test-class.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.regression.mixed_linear_model import MixedLM\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy import stats\n",
    "from viewser.operations import fetch\n",
    "from viewser import Queryset, Column\n",
    "import subprocess\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from MonthToAnnual import MonthToAnnualRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "  > Ged2_cm\n",
      "  -------------------------------------------------------\n",
      "  | name                    | path                    |\n",
      "  |:------------------------|:------------------------|\n",
      "  | country_month_id        | country_month_id        |\n",
      "  | ged_sb_best_sum_nokgi   | ged_sb_best_sum_nokgi   |\n",
      "  | ged_ns_best_sum_nokgi   | ged_ns_best_sum_nokgi   |\n",
      "  | ged_os_best_sum_nokgi   | ged_os_best_sum_nokgi   |\n",
      "  | ged_sb_best_count_nokgi | ged_sb_best_count_nokgi |\n",
      "  | ged_ns_best_count_nokgi | ged_ns_best_count_nokgi |\n",
      "  | ged_os_best_count_nokgi | ged_os_best_count_nokgi |\n",
      "  | ged_sb_high_sum_nokgi   | ged_sb_high_sum_nokgi   |\n",
      "  | ged_ns_high_sum_nokgi   | ged_ns_high_sum_nokgi   |\n",
      "  | ged_os_high_sum_nokgi   | ged_os_high_sum_nokgi   |\n",
      "  | ged_sb_high_count_nokgi | ged_sb_high_count_nokgi |\n",
      "  | ged_ns_high_count_nokgi | ged_ns_high_count_nokgi |\n",
      "  | ged_os_high_count_nokgi | ged_os_high_count_nokgi |\n",
      "  | test                    | test                    |\n",
      "  \n",
      "  -------------------------------------------------------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!viewser tables show ged2_cm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " .    "
     ]
    }
   ],
   "source": [
    "num_lags = 36\n",
    "\n",
    "queryset = (Queryset('month_to_annual','country_month'))\n",
    "\n",
    "for lag in range(1, num_lags + 1):\n",
    "    tlag_variable_name = f\"ged_sb_tlag{lag}\"\n",
    "    # Apply the tlag transformation\n",
    "    queryset = queryset.with_column(\n",
    "        Column(tlag_variable_name, from_table=\"ged2_cm\", from_column=\"ged_sb_best_sum_nokgi\")\n",
    "        .transform.missing.replace_na()\n",
    "        .transform.ops.ln()\n",
    "        .transform.temporal.tlag(lag)\n",
    "        .transform.missing.replace_na()\n",
    "    )\n",
    "\n",
    "queryset = queryset.with_column(\n",
    "    Column('wdi_sh_dyn_mort_fe', from_table=\"wdi_cy\", from_column=\"wdi_sh_dyn_mort_fe\")\n",
    "    .transform.missing.fill()\n",
    "    .transform.ops.ln()\n",
    "    .transform.missing.replace_na()\n",
    "\n",
    ")\n",
    "\n",
    "queryset = queryset.with_column(\n",
    "    Column('wdi_ny_gdp_mktp_kd', from_table=\"wdi_cy\", from_column=\"wdi_ny_gdp_mktp_kd\")\n",
    "    .transform.missing.fill()\n",
    "    .transform.ops.ln()\n",
    "    .transform.missing.replace_na()\n",
    "\n",
    ")\n",
    "\n",
    "queryset = queryset.with_column(\n",
    "    Column('wdi_sp_pop_totl', from_table=\"wdi_cy\", from_column=\"wdi_sp_pop_totl\")\n",
    "    .transform.missing.fill()\n",
    "    .transform.ops.ln()\n",
    "    .transform.missing.replace_na()\n",
    "\n",
    ")\n",
    "\n",
    "queryset = queryset.with_column(\n",
    "    Column('country_name', from_table=\"country\", from_column=\"name\")\n",
    "\n",
    ")\n",
    "    \n",
    "df = queryset.publish().fetch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " .    "
     ]
    }
   ],
   "source": [
    "df = Queryset(\"month_to_annual\", \"country_month\").fetch()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>wdi_sh_dyn_mort_fe</th>\n",
       "      <th>wdi_ny_gdp_mktp_kd</th>\n",
       "      <th>wdi_sp_pop_totl</th>\n",
       "      <th>ged_sb_tlag1</th>\n",
       "      <th>ged_sb_tlag2</th>\n",
       "      <th>ged_sb_tlag3</th>\n",
       "      <th>ged_sb_tlag4</th>\n",
       "      <th>ged_sb_tlag5</th>\n",
       "      <th>ged_sb_tlag6</th>\n",
       "      <th>...</th>\n",
       "      <th>ged_sb_tlag27</th>\n",
       "      <th>ged_sb_tlag28</th>\n",
       "      <th>ged_sb_tlag29</th>\n",
       "      <th>ged_sb_tlag30</th>\n",
       "      <th>ged_sb_tlag31</th>\n",
       "      <th>ged_sb_tlag32</th>\n",
       "      <th>ged_sb_tlag33</th>\n",
       "      <th>ged_sb_tlag34</th>\n",
       "      <th>ged_sb_tlag35</th>\n",
       "      <th>ged_sb_tlag36</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_id</th>\n",
       "      <th>country_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>4.151040</td>\n",
       "      <td>21.645497</td>\n",
       "      <td>13.564709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>3.953165</td>\n",
       "      <td>21.704949</td>\n",
       "      <td>12.834983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.994549</td>\n",
       "      <td>13.935826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>3.711130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.537493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>3.718438</td>\n",
       "      <td>19.784920</td>\n",
       "      <td>12.013131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">852</th>\n",
       "      <th>242</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.793239</td>\n",
       "      <td>24.884573</td>\n",
       "      <td>17.967941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>2.844909</td>\n",
       "      <td>25.542467</td>\n",
       "      <td>17.428496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.728763</td>\n",
       "      <td>15.344817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>3.927896</td>\n",
       "      <td>25.101865</td>\n",
       "      <td>17.636672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4.552824</td>\n",
       "      <td>23.207989</td>\n",
       "      <td>16.190256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158230 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country_name  wdi_sh_dyn_mort_fe  \\\n",
       "month_id country_id                                            \n",
       "1        1                        Guyana            4.151040   \n",
       "         2                      Suriname            3.953165   \n",
       "         3           Trinidad and Tobago            3.616309   \n",
       "         4                     Venezuela            3.711130   \n",
       "         5                         Samoa            3.718438   \n",
       "...                                  ...                 ...   \n",
       "852      242                    Tanzania            3.793239   \n",
       "         243                     Morocco            2.844909   \n",
       "         244                  Mauritania            3.616309   \n",
       "         245                       Sudan            3.927896   \n",
       "         246                 South Sudan            4.552824   \n",
       "\n",
       "                     wdi_ny_gdp_mktp_kd  wdi_sp_pop_totl  ged_sb_tlag1  \\\n",
       "month_id country_id                                                      \n",
       "1        1                    21.645497        13.564709           0.0   \n",
       "         2                    21.704949        12.834983           0.0   \n",
       "         3                    22.994549        13.935826           0.0   \n",
       "         4                     0.000000        16.537493           0.0   \n",
       "         5                    19.784920        12.013131           0.0   \n",
       "...                                 ...              ...           ...   \n",
       "852      242                  24.884573        17.967941           0.0   \n",
       "         243                  25.542467        17.428496           0.0   \n",
       "         244                  22.728763        15.344817           0.0   \n",
       "         245                  25.101865        17.636672           0.0   \n",
       "         246                  23.207989        16.190256           0.0   \n",
       "\n",
       "                     ged_sb_tlag2  ged_sb_tlag3  ged_sb_tlag4  ged_sb_tlag5  \\\n",
       "month_id country_id                                                           \n",
       "1        1                    0.0           0.0           0.0           0.0   \n",
       "         2                    0.0           0.0           0.0           0.0   \n",
       "         3                    0.0           0.0           0.0           0.0   \n",
       "         4                    0.0           0.0           0.0           0.0   \n",
       "         5                    0.0           0.0           0.0           0.0   \n",
       "...                           ...           ...           ...           ...   \n",
       "852      242                  0.0           0.0           0.0           0.0   \n",
       "         243                  0.0           0.0           0.0           0.0   \n",
       "         244                  0.0           0.0           0.0           0.0   \n",
       "         245                  0.0           0.0           0.0           0.0   \n",
       "         246                  0.0           0.0           0.0           0.0   \n",
       "\n",
       "                     ged_sb_tlag6  ...  ged_sb_tlag27  ged_sb_tlag28  \\\n",
       "month_id country_id                ...                                 \n",
       "1        1                    0.0  ...            0.0            0.0   \n",
       "         2                    0.0  ...            0.0            0.0   \n",
       "         3                    0.0  ...            0.0            0.0   \n",
       "         4                    0.0  ...            0.0            0.0   \n",
       "         5                    0.0  ...            0.0            0.0   \n",
       "...                           ...  ...            ...            ...   \n",
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       "         246                  0.0  ...            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag29  ged_sb_tlag30  ged_sb_tlag31  \\\n",
       "month_id country_id                                                \n",
       "1        1                     0.0            0.0            0.0   \n",
       "         2                     0.0            0.0            0.0   \n",
       "         3                     0.0            0.0            0.0   \n",
       "         4                     0.0            0.0            0.0   \n",
       "         5                     0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "852      242                   0.0            0.0            0.0   \n",
       "         243                   0.0            0.0            0.0   \n",
       "         244                   0.0            0.0            0.0   \n",
       "         245                   0.0            0.0            0.0   \n",
       "         246                   0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag32  ged_sb_tlag33  ged_sb_tlag34  \\\n",
       "month_id country_id                                                \n",
       "1        1                     0.0            0.0            0.0   \n",
       "         2                     0.0            0.0            0.0   \n",
       "         3                     0.0            0.0            0.0   \n",
       "         4                     0.0            0.0            0.0   \n",
       "         5                     0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "852      242                   0.0            0.0            0.0   \n",
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       "         244                   0.0            0.0            0.0   \n",
       "         245                   0.0            0.0            0.0   \n",
       "         246                   0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag35  ged_sb_tlag36  \n",
       "month_id country_id                                \n",
       "1        1                     0.0            0.0  \n",
       "         2                     0.0            0.0  \n",
       "         3                     0.0            0.0  \n",
       "         4                     0.0            0.0  \n",
       "         5                     0.0            0.0  \n",
       "...                            ...            ...  \n",
       "852      242                   0.0            0.0  \n",
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       "         244                   0.0            0.0  \n",
       "         245                   0.0            0.0  \n",
       "         246                   0.0            0.0  \n",
       "\n",
       "[158230 rows x 40 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>848</th>\n",
       "      <th>28</th>\n",
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       "    <tr>\n",
       "      <th>851</th>\n",
       "      <th>28</th>\n",
       "      <td>Colombia</td>\n",
       "      <td>2.525729</td>\n",
       "      <td>26.524291</td>\n",
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       "    <tr>\n",
       "      <th>852</th>\n",
       "      <th>28</th>\n",
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       "<p>852 rows × 40 columns</p>\n",
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      "text/plain": [
       "                    country_name  wdi_sh_dyn_mort_fe  wdi_ny_gdp_mktp_kd  \\\n",
       "month_id country_id                                                        \n",
       "1        28             Colombia            3.979682           25.179415   \n",
       "2        28             Colombia            3.979682           25.179415   \n",
       "3        28             Colombia            3.979682           25.179415   \n",
       "4        28             Colombia            3.979682           25.179415   \n",
       "5        28             Colombia            3.979682           25.179415   \n",
       "...                          ...                 ...                 ...   \n",
       "848      28             Colombia            2.525729           26.524291   \n",
       "849      28             Colombia            2.525729           26.524291   \n",
       "850      28             Colombia            2.525729           26.524291   \n",
       "851      28             Colombia            2.525729           26.524291   \n",
       "852      28             Colombia            2.525729           26.524291   \n",
       "\n",
       "                     wdi_sp_pop_totl  ged_sb_tlag1  ged_sb_tlag2  \\\n",
       "month_id country_id                                                \n",
       "1        28                17.080361           0.0           0.0   \n",
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       "3        28                17.080361           0.0           0.0   \n",
       "4        28                17.080361           0.0           0.0   \n",
       "5        28                17.080361           0.0           0.0   \n",
       "...                              ...           ...           ...   \n",
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       "850      28                17.757414           0.0           0.0   \n",
       "851      28                17.757414           0.0           0.0   \n",
       "852      28                17.757414           0.0           0.0   \n",
       "\n",
       "                     ged_sb_tlag3  ged_sb_tlag4  ged_sb_tlag5  ged_sb_tlag6  \\\n",
       "month_id country_id                                                           \n",
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       "...                           ...           ...           ...           ...   \n",
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       "851      28                   0.0           0.0           0.0           0.0   \n",
       "852      28                   0.0           0.0           0.0           0.0   \n",
       "\n",
       "                     ...  ged_sb_tlag27  ged_sb_tlag28  ged_sb_tlag29  \\\n",
       "month_id country_id  ...                                                \n",
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       "...                  ...            ...            ...            ...   \n",
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       "\n",
       "                     ged_sb_tlag30  ged_sb_tlag31  ged_sb_tlag32  \\\n",
       "month_id country_id                                                \n",
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       "\n",
       "                     ged_sb_tlag33  ged_sb_tlag34  ged_sb_tlag35  \\\n",
       "month_id country_id                                                \n",
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       "2        28                    0.0            0.0            0.0   \n",
       "3        28                    0.0            0.0            0.0   \n",
       "4        28                    0.0            0.0            0.0   \n",
       "5        28                    0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "848      28                    0.0            0.0            0.0   \n",
       "849      28                    0.0            0.0            0.0   \n",
       "850      28                    0.0            0.0            0.0   \n",
       "851      28                    0.0            0.0            0.0   \n",
       "852      28                    0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag36  \n",
       "month_id country_id                 \n",
       "1        28                    0.0  \n",
       "2        28                    0.0  \n",
       "3        28                    0.0  \n",
       "4        28                    0.0  \n",
       "5        28                    0.0  \n",
       "...                            ...  \n",
       "848      28                    0.0  \n",
       "849      28                    0.0  \n",
       "850      28                    0.0  \n",
       "851      28                    0.0  \n",
       "852      28                    0.0  \n",
       "\n",
       "[852 rows x 40 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('country_id == 28')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15 0\n",
      "27 832\n",
      "35 830\n",
      "36 808\n",
      "44 0\n",
      "51 0\n",
      "56 692\n",
      "57 692\n",
      "59 379\n",
      "61 0\n",
      "63 709\n",
      "65 709\n",
      "68 0\n",
      "71 0\n",
      "72 0\n",
      "75 0\n",
      "83 705\n",
      "84 705\n",
      "86 710\n",
      "88 0\n",
      "91 0\n",
      "92 712\n",
      "95 0\n",
      "98 696\n",
      "102 696\n",
      "103 705\n",
      "106 0\n",
      "110 713\n",
      "111 712\n",
      "113 712\n",
      "114 712\n",
      "115 713\n",
      "117 709\n",
      "122 711\n",
      "123 713\n",
      "124 607\n",
      "125 709\n",
      "126 709\n",
      "131 607\n",
      "134 713\n",
      "137 712\n",
      "140 804\n",
      "141 0\n",
      "144 675\n",
      "152 771\n",
      "153 770\n",
      "163 730\n",
      "170 730\n",
      "175 0\n",
      "176 584\n",
      "182 847\n",
      "184 723\n",
      "185 130\n",
      "186 130\n",
      "187 156\n",
      "188 143\n",
      "189 140\n",
      "190 0\n",
      "191 161\n",
      "192 123\n",
      "193 0\n",
      "194 0\n",
      "195 0\n",
      "196 125\n",
      "197 125\n",
      "200 0\n",
      "201 0\n",
      "202 0\n",
      "203 0\n",
      "204 0\n",
      "207 0\n",
      "208 269\n",
      "209 584\n",
      "210 0\n",
      "211 0\n",
      "212 0\n",
      "215 0\n",
      "216 0\n",
      "217 0\n",
      "219 0\n",
      "221 0\n",
      "224 0\n",
      "225 0\n",
      "226 0\n",
      "227 171\n",
      "228 0\n",
      "229 0\n",
      "230 21\n",
      "231 535\n",
      "232 514\n",
      "233 514\n",
      "236 193\n",
      "238 0\n",
      "239 246\n",
      "240 122\n",
      "241 0\n",
      "242 659\n",
      "245 474\n",
      "246 474\n",
      "Number of countries with missing data is  99\n"
     ]
    }
   ],
   "source": [
    "j = 0\n",
    "for i in range(1,247,1):\n",
    "    count = MonthToAnnualRegression.countries_with_missing_data(df,i)\n",
    "    if count != 852 :\n",
    "        print(i, count)\n",
    "        j=j+1\n",
    "print('Number of countries with missing data is ',j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fixed_effect_column = 'country_id'\n",
    "df[fixed_effect_column] = df.index.get_level_values(fixed_effect_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>wdi_sh_dyn_mort_fe</th>\n",
       "      <th>wdi_ny_gdp_mktp_kd</th>\n",
       "      <th>wdi_sp_pop_totl</th>\n",
       "      <th>ged_sb_tlag1</th>\n",
       "      <th>ged_sb_tlag2</th>\n",
       "      <th>ged_sb_tlag3</th>\n",
       "      <th>ged_sb_tlag4</th>\n",
       "      <th>ged_sb_tlag5</th>\n",
       "      <th>ged_sb_tlag6</th>\n",
       "      <th>...</th>\n",
       "      <th>ged_sb_tlag28</th>\n",
       "      <th>ged_sb_tlag29</th>\n",
       "      <th>ged_sb_tlag30</th>\n",
       "      <th>ged_sb_tlag31</th>\n",
       "      <th>ged_sb_tlag32</th>\n",
       "      <th>ged_sb_tlag33</th>\n",
       "      <th>ged_sb_tlag34</th>\n",
       "      <th>ged_sb_tlag35</th>\n",
       "      <th>ged_sb_tlag36</th>\n",
       "      <th>country_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_id</th>\n",
       "      <th>country_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>4.151040</td>\n",
       "      <td>21.645497</td>\n",
       "      <td>13.564709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>3.953165</td>\n",
       "      <td>21.704949</td>\n",
       "      <td>12.834983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.994549</td>\n",
       "      <td>13.935826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>3.711130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.537493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>3.718438</td>\n",
       "      <td>19.784920</td>\n",
       "      <td>12.013131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">852</th>\n",
       "      <th>242</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.793239</td>\n",
       "      <td>24.884573</td>\n",
       "      <td>17.967941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>2.844909</td>\n",
       "      <td>25.542467</td>\n",
       "      <td>17.428496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.728763</td>\n",
       "      <td>15.344817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>3.927896</td>\n",
       "      <td>25.101865</td>\n",
       "      <td>17.636672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4.552824</td>\n",
       "      <td>23.207989</td>\n",
       "      <td>16.190256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158230 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country_name  wdi_sh_dyn_mort_fe  \\\n",
       "month_id country_id                                            \n",
       "1        1                        Guyana            4.151040   \n",
       "         2                      Suriname            3.953165   \n",
       "         3           Trinidad and Tobago            3.616309   \n",
       "         4                     Venezuela            3.711130   \n",
       "         5                         Samoa            3.718438   \n",
       "...                                  ...                 ...   \n",
       "852      242                    Tanzania            3.793239   \n",
       "         243                     Morocco            2.844909   \n",
       "         244                  Mauritania            3.616309   \n",
       "         245                       Sudan            3.927896   \n",
       "         246                 South Sudan            4.552824   \n",
       "\n",
       "                     wdi_ny_gdp_mktp_kd  wdi_sp_pop_totl  ged_sb_tlag1  \\\n",
       "month_id country_id                                                      \n",
       "1        1                    21.645497        13.564709           0.0   \n",
       "         2                    21.704949        12.834983           0.0   \n",
       "         3                    22.994549        13.935826           0.0   \n",
       "         4                     0.000000        16.537493           0.0   \n",
       "         5                    19.784920        12.013131           0.0   \n",
       "...                                 ...              ...           ...   \n",
       "852      242                  24.884573        17.967941           0.0   \n",
       "         243                  25.542467        17.428496           0.0   \n",
       "         244                  22.728763        15.344817           0.0   \n",
       "         245                  25.101865        17.636672           0.0   \n",
       "         246                  23.207989        16.190256           0.0   \n",
       "\n",
       "                     ged_sb_tlag2  ged_sb_tlag3  ged_sb_tlag4  ged_sb_tlag5  \\\n",
       "month_id country_id                                                           \n",
       "1        1                    0.0           0.0           0.0           0.0   \n",
       "         2                    0.0           0.0           0.0           0.0   \n",
       "         3                    0.0           0.0           0.0           0.0   \n",
       "         4                    0.0           0.0           0.0           0.0   \n",
       "         5                    0.0           0.0           0.0           0.0   \n",
       "...                           ...           ...           ...           ...   \n",
       "852      242                  0.0           0.0           0.0           0.0   \n",
       "         243                  0.0           0.0           0.0           0.0   \n",
       "         244                  0.0           0.0           0.0           0.0   \n",
       "         245                  0.0           0.0           0.0           0.0   \n",
       "         246                  0.0           0.0           0.0           0.0   \n",
       "\n",
       "                     ged_sb_tlag6  ...  ged_sb_tlag28  ged_sb_tlag29  \\\n",
       "month_id country_id                ...                                 \n",
       "1        1                    0.0  ...            0.0            0.0   \n",
       "         2                    0.0  ...            0.0            0.0   \n",
       "         3                    0.0  ...            0.0            0.0   \n",
       "         4                    0.0  ...            0.0            0.0   \n",
       "         5                    0.0  ...            0.0            0.0   \n",
       "...                           ...  ...            ...            ...   \n",
       "852      242                  0.0  ...            0.0            0.0   \n",
       "         243                  0.0  ...            0.0            0.0   \n",
       "         244                  0.0  ...            0.0            0.0   \n",
       "         245                  0.0  ...            0.0            0.0   \n",
       "         246                  0.0  ...            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag30  ged_sb_tlag31  ged_sb_tlag32  \\\n",
       "month_id country_id                                                \n",
       "1        1                     0.0            0.0            0.0   \n",
       "         2                     0.0            0.0            0.0   \n",
       "         3                     0.0            0.0            0.0   \n",
       "         4                     0.0            0.0            0.0   \n",
       "         5                     0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "852      242                   0.0            0.0            0.0   \n",
       "         243                   0.0            0.0            0.0   \n",
       "         244                   0.0            0.0            0.0   \n",
       "         245                   0.0            0.0            0.0   \n",
       "         246                   0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag33  ged_sb_tlag34  ged_sb_tlag35  \\\n",
       "month_id country_id                                                \n",
       "1        1                     0.0            0.0            0.0   \n",
       "         2                     0.0            0.0            0.0   \n",
       "         3                     0.0            0.0            0.0   \n",
       "         4                     0.0            0.0            0.0   \n",
       "         5                     0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "852      242                   0.0            0.0            0.0   \n",
       "         243                   0.0            0.0            0.0   \n",
       "         244                   0.0            0.0            0.0   \n",
       "         245                   0.0            0.0            0.0   \n",
       "         246                   0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag36  country_id  \n",
       "month_id country_id                             \n",
       "1        1                     0.0           1  \n",
       "         2                     0.0           2  \n",
       "         3                     0.0           3  \n",
       "         4                     0.0           4  \n",
       "         5                     0.0           5  \n",
       "...                            ...         ...  \n",
       "852      242                   0.0         242  \n",
       "         243                   0.0         243  \n",
       "         244                   0.0         244  \n",
       "         245                   0.0         245  \n",
       "         246                   0.0         246  \n",
       "\n",
       "[158230 rows x 41 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove countries that don't exist anymore\n",
    "df = df.query('country_id!=185') # German Democratic Republic\n",
    "# Remove country months that have value zero for dependent variable\n",
    "df = df.query('wdi_sh_dyn_mort_fe!=0') \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>wdi_sh_dyn_mort_fe</th>\n",
       "      <th>wdi_ny_gdp_mktp_kd</th>\n",
       "      <th>wdi_sp_pop_totl</th>\n",
       "      <th>ged_sb_tlag1</th>\n",
       "      <th>ged_sb_tlag2</th>\n",
       "      <th>ged_sb_tlag3</th>\n",
       "      <th>ged_sb_tlag4</th>\n",
       "      <th>ged_sb_tlag5</th>\n",
       "      <th>ged_sb_tlag6</th>\n",
       "      <th>...</th>\n",
       "      <th>ged_sb_tlag28</th>\n",
       "      <th>ged_sb_tlag29</th>\n",
       "      <th>ged_sb_tlag30</th>\n",
       "      <th>ged_sb_tlag31</th>\n",
       "      <th>ged_sb_tlag32</th>\n",
       "      <th>ged_sb_tlag33</th>\n",
       "      <th>ged_sb_tlag34</th>\n",
       "      <th>ged_sb_tlag35</th>\n",
       "      <th>ged_sb_tlag36</th>\n",
       "      <th>country_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_id</th>\n",
       "      <th>country_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1</th>\n",
       "      <th>1</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>4.151040</td>\n",
       "      <td>21.645497</td>\n",
       "      <td>13.564709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>3.953165</td>\n",
       "      <td>21.704949</td>\n",
       "      <td>12.834983</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.994549</td>\n",
       "      <td>13.935826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>3.711130</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.537493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>3.718438</td>\n",
       "      <td>19.784920</td>\n",
       "      <td>12.013131</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">852</th>\n",
       "      <th>242</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.793239</td>\n",
       "      <td>24.884573</td>\n",
       "      <td>17.967941</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>2.844909</td>\n",
       "      <td>25.542467</td>\n",
       "      <td>17.428496</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.728763</td>\n",
       "      <td>15.344817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>3.927896</td>\n",
       "      <td>25.101865</td>\n",
       "      <td>17.636672</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4.552824</td>\n",
       "      <td>23.207989</td>\n",
       "      <td>16.190256</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155858 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country_name  wdi_sh_dyn_mort_fe  \\\n",
       "month_id country_id                                            \n",
       "1        1                        Guyana            4.151040   \n",
       "         2                      Suriname            3.953165   \n",
       "         3           Trinidad and Tobago            3.616309   \n",
       "         4                     Venezuela            3.711130   \n",
       "         5                         Samoa            3.718438   \n",
       "...                                  ...                 ...   \n",
       "852      242                    Tanzania            3.793239   \n",
       "         243                     Morocco            2.844909   \n",
       "         244                  Mauritania            3.616309   \n",
       "         245                       Sudan            3.927896   \n",
       "         246                 South Sudan            4.552824   \n",
       "\n",
       "                     wdi_ny_gdp_mktp_kd  wdi_sp_pop_totl  ged_sb_tlag1  \\\n",
       "month_id country_id                                                      \n",
       "1        1                    21.645497        13.564709           0.0   \n",
       "         2                    21.704949        12.834983           0.0   \n",
       "         3                    22.994549        13.935826           0.0   \n",
       "         4                     0.000000        16.537493           0.0   \n",
       "         5                    19.784920        12.013131           0.0   \n",
       "...                                 ...              ...           ...   \n",
       "852      242                  24.884573        17.967941           0.0   \n",
       "         243                  25.542467        17.428496           0.0   \n",
       "         244                  22.728763        15.344817           0.0   \n",
       "         245                  25.101865        17.636672           0.0   \n",
       "         246                  23.207989        16.190256           0.0   \n",
       "\n",
       "                     ged_sb_tlag2  ged_sb_tlag3  ged_sb_tlag4  ged_sb_tlag5  \\\n",
       "month_id country_id                                                           \n",
       "1        1                    0.0           0.0           0.0           0.0   \n",
       "         2                    0.0           0.0           0.0           0.0   \n",
       "         3                    0.0           0.0           0.0           0.0   \n",
       "         4                    0.0           0.0           0.0           0.0   \n",
       "         5                    0.0           0.0           0.0           0.0   \n",
       "...                           ...           ...           ...           ...   \n",
       "852      242                  0.0           0.0           0.0           0.0   \n",
       "         243                  0.0           0.0           0.0           0.0   \n",
       "         244                  0.0           0.0           0.0           0.0   \n",
       "         245                  0.0           0.0           0.0           0.0   \n",
       "         246                  0.0           0.0           0.0           0.0   \n",
       "\n",
       "                     ged_sb_tlag6  ...  ged_sb_tlag28  ged_sb_tlag29  \\\n",
       "month_id country_id                ...                                 \n",
       "1        1                    0.0  ...            0.0            0.0   \n",
       "         2                    0.0  ...            0.0            0.0   \n",
       "         3                    0.0  ...            0.0            0.0   \n",
       "         4                    0.0  ...            0.0            0.0   \n",
       "         5                    0.0  ...            0.0            0.0   \n",
       "...                           ...  ...            ...            ...   \n",
       "852      242                  0.0  ...            0.0            0.0   \n",
       "         243                  0.0  ...            0.0            0.0   \n",
       "         244                  0.0  ...            0.0            0.0   \n",
       "         245                  0.0  ...            0.0            0.0   \n",
       "         246                  0.0  ...            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag30  ged_sb_tlag31  ged_sb_tlag32  \\\n",
       "month_id country_id                                                \n",
       "1        1                     0.0            0.0            0.0   \n",
       "         2                     0.0            0.0            0.0   \n",
       "         3                     0.0            0.0            0.0   \n",
       "         4                     0.0            0.0            0.0   \n",
       "         5                     0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "852      242                   0.0            0.0            0.0   \n",
       "         243                   0.0            0.0            0.0   \n",
       "         244                   0.0            0.0            0.0   \n",
       "         245                   0.0            0.0            0.0   \n",
       "         246                   0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag33  ged_sb_tlag34  ged_sb_tlag35  \\\n",
       "month_id country_id                                                \n",
       "1        1                     0.0            0.0            0.0   \n",
       "         2                     0.0            0.0            0.0   \n",
       "         3                     0.0            0.0            0.0   \n",
       "         4                     0.0            0.0            0.0   \n",
       "         5                     0.0            0.0            0.0   \n",
       "...                            ...            ...            ...   \n",
       "852      242                   0.0            0.0            0.0   \n",
       "         243                   0.0            0.0            0.0   \n",
       "         244                   0.0            0.0            0.0   \n",
       "         245                   0.0            0.0            0.0   \n",
       "         246                   0.0            0.0            0.0   \n",
       "\n",
       "                     ged_sb_tlag36  country_id  \n",
       "month_id country_id                             \n",
       "1        1                     0.0           1  \n",
       "         2                     0.0           2  \n",
       "         3                     0.0           3  \n",
       "         4                     0.0           4  \n",
       "         5                     0.0           5  \n",
       "...                            ...         ...  \n",
       "852      242                   0.0         242  \n",
       "         243                   0.0         243  \n",
       "         244                   0.0         244  \n",
       "         245                   0.0         245  \n",
       "         246                   0.0         246  \n",
       "\n",
       "[155858 rows x 41 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.query('month_id > 240 & month_id < 505')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = MonthToAnnualRegression(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>country_name</th>\n",
       "      <th>wdi_sh_dyn_mort_fe</th>\n",
       "      <th>wdi_ny_gdp_mktp_kd</th>\n",
       "      <th>wdi_sp_pop_totl</th>\n",
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       "      <th>ged_sb_tlag6</th>\n",
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       "      <th>ged_sb_tlag35</th>\n",
       "      <th>ged_sb_tlag36</th>\n",
       "      <th>country_id</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>month_id</th>\n",
       "      <th>country_id</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">241</th>\n",
       "      <th>1</th>\n",
       "      <td>Guyana</td>\n",
       "      <td>3.732896</td>\n",
       "      <td>21.779484</td>\n",
       "      <td>13.539826</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Suriname</td>\n",
       "      <td>3.349904</td>\n",
       "      <td>21.795169</td>\n",
       "      <td>13.079454</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Trinidad and Tobago</td>\n",
       "      <td>3.277145</td>\n",
       "      <td>23.309243</td>\n",
       "      <td>14.102345</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Venezuela</td>\n",
       "      <td>2.990720</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.011230</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Samoa</td>\n",
       "      <td>3.005683</td>\n",
       "      <td>20.098032</td>\n",
       "      <td>12.122740</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">504</th>\n",
       "      <th>242</th>\n",
       "      <td>Tanzania</td>\n",
       "      <td>3.793239</td>\n",
       "      <td>24.884573</td>\n",
       "      <td>17.967941</td>\n",
       "      <td>1.098612</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>Morocco</td>\n",
       "      <td>2.844909</td>\n",
       "      <td>25.542467</td>\n",
       "      <td>17.428496</td>\n",
       "      <td>1.386294</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Mauritania</td>\n",
       "      <td>3.616309</td>\n",
       "      <td>22.728763</td>\n",
       "      <td>15.344817</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Sudan</td>\n",
       "      <td>3.927896</td>\n",
       "      <td>25.101865</td>\n",
       "      <td>17.636672</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.890372</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.708050</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>2.302585</td>\n",
       "      <td>245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>South Sudan</td>\n",
       "      <td>4.552824</td>\n",
       "      <td>23.207989</td>\n",
       "      <td>16.190256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>4.189655</td>\n",
       "      <td>3.496508</td>\n",
       "      <td>1.791759</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>...</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.098612</td>\n",
       "      <td>2.197225</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>3.218876</td>\n",
       "      <td>3.663562</td>\n",
       "      <td>3.688879</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>49580 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            country_name  wdi_sh_dyn_mort_fe  \\\n",
       "month_id country_id                                            \n",
       "241      1                        Guyana            3.732896   \n",
       "         2                      Suriname            3.349904   \n",
       "         3           Trinidad and Tobago            3.277145   \n",
       "         4                     Venezuela            2.990720   \n",
       "         5                         Samoa            3.005683   \n",
       "...                                  ...                 ...   \n",
       "504      242                    Tanzania            3.793239   \n",
       "         243                     Morocco            2.844909   \n",
       "         244                  Mauritania            3.616309   \n",
       "         245                       Sudan            3.927896   \n",
       "         246                 South Sudan            4.552824   \n",
       "\n",
       "                     wdi_ny_gdp_mktp_kd  wdi_sp_pop_totl  ged_sb_tlag1  \\\n",
       "month_id country_id                                                      \n",
       "241      1                    21.779484        13.539826      0.000000   \n",
       "         2                    21.795169        13.079454      0.000000   \n",
       "         3                    23.309243        14.102345      0.000000   \n",
       "         4                     0.000000        17.011230      0.000000   \n",
       "         5                    20.098032        12.122740      0.000000   \n",
       "...                                 ...              ...           ...   \n",
       "504      242                  24.884573        17.967941      1.098612   \n",
       "         243                  25.542467        17.428496      1.386294   \n",
       "         244                  22.728763        15.344817      0.000000   \n",
       "         245                  25.101865        17.636672      0.000000   \n",
       "         246                  23.207989        16.190256      0.000000   \n",
       "\n",
       "                     ged_sb_tlag2  ged_sb_tlag3  ged_sb_tlag4  ged_sb_tlag5  \\\n",
       "month_id country_id                                                           \n",
       "241      1               0.000000      0.000000      0.000000      0.000000   \n",
       "         2               0.000000      0.000000      0.000000      0.000000   \n",
       "         3               0.000000      0.000000      0.000000      0.000000   \n",
       "         4               0.000000      0.000000      0.000000      0.000000   \n",
       "         5               0.000000      0.000000      0.000000      0.000000   \n",
       "...                           ...           ...           ...           ...   \n",
       "504      242             0.000000      0.000000      1.791759      0.000000   \n",
       "         243             0.000000      0.000000      0.000000      0.000000   \n",
       "         244             0.000000      0.000000      0.000000      0.000000   \n",
       "         245             1.386294      0.000000      0.000000      2.890372   \n",
       "         246             1.098612      4.189655      3.496508      1.791759   \n",
       "\n",
       "                     ged_sb_tlag6  ...  ged_sb_tlag28  ged_sb_tlag29  \\\n",
       "month_id country_id                ...                                 \n",
       "241      1               0.000000  ...       0.000000       0.000000   \n",
       "         2               0.000000  ...       0.000000       0.000000   \n",
       "         3               0.000000  ...       0.000000       0.000000   \n",
       "         4               0.000000  ...       0.000000       0.000000   \n",
       "         5               0.000000  ...       0.000000       0.000000   \n",
       "...                           ...  ...            ...            ...   \n",
       "504      242             0.000000  ...       0.000000       0.000000   \n",
       "         243             0.000000  ...       0.000000       0.000000   \n",
       "         244             0.000000  ...       0.000000       0.000000   \n",
       "         245             0.000000  ...       0.000000       0.000000   \n",
       "         246             2.397895  ...       1.386294       2.197225   \n",
       "\n",
       "                     ged_sb_tlag30  ged_sb_tlag31  ged_sb_tlag32  \\\n",
       "month_id country_id                                                \n",
       "241      1                     0.0       0.000000       0.000000   \n",
       "         2                     0.0       0.000000       0.000000   \n",
       "         3                     0.0       0.000000       0.000000   \n",
       "         4                     0.0       0.000000       0.000000   \n",
       "         5                     0.0       0.000000       0.000000   \n",
       "...                            ...            ...            ...   \n",
       "504      242                   0.0       0.000000       0.000000   \n",
       "         243                   0.0       0.000000       0.000000   \n",
       "         244                   0.0       0.000000       0.000000   \n",
       "         245                   0.0       0.000000       0.000000   \n",
       "         246                   0.0       1.098612       2.197225   \n",
       "\n",
       "                     ged_sb_tlag33  ged_sb_tlag34  ged_sb_tlag35  \\\n",
       "month_id country_id                                                \n",
       "241      1                0.000000       0.000000       0.000000   \n",
       "         2                0.000000       0.000000       0.000000   \n",
       "         3                0.000000       0.000000       0.000000   \n",
       "         4                0.000000       0.000000       0.000000   \n",
       "         5                0.000000       0.000000       0.000000   \n",
       "...                            ...            ...            ...   \n",
       "504      242              0.000000       0.000000       0.000000   \n",
       "         243              0.000000       0.000000       0.000000   \n",
       "         244              0.000000       0.000000       0.000000   \n",
       "         245              2.708050       1.609438       1.791759   \n",
       "         246              1.386294       3.218876       3.663562   \n",
       "\n",
       "                     ged_sb_tlag36  country_id  \n",
       "month_id country_id                             \n",
       "241      1                0.000000           1  \n",
       "         2                0.000000           2  \n",
       "         3                0.000000           3  \n",
       "         4                0.000000           4  \n",
       "         5                0.000000           5  \n",
       "...                            ...         ...  \n",
       "504      242              0.000000         242  \n",
       "         243              0.000000         243  \n",
       "         244              0.000000         244  \n",
       "         245              2.302585         245  \n",
       "         246              3.688879         246  \n",
       "\n",
       "[49580 rows x 41 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "independent_variable = ['wdi_ny_gdp_mktp_kd','wdi_sp_pop_totl','ged_sb_tlag1', 'ged_sb_tlag2', 'ged_sb_tlag3', 'ged_sb_tlag4', 'ged_sb_tlag5', 'ged_sb_tlag6', 'ged_sb_tlag7', 'ged_sb_tlag8', 'ged_sb_tlag9', 'ged_sb_tlag10', 'ged_sb_tlag11', 'ged_sb_tlag12', 'ged_sb_tlag13', 'ged_sb_tlag14', 'ged_sb_tlag15', 'ged_sb_tlag16', 'ged_sb_tlag17', 'ged_sb_tlag18', 'ged_sb_tlag19', 'ged_sb_tlag20', 'ged_sb_tlag21', 'ged_sb_tlag22', 'ged_sb_tlag23', 'ged_sb_tlag24', 'ged_sb_tlag25', 'ged_sb_tlag26', 'ged_sb_tlag27', 'ged_sb_tlag28', 'ged_sb_tlag29', 'ged_sb_tlag30', 'ged_sb_tlag31', 'ged_sb_tlag32', 'ged_sb_tlag33', 'ged_sb_tlag34', 'ged_sb_tlag35', 'ged_sb_tlag36','country_id']\n",
    "dependent_variable = 'wdi_sh_dyn_mort_fe'\n",
    "data1.ols_regression(independent_variable,dependent_variable,'Fixed Effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.plot_regression(independent_variable,dependent_variable,'Fixed Effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fixed Effect': <class 'statsmodels.iolib.summary.Summary'>\n",
       " \"\"\"\n",
       "                             OLS Regression Results                            \n",
       " ==============================================================================\n",
       " Dep. Variable:     wdi_sh_dyn_mort_fe   R-squared:                       0.236\n",
       " Model:                            OLS   Adj. R-squared:                  0.229\n",
       " Method:                 Least Squares   F-statistic:                     32.45\n",
       " Date:                Fri, 17 Nov 2023   Prob (F-statistic):          6.67e-207\n",
       " Time:                        13:49:22   Log-Likelihood:                -5564.4\n",
       " No. Observations:                4133   AIC:                         1.121e+04\n",
       " Df Residuals:                    4093   BIC:                         1.146e+04\n",
       " Df Model:                          39                                         \n",
       " Covariance Type:            nonrobust                                         \n",
       " ======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       " --------------------------------------------------------------------------------------\n",
       " const                  4.6702      0.125     37.385      0.000       4.425       4.915\n",
       " wdi_ny_gdp_mktp_kd    -0.1404      0.005    -28.446      0.000      -0.150      -0.131\n",
       " wdi_sp_pop_totl        0.0973      0.009     11.446      0.000       0.081       0.114\n",
       " ged_sb_tlag1           0.0208      0.030      0.685      0.494      -0.039       0.080\n",
       " ged_sb_tlag2           0.0664      0.032      2.094      0.036       0.004       0.129\n",
       " ged_sb_tlag3           0.0453      0.031      1.483      0.138      -0.015       0.105\n",
       " ged_sb_tlag4          -0.0819      0.032     -2.563      0.010      -0.145      -0.019\n",
       " ged_sb_tlag5          -0.0212      0.033     -0.649      0.516      -0.085       0.043\n",
       " ged_sb_tlag6           0.0465      0.033      1.421      0.155      -0.018       0.111\n",
       " ged_sb_tlag7           0.0128      0.032      0.404      0.686      -0.049       0.075\n",
       " ged_sb_tlag8          -0.0316      0.031     -1.029      0.303      -0.092       0.029\n",
       " ged_sb_tlag9           0.0418      0.032      1.302      0.193      -0.021       0.105\n",
       " ged_sb_tlag10          0.0032      0.033      0.097      0.923      -0.061       0.068\n",
       " ged_sb_tlag11          0.0399      0.034      1.189      0.235      -0.026       0.106\n",
       " ged_sb_tlag12          0.0454      0.030      1.539      0.124      -0.012       0.103\n",
       " ged_sb_tlag13         -0.0299      0.032     -0.920      0.357      -0.094       0.034\n",
       " ged_sb_tlag14          0.0228      0.032      0.705      0.481      -0.041       0.086\n",
       " ged_sb_tlag15          0.0106      0.031      0.344      0.731      -0.050       0.071\n",
       " ged_sb_tlag16         -0.1150      0.032     -3.627      0.000      -0.177      -0.053\n",
       " ged_sb_tlag17          0.0037      0.032      0.115      0.909      -0.060       0.067\n",
       " ged_sb_tlag18          0.0362      0.032      1.142      0.254      -0.026       0.098\n",
       " ged_sb_tlag19          0.0150      0.033      0.461      0.645      -0.049       0.079\n",
       " ged_sb_tlag20         -0.0565      0.031     -1.824      0.068      -0.117       0.004\n",
       " ged_sb_tlag21          0.0482      0.032      1.509      0.131      -0.014       0.111\n",
       " ged_sb_tlag22          0.0104      0.032      0.327      0.744      -0.052       0.073\n",
       " ged_sb_tlag23          0.0077      0.033      0.234      0.815      -0.057       0.072\n",
       " ged_sb_tlag24          0.0607      0.029      2.110      0.035       0.004       0.117\n",
       " ged_sb_tlag25         -0.0091      0.032     -0.281      0.779      -0.072       0.054\n",
       " ged_sb_tlag26          0.0254      0.032      0.787      0.431      -0.038       0.089\n",
       " ged_sb_tlag27         -0.0039      0.030     -0.129      0.898      -0.064       0.056\n",
       " ged_sb_tlag28         -0.0979      0.031     -3.118      0.002      -0.159      -0.036\n",
       " ged_sb_tlag29         -0.0120      0.032     -0.376      0.707      -0.074       0.050\n",
       " ged_sb_tlag30          0.0395      0.031      1.292      0.197      -0.020       0.099\n",
       " ged_sb_tlag31          0.0122      0.032      0.382      0.703      -0.050       0.075\n",
       " ged_sb_tlag32         -0.0440      0.031     -1.429      0.153      -0.104       0.016\n",
       " ged_sb_tlag33          0.0281      0.031      0.903      0.367      -0.033       0.089\n",
       " ged_sb_tlag34          0.0098      0.032      0.306      0.759      -0.053       0.073\n",
       " ged_sb_tlag35         -0.0113      0.032     -0.352      0.725      -0.074       0.052\n",
       " ged_sb_tlag36          0.0770      0.028      2.779      0.005       0.023       0.131\n",
       " country_id             0.0013      0.000      5.502      0.000       0.001       0.002\n",
       " ==============================================================================\n",
       " Omnibus:                       80.862   Durbin-Watson:                   1.012\n",
       " Prob(Omnibus):                  0.000   Jarque-Bera (JB):               99.456\n",
       " Skew:                          -0.271   Prob(JB):                     2.53e-22\n",
       " Kurtosis:                       3.532   Cond. No.                     1.09e+03\n",
       " ==============================================================================\n",
       " \n",
       " Notes:\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       " [2] The condition number is large, 1.09e+03. This might indicate that there are\n",
       " strong multicollinearity or other numerical problems.\n",
       " \"\"\"}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "independent_variable = ['wdi_ny_gdp_mktp_kd', 'wdi_sp_pop_totl', 'ged_sb_tlag1', 'ged_sb_tlag2', 'ged_sb_tlag3', 'ged_sb_tlag4', 'ged_sb_tlag5', 'ged_sb_tlag6', 'ged_sb_tlag7', 'ged_sb_tlag8', 'ged_sb_tlag9', 'ged_sb_tlag10', 'ged_sb_tlag11', 'ged_sb_tlag12', 'ged_sb_tlag13', 'ged_sb_tlag14', 'ged_sb_tlag15', 'ged_sb_tlag16', 'ged_sb_tlag17', 'ged_sb_tlag18',\n",
    "                        'ged_sb_tlag19', 'ged_sb_tlag20', 'ged_sb_tlag21', 'ged_sb_tlag22', 'ged_sb_tlag23', 'ged_sb_tlag24', 'ged_sb_tlag25', 'ged_sb_tlag26', 'ged_sb_tlag27', 'ged_sb_tlag28', 'ged_sb_tlag29', 'ged_sb_tlag30', 'ged_sb_tlag31', 'ged_sb_tlag32', 'ged_sb_tlag33', 'ged_sb_tlag34', 'ged_sb_tlag35', 'ged_sb_tlag36']\n",
    "dependent_variable = 'wdi_sh_dyn_mort_fe'\n",
    "data1.ols_regression(independent_variable, dependent_variable,'No effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fixed Effect': <class 'statsmodels.iolib.summary.Summary'>\n",
       " \"\"\"\n",
       "                             OLS Regression Results                            \n",
       " ==============================================================================\n",
       " Dep. Variable:     wdi_sh_dyn_mort_fe   R-squared:                       0.236\n",
       " Model:                            OLS   Adj. R-squared:                  0.229\n",
       " Method:                 Least Squares   F-statistic:                     32.45\n",
       " Date:                Fri, 17 Nov 2023   Prob (F-statistic):          6.67e-207\n",
       " Time:                        13:49:22   Log-Likelihood:                -5564.4\n",
       " No. Observations:                4133   AIC:                         1.121e+04\n",
       " Df Residuals:                    4093   BIC:                         1.146e+04\n",
       " Df Model:                          39                                         \n",
       " Covariance Type:            nonrobust                                         \n",
       " ======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       " --------------------------------------------------------------------------------------\n",
       " const                  4.6702      0.125     37.385      0.000       4.425       4.915\n",
       " wdi_ny_gdp_mktp_kd    -0.1404      0.005    -28.446      0.000      -0.150      -0.131\n",
       " wdi_sp_pop_totl        0.0973      0.009     11.446      0.000       0.081       0.114\n",
       " ged_sb_tlag1           0.0208      0.030      0.685      0.494      -0.039       0.080\n",
       " ged_sb_tlag2           0.0664      0.032      2.094      0.036       0.004       0.129\n",
       " ged_sb_tlag3           0.0453      0.031      1.483      0.138      -0.015       0.105\n",
       " ged_sb_tlag4          -0.0819      0.032     -2.563      0.010      -0.145      -0.019\n",
       " ged_sb_tlag5          -0.0212      0.033     -0.649      0.516      -0.085       0.043\n",
       " ged_sb_tlag6           0.0465      0.033      1.421      0.155      -0.018       0.111\n",
       " ged_sb_tlag7           0.0128      0.032      0.404      0.686      -0.049       0.075\n",
       " ged_sb_tlag8          -0.0316      0.031     -1.029      0.303      -0.092       0.029\n",
       " ged_sb_tlag9           0.0418      0.032      1.302      0.193      -0.021       0.105\n",
       " ged_sb_tlag10          0.0032      0.033      0.097      0.923      -0.061       0.068\n",
       " ged_sb_tlag11          0.0399      0.034      1.189      0.235      -0.026       0.106\n",
       " ged_sb_tlag12          0.0454      0.030      1.539      0.124      -0.012       0.103\n",
       " ged_sb_tlag13         -0.0299      0.032     -0.920      0.357      -0.094       0.034\n",
       " ged_sb_tlag14          0.0228      0.032      0.705      0.481      -0.041       0.086\n",
       " ged_sb_tlag15          0.0106      0.031      0.344      0.731      -0.050       0.071\n",
       " ged_sb_tlag16         -0.1150      0.032     -3.627      0.000      -0.177      -0.053\n",
       " ged_sb_tlag17          0.0037      0.032      0.115      0.909      -0.060       0.067\n",
       " ged_sb_tlag18          0.0362      0.032      1.142      0.254      -0.026       0.098\n",
       " ged_sb_tlag19          0.0150      0.033      0.461      0.645      -0.049       0.079\n",
       " ged_sb_tlag20         -0.0565      0.031     -1.824      0.068      -0.117       0.004\n",
       " ged_sb_tlag21          0.0482      0.032      1.509      0.131      -0.014       0.111\n",
       " ged_sb_tlag22          0.0104      0.032      0.327      0.744      -0.052       0.073\n",
       " ged_sb_tlag23          0.0077      0.033      0.234      0.815      -0.057       0.072\n",
       " ged_sb_tlag24          0.0607      0.029      2.110      0.035       0.004       0.117\n",
       " ged_sb_tlag25         -0.0091      0.032     -0.281      0.779      -0.072       0.054\n",
       " ged_sb_tlag26          0.0254      0.032      0.787      0.431      -0.038       0.089\n",
       " ged_sb_tlag27         -0.0039      0.030     -0.129      0.898      -0.064       0.056\n",
       " ged_sb_tlag28         -0.0979      0.031     -3.118      0.002      -0.159      -0.036\n",
       " ged_sb_tlag29         -0.0120      0.032     -0.376      0.707      -0.074       0.050\n",
       " ged_sb_tlag30          0.0395      0.031      1.292      0.197      -0.020       0.099\n",
       " ged_sb_tlag31          0.0122      0.032      0.382      0.703      -0.050       0.075\n",
       " ged_sb_tlag32         -0.0440      0.031     -1.429      0.153      -0.104       0.016\n",
       " ged_sb_tlag33          0.0281      0.031      0.903      0.367      -0.033       0.089\n",
       " ged_sb_tlag34          0.0098      0.032      0.306      0.759      -0.053       0.073\n",
       " ged_sb_tlag35         -0.0113      0.032     -0.352      0.725      -0.074       0.052\n",
       " ged_sb_tlag36          0.0770      0.028      2.779      0.005       0.023       0.131\n",
       " country_id             0.0013      0.000      5.502      0.000       0.001       0.002\n",
       " ==============================================================================\n",
       " Omnibus:                       80.862   Durbin-Watson:                   1.012\n",
       " Prob(Omnibus):                  0.000   Jarque-Bera (JB):               99.456\n",
       " Skew:                          -0.271   Prob(JB):                     2.53e-22\n",
       " Kurtosis:                       3.532   Cond. No.                     1.09e+03\n",
       " ==============================================================================\n",
       " \n",
       " Notes:\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       " [2] The condition number is large, 1.09e+03. This might indicate that there are\n",
       " strong multicollinearity or other numerical problems.\n",
       " \"\"\",\n",
       " 'No effect': <class 'statsmodels.iolib.summary.Summary'>\n",
       " \"\"\"\n",
       "                             OLS Regression Results                            \n",
       " ==============================================================================\n",
       " Dep. Variable:     wdi_sh_dyn_mort_fe   R-squared:                       0.231\n",
       " Model:                            OLS   Adj. R-squared:                  0.223\n",
       " Method:                 Least Squares   F-statistic:                     32.28\n",
       " Date:                Fri, 17 Nov 2023   Prob (F-statistic):          2.64e-201\n",
       " Time:                        13:49:22   Log-Likelihood:                -5579.6\n",
       " No. Observations:                4133   AIC:                         1.124e+04\n",
       " Df Residuals:                    4094   BIC:                         1.148e+04\n",
       " Df Model:                          38                                         \n",
       " Covariance Type:            nonrobust                                         \n",
       " ======================================================================================\n",
       "                          coef    std err          t      P>|t|      [0.025      0.975]\n",
       " --------------------------------------------------------------------------------------\n",
       " const                  4.7042      0.125     37.569      0.000       4.459       4.950\n",
       " wdi_ny_gdp_mktp_kd    -0.1399      0.005    -28.250      0.000      -0.150      -0.130\n",
       " wdi_sp_pop_totl        0.1028      0.008     12.138      0.000       0.086       0.119\n",
       " ged_sb_tlag1           0.0223      0.031      0.730      0.465      -0.038       0.082\n",
       " ged_sb_tlag2           0.0666      0.032      2.091      0.037       0.004       0.129\n",
       " ged_sb_tlag3           0.0473      0.031      1.542      0.123      -0.013       0.107\n",
       " ged_sb_tlag4          -0.0819      0.032     -2.554      0.011      -0.145      -0.019\n",
       " ged_sb_tlag5          -0.0188      0.033     -0.573      0.567      -0.083       0.046\n",
       " ged_sb_tlag6           0.0413      0.033      1.258      0.208      -0.023       0.106\n",
       " ged_sb_tlag7           0.0124      0.032      0.390      0.696      -0.050       0.075\n",
       " ged_sb_tlag8          -0.0294      0.031     -0.955      0.340      -0.090       0.031\n",
       " ged_sb_tlag9           0.0416      0.032      1.293      0.196      -0.022       0.105\n",
       " ged_sb_tlag10          0.0021      0.033      0.065      0.949      -0.063       0.067\n",
       " ged_sb_tlag11          0.0405      0.034      1.203      0.229      -0.026       0.107\n",
       " ged_sb_tlag12          0.0448      0.030      1.512      0.131      -0.013       0.103\n",
       " ged_sb_tlag13         -0.0278      0.033     -0.852      0.394      -0.092       0.036\n",
       " ged_sb_tlag14          0.0237      0.032      0.729      0.466      -0.040       0.087\n",
       " ged_sb_tlag15          0.0119      0.031      0.387      0.699      -0.049       0.072\n",
       " ged_sb_tlag16         -0.1153      0.032     -3.624      0.000      -0.178      -0.053\n",
       " ged_sb_tlag17          0.0062      0.032      0.190      0.849      -0.057       0.070\n",
       " ged_sb_tlag18          0.0306      0.032      0.964      0.335      -0.032       0.093\n",
       " ged_sb_tlag19          0.0147      0.033      0.448      0.654      -0.050       0.079\n",
       " ged_sb_tlag20         -0.0556      0.031     -1.787      0.074      -0.117       0.005\n",
       " ged_sb_tlag21          0.0490      0.032      1.526      0.127      -0.014       0.112\n",
       " ged_sb_tlag22          0.0099      0.032      0.308      0.758      -0.053       0.073\n",
       " ged_sb_tlag23          0.0094      0.033      0.283      0.777      -0.055       0.074\n",
       " ged_sb_tlag24          0.0611      0.029      2.119      0.034       0.005       0.118\n",
       " ged_sb_tlag25         -0.0068      0.032     -0.209      0.835      -0.070       0.057\n",
       " ged_sb_tlag26          0.0247      0.032      0.763      0.445      -0.039       0.088\n",
       " ged_sb_tlag27         -0.0018      0.031     -0.059      0.953      -0.062       0.058\n",
       " ged_sb_tlag28         -0.0993      0.032     -3.153      0.002      -0.161      -0.038\n",
       " ged_sb_tlag29         -0.0100      0.032     -0.313      0.755      -0.073       0.053\n",
       " ged_sb_tlag30          0.0333      0.031      1.087      0.277      -0.027       0.094\n",
       " ged_sb_tlag31          0.0122      0.032      0.381      0.703      -0.051       0.075\n",
       " ged_sb_tlag32         -0.0425      0.031     -1.374      0.169      -0.103       0.018\n",
       " ged_sb_tlag33          0.0290      0.031      0.927      0.354      -0.032       0.090\n",
       " ged_sb_tlag34          0.0094      0.032      0.292      0.770      -0.054       0.072\n",
       " ged_sb_tlag35         -0.0107      0.032     -0.331      0.741      -0.074       0.053\n",
       " ged_sb_tlag36          0.0777      0.028      2.794      0.005       0.023       0.132\n",
       " ==============================================================================\n",
       " Omnibus:                       82.273   Durbin-Watson:                   1.005\n",
       " Prob(Omnibus):                  0.000   Jarque-Bera (JB):              101.368\n",
       " Skew:                          -0.274   Prob(JB):                     9.73e-23\n",
       " Kurtosis:                       3.537   Cond. No.                         248.\n",
       " ==============================================================================\n",
       " \n",
       " Notes:\n",
       " [1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       " \"\"\"}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:     wdi_sh_dyn_mort_fe   R-squared:                       0.236\n",
      "Model:                            OLS   Adj. R-squared:                  0.229\n",
      "Method:                 Least Squares   F-statistic:                     32.45\n",
      "Date:                Fri, 17 Nov 2023   Prob (F-statistic):          6.67e-207\n",
      "Time:                        13:49:22   Log-Likelihood:                -5564.4\n",
      "No. Observations:                4133   AIC:                         1.121e+04\n",
      "Df Residuals:                    4093   BIC:                         1.146e+04\n",
      "Df Model:                          39                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "======================================================================================\n",
      "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
      "--------------------------------------------------------------------------------------\n",
      "const                  4.6702      0.125     37.385      0.000       4.425       4.915\n",
      "wdi_ny_gdp_mktp_kd    -0.1404      0.005    -28.446      0.000      -0.150      -0.131\n",
      "wdi_sp_pop_totl        0.0973      0.009     11.446      0.000       0.081       0.114\n",
      "ged_sb_tlag1           0.0208      0.030      0.685      0.494      -0.039       0.080\n",
      "ged_sb_tlag2           0.0664      0.032      2.094      0.036       0.004       0.129\n",
      "ged_sb_tlag3           0.0453      0.031      1.483      0.138      -0.015       0.105\n",
      "ged_sb_tlag4          -0.0819      0.032     -2.563      0.010      -0.145      -0.019\n",
      "ged_sb_tlag5          -0.0212      0.033     -0.649      0.516      -0.085       0.043\n",
      "ged_sb_tlag6           0.0465      0.033      1.421      0.155      -0.018       0.111\n",
      "ged_sb_tlag7           0.0128      0.032      0.404      0.686      -0.049       0.075\n",
      "ged_sb_tlag8          -0.0316      0.031     -1.029      0.303      -0.092       0.029\n",
      "ged_sb_tlag9           0.0418      0.032      1.302      0.193      -0.021       0.105\n",
      "ged_sb_tlag10          0.0032      0.033      0.097      0.923      -0.061       0.068\n",
      "ged_sb_tlag11          0.0399      0.034      1.189      0.235      -0.026       0.106\n",
      "ged_sb_tlag12          0.0454      0.030      1.539      0.124      -0.012       0.103\n",
      "ged_sb_tlag13         -0.0299      0.032     -0.920      0.357      -0.094       0.034\n",
      "ged_sb_tlag14          0.0228      0.032      0.705      0.481      -0.041       0.086\n",
      "ged_sb_tlag15          0.0106      0.031      0.344      0.731      -0.050       0.071\n",
      "ged_sb_tlag16         -0.1150      0.032     -3.627      0.000      -0.177      -0.053\n",
      "ged_sb_tlag17          0.0037      0.032      0.115      0.909      -0.060       0.067\n",
      "ged_sb_tlag18          0.0362      0.032      1.142      0.254      -0.026       0.098\n",
      "ged_sb_tlag19          0.0150      0.033      0.461      0.645      -0.049       0.079\n",
      "ged_sb_tlag20         -0.0565      0.031     -1.824      0.068      -0.117       0.004\n",
      "ged_sb_tlag21          0.0482      0.032      1.509      0.131      -0.014       0.111\n",
      "ged_sb_tlag22          0.0104      0.032      0.327      0.744      -0.052       0.073\n",
      "ged_sb_tlag23          0.0077      0.033      0.234      0.815      -0.057       0.072\n",
      "ged_sb_tlag24          0.0607      0.029      2.110      0.035       0.004       0.117\n",
      "ged_sb_tlag25         -0.0091      0.032     -0.281      0.779      -0.072       0.054\n",
      "ged_sb_tlag26          0.0254      0.032      0.787      0.431      -0.038       0.089\n",
      "ged_sb_tlag27         -0.0039      0.030     -0.129      0.898      -0.064       0.056\n",
      "ged_sb_tlag28         -0.0979      0.031     -3.118      0.002      -0.159      -0.036\n",
      "ged_sb_tlag29         -0.0120      0.032     -0.376      0.707      -0.074       0.050\n",
      "ged_sb_tlag30          0.0395      0.031      1.292      0.197      -0.020       0.099\n",
      "ged_sb_tlag31          0.0122      0.032      0.382      0.703      -0.050       0.075\n",
      "ged_sb_tlag32         -0.0440      0.031     -1.429      0.153      -0.104       0.016\n",
      "ged_sb_tlag33          0.0281      0.031      0.903      0.367      -0.033       0.089\n",
      "ged_sb_tlag34          0.0098      0.032      0.306      0.759      -0.053       0.073\n",
      "ged_sb_tlag35         -0.0113      0.032     -0.352      0.725      -0.074       0.052\n",
      "ged_sb_tlag36          0.0770      0.028      2.779      0.005       0.023       0.131\n",
      "country_id             0.0013      0.000      5.502      0.000       0.001       0.002\n",
      "==============================================================================\n",
      "Omnibus:                       80.862   Durbin-Watson:                   1.012\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               99.456\n",
      "Skew:                          -0.271   Prob(JB):                     2.53e-22\n",
      "Kurtosis:                       3.532   Cond. No.                     1.09e+03\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 1.09e+03. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "print(data1.get_regression_matrix('Fixed Effect'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2TVi2bJnQtGlTISoqyiBRc/ny5br9tm/fLnz11VdCo0aNhCZNmghVL11ikmm1/vzzTwGAUK9ePcHPz0/Izc01+P1TTz0lPPfcc8Lq1auF3bt3C2vWrBFat24taDQaXQKxlETlqkne5gAQpkyZYrT99OnTQmhoqNCxY0chISFB2Llzp7Bhwwbhww8/FHr16qXbLyMjQ6hdu7ZQt25dISEhQdi0aZMwcuRIITo62moyrSAIwujRowWVSiVMnDhR2LBhg/Drr78KCxYsEP773/8aPM7Hx0dYvXq1cOjQIeHEiROCIAhCQUGB0KZNG6FevXrCBx98IGzdulX49ddfhaVLlwqPP/64cPDgQUEQBKG8vFzo3r274OPjI7zzzjvCli1bhNmzZwsNGzZkMi2ZxUCF3Ia5QEUQBGHEiBFGgYogCMLt27eF6dOnCzExMYJarRaioqKEyZMnCzk5OaLOefz4cWHo0KFCRESEoFarhcjISKF3797CZ599pttHaqAi9riCIAiXLl0Sxo8fL0RGRgpqtVqoU6eOMHToUOHatWu6fVatWiU0a9ZMUKvVBhd7bbus3cBbt24tRERECCUlJWb36dy5s1CrVi3dPikpKcLjjz8uhIeHC97e3kL9+vWFsWPHGgR/CxcuFGJjYwVPT0+DmUkVFRXCe++9JzRs2FDw9fUV2rdvL+zYscPkrJ8FCxYIDRo0EHx8fITmzZsLS5cuFWbPnm1XoCIIgnDvvfcKAISRI0ca/e7rr78WevXqJdx1112Ct7e37jnX3pQFQfxzKwj2Byra840fP16oW7euoFarhdq1awv33nuv8NZbbxnsl5KSIvTt21fw9fUVwsLChAkTJgjr168XFaiUl5cLH330kRAfHy94e3sLGo1G6NKli/Dzzz/r9klPTxceeOABISgoSABgcIyCggLhjTfeEJo2bap7/D333CO8+OKLQmZmpm6/3NxcYfz48UJISIjg7+8v9O3bV/jjjz8YqJBZKkEwUc2JiIiISAGYo0JERESKxUCFiIiIFIuBChERESkWAxUiIiJSLAYqREREpFgMVIiIiEix3LqEfkVFBa5evYqgoCCj0tNERESkTIIg4NatW6hTpw48PCz3mbh1oHL16lVER0fL3QwiIiKywaVLl6wucurWgYp2TZRLly4hODhY5tYQERGRGPn5+YiOjha1ZppbByra4Z7g4GAGKkRERG5GTNoGk2mJiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsdy6Mi0RWVdeIeBQWjau3ypGRJAvOsaGwdODi3gSkXtgoEJUjW06kYE31p9CduEd3bawAG+8NTgeA1pGydgyIiJxOPRDVE3N35SCZ1cmGQQpAJBdeAfPrkzC/E0pMrWsspfnQGoW1idfwYHULJRXCLK1hYiUjT0qRNXQphNX8fmeNIv7fL4nDa3qhWBAyzoualWlxFMZmL3+FK7d+ieAuivIG3MHx6NfPHt5iMgQe1SIqpnyCgH//uGEqH3//cMJl/ZmJJ7KwKQVSQZBCgBcu3UHk1YkIfFUhsvaQkTugYEKUTVz8K8sFJaUi9q3sKQcTWZuwvnMAgDA+cwCNJm5EQ1e24gmMzfqtjtCeYWAl74/bnGfl74/zmEgIjKgEgTBba8K+fn50Gg0yMvLQ3BwsNzNIXK585kF6P/f3SitANQewObne+DH5MtYvCvVpe1YPqwdereONNmexpGBOJ9ZgL4Ld0PMxUYFYOu0yscBpv9G7TGrbgeg2+apAspNnFD/GJaOX5XY/Uyx57Fyn5vIGaTcvxmoELmp2Nc2irrxuyvtBGpn/Y2Wjq8CkLZgoO5nc8911f1Mseexcp+byFmk3L859EPkhqp7kAJUBhDO/BstHV9A5XMMWH6u9fczxZ7Hyn1uIqVgoELkZs5nFlT7IEUJBAA7kjOtPtcCYDKXR8zrZO6x9j7e3nMTKQkDFSI30/+/u+VuQo0xfvVRUfuZek3Evk7m9rPn8faem0hJGKgQuZnSCrlbQFWZek3Evk7m9rPn8faem0hJGKgQuRk1P7WKY+o1Efs6mdvPnsfbe24iJeHblMjNaKfhkvMtH9ZO1H6mXhOxr5O5/ex5vL3nJlISBipEbqZxZCC49rHzqQD0bh1p9blWASbrkoh5ncw91t7H23tuIiVhoELkhtIWDDR7I6ouQYwKzv1bLB1fv86ItefaUj0Sex4r97mJlIIF34jcmKmqozcKSzB86UGbjvfOgKaYuelPyY8TU5m238e7USbiauOlAhJfYGVapZybyBlYmZaoBluffAUvrE4Wvf+qpzujS6Nw5zVIT+KpDExekQTAsNia9pv/klFtuYIyUQ3AyrRENVhEkK/ofUP81OgYG+bE1hjqFx+FJaPaIlJj2MZIjS+DFCIyyUvuBhCRY3WMDUNYgDeyC+9Y3Xdc1wbw9HBtVku/+Cj0jYvEobRsXL9VjIggX3SMDXN5O4jIPTBQIapmPD1UeGtwPJ5dmWRxv1B/Nab2buKiVhny9FC5bLiJiNwbh36IqqEBLaPwTPdYs79XAZg/5B72YhCR4jFQIaqmZgyIw+IRbRAWoDbYHsV8ECJyIxz6IarGBrSsgwfjo5gPQkRui4EKUTXHfBAicmcc+iEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFkj1QuXLlCkaNGoXw8HD4+/ujdevWOHr0qNzNIiIiIgWQtYR+Tk4Ounbtil69emHz5s2IiIhAamoqQkJC5GwWERERKYSsgcq7776L6OhoJCQk6LY1aNBAvgYRERGRosg69LNhwwa0b98ejz/+OCIiItCmTRssXbrU7P4lJSXIz883+EdERETVl6yByl9//YUlS5agSZMm+PXXXzFp0iQ8//zz+Oabb0zuP3/+fGg0Gt2/6OhoF7eYiIiIXEklCIIg18m9vb3Rvn17/Pbbb7ptzz//PA4fPowDBw4Y7V9SUoKSkhLdz/n5+YiOjkZeXh6Cg4Nd0mYiIiKyT35+PjQajaj7t6w9KlFRUYiLizPY1rx5c1y8eNHk/j4+PggODjb4R0RERNWXrIFK165d8eeffxpsO3v2LGJiYmRqERERESmJrIHKiy++iIMHD+Kdd97B+fPnsXLlSnzxxReYMmWKnM0iIiIihZA1UOnQoQPWrVuHVatWIT4+HvPmzcPChQsxcuRIOZtFRERECiFrMq29pCTjEBERkTK4TTItERERkSUMVIiIiEixGKgQERGRYjFQISIiIsVioEJERESKxUCFiIiIFIuBChERESkWAxUiIiJSLAYqREREpFgMVIiIiEixGKgQERGRYjFQISIiIsVioEJERESKxUCFiIiIFIuBChERESkWAxUiIiJSLAYqREREpFgMVIiIiEixGKgQERGRYjFQISIiIsVioEJERESKxUCFiIiIFIuBChERESkWAxUiIiJSLAYqREREpFgMVIiIiEixGKgQERGRYjFQISIiIsVioEJERESKxUCFiIiIFIuBChERESkWAxUiIiJSLC+5G0BERKQU5RUCDqVl4/qtYkQE+aJjbBg8PVRyN6tGY6BCREQEIPFUBub+nIKMvGLdtiiNL2YPikO/+CgZW1azceiHiIhqvMRTGZi8IskgSAGAzLxiTF6RhMRTGQ47V3mFgAOpWViffAUHUrNQXiE47NjVEXtUiMhl2K1OSlReIWDuzykwFS4IAFQA5v6cgr5xkXa/X9lrIx0DFSJyCV6gSakOpWUb9aToEwBk5BXjUFo2ujQKt/k82l6bqgGRttdmyai2/CyYwKEfInI6V3ary4Fd+e7t+i3zQYq+zHxx+5lirdcGqOy14XvHGHtUiMipXNmtLofEUxmYs+E0MvNLdNsig30w5+EW/HbsJiKCfEXtN++X0/BTe9j0urqq16Y6Yo8KETmVlAu0u0k8lYFJK5IMghQAyMwvwSQX9xSxV8d27WJCISZGzi4stbkHUGyvjdj9ahL2qBCRU1XXC3R5hYDXfjxpcZ/Xfjzpkp4i5v/Y5+iFHEiJ62zpARTbayN2v5qEPSpE5FTV9QJ9MDULuUWlFvfJLSrF8n1/ObWXo7rn/7iClCDZ1h7AjrFhiNL4wlxoo0JlcNkxNkzScWsC9qgQkVPlFN6xuo87XqAP/HVT1H5vb/pD9//IYF/MedhxvRzVPf/HVWwJkqX2AHp6qDB7UBwmr0iCCjB4zbSvzOxBcXydTGCPChE5TXmFgHkbU6zu91BLd7yRSm9vZn6xQ3NXqnP+jytZ6+0wxZbgpl98FJaMaotIjeFjIzW+nJpsAXtUiMhprN1ItVYeuoTX+rvXt8kujcKxaOd5mx47w0G5K9U1/8fVLPV2mBLir0ZFhYDyCkHya9gvPgp94yJZ+FAC9qgQkdOIvUEWlpTjYGqWk1vjWB0ahEFl470lp6gUB/+y/++trvk/rlB1llTfuEiTvR2m5BaVYuSy39Ht3R029Y55eqjQpVE4Breuiy6NwhmkWMEeFSJyGik3yN9Sb6Jrk1pObI1jHb2QA8GO3NgDqVno2ti+v7djbBj8vT1RdKfc7D7+3p5ul//jbJZmSe2b3huH0rKxLSUT65KvILvQfMI0K8q6BntUiMhpOsaGwVct7jJzNfe2k1vjWPYOpwhWBxisK68QcLvUfJACALdLy1lTRY+1WVJbUzLRpVE4Zg1qgcOv98V3T3VCiJ/a5LFYUdY1GKgQkdN4eqjQLDJI1L51Qv2c3BrHqhXoY9fjA33s79D+9kC61V4dQajcjwXhpJex9/RQwUOlQu5t870qTFh2PlmHfubMmYO5c+cabLvrrruQmZkpU4uIyJHKKwRczBbXU3JvQ/cZ9gFgPePSipSMPLubcCG7SNR+e8/dwJf70mp8QThbytgzYVl+sueotGjRAtu2bdP97OnpKWNriMiRDqVlI1tEHRUA6OBmeRSZefYNVWXk2n9jiwnzF7XfrrPGNV8y8yqnSo/v2gB94yJrxMwTW4IOsT1n9vawkXmyBypeXl6IjIyUuxlE5ARSvmUevZDjVouxJV3KsevxdULsH+oa0SkG8zaesemx2g6h5fvTsXx/OiKDfTG8Y300qOVfbafMSp0lVV4hIOVqvriD17yRNJeRPVA5d+4c6tSpAx8fH3Tq1AnvvPMOGjZsKHeziMgBpMz6cbeu8+v54nqKzGlRR2N3G5Iv5dp9DK3M/GJ8tO2s7ufqODSkLeyWmVdsMq5QobL4WsfYMJMzgyy5WVhifSeyiazJtJ06dcI333yDX3/9FUuXLkVmZibuvfdeZGWZri9QUlKC/Px8g39EpFwdY8NEJ426W9d5oI99w9QRwfbXNnFmcKcdGnrz59PVJvlWW9gNMK4rrP151sA4LNpxDpNMzAyyREpQzsRmaWTtUenfv7/u//fccw+6dOmCRo0a4euvv8ZLL71ktP/8+fONkm+JSNnKyivE7ehm1+pH2tTFuuSrNj8+Isj+wMyZhdyqDg1FaXwxc0BzXM8vxoXsIsSE+WN0lwbw9nKvyaPaMvZVe0siNb54uFUU3vwlBZn50gJADxXQLiZU1L5c6Vo62Yd+9AUEBOCee+7BuXPnTP5+xowZBgFMfn4+oqOjXdU8IpLoYGoWisvEBSru1nXuYcNaPwbMBGblFYLo8uodY8MQ4q+2uoqzI2TkFeO5VccMtr296Qyevi8WMwbEOf38gLTnxhJTZexzCkswZeUxm+LlCkFcjpW2hkvVc7BwnGWKClRKSkpw5swZ3HfffSZ/7+PjAx8f9+oeJqrJxK4wDABh/t5ObInj/Z5uXwl8U4GZu33brhCAz/ekAYBTghX9wCT9ZhFWHbpo0Nthz3OjLWOvPU+3d3fY1alnbRiOK13bTtY+u1deeQW7d+9GWloafv/9dzz22GPIz8/HmDFj5GwWETlAeYWAyznip/D+keleOWf2jlRVHbYxVzE14+9v26bWlDmUlu2S3hRrlu5Nwx2RPWdiJZ7KQLd3d2D40oN4YXUyPtp21mhIxtJzI4XYxTMtsTYMx5WubSdroHL58mUMHz4cTZs2xZAhQ+Dt7Y2DBw8iJiZGzmYRkZ02nchAh7e34ScJORxii5cpRYif7T1AIf5qg/V3LH3bBipvYqbKtEvNpXCWir+r3zqKuaDNFAGVq1Hbk5B6Nce+916In9rqekosHGc7WYd+Vq9eLefpiaolR43j22r+phTdcIAkbpZMWyvQ9kCl6qsh5ht91YqpAHDzlnLyeuwNNLXv28z8Ysz75bSkt0NOUSkW7TiHF/rcLfm88zel4Iu9Nrxf9dzXxPoKyOk3C0UdiytdG1NUjgoR2UfuHIdNJ67aFqQACPY1vfCbUkVqbC/YllNUahB0iP1GX7nfP4FKTpF9tVwcKaeoBAdSs2wKjKXWLDElYX86pvZuIuncNgfVVRxOz0F5hWD23ImnMvDRNtOTRLT0a7iQIfeaV0ZEZllbFdbecXxryisEvLH+lM2P93Czq5G2eJit9Idtjoks3FZ1PyXlXP58PBPDlx5E1wU78PG2c6JrhEgZ5rEk93appPyOO2UVWGpnT4pWZn6J2XNrh/XEmD0ojom0JrjZpYGITJG6KqwzVK7rY3tiZxc3W5RQWzzM1tvKTb1chGsi8xKq7qfE50xb4faF1cl/By7bzQbJ1nJzpJKS3/HtgXSI+Tj4qsXdJs2dW2yi7rQ+dytyZpcSMFAhqgaUMKPAnsROXy8VOrvROj9a2uJhkcHSyyboz9YJ8BY3Cl91v7Yii4zJKTO/BJNWJJnsZXHEbBt9N2+ViA7G07PE5Yx0aywuGDRXWVls8NSglrgFJmsi5qgQVQNKmFGQXWB7Yqf7L4Anve0qVeVjyisENI8Kwk/J1h/zaJt6Bj+vOJgu+bxyqbqO0MwBzbH9zDWHnmPexjP4cl+aQ3OyxE67fvn7ZMx5uIXReaUuhEjG2KNCVA0o4WJ42Y4pnmov+9bNkYs2v8KW3qQujcJ1tULmb/7T6v7+3p64t4nht/vD6fat4CwXbZVbKdPXpRxbTE5W62hxvVHHLop7jjPzS0yeV5vLZC6UVaEycGMSrXkMVIiqAbkvhomnMpDw2wWbH198p9yBrXENe/IrQv3VyCu6IymJ9MOhrYx6nQK83TPAczYBwGs/nsT+czfNDgXVCRE3a+tWifj3pql6N2IWQmQSrWUMVIiqAUsXQ6DyAjqsQ32nnFvKrAZzWtbTOKg1rmNPfsXbj9yDeRvPiApy7gryxmdm1oAZ0raeiUcQUJkDNHLZ7+j27g6TvStiZm2F+EufMm8qF0yXy1TlfJEaX67vIwIDFaJqwtzFUOujbWfNXrTt4YiEyG5NajuoNc5XXiHgQGoWNtvwPEZpfPHZqLYIDfAW/Zxpc1lMubdxLfaqWGFuer6nhwqzBppfn0gFYGyXBjadc2tKptG2fvFR2De9N1Y93RkfD2uNVU93xr7pvWUPUrTvZ7HTyeWgEgRBea0SKT8/HxqNBnl5eQgODpa7OUSKUF4hYNGOcyYLTGlveY78Frcu6TJe/P64zY8P9VfjyBt93aLr25bCZLMGNketIB+DKsHrk6/ghdXJoh5v7TVLPJWBSSuSRLenJlIBuCvYBx8MbY2bBSW61ZLnbTxj8rWM0vji4VZR+OnYVVyzofpveIA3Dr3eR/HvaTkLREq5f3PWD1E1tPrwJZPbnbFKa3ahfdVR2zcIVfwFHfgncVbsNzttpdGxXWON/j4pSc3WXrN+8VH4bFRbzNmQopi1f5RGQGWy68gvfxe1f5tojV0Va7MK7xgtd6A05t7P2h4oJQ1JceiHqJpxdU2VMDP1I8TamnIdm044fvaHI0lNnLWWJNkuJlRSVVlrr1m/+Cjsf603vnuqE/w5FGS3Tafsnzat5MUFlVAgUgrJgcrt27dRVPTPNMQLFy5g4cKF2LJli0MbRkS2cXVNlchg+6c8v/7TKcVcFE2RmodjLUny6IUcUVVRq7L0mnl6qOChUqHIDWdQVUemis+5Ih9EzDmUUCBSCslDP4MHD8aQIUMwadIk5ObmolOnTlCr1bh58yY+/PBDTJ482RntJCKRXF1TRTt7wp6E2qqL9CmN2KDuyS4x6B8fZbKAnf6q1ueu3bKpHdZeMyV/i69p5m08g6V70zC8Y300qOWP9JtFWHXoosHwnKPzQcTmnCihQKQUkntUkpKScN999wEA/ve//+Guu+7ChQsX8M033+C///2vwxtIRNK4uqaK/tRoeyjlomiK2KCuf3wUujQKNwpStIXdhi89iBdWJ2PRzlRJ5xf7mrG6qbLor3v00bazRjlEjlwwVMqipEooECmF5EClqKgIQUFBAIAtW7ZgyJAh8PDwQOfOnXHhgu0Fn4jIMdy1wJRSLoqm2BP8OWp1YGuvWXmFgApBQIif9NofJA8BpovESSU150TuApFSSQ5UGjdujJ9++gmXLl3Cr7/+igceeAAAcP36dU4RJlIIVxaYckTBt8hgH8VcFE2xNfhzxOrAHipgYvdYi6+Ztsdm5Je/I/e27StYkzwy8oqxaIdxOQGxpOacuNuXGck5Kv/5z38wYsQIvPjii+jduze6dOkCoLJ3pU2bNg5vIBHZpl98FPrGReryIvTreDiSIwq+zXm4hWIuiuZog7+qOQCRFvIMHPHcCALwxZ40tKkfaraOipRp06RMH207h6aRQTZ9Zm3JOTH3fg4L8Mbg1nWg8fNGeYWgiM+lTQXfMjMzkZGRgVatWsHDo7JT5tChQwgODkazZs0c3khzWPCNSH5SipdVFeKvxoIh9yimXoMY+kmx1m4kYp+bKT0b4bvfL1rsDYnS+GLf9N4G5yqvENDt3R12B0OkDBo/L/ipvSQn3B5IzcLwpQetHn/V052NEta17+dtKZlYl3wF2YX/vAedWfzN6QXfIiMjUVBQgK1bt6J79+7w8/NDhw4dLJZ6JqLqyZbckkda18Hj7aLR2UTiqSlSggNn8/RQmb3YV22f2OcmLMDb6pCNtute/9yO6LEh5ci7XYa822UG28QUYNPmnGTmFZvsWdMWHzQ3vHooLQvL9qcbbVdK8TfJgUpWVhaGDh2KnTt3QqVS4dy5c2jYsCGeeuophISE4IMPPnBGO4nIDLlv4lKnJ3uogPceawVvL3EpcnKV+Rb7vFpqX9+4SFE3kJsF4sq0G88auS3lTyI3JKaatDbnZPKKJKgAg/eapZyTxFMZFisaO6OStS0kJ9O++OKLUKvVuHjxIvz9/XXbn3jiCSQmJjq0cURkWdVpr8OXHnTKwoOWeHqoEF9X/NBrhVBZ8Mya8goBH287i0kmZsxk5BVj0ookvPnzaacUzhL7vFqbEvrrqUwM61DfbJAiALhdWo4lu/8S1a6bVdadsXf5AnIPYgqwSU2g1753rS27oITib5J7VLZs2YJff/0V9eoZLi/epEkTTk8mciGlrNVxp6wC289cl/QYaxdHa9/0tJbvT8fy/ekO7WER+7yKmRI6dVWS2Qq0If5q5BSVIrdI/CydnCLDwMTe5QvIvVhLmhWbQG/LbDQ56xxJ7lEpLCw06EnRunnzJnx8+KEhcgUlrdXx7YF0yeXgq/YM6BP7TU9fhsTCWebKjEt5XsXkh5h7Xqbd3wQ+Ioe+9FXteXfE8gXkPs5dK7Dag6jNoRrcuq7J4oOAbblNctY5ktyj0r17d3zzzTeYN28eAEClUqGiogLvv/8+evXq5fAGEpExsXUTZq8/hQfjIpF9+47T8lfSsgolPyar0HSgYk/dEW3hLGtj6ZZySjR+3qKe16/2p9ncm6EC8O3BC8iyYdimS8NaBj87YvkCch+Ldp7Hop3n7e5BlNI7Yi0R1xUkByrvv/8+evbsiSNHjuDOnTt49dVXcfr0aWRnZ2P//v3OaCMRVSH2QrPi94tY8ftF3c/OSEK1ocIBTl7OM1mjwd5ZLKZmxuizNqwzrmsDUeeZt/EMwgJsqwArADYFKSH+anSu8ndpkygnrUiyqS3knuwd3pXSOyJA/uJvkvse4+LicOLECXTs2BF9+/ZFYWEhhgwZgmPHjqFRo0bOaCOR23DF6qiA7d2wjlxbREvj6y35MftTs0wmpzpiHPxqrumZMGKGddYnXxV9Hv16E66wYMg9Jm8W/eKjsHhEW6NhIaq+7B3etVZCX2lsrqMyd+5cR7eFyK2ZG1KYNTAOoQHeDp0+bK1ugjnOmG7oYeMxTH0rdMQ4+OwNpxDg42n0TVPMcFlW4R2EBaiRU1iquEqvxy7mmP32PKBlFBahDZ5deczFrSK56M/GkbrquKeHCjMebIrnvz9udV8lTE+WHKjs2bPH4u+7d+9uc2OI3JW5IYWMvGI8u9KwW94Rwy+W6iZYY88FzpQujcKxaOd5yY8zFTTZGoDpKygpx6QVSfisSre42N6awa3q4KvfLkh+XqVQe6hQKvGb8NK9aXj5gWZm688MaFkHn3mojIJlqt5s6YV8+pvD2Joibqaeo68XtpAcqPTs2dNom35F2vLycrsaRORu7pRVYOa6k6Jvapl/1wB5sU8TNKgVYHMvi7m1OsRy1HTDDg1sT7KrehHUD8DsNePHkwbfAsX21qw/fhUTu8di5aFLuFVcZv0BNpAapACVM4i+PZCOCfc1NLtPv/go9G52F77+LR0fbT2LolJp12MvDxXKXDBTjBxHai+klCBFn1tNT87JyTH4d/36dSQmJqJDhw7YsmWLM9pIpFiJpzLQef52SfkK2tvAR9vO2V2krV98FPZN741RnaIlP7ZWgGPKCSzeafuqr1rbUjJ1/9cGYEG+No1M6+QUleK38zd1P4sdl88uLMUXe9IQG25chkFuF7KLLP4+8VQGery/E29vOiM5SAHAIMXNREmcjXP7TrlNQQog7/RkyYGKRqMx+FerVi307dsX7733Hl599VVntJFIkbTDPY6oDmpPkuvWlExsO3ND8uMqbJitU1V5hYCl+9LsPs665CsGSYH94qNwaGYfu4876bujuufU0tL2ppy7XmD3+R0tOtR88GSuSi5VX7dLy7FVL8i35p1NKZLPoYL0gMjRpFccMqN27dr4888/HXU4IkWzp96HKZay+C3NJLKlOJrW7w4oiX0oLRuFJfYP92YXlhqV6Pb28oDa077kvcKScoMAUNtbExpgeaZSZWn7CrvO7QzN7goyud3R70dyD3lFpZK+4KRnWe6RM0UJ05Ml962eOHHC4GdBEJCRkYEFCxagVatWDmsYkZI5Y9VaU0lr1ha8s+/mZP9tzZGL4m1LyTRI1vvt/E2Uljvm1qufsNsvPgq375TjRREzHlylfUwIjlzItbpf9m3TvXdcRbn6spTULXUWX4Nwf+y1f6TW5SQHKq1bt4ZKpTIq8tS5c2csX77cYQ0jciWpKxA7M7FMe2xrxcmm9bnbrptTpwb2Z/A7clG8ZfvT0SE2TDdT539HLlp5hDimAsBIjZ9Dju0ozaOCRQUq5vIE5Ex0JOcKDfC2+Dmr+v62dC2bOSAO3x6U9rlyy+nJaWmG49EeHh6oXbs2fH255gS5J0u9FuamEDszsSwiyNdqcTIVgITf7MsN8bBzWAVw/KJ42gvie4lnsP6E+LF3MfRv5h1jwxAZ7GvTkJmjeahg9eZhrYy5nImO5FzDOtTD4l3WV9e+fqvY6rXMz9sTfeMiJCXUKmF6suQclZiYGIN/0dHRDFLIbZlLQLSW3CpmBkl4gDcWDWsteuE4/aQ1McXJpKy6a8rNAvMLA4rl6EXxMvKK8cLqY/h8j/0JulXV0guqPD1UGNZB+kwpZ7A20Ub7HrOUJ+DMSqN+aoelMpINPFTinv/0m0WirmVLn+yAvnERktshZ6+dqB6V//73v6IP+Pzzz9vcGCJXEtNrYa7L01LBNe2eb/8rXu9ollW9GYm9KIT4qZF327Yqqo74Ft4uJtTuY1S18YTjyvsbqPIkyT0VV6UCxEy8ihRRINCZa/4oMam4JhEgICxAbbEEQmSwD1Yduij6WvbZqPbo8s5WXC8Q/2VH8asnf/TRR6IOplKpGKiQIpkatxW7ArG5Lk9zBde0NxYAJnNMTKl6MxJ7URjXtQEWbpOeHeehckyQcfRCjt3HqMpZ4cN1ox4k1wUq2mBWW+Tven4J3t50xurjXh/QHOO7xco644Lk9enOVKv75N4uRbGFgLLqtexQWrakIMVR1wtbiQpUqualELkTc+O2/eMjRT1ev3ejasDTNy4SfeMijYIgAOj27g6Lt8KwADVmPdQCkcHGybvWSslrcxam9m6CppFBeHntcUnThCuEyiDD3jFnR876cbZ5v5yGn9pDFwx2aVgLi0TcBByhaiD6fuIfoh53+kqeqERvbe8g1UyWghR92muZ1GEcR10vbGVf6UcihbM0c2b5/nRRx9D2bkhJuj2QmmV1Rk52YSlSrxeYzPMQM7SkHSbqGxeJF3oV4R2RNz+tfedv2L1QoiNn/ThbdmGpwSKInRuFw9vLA3fKnDu0YapXxNwKz1X9dPwqNpy4apDHUvU9V14h4Kv9aZyeTFZpr2W2DOMoPkelqsuXL2PDhg24ePEi7twxvFB9+OGHDmkYkb3E5KDAQp6A/kwLa1OFl1RZAE/sbJJFO89j0c7ziAz2xfCO9dGglr8ucLA2tNQvPspk8CSWfpeyqfNbC1zKKwS3ClSAytf9tR9OIshHjZzCO04PUlQqYMy9DYyeSynTo6um0ui/5wBwEUISTTt80zE2DCH+akkJ+YrPUdG3fft2PPzww4iNjcWff/6J+Ph4pKenQxAEtG3b1hltJLKJmBwUc2Mz+r0WAEQn3WrPu+dPaetpZOYX46NtZ3U/639r7t3sLnx7IB0XsosQE+aP0V0awNvLw2zwZAtL5zfFngBJbrm3SzFy2e9QuSDtQ/i7y1ybE6XtwQrxt70zW/t6v7L2BApKnLNoIlVPv6dm4b6mtSU9xtrUeFeQ/GmZMWMGXn75Zbz55psICgrCDz/8gIiICIwcORL9+vVzRhuJbGJPV6V+r4W1YRxtotqiHeex+vBFh9y8td+aJ3aPxYbjGQbH/HJfGmYNjMO8jc4rmW6upwgwP5zmbhyw1JEoX+1Pw4trkg162TR+aruPyyCFpPrh2GXc17Q2DqVlS+pNcbsS+mfOnMGqVasqH+zlhdu3byMwMBBvvvkmBg8ejMmTJzu8kUS2sLWrMjzAG7v/3QveXpX1C8QGPPo9EvbS3kNN1RPJzCvGsysdPw216vlNTc/mmjLS/ZpyzWhb3m37auAQ2aKwpAwHUrOwWeTaQCF+aix49B6LU+NdQXKgEhAQgJKSyml+derUQWpqKlq0aAEAuHnzpqWHErmULeOwAJBVeAffHkhHrSAfRAT5olaAY6uv2stVQYKp6dlcU4bIfR1My8bWM+KHpT8d2RZdG9dyYovEkRyodO7cGfv370dcXBwGDhyIl19+GSdPnsSPP/6Izp07O6ONRC43b+M/NS4ig30R4q9GXpFthdXcnX6PkjtNRyYiQ7eKxQ8XeqiADg3ky0vRJzpQuXHjBmrXro0PP/wQBQUFAIA5c+agoKAAa9asQePGjUUXhiNyBanjsOZcy/+nlomllUyrq3PXCnAgNQs5hSV48xfW6iCqCeSunaJPdKBSt25dPPzww5gwYYIuadbf3x+LFy92WuOI7OGoef/afI0QfzV8vDyQmW//GjmO5OzgSTuFmojcjz3XB6Wsyi16tamvv/4a+fn5GDRoEKKjozFr1iykprqmqiORLRw5718AkFNUig+GtsZ3T3VCgLenw45tC+0ChotHtEGkhouCEpEhLw8VRnaKxvuPtbT5GLUcvDq6rUQHKsOHD8eWLVuQlpaGp59+Gt999x3uvvtu9OrVC9999x2Ki+2LvObPnw+VSoVp06bZdRwirdbRIQ5fTfZmQQk8VCoU3hFfrt7R9Gu8DGhZB/um98Z3T3VCiMQpr9GhftD4sjg1UXVUViHgu98v4Z3N1teUMksh49yS1++Ojo7G7Nmz8ddff2HLli2oW7cuJk6ciKioKDz77LM2NeLw4cP44osv0LKl7ZEfWVZeIeBAahbWJ1/BgdQslMu8cqyzJZ7KQFcra+3YIiLIF1tTMh18VMvCAgwDkEiNr0F9E08PFbo2roUFj94jKTC7lHMbeRKS64jI/Vhaddmam4XKGOa26+vU/fffj/vvvx8//PADJk6ciM8//1xyzkpBQQFGjhyJpUuX4q233rKnOWSGlDVqxBKzUJpcnFGQTFudMetWieg1ghzljQFxyCm6Y1SZVkv7WpSUVWBanyZI2PcXcovl6/EhoupBzrL5+mwOVNLT05GQkICvv/4aly9fRq9evTBhwgTJx5kyZQoGDhyIPn36WA1USkpKdDVcACA/P1/y+WoaqWvUiD2mowMfR3FmQbKHWkbh+TXHnHBky97alGLwrejLfWkW1/pRRrhIREqjsrC2WVVRMpfN1ycpUCkuLsbatWuRkJCAPXv2oG7duhg7dizGjRuHBg0aSD756tWrkZSUhMOHD4vaf/78+Zg7d67k89RUYhblq1p51BpnBD6O5IyCZCF+ajzRoZ7JKrGuULXrVr+8/hd70oxei+o9qEdEtpKybMSwDvWd1xCJROeoTJw4EZGRkXj66adRu3ZtbNy4Eenp6Zg7d65NQcqlS5fwwgsvYMWKFfD1Fde9NGPGDOTl5en+Xbp0SfJ5axIxi/JpK4+KYS3wASoDHznzX5wxne6T4W2w4bi4ktOuoH12l+41DlKIiBzho21n0e3dHUgUWW7fmUT3qBw8eBBz587F6NGjERZmf3fQ0aNHcf36dbRr1063rby8HHv27MGiRYtQUlICT0/DKaA+Pj7w8VHGdCl3IPamLXY/KYGPXEWCHD2mGqXxhYdKpbiy8QJct6geEdVMGQrpKRcdqJw4ccKhJ77//vtx8uRJg23jxo1Ds2bNMH36dKMghaQTe9PW389SkqyjAx9n6BgbhiiNr8MCi2EdorHljGtn+RARKYUA6SkCjiZbEYWgoCDEx8cbbAsICEB4eLjRdrKN9qadmVdscohAO5NFmzBlLUnWlsDH1Tw9VHi4VZTD8kk+2nbOIcchInJXcveUS66jQu7D00OF2YPiABjPBNEvGubpodIlyVbtidAmbiaeytAFPuZiam21VDkzxRNPZeALmZJeXU0hs8GJqAaQc0FSRQUqu3btwsKFC+VuRrXSLz4KS0a1NSqzrl80TGySLADRgY8cBeacOTVZieqF+sndBCKqIW4W3JHt3KyfXQP0i49C37hIs7knUpJktYFP1SGiSL0hIrnqrDhjarKSXcyW7xsOEdUsubcVHqhISaRlGXxl8vRQmR1flJokaynwkbPOilJW+iQiqm7kHGkWFai0bt0aKpUKgiBApbLc3PJylu52N+k3i0TtdyQ9G/3jo+Dt5WEy8HFGgTkplFLumYiouukUK08iLSAyRyUtLQ1//fUX0tLS8MMPPyA2NhaLFy/GsWPHcOzYMSxevBiNGjXCDz/84Oz2koMlnsrAwm1nRe377cGLaDZrM+ZvSjH5eylDSM7IYdEm+xIRkWN5WOmkcCZRPSoxMTG6/z/++OP473//iwEDBui2tWzZEtHR0Zg1axYeeeQRhzeSnMOW5NMKAfh8TxoqBOD1gXG64xxKy8ZmkRUMt6Vk4qXvkx2ew+LpocKsgXF4dmWSzccgIiJjcq6kLDmZ9uTJk4iNjTXaHhsbi5QU09+0SZnsST5dujcNrepq4OXlYZQ4a80yE6sPOyqHJTTA2+bHEhGRaXIOrUuenty8eXO89dZbKC7+58ZUUlKCt956C82bN3do48i57E0+nbo6GZNM1F6xhaPWCnLWXH/WLCGimkgJ9bEk96h89tlnGDRoEKKjo9GqVSsAwPHjx6FSqfDLL784vIHkPEpLPnXEWkH7z990bKMAdG0UhqEd6mPa6uQaU6OFiKhqfSy5SA5UOnbsiLS0NKxYsQJ//PEHBEHAE088gREjRiAgIMAZbSQHMLWGj7US+3KxtaenvELAplOOX5dnf2o2fkvNxv3Na2Pf+SwUl1Y4/BxERHIK8asMB3Jvl+m23RXsgzkPt5B1QULAxoJv/v7+mDhxoqPbQk5iqQDb7EFxmLwiCSpAMcGKrT09B1OzUHTHOdPjBQDbztxwyrGJiOT0WNu6CPL1wk/JV6v8Rhlj3jaV0P/222/RrVs31KlTBxcuXAAAfPTRR1i/fr1DG0fGpE7rtbSGz6QVSTiUlo1pfZrgrmAfZzZbFHvHQg/85fhhHyKi6irUX40QfzX+l3QFCb9dQE5RqcHvr+X/s9abnCQHKkuWLMFLL72E/v37IycnR1fgLTQ0lOv0OFniqQx0e3cHhi89iBdWJ2P40oPo9u4Os28iMWv4LN+f/vcKwSq82OdufDysNWYNtD8pWgUgXMIMHG3c3rVROOb+fBrL9v6FO2VSh1iUEf0TESld//i7kFNUitwqwYk+R01ysJfkQOWTTz7B0qVL8frrr8PL65+Ro/bt2+PkyZMObRz9Q8zqxlVJmX58Lb8YC7edhY+XB8Z2jbW4SnJV5hYoHN05BiF+alHH8PfxBAD8L+kKvjlwAfM2njFbXM5cr1InGbPSiYjcye9pOaL205/kIBfJOSppaWlo06aN0XYfHx8UFhY6pFFkyNbS9FKSUqsex1ruSoifF8Z1bYj823ewLvkKsgv/icpD/NUQACzcfs7qecd3bYAbt4rx8wnjJFhtcTkAmDGgsricpXybIB9xQRERUU2mApBdKG2RQTnXUpPcoxIbG4vk5GSj7Zs3b0ZcXJwj2kRVSClNr09qUqqpVZLvCjZ9jPziMny07SyW7U9HdmEpwgK8Mb5rA7zYp4nV7kR9FRUCfjERpOhbujcNd8oqrPYqbUmRdxyViMgd2DKII2c5C8k9Kv/+978xZcoUFBcXQxAEHDp0CKtWrcL8+fPx5ZdfOqONNZ7U1Y21bJ1+rH+c26VlJvepOlyZXXgHy/enI/DvIRyxvjpwweo+FQLw9W9pWL4/3WKv0vdHLks6NxERWRfip0aFIKC8QpClnorkQGXcuHEoKyvDq6++iqKiIowYMQJ169bFxx9/jGHDhjmjjTWe2Ei26n6eHiqbph9HBPnqei+kRt4FJc6ZHnw4Pcdqr9Jt1jchInK43NulGPnl7w5Zk80WNk1Pfvrpp3HhwgVcv34dmZmZuHTpEiZMmODottHftD0j5uJYS9N6tUM4kSJWFdYep11MqOTFCp0twFtaTw0RETmWpckbziQ5UOnduzdyc3MBALVq1UJERAQAID8/H71793Zo46iStmcEMD/DxlKJ437xUdg3vTdWPd0ZE7o2MLmP/nGOXrDce+FqKgAPt6ojdzOIiGo0uaYrSw5Udu3ahTt3jLOFi4uLsXfvXoc0ioyZ6xmJ1PiKWnHY00OFLo3CMWtQC3w2qi2iLBxHzuxuUwQAL//vhKh9o8wk/xIRkf3kmK4sOkflxIl/bhQpKSnIzPxnpkZ5eTkSExNRt25dx7aODPSLj0LfuEijNXukJjeZOk67mFAcvZCD9clXcPNWiZP+AtuJnUqXma+sIIuIqDpy5Rda0YFK69atoVKpoFKpTA7x+Pn54ZNPPnFo48iYtmdEKlOLEmqPk3gqAz3e32kw3KOktX+kcMc2ExG5G1dOVxYdqKSlpUEQBDRs2BCHDh1C7dq1db/z9vZGREQEPD2Z8KhEloqkATA5u4c3fCIiqkqFylQBW9dks4XoQCUmJgYAUFHBKaDuxNw0Y232tubvKrJERETWCLA8ecMZJCfTzp8/H8uXLzfavnz5crz77rsOaRQ5hrXS+wIguoIsERGRHCQHKp9//jmaNWtmtL1Fixb47LPPHNIoMs/cgnymSFmUkIiISAxXT0+WXJk2MzMTUVHGU2Fr166NjAyuteJMlnJNTE1PVto0YyIicn/a6cm2TOywheQelejoaOzfv99o+/79+1GnDotyOYu1Bfn0KwVqe13OXStwdTOJiKgGcGUpCMk9Kk899RSmTZuG0tJS3TTl7du349VXX8XLL7/s8AaS9VwTFSq74vrGRWJrSqZRr4s52lQoJtMSEZEUrqy3JTlQefXVV5GdnY1nn31WV6HW19cX06dPx4wZMxzeQLKea6KtFLhoxzks3HZOVODhrnVSiIhIfjlFCg5UVCoV3n33XcyaNQtnzpyBn58fmjRpAh8fH2e0jyA+1yRhf7ro4EPjp0aQrxcu5dy2vWFERFQjqcwuk+t4kgMVrcDAQHTo0MGRbSEzxFYAzL0tfqpx7u1SSfsTERFphfh7u+xcogKVIUOG4KuvvkJwcDCGDBlicd8ff/zRIQ2jf3SMDUOUxheZecUme0xUADT+atZEISIilwgLcF2gImrWj0ajgUql0v3f0j9yPE8PFWYPijM7rCMAGHdvrCubRERENZjYhWIdQVSPSkJCgsn/k3I0iQi02OtCRETkKK5MppVcR4VcTzs92ZJ5G1Mwa2BzF7WIiIhqNoUl07Zp00Y39GNNUlKSXQ0iY2JK4WfkFSM0wAdLRrXFzHWnXNotR0RENUuo0pJpH3nkEd3/i4uLsXjxYsTFxaFLly4AgIMHD+L06dN49tlnndLImk5sBcDM/GL4qT2gduGqlkREVPPUCnJdSRJRgcrs2bN1/3/qqafw/PPPY968eUb7XLp0ybGtIwBAdoG4scDvD1/Awb9ymKNCREROFRksrmyGI0jOUVm7di2efPJJo+2jRo3CDz/84JBGkSGx08AOMEghIiIni9L4omNsmMvOJzlQ8fPzw759+4y279u3D76+rouwapJIjZ/cTSAiIgIAPNwqCp4uTDGQXJl22rRpmDx5Mo4ePYrOnTsDqMxRWb58Of7zn/84vIEEtI4OkbsJREREAIANxzPwar/mLgtWJAcqr732Gho2bIiPP/4YK1euBAA0b94cX331FYYOHerwBhKw8vcLcjeBiIgIQOUs00Np2ejSKNwl57NprZ+hQ4cyKHGhC9lFcjeBiIhIR+xsVEewqeBbbm4uvvzyS8ycORPZ2dkAKuunXLlyxaGNo0rRof5yN4GIiEhH7GxUR5Dco3LixAn06dMHGo0G6enpeOqppxAWFoZ169bhwoUL+Oabb5zRzhrt7tqBcjeBiIhIR3GLEup76aWXMHbsWJw7d85glk///v2xZ88ehzaOKh2+mC13E4iIiHRcORtVcqBy+PBhPPPMM0bb69ati8zMTIc0iqpipVkiInIuFSprpGj8LA+2hPirlV1HxdfXF/n5+Ubb//zzT9SuXdshjSJDrsqsJiKimm3WwDira/u5+quz5EBl8ODBePPNN1FaWgoAUKlUuHjxIl577TU8+uijko61ZMkStGzZEsHBwQgODkaXLl2wefNmqU2q9jo3DEeAt6fczSAiIjekAuDv7QlLZU88VMDE7rEIDfBGblGpxePlFJXiUJrrUhIkByr/93//hxs3biAiIgK3b99Gjx490LhxYwQFBeHtt9+WdKx69ephwYIFOHLkCI4cOYLevXtj8ODBOH36tNRmKc75zAI0mbkRDV7biCYzN+J8ZoFdx1N72TRBi4iIajBtbPLh0Fb4Y15/PNa2rsn9BAH4Yk8atqaIS+G4fst105NVgiDYtDzMjh07kJSUhIqKCrRt2xZ9+vRxSIPCwsLw/vvvY8KECVb3zc/Ph0ajQV5eHoKDgx1yfinOZxag/393o7QCUHsAm5/vgcaRgYh9baPJNXdUALZO62HyMZaOeaOwBMOXHnTZ30VERNVDlMYXswfFoV98FMorBHR7dwcy8kwHGSpUzubJKrxj9birnu5sV1qClPu3pEClrKwMvr6+SE5ORnx8vM0NNKW8vBxr167FmDFjcOzYMcTFxRntU1JSgpKSf+Zu5+fnIzo6WpZAxVwwYgsVgLQFAx16TCIiqrlC/NX4dHhbdG4Urit1fyA1S9SX3rAANXIKS81+4Y7U+GLf9N52ldCXEqhIGk/w8vJCTEwMysvLbW5cVSdPnkRgYCB8fHwwadIkrFu3zmSQAgDz58+HRqPR/YuOjnZYO6RwdEAhAGjAIIWIiBwkt6gUHh4qg2AiM++2qMf+q3Xl8FDVMET78+xBcS5dlFBy4sMbb7yBGTNm6CrS2qtp06ZITk7GwYMHMXnyZIwZMwYpKSkm950xYwby8vJ0/y5duuSQNkhxPrOAAQURESmefh5J4qkMzNt4RtTj+sRFYsmotojU+Bpsj9T4YsmotugXH+XQdlojOUelTZs2OH/+PEpLSxETE4OAgACD3yclJdnVoD59+qBRo0b4/PPPre4rR45Kk5kbUVrhklMRERHZTJtHkngqA5NXJFn9kl11WKe8QsChtGxcv1WMiCBfdIwNc1hPipT7t+QS+oMHD7Y6x9oegiAY5KEoDYMUIiJSukAfL1RUCLhTVoG5P6eIHgnQH9bx9FApoo6X5EBlzpw5Djv5zJkz0b9/f0RHR+PWrVtYvXo1du3ahcTERIedw9HUHgxWiIhI2QpKyjBy2e8IC1Aju9ByXRSgsjdlYvdYlw/riCE6R6WoqAhTpkxB3bp1ERERgREjRuDmzZt2nfzatWsYPXo0mjZtivvvvx+///47EhMT0bdvX7uO60ybn+8hdxOIiIhEEROkAJWTOj7fk4bEUxm6beUVAg6kZmF98hUcSM1CeYU8GZqic1T+/e9/Y/HixRg5ciR8fX2xatUq9OzZE2vXrnV2G82Sq44KpxETEVF1FOqvxpE3+mJrSibm/pxiUHNFvyaLvZxSR6VRo0Z4++23MWzYMADAoUOH0LVrVxQXF8PTU57y7nIWfLNU1C1twUA0mrER5YxmiIjIzUy7vzE+3n7e6B6nzU51xMwfpwQq3t7eSEtLQ926/5Tf9fPzw9mzZ2WrZ+KsQEW/OqynCrqAo2ol2R3JmRi/+qjR45nHQkRE7irAxxOFJabrpclR8E10Mm15eTm8vb0NH+zlhbKyMttaqVBVe0r0e0VKK4A+C3frokpzER6DFCIiclfmghSg8r6XkVeMQ2nZLpsRJDpQEQQBY8eOhY+Pj25bcXExJk2aZFBL5ccff3RsC11IbO4JR3SIiKgmc+WihKIDlTFjxhhtGzVqlEMbIydWnCUiopou0McLBSXWR0oignyt7uMoogOVhIQEZ7ZDdv3/u1vuJhAREdlsQtcG2HgyE5n5tvd2vPdoS8zbmILMvGKLixJ2jA2z+RxSSV7rp7piXgkREbmzPnGR2P9abwxqGSn5sZHBPvhsVFsMaBmF2YMqFwZ220UJqys1nwkiInJTIf5q3Vo8n4xoh2e6x0JsLBEe4I09r/bWTTnuFx/l3osSKokjpyefzyxAn4Uc/iGimifUX43colLm6bkxbaE2/Z6OO2UVmPfLaXx78KLVx2sXMNSnlEUJ2Y/wt8aRgUbdXERENUHnhpX5BrwGuq+colIcSss22Obt5YG29UNFPT4z77bRNu2ihINb10WXRuEuHe7Rx0BFT9qCgfygElGN06h2kMmufnIvpqYMZxfeEfVYsfvJQfLqydVd2oKBZivTEhFVR10ahaNr41roGxep6+pPu1Fgsow6KZepKcNhgT4m9jQmdj85MFAxoXFkIM69M9BgGxciJKLqKNRfjc4NK3MTtF39AHAgNQvC9vMuaYO2docKLKhpqygzU4Yjg8X1kondTw4c+hGJHx4iqo6Gtq9nMvfAFZVHVX//+7/HW+IzDj3ZxdyU4Y6xYYiy8ryaC3KUgoGKFeczC9DgtY1yN4OIyCk2HM9AeYXxVzFnVB6tehuN1Pji0xFtofHzRklZBf7v8VZ4vldjh5/X3T3ati563l0LvibqaIT6q/GZhSnDnh4qzB4UpwsK9Wm3ubouilQc+rGAwz1EpCQeKsBETGEXcwvMtYsJRViAGtmFpQ47lwDgxT5N0KBWACKCfJFTWIJ5G1OQkfdP742C75ey+S01C7MHxWHZ2I44mJqFA3/dBFA5TNe5ofXZONq6KHN/NnyuIzW+mD0ozuV1UaRiHRUzGKQQVX9eHkCZG1SlHndvDB5oEYWcwhJMWXkMgGOHo6f2aowmdwXqamVsTck0uqk5grb8+r7pvbE1JROTVyTJdp1tVS8Yxy/ny3R2abRhiLbYmq31TZxZF0UqKfdv9qiYwAUKiWoGdwhSAKBTw3Bdj8cSD5WoIEIFICzAG1kipp0u2vlP0mygjycKSsrtaq85Aip7cA7+lYW5P6fIep29lGNcN0SpBFS+nnN/TkFFhYB5G88YvP5RIntG9JOl3QlzVEzgAoXkTOO7RcvdBMVxxnc6jZ9938NC/LwQ4q+2ut9D90Qi2NfTrnNZor1BafNI+sVHYfe/e2HWwObocXdt3T5VHwMA8wbHI0rjK+n5dVaQou9AapbDe2ukyi4shcrBb7xQf7XTanFpg7xnVx4zeu4y8ooxaUUSNp3IcNLZ5cVAxQQuUEiOpELlN57UdwYgfcFA9G1eV+4mySrUX43IYMOaDZEaX3w2qi16N6tt17Gn9mqMj4e1xqqnOyNp1gP4bFRboxkP4QHeoo716ch2ODSzD8ICLAcr+1Kz4OHhvEupfi8EACSeykCP93di3sYz2H32BgAY3XC1a7JYWmBOXsros3Z04sOQNnVl/cumrkrCphNXZWyBc3DoxwS1B4MVchztjUabsNgxNgyRwT7IzC+Ru2kO0S4mBOevFyDvdpmo/d9+5B48GB9pcqxc4+eNHX/csLktXRvXMuja7hcfZVDELCLIF5l5t/Hi98etHutmQQmOXsixmkyaW+S4ZFNLpnyXhCc61MMXe9KMbobaBNsJXRugT1ykQe6BuURKOWhzVLo0rIVFO1NlbYszBPupEfL3uklyqBCAZ1cew2ceKsUnyErBQMWEzc/34AKF5HDauhRbUzJR7C7JEVaE+Hnh+2fuBQAs35eGtzedsfqY0ABvs2Pl2poPmXnFkr+Zhgd4m6wFUfVcB1KzRB0vIsjXJbVExMq9XYrP96SZ/b0KwKZTmZg50HiqadWA7dy1Wy4PFLQtmj0oDp0bhVerYB2o7Cn8aNs5uZsBoHKosG9cpKKnHEvBoR8TuEAhOUNEkC8ST2Vg8ook2b5xOVru7TIcSsuGp4cKEcHiSnBbuvlraz4A0ocq5g2OF3Vh1gZD5vbUDtV1jA1zSi0Ra0NJttLvuTNFf4G5ro3tG2KzJsTPy6jSqXY4ql98VLUK1gE4vaKu1M+CpfeBO2KPihlpCwZyijI5hLa7u11MKHq8v7Pavae0gYfYm7q1/WwZqnimeywGtBTX1a0NhiavSDK6weh/6/f0UNnVw6M1pVcj3NuwFm4WllQOPeUX48U1yTYezToxvUCO+LssWfBoS6MhN+1wlDZYry6fAy8PFeKignDiiuOmOletlxOp8cWsgXFGNWcsUVJvoL0YqFhQdYFCIlvNHhSHoxdyZM8RcIaIIF+UVwioqBAQ4qdG7m3TvUXagE1MqW5TuSWVxcEMp2WGBajx1uB4DGhZR1KbxRbAshTUiNWtcW2bhp5sJSZg1P+7HO2Z7rG656/q8F55hSD7tGRHK6sQHBKkdGwQiuEd6yNS44d2MaE4eiEH128Vo1agDyAANwtLMKxDtOjhJWf0BsqFgYoVVRco3JGcifGrj8rYInIn+vUN1idfkbs5DqUNPHIK76DbuzssBmFVeyrEMJXH8mB8lMMKVpkKhkwdz2xQE+yD4rIK5BWVmrzxmgvMnNWbISUQBJyTZKsC8L+jV9AsMhiRGj+j5/NQWna1DNYdIfVGIR5uXVf3fHVpFI7EUxl4Ze1xg+csxF9t9j0HSH8fuAMGKhL1bh2J9NYDjbbr97yoPYC5/Zpi5qY/jfbzVAHlgvH/9S0f1g69W0eyN8dNqVRA76a18dR9jQwu1NXpG4721vNwqyhMWWm9G99RpbodXbBK7PHMBTXa6qrWhpCqntPS0JMtwYstgSBg+HdtTcnET8lXka1XIE5qCX0BQFbhHd2sqqqFyBw1HOGMpQTkllV4x2ApA3NDZNaCFED5a/dIxRL6bqC8QkC7t7a6XQLm1F6N8MqDzXQ/V+fFHR9pXQfBfmrEhPljdJcG8PYyzlMvrxDQ7d0dTssLcKUojS9mDWxuNBRTVYifGp+ObCtqPRJ3lXgqw6hXQkylUHOPe7hVFL74e3aPqeBnYvdYbDieYVNlUmuqlljX5lXZ+p6tWvr9QGoWhi89aHP7tMf7dERbhAZ464ZGXv4+GdfyS9z+c/Vklxj0j4/SPe/mPlsqVPas+Hh5GMycctT7wBWk3L8ZqLiJxFMZmOSE8WRn+m5CJ3RtUkv3szsGKmEB3gbfMM1Z9XRnUd/MnZVI6O3lAX9vT7PBrJhv6p0bhuHgX9ZnCswa2Bxju8biUFq2qJuO2OfGnTl67RVrwY+r1mwprxCwaMd5fLTtrM3H0F/fB4CoYF37fq1ak8TcjVj7uQIc00MlN7HXne+e6gQPlUoRa/dIxbV+qqF+8VFYPKItpq5KcosuzxB/NTpXuTmZG+pSohB/NT4d3hYdYsOsfqMMC1CjXUyoqOP2i4/Cp054HUvLKvDO0FYIDfDBtpRMrEu+YtBlrx16AWB0A9QmpD4YH2XxJqK94YztGgtPD5XobvzqNPvAHFuHpMw9TjskY2qlXHvOJ4WpYMkWVQseiklO1r5fxeQQAZaTo4d1qG9XoGWrxrUDkHqj0KZASUyQAlQWJRzcuvpXumag4kZCA7ydHqT4qz1QZCIpxt/bE/3jI7Hv3A1cv3XH6odvwZB7jC4ov74gfyE9bYusfZtbMOQeXW+QtQtrdmEpery/U3SXq7Nex3kbz2Df9N7o0igcMwfGmb3AW7r4i522CzhuOnJ1Zk/PR9UVjBftPO+yrn1n9PxtPlW5Dk3fuEiTQUVYgBr/al3XqLKu2IDMXB4RAKw+fNEhQ67WElm1PFTAphe6AwC+PZCOC9lFKCopww9JlQn1jnpea8pni0M/bmR98hW8sDrZacfvcXctPNKmHrILSnTTTMMCfRAZbFwDATD9YQvx88KCR1uavZAqoTbNY23rYr+ZRdEsdS1b+nZZdSzeEme+jo4YZhGbc2Et50a/y99duqMdydbcFe1jTQUKUt5nttK+rpbe6xp/NXy9PJGZL723JUpib4kjWLtuifFM91i0qR8qKoB7pnssZgyIM9kOR/RSVYfPFnNUqqmpK5Pwi4tWx7R0QTX1YQvxV2PcvbGY2rux1Q+Orbkq26b1QOPIQKPt8zelWCwtXtWTXWIwe1ALHErLRmZ+MbILShAW4G1yOqW+O2UV6Dx/u9luWbEXD3sTCi35eFhrg65gU9/oAVi9QYjtCbCUGwA494aqZPYEGmICBWfepMS+P7+b0AkeHipk5hdj3i+nRc8Okuu9YWuQULVWj6XjeKiAp+8zHaRoaT9bm09l4JsDF6T9Eag+ny3mqFRD8zeluCxIAYDMvGJMXpFk8sMgtv6EOanvDEDrWZtwy8Jq8lESLsTai4LYYKW8rAK/nLiKiCBfPNyq8uKj/VsOpWWb/VsqF6gzP3ZcdSzeHG0dDWfUk7h5qwTrk6+YLZAW4l9Zvt1agqKUabtiCqfVJJaKmmm3WVqLxVqtEbHvM1uJzSm6WfhPfoSf2kN0j4WAyputq9ej0b9uLd2banHxy4daRqFv3F0mr236x8nIvY1jl3IgAIgNDzA740+f/mdLTKBSdYp4TfxsMVBxA3fKKvDFXvE9Bo5g7WJiTzKfp4cK7w9va7YLVQXpdQBmDIhDoK8XPthivWrjd4cv4bvDlwCIv3ED4i/g1vbz9FDh4VZRknqBxJq30fKigKZmBVkKSsWwN3CtbsQUNcvIK8aiHefwQp+7jX4nd5KyLblHUovHOTvYMke7LMJL3ydb3O/ohRx8PKyN2few/vVvSLt6NrXFWuE/bc/Z7n/30lWpramfLS5K6Aa+/i0dcgzQWVvkzB7aC1tklYXsIoN9bL5hHr+UJ/kxuUWlRjdv7Y078ZRhD5ajkkfLKwRsOO663jFr9L/ll9uY5au/4F2XRtW3ZooYYgOIj7adM3qPAfImKZdXCKgQKpdCMEd/0UZ9/eKjsG96b6x6ujOe7BIj6nxyzAgTG0g6e1E/Swtw6ieve3t51PjPFgMVN3A4XdzaID6eznkDO/diYu4jKk3iqQzsO3/T/ubA/I1byqq7liixjLgzg9KaRkoAYSo4dNT7TKrEUxno9u4OjPzyd4vrNQHmezy1AWt/kV80XDVrpbxCwIHULKxPvoL9580P+ehzRRCl+8KmMb/SNHHoxy34e4t7meqH+ePcjUKHn98ZFxNzyYbX8qUPQzhjKqWprmkpq+5aouS6IkpumzvQ75Ewd7PXZ2r4w1HvM/02WRuWE/sZEpsfIXZYwxXr0diaROuqIIpDp9YxUHEDj7ath5+Sr1rd70ZBidV99KlQuS6Nud5+Z11MrCUbSkm0c/ZqrFVv3I5IHlVy7YNaAT7WdyKTbL0hmgoOHZWkLGaKtJjPkLYAYmcrQw/6QZF2pV9HBFuWziNmVpqU64Mci/q5ooCfO2Og4gbubVwL/t6eKLpjfpqMr5cHcm+XSTquAOhyX5xxMTHHkbManD2MYiqosPcbkJjVc2VbdI1f4mxiT6+eucBV6vus6s07p7AEU1YeM2pT1eRpMZ+h3KJSeHioLL7HzZUt0D5ey95ZK1Lq/Ej9ElNdF/VzdwxU3ICnhwofDm1lca2fEZ3qY/n+dJuOP6FrA2w6lemy6aWOnNUg9lhS1/yw9q3K3llP1rr2Fw1vC42fGlNWJokaQnCUmxJ75cj2Xj0x39zFvs9M3bw9VKbf81V7LR3xebS00i8AvNjnbjSo5W/3sIa585iauWbLl5iaOPXXHTBQcRP94qPw2ai2mL3+NK7d+udmEhnsgzkPt4DGz9vmQKVPXKTFkuuO5shZDWKP9cL9TfDx9sqpy9ZuKK74ViW2a3/Bo/fYXVFTCiUPSymVLTdER77HzN28LfXI6fda2vt5FDOUu/rwRbsL1EkdMhYbgE3t1RhN7gpkboiCMVBxI5a6gssrBKvDCVXpf6Nz5RipIxPt2sWEWh0m8VABz/ZqjGZRQUaBQai/GgIc2zUtlpiufXMBTdTfi61pv6XmFJbgzV9SDJZ8l0KOcfnqwpYEZEe9x+zN0bp+qxgPtaxj1+fRVQXqpJ5HbADWtXEt5ocoHAMVN2MuoLA0nGCKnGOxjpzVcPRCjtVcjgqhcj9Li5bJlXEvJkAUm6vwYHwUFu04b3KlWP3n2ZX5SDWB2BvirIHNUSvIx6HvMXtztCKCfO3+PLqqQJ3U8yhp5hHZh4FKNWLu27ecvQbmOGpWg9SLl7nAQOnfqMQENJ4eKrzQpwmaRgaafV4BsNy9g4m9IY7tGuuwQFB/vRhbVL1J2/N5dFWBOrGP119GYtbA5piy8hiDczfHRQndjJhpebYuRCcHsdMMzRG7gJojVhV2J5aeV3ufczLmysUZ7V2B11KbbHlvuGoVbWvnAYxny0VpfPFwqyhsOJ5h0yrW5DxcPbmasmfZ+OrKVRdJImtc8fm0ZRq0qZu3o68ZrgrUzJ3HHO35Px3RFqEB3gzOFYSBSjVkz7Lx1Z21i9eLfe7G1N6NeWEip3Nmb5U2KBfbk+Lqm7SrvkiZm4ptrXAlv6woCwOVasbaBYofROvd4TW954ncn9hhTi053vOuGlbUP8/NWyVWVw0Hat7wr9JJuX/Luijh/Pnz0aFDBwQFBSEiIgKPPPII/vzzTzmbpEhSpuXVVNqVW1/s08Tk782tiEzkLsQmjj/ZJQarnu6MfdN7WwxS9BfqO5CaZfPK2fpctYq2/nlqBYlb9oHrWLkvWWf97N69G1OmTEGHDh1QVlaG119/HQ888ABSUlIQEBAgZ9MURerMlpqcLLn68CWT26WuIUSkNGJnvfSPj7Lac1Cd8t1cNeuI5CNroJKYmGjwc0JCAiIiInD06FF0795dplYpj5QPYnW6AEnlqsJTRHJwVF0QKWXo3QHrpVR/sg79VJWXlwcACAvjG0qf9oNorg9AhcpgJKewBJNXJBndrGvKsIerCk8RyUFbmA0wXjtSbF0Qa2XogcpeR0cMA7mKI54XUjbFBCqCIOCll15Ct27dEB8fb3KfkpIS5OfnG/yrCcR8EGcNjMO8jWeq1QVIKnYBU3WnLcwWqTF8D0dqfEX1hFTXfDd7nxdSNsVUpp06dSpOnDiBffv2md1n/vz5mDt3rgtbpRzWKkdq/Lxr/LBHu5hQhAWokV1oerVhdgFTdSB2SQVTqnOvoz3Pi76anOOnVIoIVJ577jls2LABe/bsQb169czuN2PGDLz00ku6n/Pz8xEdHe2KJiqCpQ/i+uQroo7hjhcgMbS5OZaCFIBdwFQ92LqIaHXvdbR3cdWanOOnZLIGKoIg4LnnnsO6deuwa9cuxMbGWtzfx8cHPj7ipqJVV+Y+iNX9AmSJmGqdXM+GiImnllS3JOPqRNYclSlTpmDFihVYuXIlgoKCkJmZiczMTNy+fVvOZrklsQm31e0CJGaZ+/AAb+z+dy9eZKjGY+KpadUxybg6kTVQWbJkCfLy8tCzZ09ERUXp/q1Zs0bOZrmlmnoBErPMfVbhHRy9kOOiFhEpGxNPjVXXJOPqQvahH3Ice5Zqd1fVOTmQyFkclXhaXfA6omyKSKYlx6lpF6CanJtDZA97E0+rE15HlI2BSjVUky5AHWPDEOKvRm6R6dk+ABDqr652uTlE5DhMMlY2xRR8I3IWDjASkSU1NcfPXTBQIbd2KC3bYm8KAOQWlTIJjogsYpKxcnHoh9wak+CIyFFqWo6fu2CgQm6NSXBE5Eg1KcfPXXDoh9xaTS10R0RUUzBQIbfGJDgiouqNgQq5PSbBERFVX8xRoWqBSXBERNUTAxWqNpgER0RU/XDoh4iIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBTLS+4GEBERuZvyCgGH0rJx/VYxIoJ80TE2DJ4eKrmbVS0xUCEiIpIg8VQG5v6cgoy8Yt22KI0vZg+KQ7/4KBlbVj1x6IeIiEikxFMZmLwiySBIAYDMvGJMXpGExFMZMrWs+mKgQkREJEJ5hYC5P6dAMPE77ba5P6egvMLUHmQrBipEREQiHErLNupJ0ScAyMgrxqG0bNc1qgZgoEJERCTC9VvmgxRb9iNxZA1U9uzZg0GDBqFOnTpQqVT46aef5GwOERGRSeUVAm7eKhG1b0SQr5NbU7PIGqgUFhaiVatWWLRokZzNICIiMivxVAa6vbsD8zaesbifCpWzfzrGhrmmYTWErNOT+/fvj/79+8vZBCIiIrO0s3yspcdqK6jMHhTHeioO5lZ1VEpKSlBS8k/XW35+voytISKi6szSLJ+qIllHxWncKlCZP38+5s6dK3cziIioBrA2y0dr1sDmGNs1lj0pTuJWs35mzJiBvLw83b9Lly7J3SQiIqqmxM7eqRXkwyDFidyqR8XHxwc+Pj5yN4OIiGoAsbN3OMvHudyqR4WIiMhVOsaGIUrjC3N9JZzl4xqyBioFBQVITk5GcnIyACAtLQ3Jycm4ePGinM0iIiKCp4cKswfFAYBRsMJZPq6jEgRBtkUJdu3ahV69ehltHzNmDL766iurj8/Pz4dGo0FeXh6Cg4Od0EIiIqrpuFqy40m5f8saqNiLgQoREblCeYWAQ2nZuH6rGBFBlcM97EmxnZT7t1sl0xIREcnB00OFLo3C5W5GjcRkWiIiIlIsBipERESkWAxUiIiISLEYqBAREZFiMVAhIiIixWKgQkRERIrFQIWIiIgUi4EKERERKRYDFSIiIlIst65Mq63+n5+fL3NLiIiISCztfVvMKj5uHajcunULABAdHS1zS4iIiEiqW7duQaPRWNzHrRclrKiowNWrVxEUFASVyv7FofLz8xEdHY1Lly5xkUMF4euiXHxtlIuvjXLxtansSbl16xbq1KkDDw/LWShu3aPi4eGBevXqOfy4wcHBNfbNo2R8XZSLr41y8bVRrpr+2ljrSdFiMi0REREpFgMVIiIiUiwGKnp8fHwwe/Zs+Pj4yN0U0sPXRbn42igXXxvl4msjjVsn0xIREVH1xh4VIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUAGwZ88eDBo0CHXq1IFKpcJPP/0kd5MIwPz589GhQwcEBQUhIiICjzzyCP7880+5m0UAlixZgpYtW+oKVnXp0gWbN2+Wu1lUxfz586FSqTBt2jS5m1LjzZkzByqVyuBfZGSk3M1yCwxUABQWFqJVq1ZYtGiR3E0hPbt378aUKVNw8OBBbN26FWVlZXjggQdQWFgod9NqvHr16mHBggU4cuQIjhw5gt69e2Pw4ME4ffq03E2jvx0+fBhffPEFWrZsKXdT6G8tWrRARkaG7t/JkyflbpJbcOsS+o7Sv39/9O/fX+5mUBWJiYkGPyckJCAiIgJHjx5F9+7dZWoVAcCgQYMMfn777bexZMkSHDx4EC1atJCpVaRVUFCAkSNHYunSpXjrrbfkbg79zcvLi70oNmCPCrmNvLw8AEBYWJjMLSF95eXlWL16NQoLC9GlSxe5m0MApkyZgoEDB6JPnz5yN4X0nDt3DnXq1EFsbCyGDRuGv/76S+4muQX2qJBbEAQBL730Erp164b4+Hi5m0MATp48iS5duqC4uBiBgYFYt24d4uLi5G5Wjbd69WokJSXh8OHDcjeF9HTq1AnffPMN7r77bly7dg1vvfUW7r33Xpw+fRrh4eFyN0/RGKiQW5g6dSpOnDiBffv2yd0U+lvTpk2RnJyM3Nxc/PDDDxgzZgx2797NYEVGly5dwgsvvIAtW7bA19dX7uaQHv30gnvuuQddunRBo0aN8PXXX+Oll16SsWXKx0CFFO+5557Dhg0bsGfPHtSrV0/u5tDfvL290bhxYwBA+/btcfjwYXz88cf4/PPPZW5ZzXX06FFcv34d7dq1020rLy/Hnj17sGjRIpSUlMDT01PGFpJWQEAA7rnnHpw7d07upigeAxVSLEEQ8Nxzz2HdunXYtWsXYmNj5W4SWSAIAkpKSuRuRo12//33G80kGTduHJo1a4bp06czSFGQkpISnDlzBvfdd5/cTVE8BiqozJA/f/687ue0tDQkJycjLCwM9evXl7FlNduUKVOwcuVKrF+/HkFBQcjMzAQAaDQa+Pn5ydy6mm3mzJno378/oqOjcevWLaxevRq7du0ymqlFrhUUFGSUwxUQEIDw8HDmdsnslVdewaBBg1C/fn1cv34db731FvLz8zFmzBi5m6Z4DFQAHDlyBL169dL9rB0vHDNmDL766iuZWkVLliwBAPTs2dNge0JCAsaOHev6BpHOtWvXMHr0aGRkZECj0aBly5ZITExE37595W4akSJdvnwZw4cPx82bN1G7dm107twZBw8eRExMjNxNUzyVIAiC3I0gIiIiMoV1VIiIiEixGKgQERGRYjFQISIiIsVioEJERESKxUCFiIiIFIuBChERESkWAxUiIiJSLAYqRKQIKpUKP/30k1PP0bNnT0ybNs2p5yAix2KgQlTD/Pbbb/D09ES/fv0kP7ZBgwZYuHCh4xtlxaBBg9CnTx+Tvztw4ABUKhWSkpJc3CoicgUGKkQ1zPLly/Hcc89h3759uHjxotzNEWXChAnYsWMHLly4YPS75cuXo3Xr1mjbtq0MLSMiZ2OgQlSDFBYW4vvvv8fkyZPx0EMPmVzLasOGDWjfvj18fX1Rq1YtDBkyBEDlsMmFCxfw4osvQqVSQaVSAQDmzJmD1q1bGxxj4cKFaNCgge7nw4cPo2/fvqhVqxY0Gg169OghqQfkoYceQkREhFF7i4qKsGbNGkyYMAFZWVkYPnw46tWrB39/f9xzzz1YtWqVxeOaGm4KCQkxOM+VK1fwxBNPIDQ0FOHh4Rg8eDDS09N1v9+1axc6duyIgIAAhISEoGvXriYDKiKyDQMVohpkzZo1aNq0KZo2bYpRo0YhISEB+st9bdy4EUOGDMHAgQNx7NgxbN++He3btwcA/Pjjj6hXrx7efPNNZGRkICMjQ/R5b926hTFjxmDv3r04ePAgmjRpggEDBuDWrVuiHu/l5YUnn3wSX331lUF7165dizt37mDkyJEoLi5Gu3bt8Msvv+DUqVOYOHEiRo8ejd9//110O6sqKipCr169EBgYiD179mDfvn0IDAxEv379cOfOHZSVleGRRx5Bjx49cOLECRw4cAATJ07UBXFEZD+unkxUgyxbtgyjRo0CAPTr1w8FBQXYvn27Lv/j7bffxrBhwzB37lzdY1q1agUACAsLg6enJ4KCghAZGSnpvL179zb4+fPPP0doaCh2796Nhx56SNQxxo8fj/fffx+7du3SrXa+fPlyDBkyBKGhoQgNDcUrr7yi2/+5555DYmIi1q5di06dOklqr9bq1avh4eGBL7/8Uhd8JCQkICQkBLt27UL79u2Rl5eHhx56CI0aNQIANG/e3KZzEZFp7FEhqiH+/PNPHDp0CMOGDQNQ2UvxxBNPYPny5bp9kpOTcf/99zv83NevX8ekSZNw9913Q6PRQKPRoKCgQFKOTLNmzXDvvffq2puamoq9e/di/PjxAIDy8nK8/fbbaNmyJcLDwxEYGIgtW7bYlYdz9OhRnD9/HkFBQQgMDERgYCDCwsJQXFyM1NRUhIWFYezYsXjwwQcxaNAgfPzxx5J6mojIOvaoENUQy5YtQ1lZGerWravbJggC1Go1cnJyEBoaCj8/P8nH9fDwMBiOAYDS0lKDn8eOHYsbN25g4cKFiImJgY+PD7p06YI7d+5IOteECRMwdepUfPrpp0hISEBMTIwusPrggw/w0UcfYeHChbjnnnsQEBCAadOmWTyHSqWy2PaKigq0a9cO3333ndFja9euDaCyh+X5559HYmIi1qxZgzfeeANbt25F586dJf1tRGQae1SIaoCysjJ88803+OCDD5CcnKz7d/z4ccTExOhuxC1btsT27dvNHsfb2xvl5eUG22rXro3MzEyDG35ycrLBPnv37sXzzz+PAQMGoEWLFvDx8cHNmzcl/x1Dhw6Fp6cnVq5cia+//hrjxo3TDcns3bsXgwcPxqhRo9CqVSs0bNgQ586ds3i82rVrG/SAnDt3DkVFRbqf27Zti3PnziEiIgKNGzc2+KfRaHT7tWnTBjNmzMBvv/2G+Ph4rFy5UvLfRkSmMVAhqgF++eUX5OTkYMKECYiPjzf499hjj2HZsmUAgNmzZ2PVqlWYPXs2zpw5g5MnT+K9997THadBgwbYs2cPrly5ogs0evbsiRs3buC9995DamoqPv30U2zevNng/I0bN8a3336LM2fO4Pfff8fIkSNt6r0JDAzEE088gZkzZ+Lq1asYO3aswTm2bt2K3377DWfOnMEzzzyDzMxMi8fr3bs3Fi1ahKSkJBw5cgSTJk2CWq3W/X7kyJGoVasWBg8ejL179yItLQ27d+/GCy+8gMuXLyMtLQ0zZszAgQMHcOHCBWzZsgVnz55lngqRAzFQIaoBli1bhj59+hj0Amg9+uijSE5ORlJSEnr27Im1a9diw4YNaN26NXr37m0wa+bNN99Eeno6GjVqpBv6aN68ORYvXoxPP/0UrVq1wqFDhwySWoHKpNecnBy0adMGo0ePxvPPP4+IiAib/pYJEyYgJycHffr0Qf369XXbZ82ahbZt2+LBBx9Ez549ERkZiUceecTisT744ANER0eje/fuGDFiBF555RX4+/vrfu/v7489e/agfv36GDJkCJo3b47x48fj9u3bCA4Ohr+/P/744w88+uijuPvuuzFx4kRMnToVzzzzjE1/GxEZUwlVB2iJiIiIFII9KkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLFYqBCREREisVAhYiIiBSLgQoREREpFgMVIiIiUiwGKkRERKRYDFSIiIhIsRioEBERkWIxUCEiIiLF+n+vbxop2Upw5AAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name = 'No effect'\n",
    "data1.plot_regression(independent_variable,dependent_variable,model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Fixed Effect': <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x13590dee0>,\n",
       " 'No effect': <statsmodels.regression.linear_model.RegressionResultsWrapper at 0x135d782b0>}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30.269202649526406, 3.9903362429162775e-08, 1.0)\n"
     ]
    }
   ],
   "source": [
    "MonthToAnnualRegression.f_test(data1.models['Fixed Effect'], data1.models['No effect'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1.plot_time_series_regression(independent_variable, dependent_variable, 'No effect', country_id=220)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method MonthToAnnualRegression.prediction of <MonthToAnnual.MonthToAnnualRegression object at 0x13590df10>>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "data1.prediction(independent_variable,dependent_variable,'No effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shapes (49580,39) and (40,) not aligned: 39 (dim 1) != 40 (dim 0)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/noorainkazmi/Documents/GitHub/Monthly-to-Annually-Link/test-class.ipynb Cell 30\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/noorainkazmi/Documents/GitHub/Monthly-to-Annually-Link/test-class.ipynb#Y103sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m data1\u001b[39m.\u001b[39;49mprediction(independent_variable, dependent_variable,\u001b[39m'\u001b[39;49m\u001b[39mFixed Effect\u001b[39;49m\u001b[39m'\u001b[39;49m)\n",
      "File \u001b[0;32m~/Documents/GitHub/Monthly-to-Annually-Link/MonthToAnnual.py:76\u001b[0m, in \u001b[0;36mMonthToAnnualRegression.prediction\u001b[0;34m(self, x_columns, y_column, model_name)\u001b[0m\n\u001b[1;32m     74\u001b[0m y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdata[y_column]\n\u001b[1;32m     75\u001b[0m model \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodels[model_name]\n\u001b[0;32m---> 76\u001b[0m y_pred \u001b[39m=\u001b[39m model\u001b[39m.\u001b[39;49mpredict(sm\u001b[39m.\u001b[39;49madd_constant(X))\n\u001b[1;32m     77\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpredictions[model_name] \u001b[39m=\u001b[39m y_pred\n",
      "File \u001b[0;32m~/miniforge3/envs/viewser/lib/python3.9/site-packages/statsmodels/base/model.py:1176\u001b[0m, in \u001b[0;36mResults.predict\u001b[0;34m(self, exog, transform, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1129\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1130\u001b[0m \u001b[39mCall self.model.predict with self.params as the first argument.\u001b[39;00m\n\u001b[1;32m   1131\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1171\u001b[0m \u001b[39mreturned prediction.\u001b[39;00m\n\u001b[1;32m   1172\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   1173\u001b[0m exog, exog_index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transform_predict_exog(exog,\n\u001b[1;32m   1174\u001b[0m                                                 transform\u001b[39m=\u001b[39mtransform)\n\u001b[0;32m-> 1176\u001b[0m predict_results \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmodel\u001b[39m.\u001b[39;49mpredict(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mparams, exog, \u001b[39m*\u001b[39;49margs,\n\u001b[1;32m   1177\u001b[0m                                      \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1179\u001b[0m \u001b[39mif\u001b[39;00m exog_index \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mhasattr\u001b[39m(predict_results,\n\u001b[1;32m   1180\u001b[0m                                           \u001b[39m'\u001b[39m\u001b[39mpredicted_values\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m   1181\u001b[0m     \u001b[39mif\u001b[39;00m predict_results\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n",
      "File \u001b[0;32m~/miniforge3/envs/viewser/lib/python3.9/site-packages/statsmodels/regression/linear_model.py:411\u001b[0m, in \u001b[0;36mRegressionModel.predict\u001b[0;34m(self, params, exog)\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[39mif\u001b[39;00m exog \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m    409\u001b[0m     exog \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mexog\n\u001b[0;32m--> 411\u001b[0m \u001b[39mreturn\u001b[39;00m np\u001b[39m.\u001b[39;49mdot(exog, params)\n",
      "File \u001b[0;32m<__array_function__ internals>:200\u001b[0m, in \u001b[0;36mdot\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: shapes (49580,39) and (40,) not aligned: 39 (dim 1) != 40 (dim 0)"
     ]
    }
   ],
   "source": [
    "data1.prediction(independent_variable, dependent_variable,'Fixed Effect')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'No effect': month_id  country_id\n",
       " 241       1             3.048448\n",
       "           2             2.998919\n",
       "           3             2.892203\n",
       "           4             6.453294\n",
       "           5             3.138057\n",
       "                           ...   \n",
       " 504       242           3.023980\n",
       "           243           2.881314\n",
       "           244           3.101186\n",
       "           245           3.383993\n",
       "           246           3.040280\n",
       " Length: 49580, dtype: float64}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'No effect': month_id  country_id\n",
       " 241       1             3.048448\n",
       "           2             2.998919\n",
       "           3             2.892203\n",
       "           4             6.453294\n",
       "           5             3.138057\n",
       "                           ...   \n",
       " 504       242           3.023980\n",
       "           243           2.881314\n",
       "           244           3.101186\n",
       "           245           3.383993\n",
       "           246           3.040280\n",
       " Length: 49580, dtype: float64,\n",
       " 'Fixed Effect': month_id  country_id\n",
       " 241       1             2.930206\n",
       "           2             2.884478\n",
       "           3             2.772633\n",
       "           4             6.330368\n",
       "           5             3.033534\n",
       "                           ...   \n",
       " 504       242           3.180417\n",
       "           243           3.038706\n",
       "           244           3.280116\n",
       "           245           3.552769\n",
       "           246           3.198333\n",
       " Length: 49580, dtype: float64}"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1.predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df.query('month_id > 240 & month_id <=492')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = MonthToAnnualRegression(df_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data2.prediction(independent_variable, dependent_variable,'Fixed Effect')"
   ]
  }
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