpyQPSoHDgA7dgB//VW6Xclel/Dw6q+zghhciIiI1EoIICVFBhR9UDl8GLhzp3zP1/e6DBwIaDRVW6uFMLgQERGpRVqaDCj6kHLwIHDzZul2tWsD3boB9eoBq1ff+/VU2OvC4EJERGSNsrOB+HjT3pSLF0u3c3EBgoOBhx4CuneXfwYGyh6UkBDZq6IflFsWlfW6MLgQEREpraAAOHrUNKQkJ5ceOKvRAG3bGgNK9+5Ax46Aq2vp19y61bhuy/2orNeFwYWIiKg66XTA6dMynOiDSmKiDC93a9LENKR07Qp4eDz4PUqu23K/3hY9FfW6MLgQERFVFSGAy5dNx6QcOgRotaXb1qljermne3fAx6di71tyldzyUFGvC4MLERGRpWRklB48m5paul2NGrL3RB9SHnoICAiwTG+HvrfFwUH27pSXg4Mqel0YXIiIiCrizh0gIcE0pJw9W7qdoyMQFGTam9KuHeBURV/BBQVyEK85oQWQ7S9dks8va8yMlWBwISIiepCiIiApyXTw7PHjZY8fadnS2IvSvTvQqRPg7l59tbq6yhqvXzf/uQ0aWHVoARhciIiITAkBnDtXelG33NzSbX19TUNKt25yrIrS/P3lYYMYXIiIyL6lpppe7jl4UI5VuZuHhwwmJYNKo0ZWPR7EFjG4EBGR/dBqSy/qdulS6XYuLkDnzqYzfFq3Lr1RIVU7BhciIrJN+fnAkSOmvSknT5a9qFu7dqaDZ4OCZHghq8PgQkRE6ldcDJw6ZQwpBw7I0FJYWLpt06amIaVLF7m3D6kCgwsREamLEPLyTsnLPfHxcm+fu9WrZ3q5p3t3OXOGVIvBhYiIrNvNm6aLuh04AKSnl27n7i4XdSvZm9KsGQfP2hgGFyIish45OXJRt5K9KefOlW7n5CQ3FyzZm9K2bdUt6kZWgz9hIiJSRmFh2Yu6lbXia+vWpj0pwcFy2XyyOwwuRERU9YSQy+GXnOFz+DCQl1e6rZ+faUjp1g3w8qr2ksk6MbgQEZHlXbtm2pNy6BBw61bpdp6exkGzJRd1I7oHBhciIqqcrCwZTEr2ply+XLqdq6tc1K1kb0rLllzUjczC4EJEROWXl2e6qNuBA3L9lLs5OBgXddMHlQ4duKgbVRqDCxERla24WK40W/KSz9GjZS/qFhBgermnSxegVq3qr5lsHoMLERHJwbMXLxp7UQ4elIu63b5dum39+qUHz9avX/01k11icCEiskc3bpiOSTlwALh+vXS7mjVlMCnZm9K0KRd1I8UwuBAR2brbt+XU45JBJSWldDtnZ7moW8nelDZtAEfH6q+Z6B4YXIiIbElhIXDsmGlISUoqe1G3wMDSi7q5uVV/zURmUHQO2p49ezBkyBD4+flBo9EgKirK5LwQAnPmzEHDhg1Ro0YNhIWF4cyZMyZtMjIyMGrUKHh4eMDLywvjx4/H7buuyR49ehS9e/eGm5sb/P39sWjRolK1rF69Gm3atIGbmxuCgoKwefNmi39eIiKL0umA06eBVauASZOA0FDAw0Pu1/Paa8A338gQo9MBjRsDTzwBzJ8PbN8u11Q5eRL47jvgzTeBkBCGFlIFRXtccnJyEBwcjHHjxuHJJ58sdX7RokVYsmQJvv32WwQEBGD27NkIDw/HiRMn4Pa/X7BRo0bh2rVriImJQWFhIcaOHYtXXnkFP/zwAwBAq9Vi4MCBCAsLw/Lly3Hs2DGMGzcOXl5eeOWVVwAA+/btw8iRIzF//nwMHjwYP/zwAyIjI3H48GF06NCh+v5CiIju5+rV0ou6ZWaWbuflVXpHZD+/6q6WqGoIKwFArF271nBfp9MJX19f8fHHHxsey8zMFK6uruLHH38UQghx4sQJAUAcPHjQ0GbLli1Co9GIK1euCCGE+OKLL4S3t7fIz883tJk+fboIDAw03H/mmWdERESEST0hISHi1VdfLXf9WVlZAoDIysoq93OIiO7p1i0hYmKE+PvfhYiMFMLPTwg598f0cHMTokcPISZNEmLVKiFOnxZCp1O6eiKzmPMdarVjXFJSUpCamoqwsDDDY56enggJCUFcXByeffZZxMXFwcvLC926dTO0CQsLg4ODA/bv348nnngCcXFx6NOnD1xKLHoUHh6OhQsX4tatW/D29kZcXBymTp1q8v7h4eGlLl2VlJ+fj/z8fMN9rVZrgU9NRHYpLw9ITDTtTTl9unQ7Bwe5iJu+N+Whh4D27eWgWiI7YbXBJTU1FQDg4+Nj8riPj4/hXGpqKho0aGBy3snJCXXq1DFpExAQUOo19Oe8vb2Rmpp63/cpy/z58zF37twKfDIismvFxUBysul6KUePAkVFpds2b246eLZzZzk9mciOWW1wsXYzZ8406aXRarXw9/dXsCIisjpCAOfPm87wiY8HcnJKt23QwHR5/G7dgHr1qr1kImtntcHF19cXAJCWloaGDRsaHk9LS0OnTp0MbdLT002eV1RUhIyMDMPzfX19kZaWZtJGf/9BbfTny+Lq6gpXV9cKfDIiUsT27cBbbwFLlgAlLkFbVHq6DCclg8qNG6Xb1aolg0nJ3hR/fy7qRlQOVhtcAgIC4Ovrix07dhiCilarxf79+/H6668DAEJDQ5GZmYn4+Hh07doVALBz507odDqEhIQY2rz77rsoLCyE8/+uA8fExCAwMBDe3t6GNjt27MDkyZMN7x8TE4PQ0NBq+rREVKWEAGbNkpdoZs0CBgyofEi4fVv2npQMKefPl27n7CzXRynZmxIYyEXdiCqqGgYL31N2drZISEgQCQkJAoD49NNPRUJCgrhw4YIQQogFCxYILy8vsW7dOnH06FExbNgwERAQIHJzcw2vMWjQING5c2exf/9+8ccff4hWrVqJkSNHGs5nZmYKHx8fMXr0aHH8+HHx008/CXd3d/Hll18a2uzdu1c4OTmJTz75RCQnJ4v3339fODs7i2PHjpX7s3BWEZEVi442nYkTHW3e8/PzhTh0SIgvvhBi7Fgh2rcXwsGh9AwfjUaItm2FeOEFIf71LyH27xciL69qPhORDTHnO1TR4LJr1y4BoNQxZswYIYScEj179mzh4+MjXF1dxYABA8SpU6dMXuPmzZti5MiRolatWsLDw0OMHTtWZGdnm7Q5cuSI6NWrl3B1dRWNGjUSCxYsKFXLL7/8Ilq3bi1cXFxE+/btxaZNm8z6LAwuRFZKpxOie3chHB1luHB0lPfvNWW4uFiIkyeF+O47ISZOFCIkRAhX17KnIvv7CzF8uBALFgixc6cQ/P0nqhBzvkM1QgihVG+PLdFqtfD09ERWVhY8PDyULoeI9LZuBQYNKv14dDQQHg5cuWI6w+fgQaCs5Q28vU3HpHTvDtxnHBwRlZ8536FWO8aFiKjShABmz5bjSYqLjY9rNMCzz8ol7sta9qBGDaBLF9Og0rw5B88SWQEGFyKyXVu2yB6UuwlhXCrf0VEu6lYypLRvDzjxn0cia8TfTCKyLbm5QEwMEBUF/Pe/926n0cjZPYcOcVE3IhVhcCEi9btxA9i4EVi3Dti2Dbhz58HPEULujvzHH3KsCxGpAoMLEanTuXMyqERFyfCh0xnP+fsD+fky0JR8/G6OjnIMzMCBHL9CpBIMLkSkDkLIBd/0YeX4cdPznToBw4bJIy0NeOyxB79mcbEcA7NtG3tdiFSCwYWIrFdBARAbK8PKunVy6rKeoyPQpw8QGQkMHQo0ayYfFwIICSk9k+he2OtCpCoMLkRkXbKy5GygdeuAzZtN11SpWVOuyRIZCTz+OFCnTunnb9tW9kyie2GvC5GqMLgQkfIuXwbWr5dhZdcuoLDQeM7HR/aoREYCjzwi1165F/26LQ4O9x/bcjcHB/a6EKkEgwsRVT8hgKQkOVZl3To5JbmkNm2M41VCQmSwKI+CAuDiRfNCCyDbX7okn89d34msGoMLEVWPoiJg3z5jWDl3znhOowFCQ41hJTCwYu/h6iov+1y/bv5zGzRgaCFSAQYXIqo6d+7IsSNRUXKdlZs3jedcXYFHH5VBZcgQeUnIEvz95UFENonBhYgs6/p1YMMG42JweXnGc97ewODBcrzKwIFArVqKlUlE6sTgQkSVd+aMccryvn2mY0yaNZO9KpGRQK9e3AOIiCqF/4IQkfl0OjmgVj9e5cQJ0/NduhjDSlAQZ+oQkcUwuBBR+eTny6nKUVFy6vK1a8ZzTk5Av34yrAwdCjRpolSVRGTjGFyI6N4yM+UicFFRQHQ0kJ1tPFerllwEbtgw+aeXl0JFEpE9YXAhIlOXLhnHq8TGymnMeg0bGqcs9+/P6cNEVO0YXIjsnRDA0aPGsHL4sOn5du3kWJVhw4Bu3cq/GBwRURVgcCGyR0VFwO+/G8PK+fPGcxoN0LOnsWelVSvFyiQiuhuDC5G9uH3buBjcpk1ARobxnJubXFdl2DC5zkqDBoqVSUR0PwwuRLYsLU0uBhcVBWzfLmcG6dWtK1esHTZMrmBbs6ZiZRIRlReDC5GtOXVKXv6JigL+/FOOYdFr3tw4XqVHDy4GR0Sqw3+1iNROpwP27zeGlVOnTM9362YMK+3bczE4IlI1BhciNcrLA3bskGFl/Xp5SUjP2VlOVdYvBte4sXJ1EhFZGIMLkVpkZMhBtevWycXgcnKM5zw8jIvBPfYY4OmpXJ1ERFWIwYXImp0/b5yyvGcPUFxsPNeokXHKcr9+gIuLUlUSEVUbBhciayIEkJho3LzwyBHT8x06GMerdO3K8SpEZHcYXIiUVlgoe1P0PSsXLxrPOTgAvXoZw0rz5oqVSURkDRhciJSQnS3HqaxbJ8etZGYaz9WoAYSHy7ASEQHUq6dUlUREVofBhai6XLsmZwCtWydnBBUUGM/Vr29cDC4sDHB3V65OIiIrxuBCVFWEAE6eNI5X2b/f9HzLlsZLQKGhgKOjElUSEakKgwuRJRUXy9Vq9WHlzBnT8yEhxplAbdtycC0RkZkYXIgqKzdX7gOkXwzu+nXjORcX4JFHZM/KkCGAn59iZRIR2QIGF6KKuHkT2LhRhpWtW4E7d4znPD3loNrISDnI1sNDsTKJiGwNgwtReZ07Z5yy/Pvvco8gPX9/efknMhLo00cuu09ERBbH4EJ0L0IAhw8bx6scO2Z6PjjYGFY6deJ4FSKiasDgQlRSQQGwe7cMK+vXA5cvG885OsreFP3mhQEBipVJRGSvGFyItFpgyxYZVjZvlvf1atYEBg2SYSUiAqhTR7EyiYiIwYXs1ZUrskclKgrYtUsuu6/n4yN7VIYNAwYMANzcFCuTiIhMMbiQfRACSEoyDq49eND0fGCgcbxKSIjcI4iIiKwOgwvZruJiYO9eY1j56y/jOY0GePhh42JwbdooVycREZUbgwvZljt3gG3bZFDZuBG4ccN4ztVV7gM0bJhcDM7XV7k6iYioQhhcSP2uXwc2bJBhJSZGrmSr5+0NDB4sw0p4OFCrlnJ1EhFRpTG4kDqdPWtcX2XfPtPF4Jo2NY5X6dWLi8EREdkQBhdSB50OOHRIBpWoKODECdPznTsbw0rHjlwMjojIRjG4kPXKz5dTlfWbF169ajzn5AT07WtcDK5pU+XqJCKiasPgQtYlM1MuArdunVwULjvbeK5WLeCxx2RYefxxOX6FiIjsCoMLKe/SJeOU5dhYoKjIeM7X1zhl+ZFH5MwgIiKyWwwuVP2EkBsW6gfXHj5ser5tWzlWZdgwoHt3LgZHREQGDC5UPYqKgD/+MA6uPX/eeE6jAXr0MIaVVq0UKpKIiKwdgwtVnZwcYOtW42JwGRnGc25uwKOPyqAyeLDcH4iIiOgBGFzIstLSTBeDy883nqtb17gY3MCBcudlIiIiMzC4UOWdPm0crxIXJ8ew6DVvbhxc27OnnMZMRERUQfwWIfPpdMCBA8awcvKk6fmuXY3jVTp04GJwRERkMQwu9mD7duCtt4AlS+QmgxWRlwfs3CnDyoYNQGqq8ZyTE9C/vwwrQ4cCjRtbomoiIqJSGFxsnRDArFlAcrL8c8CA8veA3LoFbNokw0p0tBxsq1e7tlwELjJSLgrn6VkV1RMREZlgcLF127YBBw/K2wcPyvvh4fduf+GCcTG43buB4mLjOT8/43iVfv24GBwREVU7BhdbJgQwezbg6CgDiKOjvD9woLHXRQggMdEYVhITTV+jfXvjeJWuXbkYHBERKYrBxZaV7G0BZHg5eFDuBeTmZgwrFy8a2zg4yNk/+rDSokW1l01ERHQvDC626u7eFj2NRgaSko/VqCF7YSIjgYgIoH79ai+XiIioPBhcbNXdvS16QsjQ4uEBDB8uw0pYGODuXu0lEhERmYvBxRbdq7dFz8EBaN0a+PprrrFCRESqYtUjLYuLizF79mwEBASgRo0aaNGiBebNmwdRYmVWIQTmzJmDhg0bokaNGggLC8OZM2dMXicjIwOjRo2Ch4cHvLy8MH78eNy+fdukzdGjR9G7d2+4ubnB398fixYtqpbPWCX0vS1lhRZALiB36JBsR0REpCJWHVwWLlyIZcuW4V//+heSk5OxcOFCLFq0CJ9//rmhzaJFi7BkyRIsX74c+/fvR82aNREeHo68vDxDm1GjRiEpKQkxMTHYuHEj9uzZg1deecVwXqvVYuDAgWjatCni4+Px8ccf44MPPsBXX31VrZ/XIkr2ttyPfoZRyeX5iYiIrJ2wYhEREWLcuHEmjz355JNi1KhRQgghdDqd8PX1FR9//LHhfGZmpnB1dRU//vijEEKIEydOCADi4MGDhjZbtmwRGo1GXLlyRQghxBdffCG8vb1Ffn6+oc306dNFYGBguWvNysoSAERWVpb5H9SSoqOFkHGkfEd0tLL1EhGR3TPnO9Sqe1x69OiBHTt24PTp0wCAI0eO4I8//sBjjz0GAEhJSUFqairCSixj7+npiZCQEMTFxQEA4uLi4OXlhW7duhnahIWFwcHBAfv37ze06dOnD1xcXAxtwsPDcerUKdy6davM2vLz86HVak0OxZW3t0WPvS5ERKQyVh1cZsyYgWeffRZt2rSBs7MzOnfujMmTJ2PUqFEAgNT/7Zfj4+Nj8jwfHx/DudTUVDRo0MDkvJOTE+rUqWPSpqzXKPked5s/fz48PT0Nh7+/fyU/rQU8aGzL3fTrunCsCxERqYRVB5dffvkFq1atwg8//IDDhw/j22+/xSeffIJvv/1W6dIwc+ZMZGVlGY5Lly4pW5C+t8XclW0dHNjrQkREqmHV06GnTZtm6HUBgKCgIFy4cAHz58/HmDFj4OvrCwBIS0tDw4YNDc9LS0tDp06dAAC+vr5IT083ed2ioiJkZGQYnu/r64u0tDSTNvr7+jZ3c3V1has17dVTUCBXwNXpzHueTgdcuiSfb02fh4iIqAxWHVzu3LkDh7t6EBwdHaH735dzQEAAfH19sWPHDkNQ0Wq12L9/P15//XUAQGhoKDIzMxEfH4+uXbsCAHbu3AmdToeQkBBDm3fffReFhYVwdnYGAMTExCAwMBDe3t7V8VErz9VVXva5ft385zZowNBCRETqUA2DhStszJgxolGjRmLjxo0iJSVFrFmzRtSrV0+88847hjYLFiwQXl5eYt26deLo0aNi2LBhIiAgQOTm5hraDBo0SHTu3Fns379f/PHHH6JVq1Zi5MiRhvOZmZnCx8dHjB49Whw/flz89NNPwt3dXXz55ZflrtVqZhURERGpjDnfoVYdXLRarZg0aZJo0qSJcHNzE82bNxfvvvuuybRlnU4nZs+eLXx8fISrq6sYMGCAOHXqlMnr3Lx5U4wcOVLUqlVLeHh4iLFjx4rs7GyTNkeOHBG9evUSrq6uolGjRmLBggVm1crgQkREVDHmfIdqhOCoTEvQarXw9PREVlYWPDw8lC6HiIhINcz5DrXqWUVEREREJTG4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWowuBAREZFqMLgQERGRajC4EBERkWo4KV2ArRBCAAC0Wq3ClRAREamL/rtT/116PwwuFpKdnQ0A8Pf3V7gSIiIidcrOzoanp+d922hEeeINPZBOp8PVq1dRu3ZtaDQapcsxodVq4e/vj0uXLsHDw0PpcuwSfwbK489AefwZKM9afwZCCGRnZ8PPzw8ODvcfxcIeFwtxcHBA48aNlS7jvjw8PKzqP1R7xJ+B8vgzUB5/Bsqzxp/Bg3pa9Dg4l4iIiFSDwYWIiIhUg8HFDri6uuL999+Hq6ur0qXYLf4MlMefgfL4M1CeLfwMODiXiIiIVIM9LkRERKQaDC5ERESkGgwuREREpBoMLkRERKQaDC42YM+ePRgyZAj8/Pyg0WgQFRX1wOfExsaiS5cucHV1RcuWLbFy5coqr9NWmfv3HxsbC41GU+pITU2tnoJt0Pz589G9e3fUrl0bDRo0QGRkJE6dOvXA561evRpt2rSBm5sbgoKCsHnz5mqo1jZV5GewcuXKUr8Hbm5u1VSx7Vm2bBk6duxoWFwuNDQUW7Zsue9z1Pg7wOBiA3JychAcHIylS5eWq31KSgoiIiLQv39/JCYmYvLkyXjppZewdevWKq7UNpn796936tQpXLt2zXA0aNCgiiq0fbt378aECRPw559/IiYmBoWFhRg4cCBycnLu+Zx9+/Zh5MiRGD9+PBISEhAZGYnIyEgcP368Giu3HRX5GQByBdeSvwcXLlyopoptT+PGjbFgwQLEx8fj0KFDeOSRRzBs2DAkJSWV2V61vwOCbAoAsXbt2vu2eeedd0T79u1NHhsxYoQIDw+vwsrsQ3n+/nft2iUAiFu3blVLTfYoPT1dABC7d+++Z5tnnnlGREREmDwWEhIiXn311aouzy6U52ewYsUK4enpWX1F2SFvb2/xn//8p8xzav0dYI+LHYqLi0NYWJjJY+Hh4YiLi1OoIvvUqVMnNGzYEI8++ij27t2rdDk2JSsrCwBQp06de7bh70HVKs/PAABu376Npk2bwt/f/769A2Se4uJi/PTTT8jJyUFoaGiZbdT6O8DgYodSU1Ph4+Nj8piPjw+0Wi1yc3MVqsp+NGzYEMuXL8dvv/2G3377Df7+/ujXrx8OHz6sdGk2QafTYfLkyejZsyc6dOhwz3b3+j3gWKPKK+/PIDAwEN988w3WrVuH77//HjqdDj169MDly5ersVrbcuzYMdSqVQuurq547bXXsHbtWrRr167Mtmr9HeDu0ETVLDAwEIGBgYb7PXr0wF9//YXFixfjv//9r4KV2YYJEybg+PHj+OOPP5QuxW6V92cQGhpq0hvQo0cPtG3bFl9++SXmzZtX1WXapMDAQCQmJiIrKwu//vorxowZg927d98zvKgRe1zskK+vL9LS0kweS0tLg4eHB2rUqKFQVfbtoYcewtmzZ5UuQ/UmTpyIjRs3YteuXWjcuPF9297r98DX17cqS7R55vwM7ubs7IzOnTvzd6ESXFxc0LJlS3Tt2hXz589HcHAwPvvsszLbqvV3gMHFDoWGhmLHjh0mj8XExNzzOihVvcTERDRs2FDpMlRLCIGJEydi7dq12LlzJwICAh74HP4eWFZFfgZ3Ky4uxrFjx/i7YEE6nQ75+fllnlPt74DSo4Op8rKzs0VCQoJISEgQAMSnn34qEhISxIULF4QQQsyYMUOMHj3a0P7cuXPC3d1dTJs2TSQnJ4ulS5cKR0dHER0drdRHUDVz//4XL14soqKixJkzZ8SxY8fEpEmThIODg9i+fbtSH0H1Xn/9deHp6SliY2PFtWvXDMedO3cMbUaPHi1mzJhhuL93717h5OQkPvnkE5GcnCzef/994ezsLI4dO6bER1C9ivwM5s6dK7Zu3Sr++usvER8fL5599lnh5uYmkpKSlPgIqjdjxgyxe/dukZKSIo4ePSpmzJghNBqN2LZtmxDCdn4HGFxsgH567d3HmDFjhBBCjBkzRvTt27fUczp16iRcXFxE8+bNxYoVK6q9blth7t//woULRYsWLYSbm5uoU6eO6Nevn9i5c6cyxduIsv7+AZj8d923b1/Dz0Tvl19+Ea1btxYuLi6iffv2YtOmTdVbuA2pyM9g8uTJokmTJsLFxUX4+PiIxx9/XBw+fLj6i7cR48aNE02bNhUuLi6ifv36YsCAAYbQIoTt/A5ohBCi+vp3iIiIiCqOY1yIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIiIiEg1GFyIiIhINRhciIiISDUYXIjIpr344ovQaDSljkGDBildGhFVgJPSBRARVbVBgwZhxYoVJo+5uroqVA0RVQaDCxHZPFdXV/j6+ipdBhFZAC8VERERkWowuBCRzdu4cSNq1aplcvzf//2f0mURUQXwUhER2bz+/ftj2bJlJo/VqVNHoWqIqDIYXIjI5tWsWRMtW7ZUugwisgBeKiIiIiLVYI8LEdm8/Px8pKammjzm5OSEevXqKVQREVUUgwsR2bzo6Gg0bNjQ5LHAwECcPHlSoYqIqKI0QgihdBFERERE5cExLkRERKQaDC5ERESkGgwuREREpBoMLkRERKQaDC5ERESkGgwuREREpBoMLkRERKQaDC5ERESkGgwuREREpBoMLkRERKQaDC5ERESkGgwuREREpBr/D7MvZpHVaspnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "U = S - X * interX_lm32.params[\"X\"]\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "interaction_plot(\n",
    "    E, M, U, colors=[\"red\", \"blue\"], markers=[\"^\", \"D\"], markersize=10, ax=plt.gca()\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Minority Employment Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:06.158110Z",
     "iopub.status.busy": "2022-11-02T17:11:06.157619Z",
     "iopub.status.idle": "2022-11-02T17:11:06.706278Z",
     "shell.execute_reply": "2022-11-02T17:11:06.705421Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6876/3843909548.py:12: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for factor, group in factor_group:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'JPERF')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    jobtest_table = pd.read_table(\"jobtest.table\")\n",
    "except:  # do not have data already\n",
    "    url = \"http://stats191.stanford.edu/data/jobtest.table\"\n",
    "    jobtest_table = pd.read_table(url)\n",
    "\n",
    "factor_group = jobtest_table.groupby([\"MINORITY\"])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "colors = [\"purple\", \"green\"]\n",
    "markers = [\"o\", \"v\"]\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor],\n",
    "        marker=markers[factor],\n",
    "        s=12 ** 2,\n",
    "    )\n",
    "ax.set_xlabel(\"TEST\")\n",
    "ax.set_ylabel(\"JPERF\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:06.710584Z",
     "iopub.status.busy": "2022-11-02T17:11:06.710143Z",
     "iopub.status.idle": "2022-11-02T17:11:06.726877Z",
     "shell.execute_reply": "2022-11-02T17:11:06.726301Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.517\n",
      "Model:                            OLS   Adj. R-squared:                  0.490\n",
      "Method:                 Least Squares   F-statistic:                     19.25\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           0.000356\n",
      "Time:                        17:11:06   Log-Likelihood:                -36.614\n",
      "No. Observations:                  20   AIC:                             77.23\n",
      "Df Residuals:                      18   BIC:                             79.22\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.0350      0.868      1.192      0.249      -0.789       2.859\n",
      "TEST           2.3605      0.538      4.387      0.000       1.230       3.491\n",
      "==============================================================================\n",
      "Omnibus:                        0.324   Durbin-Watson:                   2.896\n",
      "Prob(Omnibus):                  0.850   Jarque-Bera (JB):                0.483\n",
      "Skew:                          -0.186   Prob(JB):                        0.785\n",
      "Kurtosis:                       2.336   Cond. No.                         5.26\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm = ols(\"JPERF ~ TEST\", data=jobtest_table).fit()\n",
    "print(min_lm.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:06.730623Z",
     "iopub.status.busy": "2022-11-02T17:11:06.730152Z",
     "iopub.status.idle": "2022-11-02T17:11:06.914126Z",
     "shell.execute_reply": "2022-11-02T17:11:06.913479Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6876/1345731516.py:2: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for factor, group in factor_group:\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgsAAAINCAYAAACwBjzTAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAABd+klEQVR4nO3deXhU9d3+8feZyUz2BEJYJRiWQNiXBBcWwRaqgohaFyJQqfYRAqhotVVxqbUW7eKKIrZWfw9g0FZRAVdUUFSUJKAgBAIa9i0syWQhM5k5vz8oPKIQJslMzmRyv65rrjbhZObOJM7c+ZxzvscwTdNERERE5DRsVgcQERGR0KayICIiIjVSWRAREZEaqSyIiIhIjVQWREREpEYqCyIiIlIjlQURERGpkcqCiIiI1CjC6gD14fP52L17N/Hx8RiGYXUcERGRRsM0TVwuF+3atcNmq3l20KjLwu7du0lJSbE6hoiISKO1Y8cO2rdvX+M2jbosxMfHA8e+0YSEBIvTiIiINB6lpaWkpKSceC+tSaMuC8d3PSQkJKgsiIiI1IE/u/F1gKOIiIjUSGVBREREaqSyICIiIjVSWRAREZEaqSyIiIhIjVQWREREpEYqCyIiIlIjlQURERGpkcqCiIiI1KhRr+AoIiIS6rYe2soLa17ANM0634dhGNzY/0Y6J3UOYDL/qSyIiIgE0YptK5i1chZ2w47NqP1A32f68JpeuiR1sawsaDeEiIhIEI3rNY6k6CS8phePz1Prm9f00iK6BeN6jbPse1BZEBERCaIYRwwzh87E4MwXbDoVA4OZQ2cS44gJcDL/qSyIiIgE2ZTMKTSPbl6nr02KTmJy5uQAJ6odlQUREZEgq+t0IRSmCgCGWZ/DMy1WWlpKYmIiJSUlJCQkWB1HRKTRCIcj9BubCk8FKY+ncKjykN9f0yK6Bdtv2x6UslCb91CdDSEi0gSFwxH6jc3x6cId79+ByZlLWqhMFUCTBRGRJqkuf+X+WDD/6g1XtXneoyKimDZwGg6bw6/7ru2kR5MFERGpUW3/yv2xUPqrtzGpzfPu8Xp46sun/LrfYE96dICjiEgT1diP0G+s/H3ea7MuQ7DXYlBZEBFpohr7EfqNVX3XXTiVYP9MVBZERJqwukwXNFWov/pMdU4l2D8TS8uC1+vlvvvuo2PHjkRHR9O5c2ceeuihep3KIyIi/qvtX7maKgRGIKcLDfEzsbQsPProo8yZM4fZs2ezceNGHn30Uf7yl7/w9NNPWxlLRKRJqc1fuZoqBM6pnvekqCSSopJqdT8N8TOxtCx8/vnnjB07ltGjR5OamspVV13FL37xC7766isrY4mINCn+/pWrqUJg/fh5NzC494J7mXlB6E16LC0LgwYN4sMPP2Tz5s0AfP3116xcuZJLLrnklNtXVVVRWlp60k1EROrPn+mCpgqB98Pn/fjzG4qTHkvLwl133cW4ceNIT0/H4XDQv39/ZsyYwfjx40+5/axZs0hMTDxxS0lJaeDEIiLh6UzTBU0VguP48w6ceH5DcdJj6QqOCxcu5M477+Svf/0rPXv2ZO3atcyYMYPHHnuM66+//ifbV1VVUVVVdeLj0tJSUlJStIKjiEgA1LS6oFZrDJ6j1UdZuH4hWb2yiIyIBPxb6bG+P5ParOBo6WThzjvvPDFd6N27NxMnTuS2225j1qxZp9w+MjKShISEk24iIhIYp/uLVlOF4IqKiGJSv0knigKE3qTH0rJQUVGBzXZyBLvdjs/nsyiRiEjTdsoj9HWsgiVqOnahoX8mlpaFMWPG8PDDD7N06VKKiopYtGgRjz32GFdccYWVsUREmqxTHaGvqYI1QmnSY+kxCy6Xi/vuu49Fixaxf/9+2rVrR1ZWFvfffz9Op/OMX6+rToqIBN4P95frWAVrnerYhUD9TBrNMQvx8fE88cQTbNu2jcrKSrZu3cqf/vQnv4qCiIgEx6mO0BdrhMqkx9LJQn1psiAiEhynOkJfrBGsSU+jmSyIiEhoOtUR+mKNUJj0RDT4I4qIiEitTB04laToJLJ6ZVny+CoLIiIiIe74pMcq2g0hIiIiNVJZEBERkRqpLIiIiEiNVBZERESkRioLIiIiUiOVBREREamRyoKIiIjUSGVBREREaqSyICIiIjVSWRAREZEaablnEZEg2npoKy+seYH6XODXMAxu7H8jnZM6BzCZiP9UFkREgmjFthXMWjkLu2HHZtR+mOszfXhNL12SuqgsiGW0G0JEJIjG9RpHUnQSXtOLx+ep9c1remkR3YJxvcZZ/a1IE6ayICISRDGOGGYOnYmBUaevNzCYOXQmMY6YACcT8Z/KgohIkE3JnELz6OZ1+tqk6CQmZ04OcCKR2lFZEBEJsrpOFzRVkFChsiAi0gDqMl3QVEFChcqCiEgDqO10QVMFCSUqCyIiDaQ20wVNFSSUqCyIiDQQf6cLmipIqFFZEBFpQP5MFzRVkFCjsiAi0oDONF3QVEFCkcqCiEgDq2m6oKmChCKVBRGRBna66YKmChKqVBZERCxwqumCpgoSqlQWREQs8OPpgqYKEspUFkRELPLD6YKmChLKVBZERCxyfLoAaKogIS3C6gAiIk3Z1IFTSYpOIqtXltVRRE5LZUFExEJREVFM6jfJ6hgiNdJuCBEREamRyoKIiIjUSGVBREREaqSyICIiIjVSWRAREZEaqSyIiIhIjVQWREREmqBdhyv83lZlQUREpIlZsfkA18z9wu/ttSiTiIhIE+HzmTz90Rae+HAz3qPVfn+dJgsiIiJNwJEKNzf8v9U8vmwzpglXZbT3+2s1WRAREQlz63aWMGV+HruOVBIZYeNPl/fioq6JPO7n16ssiIiIhCnTNFm4egcPvPktbq+Ps1vEMGd8Bj3aJVBaWur3/agsiIiIhKGjHi/3vbGef+ftBGBE91b8/Zp+JEY7an1fKgsiIiJhZtvBcqbMz2fjnlJsBtxxUTemXNAZm82o0/2pLIiIiISRZRv2cdura3EdraZFrJOnsvozuEtyve5TZUFERCQMeH0mj32wiWc+3grAgA7NeGb8ANomRtf7vlUWREREGrmDZVXcsnANn205CMCkQancM6o7zojArJBg6ToLqampGIbxk9u0adOsjCUiItJo5G8/zKVPr+SzLQeJdth5clw//nBZz4AVBbB4srB69Wq8Xu+Jj9evX8/IkSO5+uqrLUwlIiIS+kzT5H+/2Maflm7A4zXp1DKW5yZk0LV1fMAfy9Ky0LJly5M+fuSRR+jcuTPDhg2zKJGIiEjoq3BXc/fr63hz7W4ARvVuw6O/7EN8VO1Pi/RHyByz4Ha7mT9/PrfffjuGUbdTO0RERMLd1gNlZM/PY/O+Muw2g7svSefGIR2D+t4ZMmXhjTfe4MiRI0yaNOm021RVVVFVVXXi49qsPiUiItLYvbNuD3f+5xvKqqppGR/JM9cN4JyOSUF/3JC5kNQLL7zAJZdcQrt27U67zaxZs0hMTDxxS0lJacCEIiIi1vB4fTy8dAPZC/Ipq6rmnI5JLL1lSIMUBQDDNE2zQR6pBtu2baNTp068/vrrjB079rTbnWqykJKSQklJCQkJCQ0RVUREpEHtLz3K9JfX8FXRIQBuuqATd17UDYe9fn/vl5aWkpiY6Nd7aEjshnjxxRdp1aoVo0ePrnG7yMhIIiMjGyiViIiItb76/hDTXs7ngKuKuMgI/nZ1Hy7u1bbBc1heFnw+Hy+++CLXX389ERGWxxEREbGcaZq8sPJ7Zr1TgNdn0q11PHMmDKBTyzhL8lj+7rxs2TK2b9/ODTfcYHUUERERy7mOevjdf77hnfV7ARjbrx2zruxNjNO6t2zLy8IvfvELQuCwCREREctt3udiyvw8vjtQjsNucN+lPZh43tmWLylgeVkQEREReHPtLu56bR2VHi9tE6N4ZvwABnRobnUsQGVBRETEUu7qY6dF/r8vtgEwpEsyT47rR4u40DmgX2VBRETEIntKKpm6IJ81248AMP3CLtw2sit2W2itZKyyICIiYoHPthRzc84aDpW7SYiK4PFr+/Hz7q2tjnVKKgsiIiINyOczmbNiK39/fxM+E3q0TeC5CRl0aBFjdbTTUlkQERFpICWVHn776lqWbdwPwNUZ7Xno8l5EOewWJ6uZyoKIiEgD+HZ3Cdnz89l+qAJnhI0/XtaTced0sDqWX1QWREREguzfuTu49431VFX7aN88mjnjM+jdPtHqWH5TWRAREQmSox4vDy7+lpyvdgBwYbeWPH5tP5rFOC1OVjsqCyIiIkGw41AFUxfks25XCYYBt43oyvQLu2ALsdMi/aGyICIiEmAfb9rPjIVrKan00CzGwZPj+jOsa0urY9WZyoKIiEiA+HwmT35YyFMfFWKa0Ld9Is+MH0D75qF7WqQ/VBZERCRovB4vhUsLKS4oxl3mxhnnJDk9mbTRadhD/HTB2jpc7mbGK2tZsfkAABPO68B9l/YgMqLxf58qCyIiEnCu3S5y5+aSOyeXigMV2CJsYAAm+Kp9xLSMITM7k8zJmcS3i7c6br19veMIUxfks+tIJVEOG3++ojdXDmhvdayAMcxGfH3o0tJSEhMTKSkpISEhweo4IiICFC0vImdMDp5KD6b39G8xht3AEe0ga3EWqcNTGy5gAJmmyctfbefBtzbg9vpIbRHDnAkZdG8b+u9JtXkPtTVQJhERaQKKlhcxb+Q8PBU1FwUA02viqfAwb+Q8ipYXNUzAAKp0e/ntv79m5qL1uL0+RvZozZvThzSKolBbKgsiIhIQrt0ucsbkYPpMTJ9/Q+vj2+ZcloNrtyvICQOnqLicK579jNfzd2Ez4K5L0nl+YgaJ0Q6rowWFyoKIiARE7tzcY7se/CwKx5k+E0+5h7zn84KULLDe/3YvY2avpGCvi+Q4J/N/cy5ThnXGMBrf+gn+UlkQEZF683q85M7JPeOuh9MxfSa5c3LxerwBThY41V4fj75bwE3z8nAdrSbj7OYsuXkogzonWx0t6FQWRESk3gqXFlJxoKJe91G+v5zCtwsDlCiwisuq+NW/vmLO8q0A/HpwKgtvOo82iVEWJ2sYOnVSRETqrbigGFuEDV+1r873YdgNiguKYWwAgwVA3rZDTF2Qz77SKmKcdh79ZR/G9G1ndawGpbIgIiL15i5zH1tHoR4Mm4Hb5Q5MoAAwTZP/93kRf1q6kWqfSeeWsTw3IYO01o1/XYjaUlkQEZF6c8Y5oZ6r9pg+E2d8aFyNsbyqmrteX8fir3cDMLpPWx79ZR/iIpvm22bT/K5FRCSgktOT67ULAo6tu5Ccbv3Bglv2l5E9P4/C/WVE2AzuHtWdGwanhvXZDmeisiAiIvWWNjqNmJYx9TrIMbZVLGmj0gKYqvaWfrOH3/3na8rdXlrFR/LM+AEMTE2yNFMo0NkQIiJSb3aHnczsTAx73f76NmwGmdmZll1cyuP18dCSDUx7OZ9yt5fzOiWx5JYhKgr/pbIgIiIBkTk5E0e0A8NWu8Jg2AwcsQ4ybsoIUrKa7S89ynX/WMULK78HYPKwTsy/8VxaxTeN0yL9obIgIiIBEd8unqzFWRg2w+/CcHzbrMVZllx9ctV3Bxn11EpWFx0mPjKC5yZkcPcl3Ymw6+3xh/RsiIhIwKQOT2XiBxNxxDrOuEvi+ERh4rKJpA5LbZiA/2WaJs9/spXx//yS4rIqurWO562bh3BxrzYNmqOx0AGOIiISUKnDU5leMJ285/NY/exqKg5UYNiPTRBMn4npNYltFUtmdiYZN2U0+ESh9KiH3/37G979di8AV/Q/i4ev6EWMU2+Jp2OYplnPM2OtU5trcYuISMPzerwUvl1IcUExbpcbZ7yT5PRk0kalWXIw46a9LqbMz+P74nIcdoP7x/RkwrkdmuRpkbV5D1WNEhGRoLE77KSPTQ+JJZzfWLOLu19fR6XHS7vEKJ6dkEG/lGZWx2oUVBZERCSsVVV7eXjpRv73i20ADE1L5slx/UmKDY3VIhsDlQUREQlbu49UMnVBPmt3HAHglp914dYRXbHX8vTOpk5lQUREwtLKwmJuWbiGQ+VuEqIieGJcP36W3trqWI2SyoKIiIQVn8/k2eVb+PsHmzFN6NkugecmZJCSFGN1tEZLZUFERMJGSYWH219dy4cF+wG4NjOFB8f2JMqiZaTDhcqCiIiEhfW7SshekMeOQ5U4I2w8NLYn1w7sYHWssKCyICIijd6rq3dw75vrcVf7SEmKZs74DHqdlWh1rLChsiAiIo3WUY+XP7z1LQtX7wDgZ+mtePyafiTGOCxOFl5UFkREpFHacaiC7AV5rN9VimHAb0d2ZerwLth0WmTAqSyIiEij83HBfma8spaSSg/NYxw8ldWfoWktrY4VtlQWRESk0fD6TJ5ctpmnPtoCQN+UZjw7fgBnNYu2OFl4U1kQEZFG4VC5m1sXruHTwmIAJp53Nvde2p3ICJ0WGWwqCyIiEvLW7jjC1Pl57C45SpTDxqwre3NF//ZWx2oyVBZERCRkmabJ/C+388fF3+LxmnRMjmXOhAGkt6n5ksoSWCoLIiISkirdXmYuWsfra3YBcFHP1vz16r4kROm0yIamsiAiIiHn++JysufnUbDXhd1m8PuLu/E/QzthGDot0goqCyIiElLe+3Yvd7z6Na6qapLjIpl9XX/O69TC6lhNmsqCiIiEhGqvj7++v4m5K74DYGBqc2ZfN4DWCVEWJxOVBRERsdwBVxU35+Sz6rtDANw4pCN3XZKOw26zOJmAyoKIiFgst+gQUxfks99VRazTzl+u6svoPm2tjiU/YHll27VrFxMmTKBFixZER0fTu3dvcnNzrY4lIiJBZpom/1r5PeOeX8V+VxVdWsXx5vTBKgohyNLJwuHDhxk8eDAXXngh77zzDi1btqSwsJDmzZtbGUtERIKsrKqa37/2DUu/2QPApX3a8ugv+xAbqYF3KLL0p/Loo4+SkpLCiy++eOJzHTt2tDCRiIgE25b9LqbMz2fL/jIibAb3ju7O9YNSdVpkCLN0N8Rbb71FZmYmV199Na1ataJ///784x//OO32VVVVlJaWnnQTEZHGY8k3u7ls9mds2V9G64RIXpl8HpMGd1RRCHGWTha+++475syZw+23384999zD6tWrueWWW3A6nVx//fU/2X7WrFk8+OCDFiQVady8Hi+FSwspLijGXebGGeckOT2ZtNFp2B26CI8En8fr489vb+TFz4oAOL9TC56+rj/JcZHWBhO/GKZpmlY9uNPpJDMzk88///zE52655RZWr17NF1988ZPtq6qqqKqqOvFxaWkpKSkplJSUkJCgdcJFfsy120Xu3Fxy5+RScaACW4QNDMAEX7WPmJYxZGZnkjk5k/h28VbHlTC1t+Qo01/OJ3fbYQCyh3fmtyO7EqHTIi1VWlpKYmKiX++hlk4W2rZtS48ePU76XPfu3XnttddOuX1kZCSRkWqhIv4oWl5EzpgcPJUeTO+xvwl81b6Ttqk4UMGnD3/KqsdWkbU4i9ThqRYklXD2+dZibslZQ3GZm/jICP5+TV9+0bON1bGkliytdYMHD2bTpk0nfW7z5s2cffbZFiUSCQ9Fy4uYN3Ienor/KwqnY3pNPBUe5o2cR9HyooYJKGHPNE2eW7GVCf/8kuIyN+lt4ll88xAVhUbK0rJw2223sWrVKv785z+zZcsWXn75ZZ5//nmmTZtmZSyRRs2120XOmBxMn4np828v4/Ftcy7LwbXbFeSEEu5Kj3qYPC+PR94pwGfClQPOYtHUwaQmx1odTerI0rIwcOBAFi1aRE5ODr169eKhhx7iiSeeYPz48VbGEmnUcufmHtv14GdROM70mXjKPeQ9nxekZNIUbNxTymVPr+T9Dftw2m08fEUv/n51X6KdOpC2MbP0AMf6qs3BGSJNgdfj5bGzHqPiQEWd7yO2VSy37bxNZ0lIrb2ev5N7Fq3jqMfHWc2ieXb8APqmNLM6lpxGbd5DdSiqSBgpXFpYr6IAUL6/nMK3CwOUSJqCqmovMxet4/ZXv+aox8cFXVuy5OYhKgphROtqioSR4oJibBG2n5z1UBuG3aC4oBjGBjCYhK1dRyqZOj+Pr3eWYBhwy8/SuOXnadhtWmQpnKgsiIQRd5n72DoK9WDYDNwud2ACSVj7ZPMBbl24hsMVHhKjHTwxrh8XdmtldSwJApUFkTDijHNCPY9CMn0mznhnYAJJWPL5TGZ/vIXHl23GNKH3WYk8O34AKUkxVkeTIFFZEAkjyenJ9doFAcfWXUhOTw5QIgk3Ryrc3PbKWj7edACArHM68MCYHkTpgNiwprIgEkbSRqcR0zKm3mdDpI1KC2AqCRfrd5UwZX4eOw9XEhlh40+X9+LqzBSrY0kD0NkQImHE7rCTmZ2JYa/bgQuGzSAzO1OnTcpPvLJ6O1fO+ZydhyvpkBTD61MHqSg0ISoLImEmc3ImjmgHRi2PRjdsBo5YBxk3ZQQpmTRGRz1efvefr/n9a+twV/sY0b0Vi28eQs92iVZHkwaksiASZuLbxZO1OAvDZvhdGI5vm7U4S1eflBO2H6zgymc/59XcndgMuPOibjw/MZPEaIfV0aSBqSyIhKHU4alM/GAijljHGXdJHJ8oTFw2kdRhqQ0TUELehxv3cenTn7JhTylJsU7+94ZzmXZhF2xaP6FJ0gGOImEqdXgq0wumk/d8HqufXU3FgQoM+7EJgukzMb0msa1iyczOJOOmDE0UBACvz+TxDzYz++MtAPTv0Ixnxw+gbWK0xcnESro2hEgT4PV4KXy7kOKCYtwuN854J8npyaSNStPBjHLCwbIqbl24lpVbigGYNCiVe0Z1xxmhIXQ4qs17qCYLIk2A3WEnfWy6lnCW01qz/TBTF+Szp+Qo0Q47j/yyN2P7nWV1LAkRKgsiIk2YaZrMX7WNPy7ZgMdr0ik5ljkTMujWRrul5P+oLIiINFEV7mrueX0db6zdDcAlvdrwl6v6EB+lsx3kZCoLIiJN0HcHysien8+mfS7sNoO7Lk7nN0M7Yhg620F+SmVBRKSJeXf9Hu749zeUVVXTMj6S2Vn9ObdTC6tjSQhTWRARaSKqvT7+8t4mnv/kOwDOSU1i9nX9aZUQZXEyCXUqCyIiTcB+11Gmv7yGr74/BMD/DO3I7y5Ox2HXaZFyZioLIiJh7qvvDzHt5XwOuKqIi4zgL1f1YVTvtlbHkkZEZUFEJEyZpskLK79n1jsFeH0maa3ieG5iBp1bxlkdTRoZlQURkTBUVlXN7/7zNW+v2wvAZX3bMevK3sRG6mVfak+/NSIiYaZwn4vJ8/P47kA5DrvBvaN78Kvzz9ZpkVJnKgsiImHkzbW7uPv1dVS4vbRJiOKZ8QPIOLu51bGkkVNZEBEJA+5qH39+eyMvfV4EwKDOLXgqqz/JcZHWBpOwoLIgItLI7SmpZNqCfPK3HwFg2oWduX1kN+w27XaQwFBZEBFpxD7fUszNOWs4WO4mPiqCx6/px4gera2OJWFGZUFEpBHy+Uye+2Qrf3tvEz4TurdN4LkJAzi7RazV0Wrk9XgpXFpIcUEx7jI3zjgnyenJpI1Ow+6wWx1PTkNlQUSkkSmp9PDbV79m2cZ9AFyV0Z4/Xd6LqBB+s3XtdpE7N5fcOblUHKjAFmEDAzDBV+0jpmUMmdmZZE7OJL6dLo8dagzTNE2rQ9RVaWkpiYmJlJSUkJCQYHUcEZGg27C7lOwFeWw7WIHTbuPBsT0ZNzAlpE+LLFpeRM6YHDyVHkzv6d9yDLuBI9pB1uIsUoenNlzAJqo276FaFFxEpJH4T95Ornj2M7YdrOCsZtH8J/t8ss7pEPJFYd7IeXgqai4KAKbXxFPhYd7IeRQtL2qYgOIXlQURkRB31OPlnkXruOPfX1NV7WNY15YsuXkIfdo3szpajVy7XeSMycH0mZg+/4bYx7fNuSwH125XkBOKv1QWRERC2M7DFVwz9wte/nI7hgG3jejKi5MG0jzWaXW0M8qdm3ts14OfReE402fiKfeQ93xekJJJbaksiIiEqOWb9nPp0yv5ZmcJzWIcvDhpILeOSMPWCNZP8Hq85M7JPeOuh9MxfSa5c3LxerwBTiZ1obIgIhJifD6TJ5Zt5tcvreZIhYc+7RNZcvMQhndrZXU0vxUuLaTiQEW97qN8fzmFbxcGKJHUh06dFBEJIYfL3cx4ZS0rNh8A4LpzO/DAmB5ERoTuaZGnUlxQjC3Chq/aV+f7MOwGxQXFMDaAwaROVBZERELENzuPkD0/n11HKomMsPHwFb25KqO91bHqxF3mPraOQj0YNgO3yx2YQFIvKgsiIhYzTZOcr3bwh7e+xe31cXaLGOaMz6BHu8a7fowzzgn1XMXH9Jk440P/QM6mQGVBRMRClW4v9725nv/k7QRgRPfW/P2aviRGOyxOVj/J6cn12gUBx9ZdSE5PDlAiqQ+VBRERi2w7WM6U+fls3FOKzYA7L0pn8gWdGsXZDmeSNjqNmJYx9TrIMbZVLGmj0gKYSupKZ0OIiFhg2YZ9XPr0SjbuKaVFrJP5N55L9vDOYVEUAOwOO5nZmRj2un0/hs0gMztTF5cKESoLIiINqNrr4y/vFvCb/83FdbSaAR2asfSWoQzqEn7j9szJmTiiHRi1LECGzcAR6yDjpowgJZPaUlkQEWkgxWVV/OpfX/Hs8q0ATBqUysKbzqdNYpTFyYIjvl08WYuzMGyG34Xh+LZZi7N09ckQorIgItIA8rYd5tKnVvL51oPEOO08ldWfP1zWE2dEeL8Mpw5PZeIHE3HEOs64S+L4RGHisomkDkttmIDiFx3gKCISRKZp8v8+L+JPSzdS7TPp1DKW5yZk0LV10/mrOXV4KtMLppP3fB6rn11NxYEKDPuxCYLpMzG9JrGtYsnMziTjpgxNFEKQYZpmPc+EtU5trsUtItLQKtzV3PXaOt76ejcAo3q34S9X9SUusun+neb1eCl8u5DigmLcLjfOeCfJ6cmkjUrTwYwNrDbvoU33N1ZEmpyth7bywpoXqM/fSIZhcGP/G+mc1LnmxzpQRvb8PDbvKyPCZnD3qO7cMDgVwwiPsx3qyu6wkz42XUs4NzIqCyLSZKzYtoJZK2dhN+zYjNofK+AzfXhNL12SutRYFt5et4c7//015W4vreIjeWb8AAamJtUnuoiltBtCRJqMCk8FKY+ncKjyUJ3vo0V0C7bftp0YR8xP/s3z39Mi//Hp9wCc2zGJp6/rT6v48DzbQRq32ryHhvdhuCIiPxDjiGHm0JkYdbzCkYHBzKEzT1kU9pceZfw/vjxRFCZf0IkFvzlXRUHCgsqCiDQpUzKn0Dy6eZ2+Nik6icmZk3/y+S+/O8jop1fyVdEh4iIjeG7CAO4e1Z0Iu15iJTxY+pv8hz/8AcMwTrqlp6dbGUlEwlxdpwunmiqYpsk/PvmO6/75JQdcVXRrHc9b0wdzca+2gY4tYim/ykKHDh04ePDgiY9nz55NaWlpQAL07NmTPXv2nLitXLkyIPcrInI6dZku/Hiq4DrqYeqCfB5+eyNen8nl/dqxaNogOrWMC3RcEcv5VRZ27tyJ1+s98fE999xDcXFxQAJERETQpk2bE7fk5PBbH11EQkttpws/nips2uti7OzPeGf9Xhx2g4fG9uTxa/sR49QJZhKe6rQbIpAnUBQWFtKuXTs6derE+PHj2b59+2m3raqqorS09KSbiEhd1Ga68MOpwptrd3H5M5/xXXE5bROjeHXy+Uw8X+snSHiz9JiFc889l5deeol3332XOXPm8P333zN06FBcLtcpt581axaJiYknbikpKQ2cWETChb/TheNThQgjigfeXM+tC9dS6fEypEsyS24eQv8OdTtYUqQx8WudBZvNxp/+9Cfi4o7ti/v973/PnXfe+ZNdBrfccku9whw5coSzzz6bxx57jBtvvPEn/15VVUVVVdWJj0tLS0lJSdE6CyJSJ/6su9AiugVf3rCZO17dyJrtRwC4+WddmDGiK/ZaXnpZJJTUZp0Fv8pCauqZR2yGYfDdd9/VLukpDBw4kBEjRjBr1qwzbqtFmUSkvh774jHueP8OTH76UmhgcGv/Z1nxdWcOlbtJiIrgiXH9+Fl6awuSigRWwK8NUVRUFIhcZ1RWVsbWrVuZOHFigzyeiMiUzCk8/OnDP50umAatjV/x5hcp+Ew3Pdom8NyEDDq0+OmCTCLhLmDHLOzatavWX3PHHXewYsUKioqK+Pzzz7niiiuw2+1kZWUFKpaISI1OdeyCzYylpfteIiuvxmfCNZnteX3qIBUFabLqXRb27t3LzTffTFpaWq2/dufOnWRlZdGtWzeuueYaWrRowapVq2jZsmV9Y4mI+O2HZ0Y4fB1pU/UEMb5zcUbYePSXvfnLVX2J0uWTpQnzqywcPnyYrKwskpOTadeuHU899RQ+n4/777+fTp06sXr1al588cVaP/jChQvZvXs3VVVV7Ny5k4ULF9K5c82XfRURCbTj04W46hG0qfobDrMtCTEeXs8exLUDO1gdT8Ryfh2zcNddd/H5558zadIk3nvvPW677TbeffddbDYbH330Eeedd16wc4qIBM1Rj5c9u4bTwtMNgGrHWt69dTrtEhMtTiYSGvwqC++88w4vvfQSP/vZz5g+fTqdOnWiX79+/PnPfw52PhGRoNpxqILsBXms31UKmByJWMD9l5yjoiDyA36Vhd27d9O9e3fg2GmUUVFRTJgwIajBRESC7eNN+5mxcC0llR6axzj469U92VlVTFYvHWQt8kN+lQXTNImI+L9N7XY70dHRQQslIhJMXp/Jkx8W8vRHhZgm9G2fyLMTMjirWTQwKTiP6fFSuLSQ4oJi3GVunHFOktOTSRudhl0HT0qI87ss/PznPz9RGCorKxkzZgxOp/Ok7fLz8wOfUEQkgA6Vu5nxylo+2XwAgAnndeC+S3sQGRGcN2zXbhe5c3PJnZNLxYEKbBE2MAATfNU+YlrGkJmdSebkTOLbxQclg0h9+bWC44MPPujXnT3wwAP1DlQbWsFRRGrj6x1HmLogn11HKoly2PjzFb25ckD7oD1e0fIicsbk4Kn0YHpP/1Jr2A0c0Q6yFmeROjw1aHlEfijgyz2HKpUFEfGHaZq8/NV2HnxrA26vj9QWMcyZkEH3tsF73ShaXsS8kfMwfSam78wvs4bNwLAZTPxgogqDNIjavIf6tc7C/v37a/z36upqvvrqK/8Tiog0kEq3l9/++2tmLlqP2+vjFz1a89bNQ4JaFFy7XeSMyfG7KAAnts25LAfX7lNfeVfEKn6VhbZt255UGHr37s2OHTtOfHzw4EHOP//8wKcTEamHouJyrnj2M17P34XNgLsuSWfuxAwSohxBfdzcubnHdj34WRSOM30mnnIPec/nBSmZSN34VRZ+vKeiqKgIj8dT4zYiIlZ6/9u9jHl6JQV7XSTHOVnwm/OYMqzzGa+gW19ej5fcObk1HqNQE9NnkjsnF6/HG+BkInXn19kQ/gj2f4Aigbb10FZeWPNCvYquYRjc2P9GOidpmfJQUe318bf3N/Pciq0AZJ7dnGfGD6B1QlSDPH7h0kIqDlTU6z7K95dT+HYh6WPTA5RKpH4CVhZEGpsV21Ywa+Us7IYdm1H7a6r5TB9e00uXpC4qCyHigKuKW3LW8MV3BwG4YXBH7h6VjsMesAvsnlFxQTG2CBu+al+d78OwGxQXFMPYAAYTqQe/yoJhGLhcLqKiojBNE8MwKCsro7S0FODE/4o0JuN6jePOD+7kUOUhvGbdRr4tolswrte4ACeTusjbdoipC/LZV1pFjNPOX67qw6V92jV4DneZG+o5aDVsBm6XOzCBRALA70WZunbtetLH/fv3P+lj7YaQxub4lQbveP8OTGq/K8LAYObQmcQ4YoKQTvxlmiYvfV7Ew0s3Uu0z6dwylrkTM+jSypoFjpxxTurw63QS02fijHeeeUORBuJXWfj444+DnUPEElMyp/Dwpw9zqPJQrb82KTqJyZmTg5BK/FVeVc3vX/uGJd/sAeDSPm155Jd9iIu0bg9rcnpyvXZBAJhek+T05AAlEqk/v/6LGjZsWLBziFiirtMFTRWst2V/GVPm57FlfxkRNoN7RnXn14NTLZ9ypo1OI6ZlTL0OcoxtFUvaqLQAphKpH7+O+vH5fDz66KMMHjyYgQMHctddd1FZWRnsbCINYkrmFJpHN6/V12iqYK0l3+xm7OyVbNlfRuuESBbedB43DOloeVEAsDvsZGZnYtjrlsWwGWRmZ+riUhJS/CoLDz/8MPfccw9xcXGcddZZPPnkk0ybNi3Y2UQaxPHpguHnUWmaKljH4/Xxx8UbmP7yGsrdXs7rlMSSm4eSmZpkdbSTZE7OxBHtwLDVrjAYNgNHrIOMmzKClEykbvy6NkRaWhp33HEHkycf+0tq2bJljB49msrKSmy2hjsl6cd0bQgJlApPBSmPp/h17EKL6BZsv227ykID21d6lGkL8snddhiAKcM6c8cvuhLRgKdF1kadrw2xbCKpw1KDH1CavIBfG2L79u2MGjXqxMcjRozAMAx2795dv6QiIcLf6YKmCtZY9d1BRj+1ktxth4mPjGDuxAzuuiQ9ZIsCQOrwVCZ+MBFHrOOMuySOTxRUFCRU+fVfWnV1NVFRJ69+5nA4frLks0hj5s+xCzpWoWGZpsncFVsZ/88vKS6rIr1NPG/dPISLeraxOppfUoenMr1gOhfcewExLY8VTMNuYHPYThSI2FaxXHDfBUwvmK6iICHL73UWJk2aRGRk5InPHT16lClTphAbG3vic6+//nrgE4o0kDOdGaGpQsMqPerhzn9/zXvf7gPgyv5n8fAVvYl2Nq4D/+LbxTP8D8MZOnMohW8XUlxQjNvlxhnvJDk9mbRRaTqYUUKeX8csTJo0ya+jjF988cWAhPKXjlmQQKvp2AUdq9BwCvaWkj0/n++Ly3Habdw/pgfjz+0QEmc7iISL2ryH+jVZeOmllwKRSyTknW66oKlCw1m0Zid3v76Oox4f7RKjeHZCBv1SmlkdS6RJ82uycOWVV57xjiIiImjTpg0jR45kzJgxAQl3JposSDCcarqgqULwVVV7+dOSjcxbtQ2AoWnJPDmuP0mxWvZYJBgCfjZEYmLiGW/R0dEUFhZy7bXXcv/99wfkGxGxwo/PjNBUIfh2HankmrmrThSFW36exku/PkdFQSRE+DVZqI0lS5YwdepUtm/fHsi7PSVNFiRYfjhd0FQhuD4tPMAtOWs4XOEhMdrBE9f248L0VlbHEgl7AZ8s1MaQIUPIzMwM9N2KNKjj0wVAU4Ug8flMnv6wkF/96ysOV3jodVYCS24eoqIgEoICfmm2Zs2a6RRKCQtTB04lKTqJrF5ZVkcJOyUVHm57dS0fFewHYNzAFP5wWU+idAqhSEiy7jquIiEuKiKKSf0mWR0j7KzfVUL2gjx2HKokMsLGQ2N7cc3AFKtjiUgNVBZEpMG8sno79735Le5qHylJ0cwZn0GvsxKtjiUiZ6CyICJBd9Tj5YE3v+WV3B0A/Cy9FY9f04/EGIfFyUTEHyoLIhJU2w9WkL0gj293l2Iz4Le/6Eb2sM7Yann5ZhGxjsqCiATNRwX7mLFwLaVHq0mKdfLUuP4MSUu2OpaI1JLKgogEnNdn8uSyzTz10RYA+qU049nxA2jXLNriZCJSFyoLIhJQh8rd3LpwDZ8WFgPwq/PP5t7RPXBGBHxZFxFpICoLIhIwa7YfZtqCfHaXHCXKYeORK/twef+zrI4lIvWksiAi9WaaJvNXbeOPSzbg8Zp0TI5lzoQBpLfRMuwi4UBlQUTqpdLt5Z5F61i0ZhcAF/dsw1+v7kN8lE6LFAkXKgsiUmffF5eTPT+Pgr0u7DaD31/cjf8Z2gnD0GmRIuFEZUFE6uTd9Xu5899f46qqJjkuktnX9ee8Ti2sjiUiQaCyICK1Uu318df3NzF3xXcADExtzuzrBtA6IcriZCISLCoLIuK3/a6j3PzyGr78/hAAvxnSkd9fko7DrtMiRcKZyoKI+CW36BBTF+Sz31VFrNPOX67qy+g+ba2OJSINQGVBRGpkmib/+qyIWW9vpNpnktYqjjkTMujSKs7qaCLSQFQWROS0yqqq+f1r37D0mz0AjOnbjkeu7E1spF46RJoS/RcvIqdUuM/FlPl5bD1QToTN4N7R3bl+UKpOixRpglQWROQnFn+9m9+/9g0Vbi9tEqJ4Znx/Ms5OsjqWiFhEZUFETnBX+5j1zkZe/KwIgPM7teDp6/qTHBdpbTARsZTKgogAsLfkKNNezidv22EApg7vzO0juxKh0yJFmjyVBRHh863F3JKzhuIyN/FREfz96r78omcbq2OJSIgImT8ZHnnkEQzDYMaMGVZHEWkyTNNkzvKtTPjnlxSXuUlvE8/i6UNUFETkJCExWVi9ejVz586lT58+VkcRaTJKj3r47atf88GGfQBcOeAsHr68N9FOu8XJRCTUWD5ZKCsrY/z48fzjH/+gefPmVscRaRI27inlsqdX8sGGfTjtNv58RW/+fnVfFQUROSXLy8K0adMYPXo0I0aMOOO2VVVVlJaWnnQTkdp5PX8nVzz7GUUHKzirWTT/yT6f687toPUTROS0LN0NsXDhQvLz81m9erVf28+aNYsHH3wwyKlEwlNVtZc/Lt7Agi+3A3BB15Y8eW0/msc6LU4mIqHOsrKwY8cObr31Vj744AOiovy7tO3dd9/N7bfffuLj0tJSUlJSghUxaLweL4VLCykuKMZd5sYZ5yQ5PZm00WnYHRoDS+DtPFzBtAX5fL2zBMOAW36Wxi0/T8Nu0zRBRM7MME3TtOKB33jjDa644grs9v97c/R6vRiGgc1mo6qq6qR/O5XS0lISExMpKSkhISEh2JHrzbXbRe7cXHLn5FJxoAJbhA0MwARftY+YljFkZmeSOTmT+HbxVseVMLFi8wFuXbiGIxUemsU4ePzaflzYrZXVsUTEYrV5D7WsLLhcLrZt23bS537961+Tnp7O73//e3r16nXG+2hMZaFoeRE5Y3LwVHowvad/yg27gSPaQdbiLFKHpzZcQAk7Pp/J0x9t4YkPN2Oa0PusRJ4dP4CUpBiro4lICKjNe6hluyHi4+N/UghiY2Np0aKFX0WhMSlaXsS8kfMwfSamr+ZuZnpNPBUe5o2cx8QPJqowSJ0cqXAz45W1LN90AICsczrwwJgeRGk3l4jUgeVnQ4Q7124XOWNy/CoKxx3fNueyHFy7XUFOKOFm3c4SRj+1kuWbDhAZYeOvV/Vh1pW9VRREpM5CYlGm45YvX251hIDLnZt7bNeDn0XhONNn4in3kPd8HsP/MDw44SSsmKbJK6t3cP9b3+Ku9tEhKYY5EwbQs12i1dFEpJHTZCGIvB4vuXNyazxGoSamzyR3Ti5ejzfAySTcHPV4+d1/vuGu19fhrvYxonsrFt88REVBRAJCZSGICpcWUnGgol73Ub6/nMK3CwOUSMLR9oMVXPns5/w7byc2A+68qBvPT8wkMdphdTQRCRMhtRsi3BQXFGOLsOGr9tX5Pgy7QXFBMYwNYDAJG8s27OP2V9dSerSaFrFOnsrqz+AuyVbHEpEwo7IQRO4y97F1FOrBsBm4Xe7ABJKw4fWZPPbBJp75eCsA/Ts049nxA2ibGG1xMhEJRyoLQeSMc0I9V7EwfSbOeC3HK//nYFkVtyxcw2dbDgIwaVAq94zqjjNCexVFJDhUFoIoOT25Xrsg4Ni6C8npGivLMfnbDzNtQT57So4S7bDzyC97M7bfWVbHEpEwp7IQRGmj04hpGVOvgxxjW8WSNiotgKmkMTJNk3mrtvHQkg14vCadkmN5bmIGXVtrWXARCT7NLYPI7rCTmZ2JYa/bgQuGzSAzO1MXl2riKtzV3PbKWu5/81s8XpNRvdvw5vTBKgoi0mBUFoIsc3ImjmgHRi2v7mfYDByxDjJuyghSMmkMvjtQxuXPfMYba3djtxncO7o7z1w3gPgonRYpIg1HZSHI4tvFk7U4C8Nm+F0Yjm+btThLV59swt5Zt4fLZn/G5n1ltIyPJOd/zuM3QzthGLqstIg0LJWFBpA6PJWJH0zEEes44y6J4xOFicsmkjostWECSkip9vp4eOkGshfkU1ZVzTkdk1h6yxDO6ZhkdTQRaaJ0gGMDSR2eyvSC6eQ9n8fqZ1dTcaACw35sgmD6TEyvSWyrWDKzM8m4KUMThSZqv+so019ew1ffHwLgpgs6cedF3XDY1etFxDqGaZr1XAnAOrW5Fnco8Xq8FL5dSHFBMW6XG2e8k+T0ZNJGpelgxibsq+8PMe3lfA64qoiLjOCvV/Xhkt5trY4lImGqNu+hmixYwO6wkz42XUs4C3DstMgXVn7PrHcK8PpMuraOY86EDDq3jLM6mogIoLIgYinXUQ+/f+0b3l63F4Cx/dox68rexDj1n6aIhA69IolYZPM+F1Pm5/HdgXIcdoP7Lu3BxPPO1tkOIhJyVBZELPDm2l3c9do6Kj1e2iZG8cz4AQzo0NzqWCIip6SyINKA3NU+/vz2Rl76vAiAwV1a8NS4/rSIi7Q2mIhIDVQWRBrInpJKpi3IJ3/7EQCmX9iF20Z2xV7L1T1FRBqayoJIA/hsSzG35KzhYLmb+KgIHr+mHyN6tLY6loiIX1QWRILI5zOZs2Irf39/Ez4TerRN4LkJGXRoEWN1NBERv6ksiARJSaWH3776Ncs27gPg6oz2PHR5L6K08JaINDIqCyJB8O3uEqYuyGfbwQqcETb+eFlPrh2YotMiRaRRUlkQCbD/5O1k5qJ1VFX7aN88mjnjM+jdPtHqWCIidaayIBIgRz1eHly8gZyvtgMwvFtLnri2H81inBYnExGpH5UFkQDYcaiCqQvyWberBMOA20Z0ZfqFXbDptEgRCQMqCyL1tHzTfma8spYjFR6axTh4clx/hnVtaXUsEZGAUVkQqSOfz+TJDwt56qNCTBP6tk/kmfEDaN9cp0WKSHhRWRCpg8Plbma8spYVmw8AMP7cDtw/pgeREfU/LXLroa28sOYFTNOs830YhsGN/W+kc1LneucREVFZEKmlr3ccYeqCfHYdqSTKYePhy3vzy4z2Abv/FdtWMGvlLOyGHZthq/XX+0wfXtNLl6QuKgsiEhC1fyUSaaJM0+TlL7dz9XNfsOtIJaktYlg0dXBAiwLAuF7jSIpOwmt68fg8tb55TS8tolswrte4gOYSkaZLkwURP1S6vdz7xnpey98JwMgerfnb1X1JjHYE/LFiHDHMHDqTO96/A5Pa74owMJg5dCYxDh07IRJoXo+XwqWFFBcU4y5z44xzkpyeTNroNOxhvDqrYdZnx6jFSktLSUxMpKSkhISEBKvjSJgqKi5nyvw8Cva6sBlw50XpTL6gU1BPi6zwVJDyeAqHKg/V+mtbRLdg+23bVRZEAsi120Xu3Fxy5+RScaACW4QNDMAEX7WPmJYxZGZnkjk5k/h28VbH9Utt3kO1G0KkBh9s2MeY2Ssp2OsiOc7J/N+cS/bwzkFfP+H4dMGgdo+jqYJI4BUtL2J2t9l8+vCnVByoAI4VBJ/Hh6/aB0DFgQo+ffhTZnebTdHyIgvTBofKgsgpVHt9/OXdAv7nf3NxHa0m4+zmLLl5KIM6JzdYhimZU2ge3bxWX5MUncTkzMlBSiTS9BQtL2LeyHl4KjyY3poH8abXxFPhYd7IeWFXGFQWRH6kuKyKX/3rK55dvhWAXw9OZeFN59EmMapBc9R2uqCpgkhguXa7yBmTg+kzMX3+7bE/vm3OZTm4druCnLDhqCyI/EDetkNc+tRKPt96kBinnaey+vPAmJ447Nb8p1Kb6YKmCiKBlTs3F0+lx++icJzpM/GUe8h7Pi9IyRqeyoIIx06LfOmz77l27ir2lh6lc8tY3pw2mMv6trM0l7/TBU0VRALL6/GSOyf3jLseTsf0meTOycXr8QY4mTVUFqTJK6+q5taFa/nD4g1U+0xG927Lm9OHkNY6NI5o9me6oKmCSGAVLi08cTBjXZXvL6fw7cIAJbKWyoI0aVv2l3H5M5/x1te7ibAZ3HdpD2Zf15+4yNBZguRM0wVNFUQCr7ig+NjpkfVg2A2KC4oDlMhaKgvSZL29bg9jZ6+kcH8ZreIjybnpPG4c0hHDCL3LStc0XdBUQSTw3GVuannm8k8YNgO3yx2YQBZTWZAmx+P18aclG5i6IJ9yt5dzOyax5JYhDExNsjraaZ1uuqCpgkhwOOOc1GEB1ZOYPhNnvDMwgSymsiBNyv7So1z3j1X8c+X3AEwe1okFvzmXVvENe1pkXZxquqCpgkhwJKcnn1hwqa5Mr0lyesOtzRJMKgvSZHz53UFGPbWS1UWHiYuM4LkJGdx9SXciLDotsrZ+PF3QVEEkeNJGpxHTsn7/bcW2iiVtVFqAElmrcbxKitSDaZo8/8lWrvvnlxSXVdGtdTxvTR/Mxb3aWB2t1n44XdBUQSR47A47mdmZGPa6Hbhg2AwyszPD5uJSKgsS1lxHPWTPz+fPbxfg9Zlc0f8sFk0bRKeWcVZHq5Pj0wVAUwWRIMucnIkj2oFRy2vBGDYDR6yDjJsygpSs4YXO+WEiAbZpr4sp8/P4vrgch93g/jE9mXBuh5A826E2pg6cSlJ0Elm9sqyOIhLW4tvFk7U4i3kj5wH4tZKjYTMwbAZZi7MazdUn/aFLVEtYemPNLu5+fR2VHi/tEqN4ZvwA+neo3UWZJHR4PV4KlxZSXFCMu8yNM85JcnoyaaPTwmbMK6GraHkROZflnPFiUscnClmLs0gdltpwAeuoNu+hmixIWHFX+/jT0g387xfbABialsyT4/qTFBsepy+dydZDW3lhzQvU528AwzC4sf+NdE7qHMBkdePa7SJ3bi65c3KpOFBxbJEcAzCPXSI4pmUMmdmZZE7ODKu/4iS0pA5PZXrBdPKez2P1s6upOFCBYT82QTB9JqbXJLZVLJnZmWTclBGWv4uaLEjY2H2kkqkL8lm74wgAt/ysC7eO6Iq9lvsbG7N/rfkXN751I3bDjs2o/SFJPtOH1/TywmUvcEP/G4KQ0H9Fy4vIGZNz7EI+Nf01ZzdwRP/3r7nhqQ0XUJokr8dL4dv/nXK53Djj/zvlGtX4ply1eQ9VWZCwsLKwmFsWruFQuZuEqAieGNePn6W3tjpWg6vwVJDyeAqHKg/V+T5aRLdg+23bLT14smh5EfNGzqvVpYENm8HwB4cz+PeDG92LtogVavMeaunZEHPmzKFPnz4kJCSQkJDA+eefzzvvvGNlJGlkfD6T2R8VMvFfX3Ko3E3PdgksvWVokywK4P9VKk8nFNZucO12kTMmp1ZFAY4dfPbxfR/z97Z/5+MHPsa12xXElCJNi6VloX379jzyyCPk5eWRm5vLz372M8aOHcu3335rZSxpJEoqPPzP/+byt/c3Y5pwbWYKr2UPIiWpaZ9O6M9VKk8nFNZuyJ2be2zXQy2Kwg9VHqzk04c/ZXa32RQtLwpsOJEmytKyMGbMGEaNGkVaWhpdu3bl4YcfJi4ujlWrVlkZSxqB9btKuHT2p3xYsB9nhI1Hf9mbR6/qQ5TGz3WeLoTCVMHr8ZI7J7fGYxT8YXpNPBUe5o2cp8IgEgAhsyiT1+tl4cKFlJeXc/7551sdR0LYq7k7+OWcz9lxqJKUpGhezx7EtQM7WB0rpNRluhAKU4XCpYVUHKgIyH0d342Rc1mOdkmI1JPlZWHdunXExcURGRnJlClTWLRoET169DjltlVVVZSWlp50k6bjqMfLXa99w+/+8w1V1T5+lt6KJdOH0uusRKujhZzaThdCYaoAUFxQfOz0yAAxfSaecg95z+cF7D5FmiLLy0K3bt1Yu3YtX375JdnZ2Vx//fVs2LDhlNvOmjWLxMTEE7eUlJQGTitW2XGogque+5yFq3dgGPDbkV35568ySYxxWB0tZNVmuhAKUwUAd5mbOh6beVqmzyR3Ti5ejzewdyzShFheFpxOJ126dCEjI4NZs2bRt29fnnzyyVNue/fdd1NSUnLitmPHjgZOK1b4uGA/lz69kvW7Smke4+B/bziHm3+ehq0JrZ9QF/5OF0JlqgDgjHNCEE7mLt9fTuHbhYG/Y5EmwvKy8GM+n4+qqqpT/ltkZOSJ0yyP3yR8eX0mj72/iV+/tJqSSg99U5qx5JahDE1raXW0RsOf6UKoTBUAktOT8VX7An6/ht2guKA44Pcr0lRYutzz3XffzSWXXEKHDh1wuVy8/PLLLF++nPfee8/KWBICDpW7uXXhGj4tPPYCP/G8s7n30u5ERuhsh9o4Pl244/07ME/xJ3soTRUA0kanEdMyJmAHOR5n2AzcLndA71OkKbF0srB//35+9atf0a1bN37+85+zevVq3nvvPUaOHGllLLHY2h1HuPSpT/m0sJgoh43Hr+3LQ5f3UlGoo5qmC6E0VQCwO+xkZmdi2AO7i8n0mTjjm8b1QUSCwdLJwgsvvGDlw0uIMU2TBV9u54+LN+D2+khtEcNzEzNIb6PdTfVxuulCqE0VjsucnMmqx1Ydu8JfHRdm+jHTa5KcnhyQ+xJpikLumAVpmirdXn776tfc+8Z63F4fF/VszVs3D1FRCJBTTRdCbapwXHy7eLIWZ2HYjl3VLxBiW8WSNiotIPcl0hSpLIjlvi8u54pnP+P1NbuwGXD3Jek8NyGDhCidFhkoPz4zIlSnCselDk9l4gcTccQ66r1LwrAZZGZn6uJSIvWgsiCWeu/bvVz29EoK9rpIjotkwW/OY/KwzhiGTosMtB9OF0J1qvBDqcNTmV4wnQvuvYCopKg63YdhM3DEOsi4KSPA6USaFpUFsUS118esdzYyeV4erqpqMs9uztJbhnB+5xZWRwtbx6cLQEhPFX4ovl08w/8wnDv23sGFD114bLeEnz3y+G6MrMVZxLeLD25QkTBn6QGO0jQdcFVxc04+q747BMANgzty96h0HHZ112CbOnAqSdFJZPXKsjpKrdgddi649wI6DOlAzmU5xw5+rOFiU8cnClmLs0gdltpwQUXClGGaZhDWS2sYpaWlJCYmUlJSogWaGoncokNMXZDPflcVsU47j17Vh0v7tLM6ljQirt0u8p7PY/Wzq6k4UIFhPzZBMH0mptcktlUsmdmZZNyUoYmCSA1q8x6qsiANwjRNXvysiD+/vZFqn0mXVnE8N2EAXVrpxVzqxuvxUvh2IcUFxbhdbpzxTpLTk0kblaaDGUX8UJv3UO2GkKArr6rm9699w5Jv9gBwaZ+2PPrLPsRG6tdP6s7usJM+Nh3GWp1EJPzp1VqCast+F1Pm57NlfxkRNoOZo7szaVCqznYQEWlEVBYkaJZ8s5vf/ecbKtxeWidE8sx1A8hMTbI6loiI1JLKggScx+vjz29v5MXPigA4v1MLnsrqT8v4SGuDiYhInagsSEDtKz3KtAX55G47DMCUYZ254xddidBpkSIijZbKggTMF1sPcnNOPsVlbuIjI/jbNX25qGcbq2OJiEg9qSxIvZmmydxPvuMv7xbgMyG9TTzPTcggNTnW6mgiIhIAKgtSL6VHPdzx6te8v2EfAFf2P4uHr+hNtFPnuYuIhAuVBamzjXtKyZ6fR9HBCpx2Gw9c1oPrzumg0yJFRMKMyoLUyaI1O7n79XUc9fg4q1k0z44fQN+UZlbHEhGRIFBZkFqpqvby0JINzF+1HYChack8Oa4/SbFOi5OJiEiwqCyI33YdqWTqgny+3nEEgFt+nsatP0/DbtNuBxGRcKayIH75ZPMBbl24hsMVHhKjHTxxbT8uTG9ldSwREWkAKgtSI5/PZPbHW3h82WZME3qflciz4weQkhRjdTQREWkgKgtyWkcq3Nz2ylo+3nQAgKxzUnhgTE+idPlfEZEmRWVBTmn9rhKmzM9j5+FKIiNsPHR5L67JTLE6loiIWEBlQX7ildXbue/Nb3FX++iQFMOcCQPo2S7R6lgiImIRlQU54ajHy/1vrufV3J0A/Dy9FY9d04/EGIfFyURExEoqCwLA9oMVZC/I49vdpdgM+O0vupE9rDM2nRYpItLkqSwIH27cx22vrKX0aDVJsU6eGtefIWnJVscSEZEQobLQhHl9Jk8s28zTH20BoH+HZjxz3QDaNYu2OJmIiIQSlYUm6mBZFbcuXMvKLcUAXH/+2cwc3QNnhM3iZCIiEmpUFpqgNdsPM21BPrtLjhLtsDPryt5c3v8sq2OJiEiIUlloQkzTZP6qbfxxyQY8XpNOybHMmZBBtzbxVkcTEZEQprLQRFS4q5m5aD2L1uwC4OKebfjr1X2Ij9JpkSIiUjOVhSbguwNlZM/PZ9M+F3abwV0Xp/OboR0xDJ0WKSIiZ6ayEObeXb+HO/79DWVV1bSMj2R2Vn/O7dTC6lgiItKIqCyEqWqvj7++t4m5n3wHwMDU5jxz3QBaJURZnExERBoblYUwtN91lOkvr+Gr7w8B8D9DO/K7i9Nx2HVapIiI1J7KQphZXXSIaQvy2e+qItZp569X92VU77ZWxxIRkUZMZSFMmKbJCyu/Z9Y7BXh9Jmmt4nhuYgadW8ZZHU1ERBo5lYUwUFZVze//8w1L1+0B4LK+7Zh1ZW9iI/XjFRGR+tO7SSNXuM/F5Pl5fHegnAibwX2X9uBX55+t0yJFRCRgVBYasbe+3s1dr31DhdtLm4Qonhk/gIyzm1sdS0REwozKQiPkrvbx57c38tLnRQAM6tyCp7L6kxwXaW0wEREJSyoLjczekqNMezmfvG2HAZh2YWduH9kNu027HUREJDhUFhqRz7cUc3POGg6Wu4mPiuCxa/oxskdrq2OJiEiYU1loBHw+k+c+2crf3tuEz4TubRN4bsIAzm4Ra3U0ERFpAlQWQlxJpYffvvo1yzbuA+CXA9rzp8t7Ee20W5xMrLL10FZeWPMCpmnW+T4Mw+DG/jfSOalzAJOJSLhSWQhhG/eUMmV+HtsOVuC023hwbE/GDUzRaZFN3IptK5i1chZ2w47NqP0S3j7Th9f00iWpi8qCiPhFFwsIUa/l7eSKZz9j28EKzmoWzX+yzyfrnA4qCsK4XuNIik7Ca3rx+Dy1vnlNLy2iWzCu1zirvxURaSRUFkJMVbWXexat47f//pqjHh/DurZkyc1D6NO+mdXRJETEOGKYOXQmBnUrjgYGM4fOJMYRE+BkIhKuVBZCyM7DFVz93Be8/OV2DANmjEjjxUkDaR7rtDqahJgpmVNoHl23BbiSopOYnDk5wIlEJJypLISIFZsPcOnTK/lmZwnNYhy8OGkgM0Z0xab1E+QU6jpd0FRBROpCZcFiPp/Jk8sKmfTiVxyp8NCnfSJLbh7C8G6trI4mIa4u0wVNFUSkLiwtC7NmzWLgwIHEx8fTqlUrLr/8cjZt2mRlpAZ1uNzNDf9vNY8v24xpwnXnduDVyefTvrn+6pMzq+10QVMFEakrw6zPydr1dPHFFzNu3DgGDhxIdXU199xzD+vXr2fDhg3Exp55waHS0lISExMpKSkhISGhARIHzrqdJUyZn8euI5VERth4+IreXJXR3upYAeP1eClcWkhxQTHuMjfOOCfJ6cmkjU7D7tAaEYFS4akg5fEUDlUeOuO2LaJbsP227SoLIgLU7j3U0nUW3n333ZM+fumll2jVqhV5eXlccMEFFqUKLtM0Wbh6Bw+8+S1ur4+zW8QwZ3wGPdo1rrJzOq7dLnLn5pI7J5eKAxXYImxgACb4qn3EtIwhMzuTzMmZxLeLtzpuo3d8unDH+3dgcvrer6mCiNSHpZOFH9uyZQtpaWmsW7eOXr16/eTfq6qqqKqqOvFxaWkpKSkpjWaycNTj5d431vOfvJ0AjOjemr9f05fEaIfFyQKjaHkROWNy8FR6ML01vHHZDRzRDrIWZ5E6PLXhAoYpf6YLmiqIyI/VZrIQMgc4+nw+ZsyYweDBg09ZFODYMQ6JiYknbikpKQ2csu62HSznimc/5z95O7EZ8LuLu/H8xIywKgrzRs7DU1FzUQAwvSaeCg/zRs6jaHlRwwQMY2c6dkFTBRGpr5CZLGRnZ/POO++wcuVK2rc/9b77QEwWrFhXf9mGfdz26lpcR6tpEevk6az+DOqSXOfHDzWu3S5md5t9rCj4/H9eDZuBI9bB9ILp2iVRTzVNFzRVEJFTaTTHLBw3ffp0lixZwieffHLaogAQGRlJZGRkvR6rIdfV9/pMHvtgE898vBWAAR2a8ez4DNokRtUpe6jKnZt7bNdDLYoCgOkz8ZR7yHs+j+F/GB6ccE3E6Y5d0FRBRALB0t0Qpmkyffp0Fi1axEcffUTHjh2D/pgNta5+cVkVv/rXlyeKwqRBqSy86fywKwpej5fcObln3PVwOqbPJHdOLl6PN8DJmp5TrbugdRVEJBAsLQvTpk1j/vz5vPzyy8THx7N371727t1LZWVl0B6zIdbVz99+mEufWslnWw4S7bDz5Lh+/OGynjgjQuYQkYApXFpIxYGKet1H+f5yCt8uDFCipuvHv9uaKohIoFj67jVnzhxKSkoYPnw4bdu2PXF75ZVXgvq4wVpX3zRN/t/nRVw79wv2lh6lU8tY3pw+mLH9zqpP3JBWXFB87PTIejDsBsUFxQFK1LT98HdbUwURCRTLd0Oc6jZp0qSgPm4w1tWvcFcz45W1PPDWt3i8JqN6t+Gt6UPo2jq8D9xzl7mp45DmBMNm4Ha5AxOoiTv+uw1oqiAiARMSBzhaYUrmFB7+9GG/Vr477nR/qW09UEb2/Dw27yvDbjO4+5J0bhzSEcMI/4tAOeOc1LAWkF9Mn4kzXlfWDJSpA6eSFJ1EVq8sq6OISJgIv53ofgrUuvrvrNvD2NmfsXlfGS3jI8n5n/P4zdBOTaIoACSnJ+Or9tXrPkyvSXJ6+JxKarWoiCgm9ZtEZET9zhwSETmuyZYFqN2xCz+eKni8Ph5euoHsBfmUVVVzTscklt4yhHM6JgUrbkhKG51GTMv6jbpjW8WSNiotQIlERCTQmnRZ8He68OOpwv7So4z/x5f849PvAbjpgk68/JtzaRUfXqdF+sPusJOZnYlhr+PZJTaDzOxMXVxKRCSENemyAP5NF344Vfjyu4OMfnolXxUdIi4ygucmDOCeUd2JsDfdpzJzciaOaAeGrZYHjP53BceMmzKClExERAKh6b7D/Ze/6+pHR0Tzj0++47p/fskBVxXdWsfz1vTBXNyrbQMnDj3x7eLJWpyFYTP8LgzHt81anKWlnkVEQlyTLwtQ83QhKTqJ8b1uZOqCfB5+eyNen8nYfu1YNG0QnVrGNXDS0JU6PJWJH0zEEes44y6J4xOFicsmkjostWECiohInakscPrpgoHBlL4PMm5uPu+s34vDbvDQ2J48cW0/YpxN9qzT00odnsr0gulccO8FJw56NOwGNoftRIGIbRXLBfddwPSC6SoKIiKNRMhcdbIuanPFrDM51VX7WttG09w9jUqPj7aJUTwzfgADOtRt5cemxuvxUvh2IcUFxbhdbpzxTpLTk0kblaaDGUVEQkCju+pkKDjpqn2mnSTPjUR5x1CJjyFdknlyXD9axOm8dX/ZHXbSx6bDWKuTiIhIfWk3xA9MyZxCUmQn2lQ9Qrx3DADTL+zC/7vhHBUFERFpsjRZ+IE12ypIrvg7R80IIh1enr3uXH7evbXVsURERCylsgD4fCZzVmzl7+9vwmdG0KaZh/k3DKNLKx2fICIi0uTLQkmlh9++upZlG/cDcHVGex66vBdROghPREQEaOJl4dvdJWTPz2f7oQqcETYeGtuTawd2sDqWiIhISGmyZeHfuTu49431VFX7aN88mjnjM+jdPtHqWCIiIiGnyZWFox4vDy7+lpyvdgBwYbeWPH5tP5rFOC1OJiIiEpqaVFnYcaiCqQvyWberBMOA20Z0ZfqFXbDV8gJIIiIiTUmTKQsfb9rPjIVrKan00DzGwZPj+nNB15ZWxxIREQl5YV8WvD6Tpz4s5KmPCjFN6Ns+kWfGD6B98xiro4mIiDQKYV0WDpe7ufWVtXyy+QAAE87rwH2X9iAyQqdFioiI+Ctsy8LXO44wdUE+u45UEuWw8ecrenPlgPZWxxIREWl0wq4smKbJy19t58G3NuD2+khtEcOcCRl0b1u/q1I2BK/HS+HS/16pscyNM+6/V2ocrSs1ioiIdcKqLFS6vcx8Yx2v5+8C4Bc9WvO3a/qSEOWwOFnNXLtd5M7NJXdOLhUHKrBF2MAATPBV+4hpGUNmdiaZkzOJbxdvdVwREWliDNM0TatD1NUPr8V9yG1nyvw8Cva6sBnwu4vTmXxBJwwjtE+LLFpeRM6YHDyVHkzv6X8Uht3AEe0ga3EWqcNTGy6giIiEpR++hyYk1Dx9D4tLVH+4cR9jnl5JwV4XyXFOFvzmPKYM69woisK8kfPwVNRcFABMr4mnwsO8kfMoWl7UMAFFREQIk7Jw68K1uKqqyTi7OUtuHsr5nVtYHemMXLtd5IzJwfSZmD7/hjvHt825LAfXbleQE4qIiBwTFmUB4IbBHVl403m0SYyyOopfcufmHtv14GdROM70mXjKPeQ9nxekZCIiIicLi7Lw16v6cP+YHjjsjePb8Xq85M7JPeOuh9MxfSa5c3LxerwBTiYiIvJTjePd9Qwu6d3W6gi1Uri0kIoDFfW6j/L95RS+XRigRCIiIqcXFmWhsSkuKD52emQ9GHaD4oLiACUSERE5PZUFC7jL3MfWUagHw2bgdrkDE0hERKQGKgsWcMY5oZ6rW5g+E2e8MzCBREREaqCyYIHk9GR81b563YfpNUlOTw5QIhERkdNTWbBA2ug0YlrW7xLZsa1iSRuVFqBEIiIip6eyYAG7w05mdiaGvW4HLhg2g8zsTF1cSkREGoTKgkUyJ2fiiHZg2GpXGAybgSPWQcZNGUFKJiIicjKVBYvEt4sna3EWhs3wuzAc3zZrcZauPikiIg1GZcFCqcNTmfjBRByxjjPukjg+UZi4bCKpw1IbJqCIiAgqC5ZLHZ7K9ILpXHDvBScOejTsBjaH7USBiG0VywX3XcD0gukqCiIi0uAM0zTreca/dWpzLe7GwOvxUvh2IcUFxbhdbpzxTpLTk0kblaaDGUVEJKBq8x4a0UCZxA92h530sekw1uokIiIi/0e7IURERKRGKgsiIiJSI5UFERERqZHKgoiIiNRIZUFERERqpLIgIiIiNVJZEBERkRqpLIiIiEiNVBZERESkRo16BcfjK1WXlpZanERERKRxOf7e6c9VHxp1WXC5XACkpKRYnERERKRxcrlcJCYm1rhNo76QlM/nY/fu3cTHx2MYNV/iOdyUlpaSkpLCjh07wuIiWo2Jnnvr6Lm3hp536wTzuTdNE5fLRbt27bDZaj4qoVFPFmw2G+3bt7c6hqUSEhL0H69F9NxbR8+9NfS8WydYz/2ZJgrH6QBHERERqZHKgoiIiNRIZaGRioyM5IEHHiAyMtLqKE2Onnvr6Lm3hp5364TKc9+oD3AUERGR4NNkQURERGqksiAiIiI1UlkQERGRGqksiIiISI1UFkLYM888Q2pqKlFRUZx77rl89dVXp932pZdewjCMk25RUVENmDY8fPLJJ4wZM4Z27dphGAZvvPHGGb9m+fLlDBgwgMjISLp06cJLL70U9JzhqLbP/fLly3/yO28YBnv37m2YwGFi1qxZDBw4kPj4eFq1asXll1/Opk2bzvh1//73v0lPTycqKorevXvz9ttvN0Da8FKX596q13qVhRD1yiuvcPvtt/PAAw+Qn59P3759ueiii9i/f/9pvyYhIYE9e/acuG3btq0BE4eH8vJy+vbtyzPPPOPX9t9//z2jR4/mwgsvZO3atcyYMYPf/OY3vPfee0FOGn5q+9wft2nTppN+71u1ahWkhOFpxYoVTJs2jVWrVvHBBx/g8Xj4xS9+QXl5+Wm/5vPPPycrK4sbb7yRNWvWcPnll3P55Zezfv36Bkze+NXluQeLXutNCUnnnHOOOW3atBMfe71es127duasWbNOuf2LL75oJiYmNlC6pgEwFy1aVOM2v/vd78yePXue9Llrr73WvOiii4KYLPz589x//PHHJmAePny4QTI1Ffv37zcBc8WKFafd5pprrjFHjx590ufOPfdcc/LkycGOF9b8ee6teq3XZCEEud1u8vLyGDFixInP2Ww2RowYwRdffHHarysrK+Pss88mJSWFsWPH8u233zZE3Cbtiy++OOnnBHDRRRfV+HOSwOrXrx9t27Zl5MiRfPbZZ1bHafRKSkoASEpKOu02+r0PDn+ee7DmtV5lIQQVFxfj9Xpp3br1SZ9v3br1affHduvWjX/961+8+eabzJ8/H5/Px6BBg9i5c2dDRG6y9u7de8qfU2lpKZWVlRalahratm3Lc889x2uvvcZrr71GSkoKw4cPJz8/3+pojZbP52PGjBkMHjyYXr16nXa70/3e63iRuvP3ubfqtb5RX3VS/s/555/P+eeff+LjQYMG0b17d+bOnctDDz1kYTKR4OjWrRvdunU78fGgQYPYunUrjz/+OPPmzbMwWeM1bdo01q9fz8qVK62O0uT4+9xb9VqvyUIISk5Oxm63s2/fvpM+v2/fPtq0aePXfTgcDvr378+WLVuCEVH+q02bNqf8OSUkJBAdHW1RqqbrnHPO0e98HU2fPp0lS5bw8ccf0759+xq3Pd3vvb+vT3Ky2jz3P9ZQr/UqCyHI6XSSkZHBhx9+eOJzPp+PDz/88KRGWROv18u6deto27ZtsGIKx1r+D39OAB988IHfPycJrLVr1+p3vpZM02T69OksWrSIjz76iI4dO57xa/R7Hxh1ee5/rMFe6xv8kErxy8KFC83IyEjzpZdeMjds2GDedNNNZrNmzcy9e/eapmmaEydONO+6664T2z/44IPme++9Z27dutXMy8szx40bZ0ZFRZnffvutVd9Co+Ryucw1a9aYa9asMQHzscceM9esWWNu27bNNE3TvOuuu8yJEyee2P67774zY2JizDvvvNPcuHGj+cwzz5h2u9189913rfoWGq3aPvePP/64+cYbb5iFhYXmunXrzFtvvdW02WzmsmXLrPoWGqXs7GwzMTHRXL58ublnz54Tt4qKihPb/Pj15rPPPjMjIiLMv/3tb+bGjRvNBx54wHQ4HOa6deus+BYarbo891a91qsshLCnn37a7NChg+l0Os1zzjnHXLVq1Yl/GzZsmHn99def+HjGjBkntm3durU5atQoMz8/34LUjdvx0/F+fDv+XF9//fXmsGHDfvI1/fr1M51Op9mpUyfzxRdfbPDc4aC2z/2jjz5qdu7c2YyKijKTkpLM4cOHmx999JE14RuxUz3nwEm/xz9+vTFN03z11VfNrl27mk6n0+zZs6e5dOnShg0eBury3Fv1Wq9LVIuIiEiNdMyCiIiI1EhlQURERGqksiAiIiI1UlkQERGRGqksiIiISI1UFkRERKRGKgsiIiJSI5UFERERqZHKgoickmEYNd7+8Ic/UFRUdNp/X7VqFXBs7fpHHnmE9PR0oqOjSUpK4txzz+Wf//yn348jItbSJapF5JT27Nlz4v+/8sor3H///WzatOnE5+Li4iguLgZg2bJl9OzZ86Svb9GiBQAPPvggc+fOZfbs2WRmZlJaWkpubi6HDx/2+3FExFoqCyJySj+83HBiYiKGYfzkEsTHy0KLFi1Oe3nit956i6lTp3L11Vef+Fzfvn1r9TgiYi3thhCRoGrTpg0fffQRBw4csDqKiNSRyoKI1NugQYOIi4s76XbcY489xoEDB2jTpg19+vRhypQpvPPOOxamFZHa0m4IEam3V155he7du5/y33r06MH69evJy8vjs88+45NPPmHMmDFMmjTpxEGOIhLaVBZEpN5SUlLo0qXLaf/dZrMxcOBABg4cyIwZM5g/fz4TJ05k5syZdOzYsQGTikhdaDeEiDS4Hj16AFBeXm5xEhHxhyYLIlJvBw8eZO/evSd9rlmzZkRFRXHVVVcxePBgBg0aRJs2bfj++++5++676dq1K+np6RYlFpHa0GRBROptxIgRtG3b9qTbG2+8AcBFF13E4sWLGTNmDF27duX6668nPT2d999/n4gI/b0i0hgYpmmaVocQERGR0KXJgoiIiNRIZUFERERqpLIgIiIiNVJZEBERkRqpLIiIiEiNVBZERESkRioLIiIiUiOVBREREamRyoKIiIjUSGVBREREaqSyICIiIjVSWRAREZEa/X+6BCrl4K3ixAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor],\n",
    "        marker=markers[factor],\n",
    "        s=12 ** 2,\n",
    "    )\n",
    "\n",
    "ax.set_xlabel(\"TEST\")\n",
    "ax.set_ylabel(\"JPERF\")\n",
    "fig = abline_plot(model_results=min_lm, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:06.918147Z",
     "iopub.status.busy": "2022-11-02T17:11:06.917612Z",
     "iopub.status.idle": "2022-11-02T17:11:06.935195Z",
     "shell.execute_reply": "2022-11-02T17:11:06.934646Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.632\n",
      "Model:                            OLS   Adj. R-squared:                  0.589\n",
      "Method:                 Least Squares   F-statistic:                     14.59\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           0.000204\n",
      "Time:                        17:11:06   Log-Likelihood:                -33.891\n",
      "No. Observations:                  20   AIC:                             73.78\n",
      "Df Residuals:                      17   BIC:                             76.77\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         1.1211      0.780      1.437      0.169      -0.525       2.768\n",
      "TEST              1.8276      0.536      3.412      0.003       0.698       2.958\n",
      "TEST:MINORITY     0.9161      0.397      2.306      0.034       0.078       1.754\n",
      "==============================================================================\n",
      "Omnibus:                        0.388   Durbin-Watson:                   3.008\n",
      "Prob(Omnibus):                  0.823   Jarque-Bera (JB):                0.514\n",
      "Skew:                           0.050   Prob(JB):                        0.773\n",
      "Kurtosis:                       2.221   Cond. No.                         5.96\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm2 = ols(\"JPERF ~ TEST + TEST:MINORITY\", data=jobtest_table).fit()\n",
    "\n",
    "print(min_lm2.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:06.938858Z",
     "iopub.status.busy": "2022-11-02T17:11:06.938380Z",
     "iopub.status.idle": "2022-11-02T17:11:07.105252Z",
     "shell.execute_reply": "2022-11-02T17:11:07.104626Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6876/1032128782.py:2: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for factor, group in factor_group:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor],\n",
    "        marker=markers[factor],\n",
    "        s=12 ** 2,\n",
    "    )\n",
    "\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm2.params[\"Intercept\"],\n",
    "    slope=min_lm2.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"purple\",\n",
    ")\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm2.params[\"Intercept\"],\n",
    "    slope=min_lm2.params[\"TEST\"] + min_lm2.params[\"TEST:MINORITY\"],\n",
    "    ax=ax,\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.110285Z",
     "iopub.status.busy": "2022-11-02T17:11:07.108789Z",
     "iopub.status.idle": "2022-11-02T17:11:07.126763Z",
     "shell.execute_reply": "2022-11-02T17:11:07.126185Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.572\n",
      "Model:                            OLS   Adj. R-squared:                  0.522\n",
      "Method:                 Least Squares   F-statistic:                     11.38\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           0.000731\n",
      "Time:                        17:11:07   Log-Likelihood:                -35.390\n",
      "No. Observations:                  20   AIC:                             76.78\n",
      "Df Residuals:                      17   BIC:                             79.77\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.6120      0.887      0.690      0.500      -1.260       2.483\n",
      "TEST           2.2988      0.522      4.400      0.000       1.197       3.401\n",
      "MINORITY       1.0276      0.691      1.487      0.155      -0.430       2.485\n",
      "==============================================================================\n",
      "Omnibus:                        0.251   Durbin-Watson:                   3.028\n",
      "Prob(Omnibus):                  0.882   Jarque-Bera (JB):                0.437\n",
      "Skew:                          -0.059   Prob(JB):                        0.804\n",
      "Kurtosis:                       2.286   Cond. No.                         5.72\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm3 = ols(\"JPERF ~ TEST + MINORITY\", data=jobtest_table).fit()\n",
    "print(min_lm3.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.130630Z",
     "iopub.status.busy": "2022-11-02T17:11:07.130128Z",
     "iopub.status.idle": "2022-11-02T17:11:07.298267Z",
     "shell.execute_reply": "2022-11-02T17:11:07.297605Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6876/1594511031.py:2: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for factor, group in factor_group:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 6))\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor],\n",
    "        marker=markers[factor],\n",
    "        s=12 ** 2,\n",
    "    )\n",
    "\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm3.params[\"Intercept\"],\n",
    "    slope=min_lm3.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"purple\",\n",
    ")\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm3.params[\"Intercept\"] + min_lm3.params[\"MINORITY\"],\n",
    "    slope=min_lm3.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.302406Z",
     "iopub.status.busy": "2022-11-02T17:11:07.301855Z",
     "iopub.status.idle": "2022-11-02T17:11:07.319657Z",
     "shell.execute_reply": "2022-11-02T17:11:07.319099Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  JPERF   R-squared:                       0.664\n",
      "Model:                            OLS   Adj. R-squared:                  0.601\n",
      "Method:                 Least Squares   F-statistic:                     10.55\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           0.000451\n",
      "Time:                        17:11:07   Log-Likelihood:                -32.971\n",
      "No. Observations:                  20   AIC:                             73.94\n",
      "Df Residuals:                      16   BIC:                             77.92\n",
      "Df Model:                           3                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "Intercept         2.0103      1.050      1.914      0.074      -0.216       4.236\n",
      "TEST              1.3134      0.670      1.959      0.068      -0.108       2.735\n",
      "MINORITY         -1.9132      1.540     -1.242      0.232      -5.179       1.352\n",
      "TEST:MINORITY     1.9975      0.954      2.093      0.053      -0.026       4.021\n",
      "==============================================================================\n",
      "Omnibus:                        3.377   Durbin-Watson:                   3.015\n",
      "Prob(Omnibus):                  0.185   Jarque-Bera (JB):                1.330\n",
      "Skew:                           0.120   Prob(JB):                        0.514\n",
      "Kurtosis:                       1.760   Cond. No.                         13.8\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "min_lm4 = ols(\"JPERF ~ TEST * MINORITY\", data=jobtest_table).fit()\n",
    "print(min_lm4.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.324898Z",
     "iopub.status.busy": "2022-11-02T17:11:07.323488Z",
     "iopub.status.idle": "2022-11-02T17:11:07.502782Z",
     "shell.execute_reply": "2022-11-02T17:11:07.502131Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_6876/2552459825.py:2: FutureWarning: In a future version of pandas, a length 1 tuple will be returned when iterating over a groupby with a grouper equal to a list of length 1. Don't supply a list with a single grouper to avoid this warning.\n",
      "  for factor, group in factor_group:\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "for factor, group in factor_group:\n",
    "    ax.scatter(\n",
    "        group[\"TEST\"],\n",
    "        group[\"JPERF\"],\n",
    "        color=colors[factor],\n",
    "        marker=markers[factor],\n",
    "        s=12 ** 2,\n",
    "    )\n",
    "\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm4.params[\"Intercept\"],\n",
    "    slope=min_lm4.params[\"TEST\"],\n",
    "    ax=ax,\n",
    "    color=\"purple\",\n",
    ")\n",
    "fig = abline_plot(\n",
    "    intercept=min_lm4.params[\"Intercept\"] + min_lm4.params[\"MINORITY\"],\n",
    "    slope=min_lm4.params[\"TEST\"] + min_lm4.params[\"TEST:MINORITY\"],\n",
    "    ax=ax,\n",
    "    color=\"green\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.506831Z",
     "iopub.status.busy": "2022-11-02T17:11:07.506412Z",
     "iopub.status.idle": "2022-11-02T17:11:07.518754Z",
     "shell.execute_reply": "2022-11-02T17:11:07.518194Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0        NaN       NaN       NaN\n",
      "1      16.0  31.655473      2.0  13.912824  3.516061  0.054236\n"
     ]
    }
   ],
   "source": [
    "# is there any effect of MINORITY on slope or intercept?\n",
    "table5 = anova_lm(min_lm, min_lm4)\n",
    "print(table5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.523297Z",
     "iopub.status.busy": "2022-11-02T17:11:07.521946Z",
     "iopub.status.idle": "2022-11-02T17:11:07.533245Z",
     "shell.execute_reply": "2022-11-02T17:11:07.532705Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff   ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0       NaN       NaN       NaN\n",
      "1      17.0  40.321546      1.0  5.246751  2.212087  0.155246\n"
     ]
    }
   ],
   "source": [
    "# is there any effect of MINORITY on intercept\n",
    "table6 = anova_lm(min_lm, min_lm3)\n",
    "print(table6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.537627Z",
     "iopub.status.busy": "2022-11-02T17:11:07.536322Z",
     "iopub.status.idle": "2022-11-02T17:11:07.547315Z",
     "shell.execute_reply": "2022-11-02T17:11:07.546771Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff    ss_diff         F    Pr(>F)\n",
      "0      18.0  45.568297      0.0        NaN       NaN       NaN\n",
      "1      17.0  34.707653      1.0  10.860644  5.319603  0.033949\n"
     ]
    }
   ],
   "source": [
    "# is there any effect of MINORITY on slope\n",
    "table7 = anova_lm(min_lm, min_lm2)\n",
    "print(table7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.551618Z",
     "iopub.status.busy": "2022-11-02T17:11:07.550319Z",
     "iopub.status.idle": "2022-11-02T17:11:07.561272Z",
     "shell.execute_reply": "2022-11-02T17:11:07.560734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff  ss_diff         F    Pr(>F)\n",
      "0      17.0  34.707653      0.0      NaN       NaN       NaN\n",
      "1      16.0  31.655473      1.0  3.05218  1.542699  0.232115\n"
     ]
    }
   ],
   "source": [
    "# is it just the slope or both?\n",
    "table8 = anova_lm(min_lm2, min_lm4)\n",
    "print(table8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One-way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:07.565696Z",
     "iopub.status.busy": "2022-11-02T17:11:07.564393Z",
     "iopub.status.idle": "2022-11-02T17:11:08.071803Z",
     "shell.execute_reply": "2022-11-02T17:11:08.071177Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAq8AAAIxCAYAAABjDVGuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA6l0lEQVR4nO3de1xVdb7/8TegbNjcFBWBACUwUZFMppQx7yZamY5aOk6ljdrMCTW1zjg6nUmdErtraXYdbbxMjqY52ZiXEqwzeqYoSnMwMU3zhk4KCIII6/eHsX/tQJOLLL/wej4ePGqvvS6fjbeXy7XX9rAsyxIAAABgAE+7BwAAAAAuF/EKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCsA2Hh4emjlzpt1j1HtpaWny8PBQWlraJdebOXOmPDw8dPLkyboZ7CfmAIDKEK9APbRkyRJ5eHi4fYWEhKh3797asGGD3ePV2O7duzVz5kwdOHDA7lFQDb169arw87P8Kysrq9Jt5syZo7fffrtuBwVwVWpk9wAArpzZs2crOjpalmXp+PHjWrJkiW699Va98847uv322+0er9p2796tWbNmqVevXmrdurXd46AaIiIilJqaWmF5eHi4HnnkEf3+9793Wz5nzhwNHz5cQ4YMqaMJAVytiFegHhs4cKB+9rOfuR6PHTtWLVu21F//+lej47UunT9/XmVlZfL29rZ7lHolKChId99990Wfb9SIP54AVI7LBoAGpEmTJvL19a0QBgUFBXrooYcUGRkph8Ohtm3b6umnn5ZlWZKks2fPKi4uTnFxcTp79qxru++++05hYWH6+c9/rtLSUknSmDFj5O/vr6+//lrJycny8/NTeHi4Zs+e7drfpXz22WcaOHCgAgMD5e/vr759+2rHjh2u55csWaI777xTktS7d2/XPzf/1PWcq1atUvv27eXj46P4+HitXbtWY8aMcTtze+DAAXl4eOjpp5/WvHnzFBMTI4fDod27d0uSPvjgA3Xv3l1+fn5q0qSJBg8erH//+99ux/nxPstVdh2nh4eHJkyYoOXLl6tt27by8fFRYmKitm3bVmH7w4cP69e//rVatmwph8OhDh066M9//nOF9b799lsNGTJEfn5+CgkJ0ZQpU1RcXHzJ782PnTx5UnfddZcCAwPVrFkzPfjggyoqKnI937NnT11//fWVbtu2bVslJydX6Xg/9uPvlYeHhwoKCvTGG2+4frzHjBnjtm52drbGjBmjJk2aKCgoSPfdd58KCwsr7HvZsmVKTEyUr6+vgoODNXLkSB06dMhtnb1792rYsGEKDQ2Vj4+PIiIiNHLkSOXm5rrW2bx5s26++WY1adJE/v7+atu2rWbMmFGj1w3g8vBXW6Aey83N1cmTJ2VZlnJycvTCCy/ozJkzbme8LMvSHXfcoa1bt2rs2LHq1KmTNm7cqP/+7//W4cOH9dxzz8nX11dvvPGGunXrpj/84Q969tlnJUkpKSnKzc3VkiVL5OXl5dpnaWmpBgwYoK5du+rJJ5/Ue++9p0cffVTnz5/X7NmzLzrvl19+qe7duyswMFC/+93v1LhxY7388svq1auX0tPT1aVLF/Xo0UOTJk3S888/rxkzZqhdu3aS5PpvZd59912NGDFCHTt2VGpqqk6dOqWxY8fqmmuuqXT9xYsXq6ioSPfff78cDoeCg4O1ZcsWDRw4UNdee61mzpyps2fP6oUXXlC3bt306aefVvvyhfT0dK1cuVKTJk2Sw+HQiy++qAEDBuhf//qX4uPjJUnHjx9X165dXbHbokULbdiwQWPHjlVeXp4mT54s6cJfMvr27auDBw9q0qRJCg8P19KlS/XBBx9Uaaa77rpLrVu3Vmpqqnbs2KHnn39ep06d0l/+8hdJ0j333KPx48dr165drhkl6eOPP9ZXX32lRx555CePUVpaWuGNYT4+PvL396+w7tKlSzVu3DjddNNNuv/++yVJMTExFWaOjo5WamqqPv30U7322msKCQnRE0884Vrn8ccf1//8z//orrvu0rhx43TixAm98MIL6tGjhz777DM1adJE586dU3JysoqLizVx4kSFhobq8OHDWr9+vU6fPq2goCB9+eWXuv3225WQkKDZs2fL4XAoOztb//u//3v532QA1WcBqHcWL15sSarw5XA4rCVLlrit+/bbb1uSrMcee8xt+fDhwy0PDw8rOzvbtWz69OmWp6entW3bNmvVqlWWJGvevHlu240ePdqSZE2cONG1rKyszLrtttssb29v68SJE67lkqxHH33U9XjIkCGWt7e3tW/fPteyI0eOWAEBAVaPHj1cy8qPvXXr1sv6fnTs2NGKiIiw8vPzXcvS0tIsSVarVq1cy/bv329JsgIDA62cnBy3fXTq1MkKCQmx/vOf/7iWff7555anp6d17733ur3+H+6z3KOPPmr9+Lfc8h+XTz75xLXsm2++sXx8fKxf/OIXrmVjx461wsLCrJMnT7ptP3LkSCsoKMgqLCy0LMuy5s2bZ0my/va3v7nWKSgosGJjYy/r+1U+4x133OG2/IEHHrAkWZ9//rllWZZ1+vRpy8fHx5o2bZrbepMmTbL8/PysM2fOXPI4PXv2rPTn5+jRo93m+CE/Pz/X85XN/Otf/9pt+S9+8QurWbNmrscHDhywvLy8rMcff9xtvZ07d1qNGjVyLf/ss88sSdaqVasuOv9zzz1nSXL7uQyg7nDZAFCPLVy4UJs3b9bmzZu1bNky9e7dW+PGjdOaNWtc6/zjH/+Ql5eXJk2a5LbtQw89JMuy3O5OMHPmTHXo0EGjR4/WAw88oJ49e1bYrtyECRNc/19+xvDcuXPasmVLpeuXlpZq06ZNGjJkiK699lrX8rCwMI0aNUofffSR8vLyqvw9OHLkiHbu3Kl7773X7axez5491bFjx0q3GTZsmFq0aOF6fPToUWVmZmrMmDEKDg52LU9ISNAtt9yif/zjH1Weq1xSUpISExNdj6OiojR48GBt3LhRpaWlsixLb731lgYNGiTLsnTy5EnXV3JysnJzc/Xpp59KuvBjGRYWpuHDh7v253Q6XWcrL1dKSorb44kTJ7r2L124XnXw4MH661//6roUpLS0VCtXrnRdsvBTWrdu7fq5Wf71u9/9rkpz/tBvf/tbt8fdu3fXf/7zH9fPmTVr1qisrEx33XWX2/cwNDRUbdq00datW12vTZI2btxY6WUH0oXLbyRp3bp1Kisrq/bMAKqHeAXqsZtuukn9+vVTv3799Ktf/Urvvvuu2rdv7wpJSfrmm28UHh6ugIAAt23L/xn+m2++cS3z9vbWn//8Z+3fv1/5+flavHhxpffj9PT0dAtQSbruuusk6aK3tzpx4oQKCwvVtm3bCs+1a9dOZWVlFa5NvBzl88fGxlZ4rrJlkhQdHV3pPi4228mTJ1VQUFDl2SSpTZs2FZZdd911Kiws1IkTJ3TixAmdPn1ar7zyilq0aOH2dd9990mScnJyXHPGxsZW+DGpbO6qzBQTEyNPT0+3H7t7771XBw8e1IcffihJ2rJli44fP6577rnnso7h5+fn+rlZ/tW+ffsqzflDUVFRbo+bNm0qSTp16pSkC9exWpalNm3aVPg+/vvf/3Z9D6OjozV16lS99tprat68uZKTk7Vw4UK3611HjBihbt26ady4cWrZsqVGjhypv/3tb4QsUEe45hVoQDw9PdW7d2/Nnz9fe/fuVYcOHaq8j40bN0qSioqKtHfv3gqhVx/4+vpWe9uL3Vy//A1tVVUeRHfffbdGjx5d6ToJCQnV2vflquw1JScnq2XLllq2bJl69OihZcuWKTQ0VP369buis1zMD6+5/qHyM8NlZWXy8PDQhg0bKl33h2fln3nmGY0ZM0br1q3Tpk2bNGnSJNf1vxEREfL19dW2bdu0detWvfvuu3rvvfe0cuVK9enTR5s2bbroLABqB/EKNDDnz5+XJJ05c0aS1KpVK23ZskX5+fluZ1/LbxbfqlUr17IvvvhCs2fP1n333afMzEyNGzdOO3fudP1Ta7mysjJ9/fXXrrOtkvTVV19J0kXf2NSiRQs5nU7t2bOnwnNZWVny9PRUZGSkpIsHYmXK58/Ozq7wXGXLLrWPi83WvHlz1z+VN23aVKdPn66w3g/PYP/Q3r17Kyz76quv5HQ6XZcuBAQEqLS09CfDsFWrVtq1a5csy3L7HlU296X8+C8l2dnZKisrc/ux8/Ly0qhRo7RkyRI98cQTevvttzV+/PgrFm41/cStmJgYWZal6Ohot5+XF9OxY0d17NhRjzzyiP75z3+qW7dueumll/TYY49JuvAXwb59+6pv37569tlnNWfOHP3hD3/Q1q1bbQt4oKHgsgGgASkpKdGmTZvk7e3tuizg1ltvVWlpqRYsWOC27nPPPScPDw8NHDjQte2YMWMUHh6u+fPna8mSJTp+/LimTJlS6bF+uD/LsrRgwQI1btxYffv2rXR9Ly8v9e/fX+vWrXP75+njx49rxYoVuvnmmxUYGChJrlCsLBJ/LDw8XPHx8frLX/7iCnbpwrv8d+7c+ZPbSxeuu+3UqZPeeOMNt2Pu2rVLmzZt0q233upaFhMTo9zcXH3xxReuZUePHtXatWsr3ff27dtd16xK0qFDh7Ru3Tr1799fXl5e8vLy0rBhw/TWW29p165dFbY/ceKE6/9vvfVWHTlyRKtXr3YtKyws1CuvvHJZr7PcwoUL3R6/8MILkuT6uVDunnvu0alTp/Sb3/ymwl0sapufn99l/XhfzNChQ+Xl5aVZs2ZVuGWbZVn6z3/+I0nKy8tz/QWvXMeOHeXp6em65dh3331XYf+dOnWSpCrflgxA1XHmFajHNmzY4DqDmpOToxUrVmjv3r36/e9/7wrBQYMGqXfv3vrDH/6gAwcO6Prrr9emTZu0bt06TZ482XVLoscee0yZmZl6//33FRAQoISEBP3xj3/UI488ouHDh7sFnI+Pj9577z2NHj1aXbp00YYNG/Tuu+9qxowZbm+E+rHHHnvMdf/MBx54QI0aNdLLL7+s4uJiPfnkk671OnXqJC8vLz3xxBPKzc2Vw+FQnz59FBISUul+58yZo8GDB6tbt2667777dOrUKS1YsEDx8fFuQXspTz31lAYOHKikpCSNHTvWdausoKAgzZw507XeyJEjNW3aNP3iF7/QpEmTVFhYqEWLFum6665zi9Ry8fHxSk5OdrtVliTNmjXLtc7cuXO1detWdenSRePHj1f79u313Xff6dNPP9WWLVtcMTV+/HgtWLBA9957rzIyMhQWFqalS5fK6XRe1msst3//ft1xxx0aMGCAtm/frmXLlmnUqFEV7u16ww03KD4+XqtWrVK7du3UuXPnKh2nKhITE7VlyxY9++yzCg8PV3R0tLp06XLZ28fExOixxx7T9OnTdeDAAQ0ZMkQBAQHav3+/1q5dq/vvv18PP/ywPvjgA02YMEF33nmnrrvuOp0/f15Lly51/SVCuvDJddu2bdNtt92mVq1aKScnRy+++KIiIiJ08803X6lvAYByNt3lAMAVVNmtsnx8fKxOnTpZixYtssrKytzWz8/Pt6ZMmWKFh4dbjRs3ttq0aWM99dRTrvUyMjKsRo0aud3+yrIs6/z589aNN95ohYeHW6dOnbIs68Ktovz8/Kx9+/ZZ/fv3t5xOp9WyZUvr0UcftUpLS922149ulWVZlvXpp59aycnJlr+/v+V0Oq3evXtb//znPyu8xldffdW69tprLS8vr8u6DdSbb75pxcXFWQ6Hw4qPj7f+/ve/W8OGDbPi4uJc65TfKuupp56qdB9btmyxunXrZvn6+lqBgYHWoEGDrN27d1dYb9OmTVZ8fLzl7e1ttW3b1lq2bNlFb5WVkpJiLVu2zGrTpo3lcDisG264odLXcvz4cSslJcWKjIy0GjdubIWGhlp9+/a1XnnlFbf1vvnmG+uOO+6wnE6n1bx5c+vBBx+03nvvvSrdKmv37t3W8OHDrYCAAKtp06bWhAkTrLNnz1a6zZNPPmlJsubMmXPJff9Qz549rQ4dOvzkHD+UlZVl9ejRw/L19a30tlo/vm1V+a+B/fv3uy1/6623rJtvvtny8/Oz/Pz8rLi4OCslJcXas2ePZVmW9fXXX1u//vWvrZiYGMvHx8cKDg62evfubW3ZssW1j/fff98aPHiwFR4ebnl7e1vh4eHWL3/5S+urr7667O8BgOrzsKzL+MgbALhMY8aM0erVqy/7jKadOnXqpBYtWmjz5s22HN/Dw0MpKSkVLtkwyfz58zVlyhQdOHCgwjv+AeBK4JpXAPVeSUlJhesY09LS9Pnnn6tXr172DFUPWJal119/XT179iRcAdQZrnkFUO8dPnxY/fr10913363w8HBlZWXppZdeUmhoaIWb2+OnFRQU6O9//7u2bt2qnTt3at26dXaPBKABIV4B1HtNmzZVYmKiXnvtNZ04cUJ+fn667bbbNHfuXDVr1szu8Yxz4sQJjRo1Sk2aNNGMGTN0xx132D0SgAaEa14BAABgDK55BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwCoI2PGjFHr1q3tHgMAjMYnbAFADXh4eFzWelu3br3CkwBAw8AnbAFADSxbtszt8V/+8hdt3rxZS5cudVt+yy23KDg4WGVlZXI4HHU5IgDUK8QrANSiCRMmaOHCheK3VgC4MrjmFQDqyI+veT1w4IA8PDz09NNPa+HChbr22mvldDrVv39/HTp0SJZl6U9/+pMiIiLk6+urwYMH67vvvquw3w0bNqh79+7y8/NTQECAbrvtNn355Zd1+MoAoO5wzSsA2Gz58uU6d+6cJk6cqO+++05PPvmk7rrrLvXp00dpaWmaNm2asrOz9cILL+jhhx/Wn//8Z9e2S5cu1ejRo5WcnKwnnnhChYWFWrRokW6++WZ99tlnvEEMQL1DvAKAzQ4fPqy9e/cqKChIklRaWqrU1FSdPXtWn3zyiRo1uvBb9YkTJ7R8+XItWrRIDodDZ86c0aRJkzRu3Di98sorrv2NHj1abdu21Zw5c9yWA0B9wGUDAGCzO++80xWuktSlSxdJ0t133+0K1/Ll586d0+HDhyVJmzdv1unTp/XLX/5SJ0+edH15eXmpS5cu3OEAQL3EmVcAsFlUVJTb4/KQjYyMrHT5qVOnJEl79+6VJPXp06fS/QYGBtbqnABwNSBeAcBmXl5eVVpefieDsrIySReuew0NDa2w3g/P2gJAfcHvbABgqJiYGElSSEiI+vXrZ/M0AFA3uOYVAAyVnJyswMBAzZkzRyUlJRWeP3HihA1TAcCVxZlXADBUYGCgFi1apHvuuUedO3fWyJEj1aJFCx08eFDvvvuuunXrpgULFtg9JgDUKuIVAAw2atQohYeHa+7cuXrqqadUXFysa665Rt27d9d9991n93gAUOv4eFgAAAAYg2teAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxrjq7vNaVlamI0eOKCAgQB4eHnaPAwAAgCvMsizl5+crPDxcnp6XPrd61cXrkSNHFBkZafcYAAAAqGOHDh1SRETEJde56uI1ICBA0oXhAwMDbZ4GAAAAV1peXp4iIyNdHXgpV128ll8qEBgYSLwCAAA0IJdzyShv2AIAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGKOR3QPAPoWFhcrKyqr29kUlpfr2VKEimjrl09ir2vuJi4uT0+ms9vYAAKDhIF4bsKysLCUmJto9hjIyMtS5c2e7xwAAAAYgXhuwuLg4ZWRkVHv77Jx8PfhmpuaP7KTYkIAazQEAAHA5iNcGzOl01uiMp/fhXDnSzqh9x06KvyaoFicDAACoHG/YAgAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABijkd0DoGb2nyxQQfF5W46dnXPG7b928HM0UnRzP9uODwAA6hbxarD9JwvU++k0u8fQ5JWZth5/68O9CFgAABoI4tVg5Wdc543opNgQ/zo/flFJqb49dVYRTX3l09irzo+fnXNGk1dm2nbmGQAA1D3itR6IDfFX/DVBthz7Z61tOSwAAGigeMMWAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYNYrXuXPnysPDQ5MnT3YtKyoqUkpKipo1ayZ/f38NGzZMx48fr+mcAAAAQPXj9eOPP9bLL7+shIQEt+VTpkzRO++8o1WrVik9PV1HjhzR0KFDazwoAAAAUK14PXPmjH71q1/p1VdfVdOmTV3Lc3Nz9frrr+vZZ59Vnz59lJiYqMWLF+uf//ynduzYUWtDAwAAoGGqVrympKTotttuU79+/dyWZ2RkqKSkxG15XFycoqKitH379ppNCgAAgAavUVU3ePPNN/Xpp5/q448/rvDcsWPH5O3trSZNmrgtb9mypY4dO1bp/oqLi1VcXOx6nJeXV9WRGqzi0iJ5+hzW/rw98vTxt3ucOrc/74w8fQ6ruLRIUpDd4wAAgDpQpXg9dOiQHnzwQW3evFk+Pj61MkBqaqpmzZpVK/tqaI4UfCO/6Bc04192T2Ifv2jpSEEnJaql3aMAAIA6UKV4zcjIUE5Ojjp37uxaVlpaqm3btmnBggXauHGjzp07p9OnT7udfT1+/LhCQ0Mr3ef06dM1depU1+O8vDxFRkZW8WU0TOF+rVSwf6Lmj+ikmJCGd+Z1X84ZPbgyU+G9W9k9CgAAqCNVite+fftq586dbsvuu+8+xcXFadq0aYqMjFTjxo31/vvva9iwYZKkPXv26ODBg0pKSqp0nw6HQw6Ho5rjN2wOLx+VFV2j6MC2at+s4f2zeVlRrsqKTsjhVTv/CgAAAK5+VYrXgIAAxcfHuy3z8/NTs2bNXMvHjh2rqVOnKjg4WIGBgZo4caKSkpLUtWvX2psaAAAADVKV37D1U5577jl5enpq2LBhKi4uVnJysl588cXaPgwAAAAaoBrHa1pamttjHx8fLVy4UAsXLqzprgEAAAA3Nfp4WAAAAKAuEa8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMEaNPx4WAHBBYWGhsrKyqr19UUmpvj1VqIimTvk09qr2fuLi4uR0Oqu9PQBczYhXAKglWVlZSkxMtHsMZWRkqHPnznaPAQBXBPEKALUkLi5OGRkZ1d4+OydfD76ZqfkjOyk2JKBGcwBAfUW8AkAtcTqdNTrj6X04V460M2rfsZPirwmqxckAoP7gDVsAAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYzSyewBU39mSUknSrsO5thy/qKRU3546q4imvvJp7FXnx8/OOVPnxwQAAPYiXg227/t4+/2anTZPYi8/Bz+NAQBoKPhT32D9O4RKkmJC/OVr05nPySszNW9EJ8WG+Nf58aUL4Rrd3M+WYwMAgLpHvBos2M9bI2+KsnsMxYb4K/6aILvHAAAADQBv2AIAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGKNK8bpo0SIlJCQoMDBQgYGBSkpK0oYNG1zP9+rVSx4eHm5fv/3tb2t9aAAAADRMjaqyckREhObOnas2bdrIsiy98cYbGjx4sD777DN16NBBkjR+/HjNnj3btY3T6azdiQEAANBgVSleBw0a5Pb48ccf16JFi7Rjxw5XvDqdToWGhtbehAAAAMD3qn3Na2lpqd58800VFBQoKSnJtXz58uVq3ry54uPjNX36dBUWFl5yP8XFxcrLy3P7AgAAACpTpTOvkrRz504lJSWpqKhI/v7+Wrt2rdq3by9JGjVqlFq1aqXw8HB98cUXmjZtmvbs2aM1a9ZcdH+pqamaNWtW9V8BAAAAGowqx2vbtm2VmZmp3NxcrV69WqNHj1Z6errat2+v+++/37Vex44dFRYWpr59+2rfvn2KiYmpdH/Tp0/X1KlTXY/z8vIUGRlZjZcCAACA+q7K8ert7a3Y2FhJUmJioj7++GPNnz9fL7/8coV1u3TpIknKzs6+aLw6HA45HI6qjgEAAIAGqMb3eS0rK1NxcXGlz2VmZkqSwsLCanoYAAAAoGpnXqdPn66BAwcqKipK+fn5WrFihdLS0rRx40bt27dPK1as0K233qpmzZrpiy++0JQpU9SjRw8lJCRcqfkBAADQgFQpXnNycnTvvffq6NGjCgoKUkJCgjZu3KhbbrlFhw4d0pYtWzRv3jwVFBQoMjJSw4YN0yOPPHKlZgcAAEADU6V4ff311y/6XGRkpNLT02s8EAAAAHAxNb7mFQAAAKgrVb7bAADUZ/tPFqig+Lwtx87OOeP2Xzv4ORopurmfbccHgJ9CvALA9/afLFDvp9PsHkOTV2baevytD/ciYAFctYhXAPhe+RnXeSM6KTbEv86PX1RSqm9PnVVEU1/5NPaq8+Nn55zR5JWZtp15BoDLQbwCwI/Ehvgr/pogW479s9a2HBYAjMEbtgAAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGCMRnYPAAAAcCWUlpbqww8/1NGjRxUWFqbu3bvLy8vL7rFQQ5x5BQAA9c6aNWsUGxur3r17a9SoUerdu7diY2O1Zs0au0dDDRGvAACgXlmzZo2GDx+ujh07avv27crPz9f27dvVsWNHDR8+nIA1HPEKAADqjdLSUj300EO6/fbb9fbbb6tr167y9/dX165d9fbbb+v222/Xww8/rNLSUrtHRTURrwAAoN748MMPdeDAAc2YMUOenu6Z4+npqenTp2v//v368MMPbZoQNUW8AgCAeuPo0aOSpPj4+EqfL19evh7MQ7wCAIB6IywsTJK0a9euSp8vX16+HsxDvAIAgHqje/fuat26tebMmaOysjK358rKypSamqro6Gh1797dpglRU8QrAACoN7y8vPTMM89o/fr1GjJkiNvdBoYMGaL169fr6aef5n6vBuNDCgAAQL0ydOhQrV69Wg899JB+/vOfu5ZHR0dr9erVGjp0qI3ToaaIVwAAUO8MHTpUgwcP5hO26iHiFQAA1EteXl7q1auX3WOglnHNKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGHw8LAB8r7i0SJ4+h7U/b488ffztHqfO7c87I0+fwyouLZIUZPc4AFAp4hUAvnek4Bv5Rb+gGf+yexL7+EVLRwo6KVEt7R4FACpFvALA98L9Wqlg/0TNH9FJMSEN78zrvpwzenBlpsJ7t7J7FAC4KOIVAL7n8PJRWdE1ig5sq/bNGt4/m5cV5aqs6IQcXj52jwIAF8UbtgAAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxqhSvC5atEgJCQkKDAxUYGCgkpKStGHDBtfzRUVFSklJUbNmzeTv769hw4bp+PHjtT40AAAAGqYqxWtERITmzp2rjIwMffLJJ+rTp48GDx6sL7/8UpI0ZcoUvfPOO1q1apXS09N15MgRDR069IoMDgAAgIanUVVWHjRokNvjxx9/XIsWLdKOHTsUERGh119/XStWrFCfPn0kSYsXL1a7du20Y8cOde3atfamBgAAQINU7WteS0tL9eabb6qgoEBJSUnKyMhQSUmJ+vXr51onLi5OUVFR2r59+0X3U1xcrLy8PLcvAAAAoDJVjtedO3fK399fDodDv/3tb7V27Vq1b99ex44dk7e3t5o0aeK2fsuWLXXs2LGL7i81NVVBQUGur8jIyCq/CAAAADQMVY7Xtm3bKjMzU//3f/+n//qv/9Lo0aO1e/fuag8wffp05ebmur4OHTpU7X0BAACgfqvSNa+S5O3trdjYWElSYmKiPv74Y82fP18jRozQuXPndPr0abezr8ePH1doaOhF9+dwOORwOKo+OQAAABqcGt/ntaysTMXFxUpMTFTjxo31/vvvu57bs2ePDh48qKSkpJoeBgAAAKjamdfp06dr4MCBioqKUn5+vlasWKG0tDRt3LhRQUFBGjt2rKZOnarg4GAFBgZq4sSJSkpK4k4DAAAAqBVVitecnBzde++9Onr0qIKCgpSQkKCNGzfqlltukSQ999xz8vT01LBhw1RcXKzk5GS9+OKLV2RwAAAANDxVitfXX3/9ks/7+Pho4cKFWrhwYY2GAgAAACpT42teAQAAgLpCvAIAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBhV/nhYAKivzpaUSpJ2Hc615fhFJaX69tRZRTT1lU9jrzo/fnbOmTo/JgBUFfEKAN/b9328/X7NTpsnsZefgz8aAFy9+B0KAL7Xv0OoJCkmxF++Np35nLwyU/NGdFJsiH+dH1+6EK7Rzf1sOTYAXA7iFQC+F+znrZE3Rdk9hmJD/BV/TZDdYwDAVYl4bcAKCwuVlZVV7e2zc/JVfCxbu3f669zxgGrvJy4uTk6ns9rbAwCAhoN4bcCysrKUmJhY4/2MeKNm22dkZKhz5841ngMAANR/xGsDFhcXp4yMjGpvf+Gd0YWKaOqs0Tuj4+Liqr0tAABoWIjXBszpdHLGEwAAGIUPKQAAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYo5HdAwBAfVFYWKisrKxqb5+dk6/iY9navdNf544HVHs/cXFxcjqd1d4eAK5mxCsA1JKsrCwlJibWeD8j3qjZ9hkZGercuXON5wCAqxHxCgC1JC4uThkZGdXevqikVN+eKlREU6d8GnvVaA4AqK+IVwCoJU6nkzOeAHCF8YYtAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDEa2T0AAADAxRQWFiorK6va2xeVlOrbU4WKaOqUT2Ovau8nLi5OTqez2tuj9hCvAADgqpWVlaXExES7x1BGRoY6d+5s9xgQ8QoAAK5icXFxysjIqPb22Tn5evDNTM0f2UmxIQE1mgNXB+IVAABctZxOZ43OeHofzpUj7Yzad+yk+GuCanEy2IU3bAEAAMAYxCsAAACMQbwCAADAGMQrAAAAjEG8AgAAwBjEKwAAAIxBvAIAAMAYxCsAAACMQbwCAADAGHzCFgAAuKL2nyxQQfF5W46dnXPG7b928HM0UnRzP9uOX99UKV5TU1O1Zs0aZWVlydfXVz//+c/1xBNPqG3btq51evXqpfT0dLftfvOb3+ill16qnYkBAIAx9p8sUO+n0+weQ5NXZtp6/K0P9yJga0mV4jU9PV0pKSm68cYbdf78ec2YMUP9+/fX7t275ef3/39Axo8fr9mzZ7seO53O2psYAAAYo/yM67wRnRQb4l/nxy8qKdW3p84qoqmvfBp71fnxs3POaPLKTNvOPNdHVYrX9957z+3xkiVLFBISooyMDPXo0cO13Ol0KjQ0tHYmBAAAxosN8Vf8NUG2HPtnrW05LK6QGr1hKzc3V5IUHBzstnz58uVq3ry54uPjNX36dBUWFl50H8XFxcrLy3P7AgAAACpT7TdslZWVafLkyerWrZvi4+Ndy0eNGqVWrVopPDxcX3zxhaZNm6Y9e/ZozZo1le4nNTVVs2bNqu4YAAAAaECqHa8pKSnatWuXPvroI7fl999/v+v/O3bsqLCwMPXt21f79u1TTExMhf1Mnz5dU6dOdT3Oy8tTZGRkdccCAABAPVateJ0wYYLWr1+vbdu2KSIi4pLrdunSRZKUnZ1dabw6HA45HI7qjAEAAIAGpkrxalmWJk6cqLVr1yotLU3R0dE/uU1mZqYkKSwsrFoDAgAAAOWqFK8pKSlasWKF1q1bp4CAAB07dkySFBQUJF9fX+3bt08rVqzQrbfeqmbNmumLL77QlClT1KNHDyUkJFyRFwAAAICGo0rxumjRIkkXPojghxYvXqwxY8bI29tbW7Zs0bx581RQUKDIyEgNGzZMjzzySK0NDAAAgIarypcNXEpkZGSFT9cCAAAAakuN7vMKAAAA1CXiFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYIxGdg8AAADqr+LSInn6HNb+vD3y9PG3e5w6tz/vjDx9Dqu4tEhSkN3j1AvEKwAAuGKOFHwjv+gXNONfdk9iH79o6UhBJyWqpd2j1AvEKwAAuGLC/VqpYP9EzR/RSTEhDe/M676cM3pwZabCe7eye5R6g3gFAABXjMPLR2VF1yg6sK3aN2t4/2xeVpSrsqITcnj52D1KvcEbtgAAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAAAAxiBeAQAAYAziFQAAAMZoZPcAAACg/jpbUipJ2nU415bjF5WU6ttTZxXR1Fc+jb3q/PjZOWfq/Jj1HfEKAACumH3fx9vv1+y0eRJ7+TlIrtrCdxIAAFwx/TuESpJiQvzla9OZz8krMzVvRCfFhvjX+fGlC+Ea3dzPlmPXR8QrAAC4YoL9vDXypii7x1BsiL/irwmyewzUAt6wBQAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxCgAAAGMQrwAAADAG8QoAAABjVCleU1NTdeONNyogIEAhISEaMmSI9uzZ47ZOUVGRUlJS1KxZM/n7+2vYsGE6fvx4rQ4NAACAhqlK8Zqenq6UlBTt2LFDmzdvVklJifr376+CggLXOlOmTNE777yjVatWKT09XUeOHNHQoUNrfXAAAAA0PI2qsvJ7773n9njJkiUKCQlRRkaGevToodzcXL3++utasWKF+vTpI0lavHix2rVrpx07dqhr1661NzkAAAAanCrF64/l5uZKkoKDgyVJGRkZKikpUb9+/VzrxMXFKSoqStu3b680XouLi1VcXOx6nJeXV5ORAABAPVJYWKisrKxqb5+dk6/iY9navdNf544HVHs/cXFxcjqd1d4etafa8VpWVqbJkyerW7duio+PlyQdO3ZM3t7eatKkidu6LVu21LFjxyrdT2pqqmbNmlXdMQAAQD2WlZWlxMTEGu9nxBs12z4jI0OdO3eu8RyouWrHa0pKinbt2qWPPvqoRgNMnz5dU6dOdT3Oy8tTZGRkjfYJAADqh7i4OGVkZFR7+6KSUn17qlARTZ3yaexVozlwdahWvE6YMEHr16/Xtm3bFBER4VoeGhqqc+fO6fTp025nX48fP67Q0NBK9+VwOORwOKozBgAAqOecTidnPOGmSncbsCxLEyZM0Nq1a/XBBx8oOjra7fnExEQ1btxY77//vmvZnj17dPDgQSUlJdXOxAAAAGiwqnTmNSUlRStWrNC6desUEBDguo41KChIvr6+CgoK0tixYzV16lQFBwcrMDBQEydOVFJSEncaAAAAQI15WJZlXfbKHh6VLl+8eLHGjBkj6cKHFDz00EP661//quLiYiUnJ+vFF1+86GUDP5aXl6egoCDl5uYqMDDwckcDAACAoarSf1WK17pAvAIAADQsVem/Kl3zCgAAANiJeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABijkd0D/JhlWZKkvLw8mycBAABAXSjvvvIOvJSrLl7z8/MlSZGRkTZPAgAAgLqUn5+voKCgS67jYV1O4tahsrIyHTlyRAEBAfLw8LB7HFxCXl6eIiMjdejQIQUGBto9DmA8fk0BtY9fV2awLEv5+fkKDw+Xp+elr2q96s68enp6KiIiwu4xUAWBgYH8hgDUIn5NAbWPX1dXv58641qON2wBAADAGMQrAAAAjEG8otocDoceffRRORwOu0cB6gV+TQG1j19X9c9V94YtAAAA4GI48woAAABjEK8AAAAwBvEKAAAAYxCvAAAAMAbxiirbtm2bBg0apPDwcHl4eOjtt9+2eyTAaKmpqbrxxhsVEBCgkJAQDRkyRHv27LF7LMBYixYtUkJCguuDCZKSkrRhwwa7x0ItIV5RZQUFBbr++uu1cOFCu0cB6oX09HSlpKRox44d2rx5s0pKStS/f38VFBTYPRpgpIiICM2dO1cZGRn65JNP1KdPHw0ePFhffvml3aOhFnCrLNSIh4eH1q5dqyFDhtg9ClBvnDhxQiEhIUpPT1ePHj3sHgeoF4KDg/XUU09p7Nixdo+CGmpk9wAAAHe5ubmSLvxhC6BmSktLtWrVKhUUFCgpKcnucVALiFcAuIqUlZVp8uTJ6tatm+Lj4+0eBzDWzp07lZSUpKKiIvn7+2vt2rVq37693WOhFhCvAHAVSUlJ0a5du/TRRx/ZPQpgtLZt2yozM1O5ublavXq1Ro8erfT0dAK2HiBeAeAqMWHCBK1fv17btm1TRESE3eMARvP29lZsbKwkKTExUR9//LHmz5+vl19+2ebJUFPEKwDYzLIsTZw4UWvXrlVaWpqio6PtHgmod8rKylRcXGz3GKgFxCuq7MyZM8rOznY93r9/vzIzMxUcHKyoqCgbJwPMlJKSohUrVmjdunUKCAjQsWPHJElBQUHy9fW1eTrAPNOnT9fAgQMVFRWl/Px8rVixQmlpadq4caPdo6EWcKssVFlaWpp69+5dYfno0aO1ZMmSuh8IMJyHh0elyxcvXqwxY8bU7TBAPTB27Fi9//77Onr0qIKCgpSQkKBp06bplltusXs01ALiFQAAAMbgE7YAAABgDOIVAAAAxiBeAQAAYAziFQAAAMYgXgEAAGAM4hUAAADGIF4BAABgDOIVAGpRr169NHnyZLvHAIB6i3gFgGoYM2aMPDw8Knw9+eST+tOf/uRar3Xr1po3b559gwJAPdPI7gEAwFQDBgzQ4sWL3Za1aNFCXl5eNk0EAPUfZ14BoJocDodCQ0Pdvvr27eu6bKBXr1765ptvNGXKFNeZWUlasmSJmjRpoo0bN6pdu3by9/fXgAEDdPToUbf9v/baa2rXrp18fHwUFxenF1980fXcuXPnNGHCBIWFhcnHx0etWrVSamqqJMmyLM2cOVNRUVFyOBwKDw/XpEmT6uabAgBXGGdeAeAKWbNmja6//nrdf//9Gj9+vNtzhYWFevrpp7V06VJ5enrq7rvv1sMPP6zly5dLkpYvX64//vGPWrBggW644QZ99tlnGj9+vPz8/DR69Gg9//zz+vvf/66//e1vioqK0qFDh3To0CFJ0ltvvaXnnntOb775pjp06KBjx47p888/r/PXDwBXAvEKANW0fv16+fv7ux4PHDjQ7fng4GB5eXkpICBAoaGhbs+VlJTopZdeUkxMjCRpwoQJmj17tuv5Rx99VM8884yGDh0qSYqOjtbu3bv18ssva/To0Tp48KDatGmjm2++WR4eHmrVqpVr24MHDyo0NFT9+vVT48aNFRUVpZtuuqnWXz8A2IHLBgCgmnr37q3MzEzX1/PPP3/Z2zqdTle4SlJYWJhycnIkSQUFBdq3b5/Gjh0rf39/19djjz2mffv2SbrwhrHMzEy1bdtWkyZN0qZNm1z7uvPOO3X27Flde+21Gj9+vNauXavz58/X0qsGAHtx5hUAqsnPz0+xsbHV2rZx48Zujz08PGRZliTpzJkzkqRXX31VXbp0cVuv/M1gnTt31v79+7VhwwZt2bJFd911l/r166fVq1crMjJSe/bs0ZYtW7R582Y98MADeuqpp5Senl7huABgGuIVAK4gb29vlZaWVmmbli1bKjw8XF9//bV+9atfXXS9wMBAjRgxQiNGjNDw4cM1YMAAfffddwoODpavr68GDRqkQYMGKSUlRXFxcdq5c6c6d+5c05cEALYiXgHgCmrdurW2bdumkSNHyuFwqHnz5pe13axZszRp0iQFBQVpwIABKi4u1ieffKJTp05p6tSpevbZZxUWFqYbbrhBnp6eWrVqlUJDQ9WkSRMtWbJEpaWl6tKli5xOp5YtWyZfX1+362IBwFRc8woAV9Ds2bN14MABxcTEqEWLFpe93bhx4/Taa69p8eLF6tixo3r27KklS5YoOjpakhQQEKAnn3xSP/vZz3TjjTfqwIED+sc//iFPT081adJEr776qrp166aEhARt2bJF77zzjpo1a3alXiYA1BkPq/wiKwAAAOAqx5lXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMYhXAAAAGIN4BQAAgDGIVwAAABiDeAUAAIAxiFcAAAAYg3gFAACAMf4fsjSfz+/V4AkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    rehab_table = pd.read_csv(\"rehab.table\")\n",
    "except:\n",
    "    url = \"http://stats191.stanford.edu/data/rehab.csv\"\n",
    "    rehab_table = pd.read_table(url, delimiter=\",\")\n",
    "    rehab_table.to_csv(\"rehab.table\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "fig = rehab_table.boxplot(\"Time\", \"Fitness\", ax=ax, grid=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.075886Z",
     "iopub.status.busy": "2022-11-02T17:11:08.075361Z",
     "iopub.status.idle": "2022-11-02T17:11:08.096978Z",
     "shell.execute_reply": "2022-11-02T17:11:08.096405Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              df  sum_sq     mean_sq          F    PR(>F)\n",
      "C(Fitness)   2.0   672.0  336.000000  16.961538  0.000041\n",
      "Residual    21.0   416.0   19.809524        NaN       NaN\n",
      "    Intercept  C(Fitness)[T.2]  C(Fitness)[T.3]\n",
      "0         1.0              0.0              0.0\n",
      "1         1.0              0.0              0.0\n",
      "2         1.0              0.0              0.0\n",
      "3         1.0              0.0              0.0\n",
      "4         1.0              0.0              0.0\n",
      "5         1.0              0.0              0.0\n",
      "6         1.0              0.0              0.0\n",
      "7         1.0              0.0              0.0\n",
      "8         1.0              1.0              0.0\n",
      "9         1.0              1.0              0.0\n",
      "10        1.0              1.0              0.0\n",
      "11        1.0              1.0              0.0\n",
      "12        1.0              1.0              0.0\n",
      "13        1.0              1.0              0.0\n",
      "14        1.0              1.0              0.0\n",
      "15        1.0              1.0              0.0\n",
      "16        1.0              1.0              0.0\n",
      "17        1.0              1.0              0.0\n",
      "18        1.0              0.0              1.0\n",
      "19        1.0              0.0              1.0\n",
      "20        1.0              0.0              1.0\n",
      "21        1.0              0.0              1.0\n",
      "22        1.0              0.0              1.0\n",
      "23        1.0              0.0              1.0\n"
     ]
    }
   ],
   "source": [
    "rehab_lm = ols(\"Time ~ C(Fitness)\", data=rehab_table).fit()\n",
    "table9 = anova_lm(rehab_lm)\n",
    "print(table9)\n",
    "\n",
    "print(rehab_lm.model.data.orig_exog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.100723Z",
     "iopub.status.busy": "2022-11-02T17:11:08.100237Z",
     "iopub.status.idle": "2022-11-02T17:11:08.111547Z",
     "shell.execute_reply": "2022-11-02T17:11:08.111001Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                   Time   R-squared:                       0.618\n",
      "Model:                            OLS   Adj. R-squared:                  0.581\n",
      "Method:                 Least Squares   F-statistic:                     16.96\n",
      "Date:                Wed, 02 Nov 2022   Prob (F-statistic):           4.13e-05\n",
      "Time:                        17:11:08   Log-Likelihood:                -68.286\n",
      "No. Observations:                  24   AIC:                             142.6\n",
      "Df Residuals:                      21   BIC:                             146.1\n",
      "Df Model:                           2                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "===================================================================================\n",
      "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
      "-----------------------------------------------------------------------------------\n",
      "Intercept          38.0000      1.574     24.149      0.000      34.728      41.272\n",
      "C(Fitness)[T.2]    -6.0000      2.111     -2.842      0.010     -10.390      -1.610\n",
      "C(Fitness)[T.3]   -14.0000      2.404     -5.824      0.000     -18.999      -9.001\n",
      "==============================================================================\n",
      "Omnibus:                        0.163   Durbin-Watson:                   2.209\n",
      "Prob(Omnibus):                  0.922   Jarque-Bera (JB):                0.211\n",
      "Skew:                          -0.163   Prob(JB):                        0.900\n",
      "Kurtosis:                       2.675   Cond. No.                         3.80\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(rehab_lm.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Two-way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.116072Z",
     "iopub.status.busy": "2022-11-02T17:11:08.114763Z",
     "iopub.status.idle": "2022-11-02T17:11:08.384040Z",
     "shell.execute_reply": "2022-11-02T17:11:08.383358Z"
    }
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    kidney_table = pd.read_table(\"./kidney.table\")\n",
    "except:\n",
    "    url = \"http://stats191.stanford.edu/data/kidney.table\"\n",
    "    kidney_table = pd.read_csv(url, delim_whitespace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Explore the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.390292Z",
     "iopub.status.busy": "2022-11-02T17:11:08.389074Z",
     "iopub.status.idle": "2022-11-02T17:11:08.400637Z",
     "shell.execute_reply": "2022-11-02T17:11:08.400104Z"
    }
   },
   "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>Days</th>\n",
       "      <th>Duration</th>\n",
       "      <th>Weight</th>\n",
       "      <th>ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Days  Duration  Weight  ID\n",
       "0   0.0         1       1   1\n",
       "1   2.0         1       1   2\n",
       "2   1.0         1       1   3\n",
       "3   3.0         1       1   4\n",
       "4   0.0         1       1   5\n",
       "5   2.0         1       1   6\n",
       "6   0.0         1       1   7\n",
       "7   5.0         1       1   8\n",
       "8   6.0         1       1   9\n",
       "9   8.0         1       1  10"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kidney_table.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Balanced panel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.405168Z",
     "iopub.status.busy": "2022-11-02T17:11:08.404064Z",
     "iopub.status.idle": "2022-11-02T17:11:08.703078Z",
     "shell.execute_reply": "2022-11-02T17:11:08.702266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "kt = kidney_table\n",
    "plt.figure(figsize=(8, 6))\n",
    "fig = interaction_plot(\n",
    "    kt[\"Weight\"],\n",
    "    kt[\"Duration\"],\n",
    "    np.log(kt[\"Days\"] + 1),\n",
    "    colors=[\"red\", \"blue\"],\n",
    "    markers=[\"D\", \"^\"],\n",
    "    ms=10,\n",
    "    ax=plt.gca(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You have things available in the calling namespace available in the formula evaluation namespace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.706418Z",
     "iopub.status.busy": "2022-11-02T17:11:08.706167Z",
     "iopub.status.idle": "2022-11-02T17:11:08.766683Z",
     "shell.execute_reply": "2022-11-02T17:11:08.766056Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   df_resid        ssr  df_diff   ss_diff        F    Pr(>F)\n",
      "0      56.0  29.624856      0.0       NaN      NaN       NaN\n",
      "1      54.0  28.989198      2.0  0.635658  0.59204  0.556748\n",
      "   df_resid        ssr  df_diff    ss_diff          F    Pr(>F)\n",
      "0      58.0  46.596147      0.0        NaN        NaN       NaN\n",
      "1      56.0  29.624856      2.0  16.971291  16.040454  0.000003\n",
      "   df_resid        ssr  df_diff   ss_diff         F   Pr(>F)\n",
      "0      57.0  31.964549      0.0       NaN       NaN      NaN\n",
      "1      56.0  29.624856      1.0  2.339693  4.422732  0.03997\n"
     ]
    }
   ],
   "source": [
    "kidney_lm = ols(\"np.log(Days+1) ~ C(Duration) * C(Weight)\", data=kt).fit()\n",
    "\n",
    "table10 = anova_lm(kidney_lm)\n",
    "\n",
    "print(\n",
    "    anova_lm(ols(\"np.log(Days+1) ~ C(Duration) + C(Weight)\", data=kt).fit(), kidney_lm)\n",
    ")\n",
    "print(\n",
    "    anova_lm(\n",
    "        ols(\"np.log(Days+1) ~ C(Duration)\", data=kt).fit(),\n",
    "        ols(\"np.log(Days+1) ~ C(Duration) + C(Weight, Sum)\", data=kt).fit(),\n",
    "    )\n",
    ")\n",
    "print(\n",
    "    anova_lm(\n",
    "        ols(\"np.log(Days+1) ~ C(Weight)\", data=kt).fit(),\n",
    "        ols(\"np.log(Days+1) ~ C(Duration) + C(Weight, Sum)\", data=kt).fit(),\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sum of squares\n",
    "\n",
    " Illustrates the use of different types of sums of squares (I,II,II)\n",
    " and how the Sum contrast can be used to produce the same output between\n",
    " the 3.\n",
    "\n",
    " Types I and II are equivalent under a balanced design.\n",
    "\n",
    " Do not use Type III with non-orthogonal contrast - ie., Treatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.770026Z",
     "iopub.status.busy": "2022-11-02T17:11:08.769591Z",
     "iopub.status.idle": "2022-11-02T17:11:08.801128Z",
     "shell.execute_reply": "2022-11-02T17:11:08.800513Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                   df     sum_sq   mean_sq          F    PR(>F)\n",
      "C(Duration, Sum)                  1.0   2.339693  2.339693   4.358293  0.041562\n",
      "C(Weight, Sum)                    2.0  16.971291  8.485645  15.806745  0.000004\n",
      "C(Duration, Sum):C(Weight, Sum)   2.0   0.635658  0.317829   0.592040  0.556748\n",
      "Residual                         54.0  28.989198  0.536837        NaN       NaN\n",
      "                                    sum_sq    df          F    PR(>F)\n",
      "C(Duration, Sum)                  2.339693   1.0   4.358293  0.041562\n",
      "C(Weight, Sum)                   16.971291   2.0  15.806745  0.000004\n",
      "C(Duration, Sum):C(Weight, Sum)   0.635658   2.0   0.592040  0.556748\n",
      "Residual                         28.989198  54.0        NaN       NaN\n",
      "                                     sum_sq    df           F        PR(>F)\n",
      "Intercept                        156.301830   1.0  291.153237  2.077589e-23\n",
      "C(Duration, Sum)                   2.339693   1.0    4.358293  4.156170e-02\n",
      "C(Weight, Sum)                    16.971291   2.0   15.806745  3.944502e-06\n",
      "C(Duration, Sum):C(Weight, Sum)    0.635658   2.0    0.592040  5.567479e-01\n",
      "Residual                          28.989198  54.0         NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "sum_lm = ols(\"np.log(Days+1) ~ C(Duration, Sum) * C(Weight, Sum)\", data=kt).fit()\n",
    "\n",
    "print(anova_lm(sum_lm))\n",
    "print(anova_lm(sum_lm, typ=2))\n",
    "print(anova_lm(sum_lm, typ=3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-11-02T17:11:08.805241Z",
     "iopub.status.busy": "2022-11-02T17:11:08.804008Z",
     "iopub.status.idle": "2022-11-02T17:11:08.839666Z",
     "shell.execute_reply": "2022-11-02T17:11:08.839041Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                               df     sum_sq   mean_sq          F    PR(>F)\n",
      "C(Duration, Treatment)                        1.0   2.339693  2.339693   4.358293  0.041562\n",
      "C(Weight, Treatment)                          2.0  16.971291  8.485645  15.806745  0.000004\n",
      "C(Duration, Treatment):C(Weight, Treatment)   2.0   0.635658  0.317829   0.592040  0.556748\n",
      "Residual                                     54.0  28.989198  0.536837        NaN       NaN\n",
      "                                                sum_sq    df          F    PR(>F)\n",
      "C(Duration, Treatment)                        2.339693   1.0   4.358293  0.041562\n",
      "C(Weight, Treatment)                         16.971291   2.0  15.806745  0.000004\n",
      "C(Duration, Treatment):C(Weight, Treatment)   0.635658   2.0   0.592040  0.556748\n",
      "Residual                                     28.989198  54.0        NaN       NaN\n",
      "                                                sum_sq    df          F    PR(>F)\n",
      "Intercept                                    10.427596   1.0  19.424139  0.000050\n",
      "C(Duration, Treatment)                        0.054293   1.0   0.101134  0.751699\n",
      "C(Weight, Treatment)                         11.703387   2.0  10.900317  0.000106\n",
      "C(Duration, Treatment):C(Weight, Treatment)   0.635658   2.0   0.592040  0.556748\n",
      "Residual                                     28.989198  54.0        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "nosum_lm = ols(\n",
    "    \"np.log(Days+1) ~ C(Duration, Treatment) * C(Weight, Treatment)\", data=kt\n",
    ").fit()\n",
    "print(anova_lm(nosum_lm))\n",
    "print(anova_lm(nosum_lm, typ=2))\n",
    "print(anova_lm(nosum_lm, typ=3))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}