statistics/ANOVA_examples.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "sharp-blank",
   "metadata": {},
   "source": [
    "# Create data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "artistic-arctic",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.481895Z",
     "start_time": "2021-03-11T12:42:40.979319Z"
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   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 2;\n",
       "                var nbb_unformatted_code = \"import pandas as pd\\nimport numpy as np\\nfrom scipy.stats import f_oneway\\nfrom statsmodels.formula.api import ols\\nfrom statsmodels.stats.anova import anova_lm\\nfrom statsmodels.stats.multicomp import MultiComparison\\nfrom statsmodels.graphics.gofplots import qqplot\\nimport warnings\\nfrom IPython.display import display, Math, Latex, Markdown\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\";\n",
       "                var nbb_formatted_code = \"import pandas as pd\\nimport numpy as np\\nfrom scipy.stats import f_oneway\\nfrom statsmodels.formula.api import ols\\nfrom statsmodels.stats.anova import anova_lm\\nfrom statsmodels.stats.multicomp import MultiComparison\\nfrom statsmodels.graphics.gofplots import qqplot\\nimport warnings\\nfrom IPython.display import display, Math, Latex, Markdown\\n\\nwarnings.filterwarnings(\\\"ignore\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import f_oneway\n",
    "from statsmodels.formula.api import ols\n",
    "from statsmodels.stats.anova import anova_lm\n",
    "from statsmodels.stats.multicomp import MultiComparison\n",
    "from statsmodels.graphics.gofplots import qqplot\n",
    "import warnings\n",
    "from IPython.display import display, Math, Latex, Markdown\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "drawn-system",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.522414Z",
     "start_time": "2021-03-11T12:42:41.483144Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 3;\n",
       "                var nbb_unformatted_code = \"cotton_weight_percent = [\\n    15,\\n    15,\\n    15,\\n    15,\\n    15,\\n    20,\\n    20,\\n    20,\\n    20,\\n    20,\\n    25,\\n    25,\\n    25,\\n    25,\\n    25,\\n    30,\\n    30,\\n    30,\\n    30,\\n    30,\\n    35,\\n    35,\\n    35,\\n    35,\\n    35,\\n]\\nobservations = [\\n    7,\\n    7,\\n    15,\\n    11,\\n    9,\\n    12,\\n    16,\\n    12,\\n    18,\\n    18,\\n    14,\\n    19,\\n    19,\\n    18,\\n    18,\\n    19,\\n    25,\\n    22,\\n    19,\\n    23,\\n    7,\\n    10,\\n    11,\\n    15,\\n    11,\\n]\";\n",
       "                var nbb_formatted_code = \"cotton_weight_percent = [\\n    15,\\n    15,\\n    15,\\n    15,\\n    15,\\n    20,\\n    20,\\n    20,\\n    20,\\n    20,\\n    25,\\n    25,\\n    25,\\n    25,\\n    25,\\n    30,\\n    30,\\n    30,\\n    30,\\n    30,\\n    35,\\n    35,\\n    35,\\n    35,\\n    35,\\n]\\nobservations = [\\n    7,\\n    7,\\n    15,\\n    11,\\n    9,\\n    12,\\n    16,\\n    12,\\n    18,\\n    18,\\n    14,\\n    19,\\n    19,\\n    18,\\n    18,\\n    19,\\n    25,\\n    22,\\n    19,\\n    23,\\n    7,\\n    10,\\n    11,\\n    15,\\n    11,\\n]\";\n",
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       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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   "source": [
    "cotton_weight_percent = [\n",
    "    15,\n",
    "    15,\n",
    "    15,\n",
    "    15,\n",
    "    15,\n",
    "    20,\n",
    "    20,\n",
    "    20,\n",
    "    20,\n",
    "    20,\n",
    "    25,\n",
    "    25,\n",
    "    25,\n",
    "    25,\n",
    "    25,\n",
    "    30,\n",
    "    30,\n",
    "    30,\n",
    "    30,\n",
    "    30,\n",
    "    35,\n",
    "    35,\n",
    "    35,\n",
    "    35,\n",
    "    35,\n",
    "]\n",
    "observations = [\n",
    "    7,\n",
    "    7,\n",
    "    15,\n",
    "    11,\n",
    "    9,\n",
    "    12,\n",
    "    16,\n",
    "    12,\n",
    "    18,\n",
    "    18,\n",
    "    14,\n",
    "    19,\n",
    "    19,\n",
    "    18,\n",
    "    18,\n",
    "    19,\n",
    "    25,\n",
    "    22,\n",
    "    19,\n",
    "    23,\n",
    "    7,\n",
    "    10,\n",
    "    11,\n",
    "    15,\n",
    "    11,\n",
    "]"
   ]
  },
  {
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   "id": "pending-graph",
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      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 4;\n",
       "                var nbb_unformatted_code = \"df = pd.DataFrame(\\n    {\\\"observations\\\": observations, \\\"cotton_weight_percent\\\": cotton_weight_percent}\\n)\";\n",
       "                var nbb_formatted_code = \"df = pd.DataFrame(\\n    {\\\"observations\\\": observations, \\\"cotton_weight_percent\\\": cotton_weight_percent}\\n)\";\n",
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       "            "
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       "<IPython.core.display.Javascript object>"
      ]
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     "metadata": {},
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   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\"observations\": observations, \"cotton_weight_percent\": cotton_weight_percent}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "statewide-valuation",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.543020Z",
     "start_time": "2021-03-11T12:42:41.529300Z"
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   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>observations</th>\n",
       "      <th>cotton_weight_percent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
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       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
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       "      <th>2</th>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
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       "      <th>3</th>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>25</td>\n",
       "      <td>30</td>\n",
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       "      <th>17</th>\n",
       "      <td>22</td>\n",
       "      <td>30</td>\n",
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       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>7</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>15</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>11</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    observations  cotton_weight_percent\n",
       "0              7                     15\n",
       "1              7                     15\n",
       "2             15                     15\n",
       "3             11                     15\n",
       "4              9                     15\n",
       "5             12                     20\n",
       "6             16                     20\n",
       "7             12                     20\n",
       "8             18                     20\n",
       "9             18                     20\n",
       "10            14                     25\n",
       "11            19                     25\n",
       "12            19                     25\n",
       "13            18                     25\n",
       "14            18                     25\n",
       "15            19                     30\n",
       "16            25                     30\n",
       "17            22                     30\n",
       "18            19                     30\n",
       "19            23                     30\n",
       "20             7                     35\n",
       "21            10                     35\n",
       "22            11                     35\n",
       "23            15                     35\n",
       "24            11                     35"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "romance-issue",
   "metadata": {},
   "source": [
    "# One-way ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "rough-advertising",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.566655Z",
     "start_time": "2021-03-11T12:42:41.544017Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>observations</td>   <th>  R-squared:         </th> <td>   0.750</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.699</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   14.96</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 11 Mar 2021</td> <th>  Prob (F-statistic):</th> <td>8.26e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>06:42:41</td>     <th>  Log-Likelihood:    </th> <td> -58.583</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    25</td>      <th>  AIC:               </th> <td>   127.2</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    20</td>      <th>  BIC:               </th> <td>   133.3</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     4</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "                 <td></td>                   <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>                      <td>    9.8000</td> <td>    1.260</td> <td>    7.777</td> <td> 0.000</td> <td>    7.171</td> <td>   12.429</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cotton_weight_percent)[T.20]</th> <td>    5.4000</td> <td>    1.782</td> <td>    3.030</td> <td> 0.007</td> <td>    1.683</td> <td>    9.117</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cotton_weight_percent)[T.25]</th> <td>    7.8000</td> <td>    1.782</td> <td>    4.377</td> <td> 0.000</td> <td>    4.083</td> <td>   11.517</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cotton_weight_percent)[T.30]</th> <td>   11.8000</td> <td>    1.782</td> <td>    6.621</td> <td> 0.000</td> <td>    8.083</td> <td>   15.517</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>C(cotton_weight_percent)[T.35]</th> <td>    1.0000</td> <td>    1.782</td> <td>    0.561</td> <td> 0.581</td> <td>   -2.717</td> <td>    4.717</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 1.322</td> <th>  Durbin-Watson:     </th> <td>   2.347</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.516</td> <th>  Jarque-Bera (JB):  </th> <td>   0.949</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.154</td> <th>  Prob(JB):          </th> <td>   0.622</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.096</td> <th>  Cond. No.          </th> <td>    5.83</td>\n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:           observations   R-squared:                       0.750\n",
       "Model:                            OLS   Adj. R-squared:                  0.699\n",
       "Method:                 Least Squares   F-statistic:                     14.96\n",
       "Date:                Thu, 11 Mar 2021   Prob (F-statistic):           8.26e-06\n",
       "Time:                        06:42:41   Log-Likelihood:                -58.583\n",
       "No. Observations:                  25   AIC:                             127.2\n",
       "Df Residuals:                      20   BIC:                             133.3\n",
       "Df Model:                           4                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "==================================================================================================\n",
       "                                     coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------------------\n",
       "Intercept                          9.8000      1.260      7.777      0.000       7.171      12.429\n",
       "C(cotton_weight_percent)[T.20]     5.4000      1.782      3.030      0.007       1.683       9.117\n",
       "C(cotton_weight_percent)[T.25]     7.8000      1.782      4.377      0.000       4.083      11.517\n",
       "C(cotton_weight_percent)[T.30]    11.8000      1.782      6.621      0.000       8.083      15.517\n",
       "C(cotton_weight_percent)[T.35]     1.0000      1.782      0.561      0.581      -2.717       4.717\n",
       "==============================================================================\n",
       "Omnibus:                        1.322   Durbin-Watson:                   2.347\n",
       "Prob(Omnibus):                  0.516   Jarque-Bera (JB):                0.949\n",
       "Skew:                           0.154   Prob(JB):                        0.622\n",
       "Kurtosis:                       2.096   Cond. No.                         5.83\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 6;\n",
       "                var nbb_unformatted_code = \"model = ols(\\\"observations ~ C(cotton_weight_percent)\\\", df).fit()\\nmodel.summary()\";\n",
       "                var nbb_formatted_code = \"model = ols(\\\"observations ~ C(cotton_weight_percent)\\\", df).fit()\\nmodel.summary()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "model = ols(\"observations ~ C(cotton_weight_percent)\", df).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "upset-carrier",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.575744Z",
     "start_time": "2021-03-11T12:42:41.568652Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 7;\n",
       "                var nbb_unformatted_code = \"res = anova_lm(model, typ=1)\";\n",
       "                var nbb_formatted_code = \"res = anova_lm(model, typ=1)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
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   "source": [
    "res = anova_lm(model, typ=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "painted-creature",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.589563Z",
     "start_time": "2021-03-11T12:42:41.577282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 8;\n",
       "                var nbb_unformatted_code = \"def model_evaluation(\\n    model,\\n    independent_name: str = \\\"cotton\\\",\\n    dependent_name: str = \\\"tensile strength\\\",\\n    alpha=0.5,\\n):\\n    p_value = model.f_pvalue\\n    display(\\n        Markdown(\\n            f\\\"\\\"\\\"\\n**Null hypothesis**: All means are equal.<br>\\n**Alternative hypothesis**: Not all mean are equal<br>\\n**Significance level**: \\u03b1 = {alpha}\\n\\nThe F-statistic of the model is {round(model.fvalue, 6)}. The p-value of the model is {round(p_value, 6)}.\\\"\\\"\\\"\\n        )\\n    )\\n    if p_value > alpha:\\n        display(\\n            Markdown(\\n                f\\\"\\\"\\\"Since the p-value is greater than the significance level of {alpha}, the differences between the means are not statistically significant.\\\"\\\"\\\"\\n            )\\n        )\\n    else:\\n        display(\\n            Markdown(\\n                f\\\"\\\"\\\"Since the p-value is less than the significance level of {alpha}, there is enough evidence to claim that the differences between some of the means are statistically significant.\\\"\\\"\\\"\\n            )\\n        )\";\n",
       "                var nbb_formatted_code = \"def model_evaluation(\\n    model,\\n    independent_name: str = \\\"cotton\\\",\\n    dependent_name: str = \\\"tensile strength\\\",\\n    alpha=0.5,\\n):\\n    p_value = model.f_pvalue\\n    display(\\n        Markdown(\\n            f\\\"\\\"\\\"\\n**Null hypothesis**: All means are equal.<br>\\n**Alternative hypothesis**: Not all mean are equal<br>\\n**Significance level**: \\u03b1 = {alpha}\\n\\nThe F-statistic of the model is {round(model.fvalue, 6)}. The p-value of the model is {round(p_value, 6)}.\\\"\\\"\\\"\\n        )\\n    )\\n    if p_value > alpha:\\n        display(\\n            Markdown(\\n                f\\\"\\\"\\\"Since the p-value is greater than the significance level of {alpha}, the differences between the means are not statistically significant.\\\"\\\"\\\"\\n            )\\n        )\\n    else:\\n        display(\\n            Markdown(\\n                f\\\"\\\"\\\"Since the p-value is less than the significance level of {alpha}, there is enough evidence to claim that the differences between some of the means are statistically significant.\\\"\\\"\\\"\\n            )\\n        )\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def model_evaluation(\n",
    "    model,\n",
    "    independent_name: str = \"cotton\",\n",
    "    dependent_name: str = \"tensile strength\",\n",
    "    alpha=0.5,\n",
    "):\n",
    "    p_value = model.f_pvalue\n",
    "    display(\n",
    "        Markdown(\n",
    "            f\"\"\"\n",
    "**Null hypothesis**: All means are equal.<br>\n",
    "**Alternative hypothesis**: Not all mean are equal<br>\n",
    "**Significance level**: α = {alpha}\n",
    "\n",
    "The F-statistic of the model is {round(model.fvalue, 6)}. The p-value of the model is {round(p_value, 6)}.\"\"\"\n",
    "        )\n",
    "    )\n",
    "    if p_value > alpha:\n",
    "        display(\n",
    "            Markdown(\n",
    "                f\"\"\"Since the p-value is greater than the significance level of {alpha}, the differences between the means are not statistically significant.\"\"\"\n",
    "            )\n",
    "        )\n",
    "    else:\n",
    "        display(\n",
    "            Markdown(\n",
    "                f\"\"\"Since the p-value is less than the significance level of {alpha}, there is enough evidence to claim that the differences between some of the means are statistically significant.\"\"\"\n",
    "            )\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "executed-shakespeare",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.599988Z",
     "start_time": "2021-03-11T12:42:41.590634Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "\n",
       "**Null hypothesis**: All means are equal.<br>\n",
       "**Alternative hypothesis**: Not all mean are equal<br>\n",
       "**Significance level**: α = 0.5\n",
       "\n",
       "The F-statistic of the model is 14.962217. The p-value of the model is 8e-06."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "Since the p-value is less than the significance level of 0.5, there is enough evidence to claim that the differences between some of the means are statistically significant."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 9;\n",
       "                var nbb_unformatted_code = \"model_evaluation(model)\";\n",
       "                var nbb_formatted_code = \"model_evaluation(model)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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    "model_evaluation(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "linear-reaction",
   "metadata": {},
   "source": [
    "# Compare Each Pair of Means Using Tukey's HSD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "rotary-anniversary",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.654383Z",
     "start_time": "2021-03-11T12:42:41.601439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 10;\n",
       "                var nbb_unformatted_code = \"comparison = MultiComparison(df[\\\"observations\\\"], df[\\\"cotton_weight_percent\\\"])\\ncomparison_results = comparison.tukeyhsd()\";\n",
       "                var nbb_formatted_code = \"comparison = MultiComparison(df[\\\"observations\\\"], df[\\\"cotton_weight_percent\\\"])\\ncomparison_results = comparison.tukeyhsd()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
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       "<IPython.core.display.Javascript object>"
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   "source": [
    "comparison = MultiComparison(df[\"observations\"], df[\"cotton_weight_percent\"])\n",
    "comparison_results = comparison.tukeyhsd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "canadian-accent",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.662076Z",
     "start_time": "2021-03-11T12:42:41.655612Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>Multiple Comparison of Means - Tukey HSD, FWER=0.05</caption>\n",
       "<tr>\n",
       "  <th>group1</th> <th>group2</th> <th>meandiff</th>  <th>p-adj</th>   <th>lower</th>   <th>upper</th>  <th>reject</th>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>15</td>     <td>20</td>      <td>5.4</td>   <td>0.0463</td>  <td>0.0668</td>  <td>10.7332</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>15</td>     <td>25</td>      <td>7.8</td>   <td>0.0024</td>  <td>2.4668</td>  <td>13.1332</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>15</td>     <td>30</td>     <td>11.8</td>    <td>0.001</td>  <td>6.4668</td>  <td>17.1332</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>15</td>     <td>35</td>      <td>1.0</td>     <td>0.9</td>   <td>-4.3332</td> <td>6.3332</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>20</td>     <td>25</td>      <td>2.4</td>   <td>0.6493</td>  <td>-2.9332</td> <td>7.7332</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>20</td>     <td>30</td>      <td>6.4</td>    <td>0.014</td>  <td>1.0668</td>  <td>11.7332</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>20</td>     <td>35</td>     <td>-4.4</td>   <td>0.1379</td>  <td>-9.7332</td> <td>0.9332</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>25</td>     <td>30</td>      <td>4.0</td>   <td>0.2043</td>  <td>-1.3332</td> <td>9.3332</td>   <td>False</td>\n",
       "</tr>\n",
       "<tr>\n",
       "    <td>25</td>     <td>35</td>     <td>-6.8</td>   <td>0.0085</td> <td>-12.1332</td> <td>-1.4668</td>  <td>True</td> \n",
       "</tr>\n",
       "<tr>\n",
       "    <td>30</td>     <td>35</td>     <td>-10.8</td>   <td>0.001</td> <td>-16.1332</td> <td>-5.4668</td>  <td>True</td> \n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 11;\n",
       "                var nbb_unformatted_code = \"comparison_results.summary()\";\n",
       "                var nbb_formatted_code = \"comparison_results.summary()\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
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       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
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       "            "
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "actual-recall",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:41.930238Z",
     "start_time": "2021-03-11T12:42:41.663347Z"
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   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 12;\n",
       "                var nbb_unformatted_code = \"fig_15 = comparison_results.plot_simultaneous(comparison_name=15)\";\n",
       "                var nbb_formatted_code = \"fig_15 = comparison_results.plot_simultaneous(comparison_name=15)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_15 = comparison_results.plot_simultaneous(comparison_name=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "current-dakota",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:42.035571Z",
     "start_time": "2021-03-11T12:42:41.931222Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 13;\n",
       "                var nbb_unformatted_code = \"fig_20 = comparison_results.plot_simultaneous(comparison_name=20)\";\n",
       "                var nbb_formatted_code = \"fig_20 = comparison_results.plot_simultaneous(comparison_name=20)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_20 = comparison_results.plot_simultaneous(comparison_name=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "entire-soviet",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:42.175621Z",
     "start_time": "2021-03-11T12:42:42.036487Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 14;\n",
       "                var nbb_unformatted_code = \"fig_25 = comparison_results.plot_simultaneous(comparison_name=25)\";\n",
       "                var nbb_formatted_code = \"fig_25 = comparison_results.plot_simultaneous(comparison_name=25)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_25 = comparison_results.plot_simultaneous(comparison_name=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "tamil-couple",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:42.349773Z",
     "start_time": "2021-03-11T12:42:42.176582Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 15;\n",
       "                var nbb_unformatted_code = \"fig_30 = comparison_results.plot_simultaneous(comparison_name=30)\";\n",
       "                var nbb_formatted_code = \"fig_30 = comparison_results.plot_simultaneous(comparison_name=30)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_30 = comparison_results.plot_simultaneous(comparison_name=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "color-nickel",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:42.453807Z",
     "start_time": "2021-03-11T12:42:42.351095Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 16;\n",
       "                var nbb_unformatted_code = \"fig_35 = comparison_results.plot_simultaneous(comparison_name=35)\";\n",
       "                var nbb_formatted_code = \"fig_35 = comparison_results.plot_simultaneous(comparison_name=35)\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_35 = comparison_results.plot_simultaneous(comparison_name=35)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "incorrect-document",
   "metadata": {},
   "source": [
    "# Check model assumptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "weighted-period",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-11T12:42:42.547920Z",
     "start_time": "2021-03-11T12:42:42.455001Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "            setTimeout(function() {\n",
       "                var nbb_cell_id = 17;\n",
       "                var nbb_unformatted_code = \"residuals = model.resid\\nplot = qqplot(residuals, line=\\\"s\\\")\";\n",
       "                var nbb_formatted_code = \"residuals = model.resid\\nplot = qqplot(residuals, line=\\\"s\\\")\";\n",
       "                var nbb_cells = Jupyter.notebook.get_cells();\n",
       "                for (var i = 0; i < nbb_cells.length; ++i) {\n",
       "                    if (nbb_cells[i].input_prompt_number == nbb_cell_id) {\n",
       "                        if (nbb_cells[i].get_text() == nbb_unformatted_code) {\n",
       "                             nbb_cells[i].set_text(nbb_formatted_code);\n",
       "                        }\n",
       "                        break;\n",
       "                    }\n",
       "                }\n",
       "            }, 500);\n",
       "            "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "residuals = model.resid\n",
    "plot = qqplot(residuals, line=\"s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "rubber-action",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "medium",
   "language": "python",
   "name": "medium"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}