diff --git a/exercicios/para-casa/analise.ipynb b/exercicios/para-casa/analise.ipynb
new file mode 100644
index 0000000..df186dd
--- /dev/null
+++ b/exercicios/para-casa/analise.ipynb
@@ -0,0 +1,1055 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"world-happiness-report.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "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>Country name</th>\n",
+       "      <th>year</th>\n",
+       "      <th>Life Ladder</th>\n",
+       "      <th>Log GDP per capita</th>\n",
+       "      <th>Social support</th>\n",
+       "      <th>Healthy life expectancy at birth</th>\n",
+       "      <th>Freedom to make life choices</th>\n",
+       "      <th>Generosity</th>\n",
+       "      <th>Perceptions of corruption</th>\n",
+       "      <th>Positive affect</th>\n",
+       "      <th>Negative affect</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>3.724</td>\n",
+       "      <td>7.370</td>\n",
+       "      <td>0.451</td>\n",
+       "      <td>50.80</td>\n",
+       "      <td>0.718</td>\n",
+       "      <td>0.168</td>\n",
+       "      <td>0.882</td>\n",
+       "      <td>0.518</td>\n",
+       "      <td>0.258</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>4.402</td>\n",
+       "      <td>7.540</td>\n",
+       "      <td>0.552</td>\n",
+       "      <td>51.20</td>\n",
+       "      <td>0.679</td>\n",
+       "      <td>0.190</td>\n",
+       "      <td>0.850</td>\n",
+       "      <td>0.584</td>\n",
+       "      <td>0.237</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>4.758</td>\n",
+       "      <td>7.647</td>\n",
+       "      <td>0.539</td>\n",
+       "      <td>51.60</td>\n",
+       "      <td>0.600</td>\n",
+       "      <td>0.121</td>\n",
+       "      <td>0.707</td>\n",
+       "      <td>0.618</td>\n",
+       "      <td>0.275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2011</td>\n",
+       "      <td>3.832</td>\n",
+       "      <td>7.620</td>\n",
+       "      <td>0.521</td>\n",
+       "      <td>51.92</td>\n",
+       "      <td>0.496</td>\n",
+       "      <td>0.162</td>\n",
+       "      <td>0.731</td>\n",
+       "      <td>0.611</td>\n",
+       "      <td>0.267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>3.783</td>\n",
+       "      <td>7.705</td>\n",
+       "      <td>0.521</td>\n",
+       "      <td>52.24</td>\n",
+       "      <td>0.531</td>\n",
+       "      <td>0.236</td>\n",
+       "      <td>0.776</td>\n",
+       "      <td>0.710</td>\n",
+       "      <td>0.268</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>3.572</td>\n",
+       "      <td>7.725</td>\n",
+       "      <td>0.484</td>\n",
+       "      <td>52.56</td>\n",
+       "      <td>0.578</td>\n",
+       "      <td>0.061</td>\n",
+       "      <td>0.823</td>\n",
+       "      <td>0.621</td>\n",
+       "      <td>0.273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>3.131</td>\n",
+       "      <td>7.718</td>\n",
+       "      <td>0.526</td>\n",
+       "      <td>52.88</td>\n",
+       "      <td>0.509</td>\n",
+       "      <td>0.104</td>\n",
+       "      <td>0.871</td>\n",
+       "      <td>0.532</td>\n",
+       "      <td>0.375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>3.983</td>\n",
+       "      <td>7.702</td>\n",
+       "      <td>0.529</td>\n",
+       "      <td>53.20</td>\n",
+       "      <td>0.389</td>\n",
+       "      <td>0.080</td>\n",
+       "      <td>0.881</td>\n",
+       "      <td>0.554</td>\n",
+       "      <td>0.339</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>4.220</td>\n",
+       "      <td>7.697</td>\n",
+       "      <td>0.559</td>\n",
+       "      <td>53.00</td>\n",
+       "      <td>0.523</td>\n",
+       "      <td>0.042</td>\n",
+       "      <td>0.793</td>\n",
+       "      <td>0.565</td>\n",
+       "      <td>0.348</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Afghanistan</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>2.662</td>\n",
+       "      <td>7.697</td>\n",
+       "      <td>0.491</td>\n",
+       "      <td>52.80</td>\n",
+       "      <td>0.427</td>\n",
+       "      <td>-0.121</td>\n",
+       "      <td>0.954</td>\n",
+       "      <td>0.496</td>\n",
+       "      <td>0.371</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Country name  year  Life Ladder  Log GDP per capita  Social support  \\\n",
+       "0  Afghanistan  2008        3.724               7.370           0.451   \n",
+       "1  Afghanistan  2009        4.402               7.540           0.552   \n",
+       "2  Afghanistan  2010        4.758               7.647           0.539   \n",
+       "3  Afghanistan  2011        3.832               7.620           0.521   \n",
+       "4  Afghanistan  2012        3.783               7.705           0.521   \n",
+       "5  Afghanistan  2013        3.572               7.725           0.484   \n",
+       "6  Afghanistan  2014        3.131               7.718           0.526   \n",
+       "7  Afghanistan  2015        3.983               7.702           0.529   \n",
+       "8  Afghanistan  2016        4.220               7.697           0.559   \n",
+       "9  Afghanistan  2017        2.662               7.697           0.491   \n",
+       "\n",
+       "   Healthy life expectancy at birth  Freedom to make life choices  Generosity  \\\n",
+       "0                             50.80                         0.718       0.168   \n",
+       "1                             51.20                         0.679       0.190   \n",
+       "2                             51.60                         0.600       0.121   \n",
+       "3                             51.92                         0.496       0.162   \n",
+       "4                             52.24                         0.531       0.236   \n",
+       "5                             52.56                         0.578       0.061   \n",
+       "6                             52.88                         0.509       0.104   \n",
+       "7                             53.20                         0.389       0.080   \n",
+       "8                             53.00                         0.523       0.042   \n",
+       "9                             52.80                         0.427      -0.121   \n",
+       "\n",
+       "   Perceptions of corruption  Positive affect  Negative affect  \n",
+       "0                      0.882            0.518            0.258  \n",
+       "1                      0.850            0.584            0.237  \n",
+       "2                      0.707            0.618            0.275  \n",
+       "3                      0.731            0.611            0.267  \n",
+       "4                      0.776            0.710            0.268  \n",
+       "5                      0.823            0.621            0.273  \n",
+       "6                      0.871            0.532            0.375  \n",
+       "7                      0.881            0.554            0.339  \n",
+       "8                      0.793            0.565            0.348  \n",
+       "9                      0.954            0.496            0.371  "
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1949, 11)"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Country name                          0\n",
+      "year                                  0\n",
+      "Life Ladder                           0\n",
+      "Log GDP per capita                   36\n",
+      "Social support                       13\n",
+      "Healthy life expectancy at birth     55\n",
+      "Freedom to make life choices         32\n",
+      "Generosity                           89\n",
+      "Perceptions of corruption           110\n",
+      "Positive affect                      22\n",
+      "Negative affect                      16\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "nulos_por_colunas = df.isnull().sum()\n",
+    "print(nulos_por_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0       0\n",
+      "1       0\n",
+      "2       0\n",
+      "3       0\n",
+      "4       0\n",
+      "       ..\n",
+      "1944    0\n",
+      "1945    0\n",
+      "1946    0\n",
+      "1947    0\n",
+      "1948    0\n",
+      "Length: 1949, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "nulos_por_linhas = df.isnull().sum(axis=1)\n",
+    "print(nulos_por_linhas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "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>year</th>\n",
+       "      <th>Life Ladder</th>\n",
+       "      <th>Log GDP per capita</th>\n",
+       "      <th>Social support</th>\n",
+       "      <th>Healthy life expectancy at birth</th>\n",
+       "      <th>Freedom to make life choices</th>\n",
+       "      <th>Generosity</th>\n",
+       "      <th>Perceptions of corruption</th>\n",
+       "      <th>Positive affect</th>\n",
+       "      <th>Negative affect</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1949.000000</td>\n",
+       "      <td>1949.000000</td>\n",
+       "      <td>1913.000000</td>\n",
+       "      <td>1936.000000</td>\n",
+       "      <td>1894.000000</td>\n",
+       "      <td>1917.000000</td>\n",
+       "      <td>1860.000000</td>\n",
+       "      <td>1839.000000</td>\n",
+       "      <td>1927.000000</td>\n",
+       "      <td>1933.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>2013.216008</td>\n",
+       "      <td>5.466705</td>\n",
+       "      <td>9.368453</td>\n",
+       "      <td>0.812552</td>\n",
+       "      <td>63.359374</td>\n",
+       "      <td>0.742558</td>\n",
+       "      <td>0.000103</td>\n",
+       "      <td>0.747125</td>\n",
+       "      <td>0.710003</td>\n",
+       "      <td>0.268544</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>4.166828</td>\n",
+       "      <td>1.115711</td>\n",
+       "      <td>1.154084</td>\n",
+       "      <td>0.118482</td>\n",
+       "      <td>7.510245</td>\n",
+       "      <td>0.142093</td>\n",
+       "      <td>0.162215</td>\n",
+       "      <td>0.186789</td>\n",
+       "      <td>0.107100</td>\n",
+       "      <td>0.085168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>2005.000000</td>\n",
+       "      <td>2.375000</td>\n",
+       "      <td>6.635000</td>\n",
+       "      <td>0.290000</td>\n",
+       "      <td>32.300000</td>\n",
+       "      <td>0.258000</td>\n",
+       "      <td>-0.335000</td>\n",
+       "      <td>0.035000</td>\n",
+       "      <td>0.322000</td>\n",
+       "      <td>0.083000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>2010.000000</td>\n",
+       "      <td>4.640000</td>\n",
+       "      <td>8.464000</td>\n",
+       "      <td>0.749750</td>\n",
+       "      <td>58.685000</td>\n",
+       "      <td>0.647000</td>\n",
+       "      <td>-0.113000</td>\n",
+       "      <td>0.690000</td>\n",
+       "      <td>0.625500</td>\n",
+       "      <td>0.206000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>2013.000000</td>\n",
+       "      <td>5.386000</td>\n",
+       "      <td>9.460000</td>\n",
+       "      <td>0.835500</td>\n",
+       "      <td>65.200000</td>\n",
+       "      <td>0.763000</td>\n",
+       "      <td>-0.025500</td>\n",
+       "      <td>0.802000</td>\n",
+       "      <td>0.722000</td>\n",
+       "      <td>0.258000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>2017.000000</td>\n",
+       "      <td>6.283000</td>\n",
+       "      <td>10.353000</td>\n",
+       "      <td>0.905000</td>\n",
+       "      <td>68.590000</td>\n",
+       "      <td>0.856000</td>\n",
+       "      <td>0.091000</td>\n",
+       "      <td>0.872000</td>\n",
+       "      <td>0.799000</td>\n",
+       "      <td>0.320000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>8.019000</td>\n",
+       "      <td>11.648000</td>\n",
+       "      <td>0.987000</td>\n",
+       "      <td>77.100000</td>\n",
+       "      <td>0.985000</td>\n",
+       "      <td>0.698000</td>\n",
+       "      <td>0.983000</td>\n",
+       "      <td>0.944000</td>\n",
+       "      <td>0.705000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              year  Life Ladder  Log GDP per capita  Social support  \\\n",
+       "count  1949.000000  1949.000000         1913.000000     1936.000000   \n",
+       "mean   2013.216008     5.466705            9.368453        0.812552   \n",
+       "std       4.166828     1.115711            1.154084        0.118482   \n",
+       "min    2005.000000     2.375000            6.635000        0.290000   \n",
+       "25%    2010.000000     4.640000            8.464000        0.749750   \n",
+       "50%    2013.000000     5.386000            9.460000        0.835500   \n",
+       "75%    2017.000000     6.283000           10.353000        0.905000   \n",
+       "max    2020.000000     8.019000           11.648000        0.987000   \n",
+       "\n",
+       "       Healthy life expectancy at birth  Freedom to make life choices  \\\n",
+       "count                       1894.000000                   1917.000000   \n",
+       "mean                          63.359374                      0.742558   \n",
+       "std                            7.510245                      0.142093   \n",
+       "min                           32.300000                      0.258000   \n",
+       "25%                           58.685000                      0.647000   \n",
+       "50%                           65.200000                      0.763000   \n",
+       "75%                           68.590000                      0.856000   \n",
+       "max                           77.100000                      0.985000   \n",
+       "\n",
+       "        Generosity  Perceptions of corruption  Positive affect  \\\n",
+       "count  1860.000000                1839.000000      1927.000000   \n",
+       "mean      0.000103                   0.747125         0.710003   \n",
+       "std       0.162215                   0.186789         0.107100   \n",
+       "min      -0.335000                   0.035000         0.322000   \n",
+       "25%      -0.113000                   0.690000         0.625500   \n",
+       "50%      -0.025500                   0.802000         0.722000   \n",
+       "75%       0.091000                   0.872000         0.799000   \n",
+       "max       0.698000                   0.983000         0.944000   \n",
+       "\n",
+       "       Negative affect  \n",
+       "count      1933.000000  \n",
+       "mean          0.268544  \n",
+       "std           0.085168  \n",
+       "min           0.083000  \n",
+       "25%           0.206000  \n",
+       "50%           0.258000  \n",
+       "75%           0.320000  \n",
+       "max           0.705000  "
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1949 entries, 0 to 1948\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column                            Non-Null Count  Dtype  \n",
+      "---  ------                            --------------  -----  \n",
+      " 0   Country name                      1949 non-null   object \n",
+      " 1   year                              1949 non-null   int64  \n",
+      " 2   Life Ladder                       1949 non-null   float64\n",
+      " 3   Log GDP per capita                1913 non-null   float64\n",
+      " 4   Social support                    1936 non-null   float64\n",
+      " 5   Healthy life expectancy at birth  1894 non-null   float64\n",
+      " 6   Freedom to make life choices      1917 non-null   float64\n",
+      " 7   Generosity                        1860 non-null   float64\n",
+      " 8   Perceptions of corruption         1839 non-null   float64\n",
+      " 9   Positive affect                   1927 non-null   float64\n",
+      " 10  Negative affect                   1933 non-null   float64\n",
+      "dtypes: float64(9), int64(1), object(1)\n",
+      "memory usage: 167.6+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "info_df = df.info()\n",
+    "print(info_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Country name', 'year', 'Life Ladder', 'Log GDP per capita',\n",
+       "       'Social support', 'Healthy life expectancy at birth',\n",
+       "       'Freedom to make life choices', 'Generosity',\n",
+       "       'Perceptions of corruption', 'Positive affect', 'Negative affect'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.rename(columns={\n",
+    "    'Country name': 'Nome do País',\n",
+    "    'year': 'Ano',\n",
+    "    'Life Ladder': 'Índice de Felicidade',\n",
+    "    'Log GDP per capita': 'PIB per capita (logaritmo)',\n",
+    "    'Social support': 'Apoio Social',\n",
+    "    'Healthy life expectancy at birth': 'Expectativa de vida saudável ao nascer',\n",
+    "    'Freedom to make life choices': 'Liberdade para fazer escolhas',\n",
+    "    'Generosity': 'Generosidade',\n",
+    "    'Perceptions of corruption': 'Percepções de corrupção',\n",
+    "    'Positive affect': 'Afeto positivo',\n",
+    "    'Negative affect': 'Afeto negativo'\n",
+    "}\n",
+    ", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Nome do País', 'Ano', 'Índice de Felicidade',\n",
+       "       'PIB per capita (logaritmo)', 'Apoio Social',\n",
+       "       'Expectativa de vida saudável ao nascer',\n",
+       "       'Liberdade para fazer escolhas', 'Generosidade',\n",
+       "       'Percepções de corrupção', 'Afeto positivo', 'Afeto negativo'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Análises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Média do índice de felicidade - Top 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Média do índice de felicidade (Life Ladder) para cada país\n",
+    "media_felicidade_paises = df.groupby('Nome do País')['Índice de Felicidade'].mean()\n",
+    "\n",
+    "# Deixar em ordem decrescente e selecionar os 20 mais felizes\n",
+    "media_felicidade_paises_decresc = media_felicidade_paises.sort_values(ascending=False)\n",
+    "media_felicidade_paises_top20 = media_felicidade_paises.sort_values(ascending=False).head(20)\n",
+    "\n",
+    "# Gráfico de barras\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.barplot(x=media_felicidade_paises_top20.values, y=media_felicidade_paises_top20.index, hue=media_felicidade_paises_top20.index, palette='coolwarm', legend=False)\n",
+    "\n",
+    "plt.title('Os 20 Países Mais Felizes do Mundo', fontsize=16)\n",
+    "plt.xlabel('Média do Índice de Felicidade (Life Ladder)')\n",
+    "plt.ylabel('País')\n",
+    "\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### A posição do Brasil no ranking"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "O Brasil está na posição 25 no índice de felicidade.\n",
+      "O Brasil está na posição 41 no índice de felicidade em 2020.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Posição do Brasil - média\n",
+    "posicao_brasil = media_felicidade_paises_decresc.index.get_loc('Brazil') + 1  \n",
+    "# +1 para obter a posição na lista iniciando em 1\n",
+    "\n",
+    "# Filtrar os dados para o ano de 2020\n",
+    "dados_2020 = df[df['Ano'] == 2020]\n",
+    "\n",
+    "# Ordenar os países pelo índice de felicidade em ordem decrescente\n",
+    "paises_ordenados_2020 = dados_2020.sort_values(by='Índice de Felicidade', ascending=False)\n",
+    "\n",
+    "# Encontrar a posição do Brasil\n",
+    "posicao_brasil_2020 = paises_ordenados_2020['Nome do País'].tolist().index('Brazil') + 1\n",
+    "\n",
+    "print(f\"O Brasil está na posição {posicao_brasil} no índice de felicidade.\")\n",
+    "print(f\"O Brasil está na posição {posicao_brasil_2020} no índice de felicidade em 2020.\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Índice de felicidade no Brasil ao longo dos anos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dados_brasil = df[df['Nome do País'] == 'Brazil']\n",
+    "\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "sns.lineplot(x='Ano', y='Índice de Felicidade', data=dados_brasil, marker='o')\n",
+    "\n",
+    "plt.title('Evolução do Índice de Felicidade no Brasil ao Longo dos Anos', fontsize=16)\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Índice de Felicidade')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1400x800 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plotar o Índice de Felicidade ao longo dos anos\n",
+    "plt.figure(figsize=(14, 8))\n",
+    "\n",
+    "# Índice de Felicidade\n",
+    "plt.subplot(3, 2, 1)\n",
+    "sns.lineplot(x='Ano', y='Índice de Felicidade', data=dados_brasil, marker='o')\n",
+    "plt.title('Evolução do Índice de Felicidade no Brasil')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Índice de Felicidade')\n",
+    "\n",
+    "# PIB per capita (logaritmo)\n",
+    "plt.subplot(3, 2, 2)\n",
+    "sns.lineplot(x='Ano', y='PIB per capita (logaritmo)', data=dados_brasil, marker='o')\n",
+    "plt.title('Evolução do PIB per capita (logaritmo) no Brasil')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('PIB per capita (logaritmo)')\n",
+    "\n",
+    "# Apoio Social\n",
+    "plt.subplot(3, 2, 3)\n",
+    "sns.lineplot(x='Ano', y='Apoio Social', data=dados_brasil, marker='o')\n",
+    "plt.title('Evolução do Apoio Social no Brasil')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Apoio Social')\n",
+    "\n",
+    "# Expectativa de vida saudável ao nascer\n",
+    "plt.subplot(3, 2, 4)\n",
+    "sns.lineplot(x='Ano', y='Expectativa de vida saudável ao nascer', data=dados_brasil, marker='o')\n",
+    "plt.title('Evolução da Expectativa de Vida Saudável no Brasil')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Expectativa de Vida Saudável')\n",
+    "\n",
+    "# Liberdade para fazer escolhas\n",
+    "plt.subplot(3, 2, 5)\n",
+    "sns.lineplot(x='Ano', y='Liberdade para fazer escolhas', data=dados_brasil, marker='o')\n",
+    "plt.title('Evolução da Liberdade para Fazer Escolhas no Brasil')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Liberdade para Fazer Escolhas')\n",
+    "\n",
+    "# Percepções de corrupção\n",
+    "plt.subplot(3, 2, 6)\n",
+    "sns.lineplot(x='Ano', y='Percepções de corrupção', data=dados_brasil, marker='o')\n",
+    "plt.title('Evolução das Percepções de Corrupção no Brasil')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Percepções de Corrupção')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Índice de generosidade - Brasil vs Top 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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Zs2djy5YtkEqlqFOnDmQyGYKCgjBr1ixcuXIFS5cuhUQiydW5GRISggEDBuDNmzeoUqUKHB0d8fTpUxw/fhwBAQH4/fff8dNPP2W5X0qlEoMGDcKlS5dgZGSEZs2a4cWLF1iyZEmmx+v27dvw8PDA8+fPUaVKFbRo0QJPnz7FoUOHcOLECXh5eWXrZvzAgQMAgA4dOqBYsWJZrp/RNQ0A/v33X0yZMgWpqamwsbGBqakpbt26BW9vbxw7dgybN29O12ReoVCgT58+iImJgZ2dHapXr46LFy/i77//xu3bt9N1y5k+fTp27NgBXV1d1KxZEwYGBggKCsL8+fPFsQ0+3YczZ87g/v37qFevHiQSCb777jtoaWnh5cuXcHd3R0REBIyMjNC0aVMkJSXh8uXLCAoKwvnz57FgwQLxpvfSpUsYOHAgUlNT4eDggBo1auDBgwfw9/fHiRMnsGbNGjRu3Pizx87T0xN//fUXpFIpGjRoAG1tbfz7778ICgrKcP2cnu+aPI++xM2bNwEA9vb2GT4/bdo0vHjxAk2bNsXdu3fFZOmcOXPg4+MDmUwGBwcH6Ovr4/79+7h+/TquX7+OmJgYTJ48Wa2c3bt3w8jICPb29tDW1sb169exdu1anDhxAvv27cuTQdC7dOmCdevW4fDhwxkmRFTvuS5dugDIm3MrM23atEGbNm0yfV7V0kuVlIqMjERaWhqqVq2qlohWUZ1HERERuYonI3fu3AHwoWWV6rM2N9fjzOS03nNynYmMjETPnj3FxJOVlRXi4uJw+vRpnD59GvPmzUP37t2zfSzMzMxgZWWFa9eu4enTpyhXrpza80eOHEFaWho6dOiQ4Rf858+fo1evXoiJiUHFihXRoEEDKJVK3Lx5E/7+/ggICMC2bds+27rk+fPn6NGjB+Li4iCXy9GiRQu8fv0aV65cQWBgIO7fv4+RI0dme59y6uLFi/j555+RmJgIuVwOOzs7REdHY/fu3Th+/DjWr1+fo4F9gQ8J+E6dOmX43Llz5/Dy5UtUq1ZNXJaSkoIRI0bg5MmTMDAwgIODA3R0dHD58mVMnjwZly9fVusGlJ/vYcqEQEQa1bdvX0Eulwu+vr65ev3mzZsFuVwutGnTRoiJiRGXv337Vhg8eLAgl8uFESNGiMsfPHggyOVyQS6XC66urkJCQoIgCIKQlpYm/Pzzz+Jz06ZNE1JSUgRBEISEhAShTZs2glwuFw4fPiyWNXHiRHH9NWvWiMsTEhKEfv36CXK5XJg1a5a43N/fX5DL5UKPHj3E7aqsXr1a3I+PtWzZUpDL5UK3bt2E9+/fC4IgCKmpqYIgCMKGDRsEuVwuDBs2TEhOThZfk5KSIvz++++CXC4X+vXrJy5PTU0VunbtKsjlcuGPP/4Q908QBGHbtm2CXC4XOnToIAiCILx//16oX7++YGNjI1y5ckUtprt37woODg6CXC4Xrl69muu6yExCQoLQuHFjwcLCQjhw4IC4/N27d4KHh4d4zC9duiQ+N2TIEEEulwtr165VK+vNmzdCjx49BLlcLvz1119ZblsQ/nfMPy7/Y4mJieL2+vTpIy6/deuWYGlpKdSsWVM4c+aMuDw1NVXw8vIS5HK50KxZM0GhUKTbVuPGjYXbt2+Lyx8+fCjUq1dPkMvlwsmTJ8Xlly5dEuRyudCrVy+1mPbs2SPI5XJh3LhxGcasOmZKpVIQhNzV79GjRwW5XC40bdpUuHv3rrj85cuXgpOTk9r7I6fnZlJSktC0aVNBLpcLK1euFNLS0sTnTp06Jdja2grW1tbCzZs3M9y/j61fv16Qy+VCz549hdevX6uVY2NjI773VZKTk8X398aNG9W2feLECcHGxkaoW7eu8Pz58yy33bFjR0Eulwtnz57Nct3MREZGCjVq1BDs7e2FwMBAcXlqaqqwZMkSQS6XC71791Z7jap+O3ToIDx8+FBcfuPGDXGfIyIixOWq86VTp07CgwcPxOVv374V32NLliwRl6vOu0/fY6pr0YgRIwS5XC4MHTpU7dp27949oVWrVoJcLhc2b94sLnd3d8/wOG3cuDFd/WR0zl+/fl2wsLAQ6tWrJ4SHh4vLY2JihBYtWoixquTmfP8a51FiYqJw9+5dtffT56iORcuWLdWWp6amCq9fvxZOnToltGzZUqhRo4YQEhKito7q88rOzk68PqelpQlpaWlCaGioIJfLBUdHRyE+Pl7tdQcOHBDkcrlQq1Yt8b386NEjQS6XCz/88IPw9u1bcV2FQiH8+OOPglwuF/bs2ZNu2zt37hSXubq6CnK5XDh//nyW+6363Pp0n96+fSvUrFlTqFevnvj5mJNzKy/dvn1bsLKyEiwsLIRbt24JgvC/z/whQ4Zk+Jq3b98KcrlcsLe3z9Y2VOf1x+/Zj7148UL8vJswYYK4PKfXY0HI+DMlp/We0+vM5MmTBblcLmzfvl0tFtVnz6fnfWZUn6v37t0T7602bdqUbr2ffvpJaNKkiZCamprh+Th79mzxPu7j93NSUpIwbNgwQS6XC7/99pu4XHV/2bRpU3HZihUrBLlcLvz5559q275+/bpgY2Mj1KxZU+2eIDPLly8X5HK5MHHixGwdA0H4cD7Uq1dPsLKyEg4dOqT23Pbt2wW5XC60atVKfO8IQvp7BUFQP56fo7pWtGzZUu06p4rd3d1dbXl8fLz43v742qCp93BRxjFEiDQsPj4eANRm88gJb29vAB9+3fp4RHV9fX0sXrwYBgYGOHr0KO7fv5/utRMnTkSJEiUAABKJROyuUrx4cUyYMAHa2toAgBIlSojZ6IzGi2jcuDEGDRokPi5RogQWLFgAHR0d7NmzB+/fvwfw4RdHR0dHjB8/XtyuimrwyYcPH2a4nz/++KP4K4rqF0ypVIrmzZtj3Lhxas1OtbW1xV/SPy7v2rVrCAsLQ/Xq1fHrr7+K+wcAvXr1Qr169aCvr49nz57h2bNnaNKkCfr37486deqoxWJubo4GDRqkK/9L6uJjx48fR3x8PFq3bo2OHTuKy/X09LBgwYIM+7FWqFABrVu3TjeavIGBgVhGZsc2M6tWrcL48ePFf+PGjYOHhweaNGmCkydPokyZMpg9e7a4/qZNm5CWloZBgwapTb2opaWFESNGoF69eoiNjRV/0fzY4MGD1aZUNDU1haOjI4C8/fVQJTf1u3XrVgAfmr9//Au5kZERxowZg++//16c6jCn5+bhw4fx9OlT1KtXDz///LNa89nmzZtj8ODBSElJwcaNG7Pct+3btwMAZs2apdYFonnz5ujVq1e69Y8dO4b79++jZcuW6Nevn9q2HR0d0atXL7x+/Rq7d+/OcttPnjwBgHS/RKp8fD59/O/jwQ83bdqE5ORkjBw5EvXq1ROXa2lpYfTo0bCwsEBQUBD++++/dOWPGTNGrQm0jY2NOCOR6pdjAFi7di0AYP78+WotTfT19TF//nxIpVL8888/6cYg0NbWhouLi1pMqqbUBgYGWLRokdq1rUqVKpg3bx4AYN26deJy1XX/03FBXFxcMHnyZAwcODDD46eyY8cOCIKA4cOHq3VDqVSpklorBpXcnO9f4zxSjY+S05Zejx49goWFhfjPysoKdevWxeDBgxEbG4vly5dn+qt1y5YtxeuzRCKBRCLBmzdv0LZtW4wdOzbdDBUdO3ZEyZIloVAo8Pz5cwAfjifwYXDXj1vs6erqYurUqZgzZw5q1aqVo336HFXrj0/H2jh+/DiSkpLQvn178fPxS8+t3Hj+/DlGjRqF1NRUdOvWDZaWlgCAxMREAMi0q6iqVaxCocjR9ubPn692/Rg7dizc3NzQokULhISEwMzMTBxLBMj59TgzOa33nF5nMqu7Nm3aYPr06Zg4cWKOBxZv3749AKQbR+Tx48f477//0L59e7VWkB8zNDRE06ZNMWrUKLX3c/HixeHs7Awg6+OW2T7VrFkTc+bMwbx583K0T3v37lV773/6782bN+K6u3btwqtXr+Di4iIeB5WffvoJLVu2VJs98Etcv34dU6ZMgUwmw8qVK8VuncnJydi0aROkUikWLVqk1t3T2NgYc+bMAQCsX79eXK6J93BRxy4zRBqmakaampqa49c+efIEDx8+ROnSpdW+OKgYGBigWbNmOHjwIC5fvqw2f3qxYsVgbW2ttr7qQl2xYsV03TJUN8Sq5MbHPh33A/jwhahmzZoIDg7G9evXUa9ePXTo0CHduklJSYiKisK1a9cAfDgOqampaskKAOIN1sdcXV3h6uqqtiwhIQGRkZE4ffo0AKh9obl8+TIAoEWLFhmOw+Lj46P2+NOxKARBwKNHjxAWFibeBKjK/5K6+NSVK1cAIMN+2iYmJqhVqxauXr2qtjyj2RSeP3+O8PBwsQl9TgeYu3DhgtpjbW1tlChRAlWqVEGTJk3g5uYGExOTdHG3bds2w/I6dOggdo36tB99Rn1iVX2/c3qznB3fffddjupXEARcuXIFWlpaaNmyZbryWrdurdbcOrfnZrt27TKM18nJCV5eXuJ6mXn69Cnu37+PsmXLZvieadWqVbrzXDXloupL8aeaNm0KHx8fBAYGYvDgwZ/dvuo6JghChs9nlAwDgHr16mHYsGEAPowrASDDGY4kEgmaNGmC8PBwXL58Od1587nzSPXlLD4+HlFRUTAwMICNjU2G61taWiI0NBRhYWFqZVauXDndDF+q875Ro0YZdmerV68eTExMEBsbi5iYGFSuXBl16tRBZGQk3Nzc0K1bNzRr1gz29vYoVqxYhn39P/W5a0SLFi2go6ODlJQUcVlOz3dNn0dZyWjaXYVCgXv37iEyMhKjR4/G1KlTMxyvI6P9adSoERo1aqS2LDk5Wewyo/rCpjo+1atXh5GREa5du4bevXvDyckJTZs2hZmZGWxtbfN8SvaOHTti0aJFOHz4MCZOnCh+fvn5+QH4X8IEwBefWzn19OlTeHh44N69e6hRowamT58uPqf6HM/o8/ZjaWlpEAQhy/VUjh8/rvZYR0cH+vr6sLKyQosWLeDq6qr2Xszp9TgzOan33Fxn6tatizNnzmD06NFi3dWrVw+6urro06dPto7NpypVqgQbGxtcu3YNcXFx4vXw0KFDEARB7UeXT2XUleX169eIiIjAuXPnAGR93OrWrYtt27Zh7ty5CAkJQcuWLdGwYUPo6+tn2F0yO/vzuTE0Ph37Ccj4swT4cE06efIkAgMDM7yPza4nT55g+PDheP/+PZYuXao2xlJYWBjevn0LCwuLDGeZsrGxQZkyZRAdHY34+HiYmJh89fcwMSFCpHEmJia4ffu2OB5DTqgGLvvc7AOqXyVUGWcVAwODdL8KqG5GVGN+ZFdmX+5V41Wo4gQ+3ITs2LEDZ8+eRWRkJOLj49PdCGX0ZSqzmJ49e4Zt27bh0qVLiIqKEo+jqryPy1Idg5yMo3H69Gns3bsX4eHhePDggTgt3ac3bl9SF59SlZXZr+wVK1ZMlxABPgxKuG3bNoSEhODevXvioIIZHYvs2Lx5c6Y3Ep+LO7NByj63/xkNXqpKFubnVLvZrd+XL19CqVSidOnS2R4YNyfnZnaPneoXysw8ffoUwOfPnU+pWnXMnz8f8+fPz7Ts2NjYz25btd3o6Gg8e/Ysw4FYPx0488KFC+laNani6dat22e3pVrvY587j1THW/U61U1qVtv4+ObbyMgo3TpZ1R3w4bjHx8cjLi4OlStXxq+//oqHDx/i/PnzWLt2LdauXQs9PT00adIEXbp0yXIsg89dI4oVK4ayZcvi8ePH6Z7L7vmu6fMoK5lNuwt86MM/fPhwTJ06FXK5PF1Ljcw+S96/f499+/bh+PHjuHv3LmJjY8Vrz6fvWZlMBk9PT4wfPx7BwcEIDg4G8OHLWqtWrdCrVy98//33X7yfKsbGxmjcuDFOnz6Nq1evom7dunjx4gUuXryIKlWqqI2X8qXnVk5ERERg6NChePToEWxtbbF+/Xq166MqeZiUlJTh61XL9fT0sp0MAT5M5ZvROfg5ObkeZyYn9Z6b60z//v0RERGBAwcOiIP/FitWDPXr10fHjh3RqVOndD8WZYeTkxNu3ryJo0ePiomhQ4cOoVKlSunG9PrUgwcPsHXrVgQFBeHevXt4/fo1gOwftw4dOuDGjRvw9vbGnj17sGfPHnFMsPbt26N79+5Zjp/3sZxMu6uqgxEjRnx2vS+5JiUmJmLYsGGIj4/HoEGD4OTkpPa86jocHh6erfPAxMTkq76H6QMmRIg0zMbGBmfPnsX169ezHCwrISEBf//9N+rVq4dGjRpl6wNc9Yvtp4MDZjTAWW5l9gGtik/1/J07d9C3b188f/4cpUqVQs2aNdGxY0dYWFigfv36aNGiRabbyKhJZ2BgIIYOHYrExESUK1dOHJDTysoKFStWTPfr4Me/mGYlLS0NP//8M06ePAmpVApra2t07doV1apVg4ODA3x8fLB///50+/o5mdXFp7K6MczoeK9btw6LFi0C8KEJfMuWLWFubg5bW1vExMRg1qxZWcb3pbI6Bp/b/5zcDOdURq2vclq/OW3BldNzM6tjp/piltW0f1kdx4ze96qy69evn+EvWCqfzuySESsrK0RHRyM4ODjdL+7ZpTrWHTp0yLQpN5DxL/3ZOY9U5asGP/2cj1tAZbf8z21Tde4bGBhgw4YNCA0NxfHjx3Hx4kXcuHEDR48exdGjR9G2bVssX7480/Jyeo3I6fmu6fPoSzRp0gQ9evTAli1bsHv37nQJkYzOqbi4OLi6uuL+/fvirB6tWrUSZ7Lq169fugRTgwYNcPz4cZw6dQqnT5/GpUuX8ODBA3h7e+Off/7B0qVLPzsAaU517doVp0+fxsGDB1G3bl0cPnwYKSkp6Wbp+NJzK7vOnz+PUaNGISEhAU2aNMHy5cvTdYVVJdQyS+SqkuOfvs/yWk6vx5+T3XrPzXVGKpVi8eLFGDZsGI4ePYoLFy7g2rVrOHv2LM6ePYsdO3Zg06ZN2Rqw+mPt27fHokWLcOTIEfE8v3nzJoYMGfLZ1/n5+WHixIlISUlBpUqV0LBhQ1StWhU1atQQu+xlx8SJE+Hq6opjx47h7NmzCA4OxpUrV3DlyhX4+Phg27Zt2RpsPqdUddCyZcsMW++pfDwAak4IgoBff/0Vt27dQrNmzTB27Nh066iui9999x1q16792fJU75+v9R6m/2FChEjD2rRpg1WrVuHkyZN4//79Z0elP3r0KNatW4fdu3fj/Pnz4k2natyCjDx48AAA0vWLzkuqXxM/pbqBVLXImDVrFp4/f46BAwdi7Nixajftql8dsksQBPz2229ITEzE9OnT0zUnDQsLS/ca1fHKLN7AwEDExcWhXr16uHDhgjhzztq1a9P9UpqQkJBh2XlRF6ptZVbWxy1uVOX++eefMDAwwKpVq9KNEaAa2yS/lS1bFg8fPsSjR49QvXr1dM+rmuXndrycz1F9ycmoNcnHfYpV/v333xzVr5GREaRSKV6/fo2kpKR0v2glJydj165d+P7779GwYcNcn5uZ1bnq3Mnq2Kn2I6PWAUD6cwf43814p06dcjwl6Ke6dOmCQ4cO4cCBAxgyZEiWCZyMlC1bFo8ePcIvv/zy2a5luaXa3+LFi2fayiAnsvPeV537n773Vc3sx4wZg7dv3+Lw4cOYO3cu/P39cfXq1XTv5Y+3ee/ePTx+/Djd+BtpaWnpvoDm9HzX9Hn0pVRfcDJqRZSRpUuX4v79++jUqRPmzp2b7nM4o2sI8GHsiHbt2old3SIjI7Fq1Srs378fCxcuzNOESKtWraCvr4+jR49i+vTp4gx1mU1bmttzKzsOHDiASZMmISUlBT169MDMmTMznUVGS0sLUVFRSEtLS5eM+nj60fySm3uFrGSn3r/kOmNubo5hw4Zh2LBhUCgUOHXqFGbOnIng4GAcPnxYrYtUdpiamqJmzZridNuqc+dz3UTevXuH6dOnQxAErFy5Ml2rhJyOu2Fqaop+/fqhX79+UCqVuHjxImbPno3o6Ghs27YNP//8c47Kyw7VddLd3T3XCfrP+fPPP3H8+HGYmZnhzz//zDDZqjoPypcvn+PzID/fw6SOg6oSaViNGjVQr149xMXFqQ0s+KlXr16Jz//444/Q0dHBd999B1NTU7x8+TLDsQXevn2L8+fPA/jQjzO/qPrgfiw2NhahoaEwMjIS+9WqBkEcOnRoul8wVXEC2esi8ezZMzx48AAlS5bMsG+tqn/rx2WpBlc8c+ZMhmUuXboU48ePR1xcnDimSffu3dN9eXj37p34/MfZ/7yqi4YNGwLI+Ibj7du36abVDAkJQVpaGurXr5/hB6TqWOS0y0xOqfbL398/w+cPHz4MIPP+vNmR2S/XqqbZqkEPP3b9+vV0y3Jav1KpFLa2tkhNTRWP58euXLmCWbNmYdOmTbk6N1XH7tOB71RUxy6j8Wk+VrZsWVSrVg3Pnz/PcPrVU6dOpVum2nZG72PgQ9epTp06YeXKlZ/dNvChT3bNmjVx7949scXS56imSM1JPOPGjUP37t1x4sSJLMvPSMWKFfHdd9/h6dOnuH37drrnFQoFOnfujD59+mRroMXatWtDIpHg/Pnz6RILwIexNV68eCFu9+XLl3B2dk43baOBgQF+/PFHNGnSBMDnm3Grbu6PHj2a7rnAwMB04+7k9HzX9Hn0pVQDV2e3e6Rq/wcOHJguGXL9+nWxXlXHZ//+/WjTpg3++usvtXXNzc3FMTSym4zJruLFi6Ndu3Z4/vw5jh07hqCgINjb26Ny5criOnlxbmUlICBAbDUwcuRIzJ07N9MWpzKZDHXr1sXbt2/F8Rw+pvqMy8+pmHNzPc5MTuo9p9eZ1NRUuLm5oUmTJmpdjGQyGdq3by8mvnJbd05OTkhLS8PRo0dx+PBhVKtW7bNdOO7cuYN3795BLpdn2EUju/cVo0ePRv369dUSxlKpFM2aNRO77+RFN7qMZHVNWrhwIbp27YqdO3fmuOx9+/Zh7dq1KFGiBFauXJlhd03gQ1JDV1cXt2/fzjCR/PTpU7Rt2xb9+vXDu3fvvsp7mNJjQoSoAPj9998hk8nw999/Y8GCBelaSzx48ABDhw4VB+T7uJlj3759AQBTp04Vf0UGPtzk/vrrr0hISEDLli0/27/9S+3bt0/ty3tCQgImTJggfsCrkh+qptKffpG5cuWK2mwl2RnczMDAAFKpFG/evEk3nsbRo0fFG5aPy2rQoAHMzc1x69YtrFixQu2DfMeOHbh27ZrYzUQV65kzZ9S62rx8+RJjxozBy5cvAagPMptXdeHo6IjKlSvjwoULaq07kpOTMXXqVHFwSBVVrNevX1dLCCiVSixbtgxnz55NF2t+cHV1hba2NtauXauWNBAEAStWrMCVK1dQrly5L+r/qvqy8ukXT1X3iatXr6p9yY6Njc3wi3lu6ld18zZ//ny1L8ovXrzAH3/8AeBDC4ncnJvt27dH2bJlcfnyZfz9999q5+aZM2ewbt06aGtri7MxfY5q0LVp06aptYa6evVqhrPUODk5wcTEBMeOHcPGjRvVth0SEoLly5cjIiIiy/7PwIeuGkuXLoWRkRE2bdqE4cOHIzIyMt164eHhGDVqlPiL2cfdKFTXDE9PT1y8eFHtddu2bYOfnx/u3LnzRbN4qN6rEyZMUJs5Kzk5Gb///jvCw8ORmJiYrXEKVOMHJCQk4NdffxXH7gE+XLunTp0K4H/nT6lSpZCamoqIiIh0rbcePnyI4OBgaGlpoUaNGplus0+fPpBKpVizZo3aORYXF5dh97jcnO9f4zxSKBSIjIzM8BzJrWvXrmHHjh0AkO5LRWYy+2yKiIjAr7/+Kj5WHZ/q1asjJiYGmzdvRlRUlNprVF2PshqbITdUg1DOmzcPgiCkaymQm3Pr8ePHiIyMzNY4Zs+ePcPkyZORmpqKYcOGZTk2A/Dh/QwAs2fPVhs/6ujRo/Dz84OJiUmOWzzkRG6ux5nJab3n5Dqjra0NAwMDxMfHY8mSJWrdNF+9eiX+kJPb86pdu3aQSCTw8fFBeHh4loOIqt4T0dHRavsqCAK2bdsmJhGyuq8wMTHBq1evsHDhQrVjnJSUJN435vUgxCo//fQT9PT0sGXLlnQzNAUEBGDz5s24fft2jrcfFBSEadOmQVtbG0uWLPlslxs9PT38+OOPSExMxK+//qp2j/bu3TtMmjQJ9+7dQ4kSJVCiRIk8+XygnGOXGaICwNzcHJs2bcKQIUOwceNGbNu2Dba2tjA2NkZsbCxCQkKQmpqKatWqYfXq1Wp9Id3c3HDt2jUcPnwYTk5OqFevHmQyGa5evYqXL1/CwsICc+fOzdf4y5YtixEjRsDBwQEmJia4cuUKXrx4gUaNGqnNJtC/f3/Mnz8fEydOxI4dO2BiYoKYmBjcunULRkZGMDExQXx8POLj4z/b3xP40GS1V69e8PHxgbu7O+rWrYuSJUvizp07iI6OFltrvH37VuzioKWlhSVLlqBfv37w8vKCn58f5HK5GEOJEiXg6ekJAOjRowd8fHxw7tw5/PDDD7CxsUFCQgKCg4ORlJSEatWq4e7du2pN0/OqLlRNbAcOHIj58+dj3759qFy5MkJCQvDixQtYW1urNfOtV6+euKxt27ZiK5GQkBA8f/4c1atXx507d7IckPNL1ahRA5MnT8bcuXMxYMAA2NnZoXz58rh9+zbu3bsHIyMjLF26NMu6/RzVjWNERAT69u0LCwsLTJkyBZUrV8YPP/yAo0eP4qeffhJb2QQGBqJatWqQy+Vq0/fmpn47dOiAixcvYteuXWL9amtrIzg4GG/evIGzs7M4oFpOz03VYH2DBw/GsmXLsG/fPlhaWuLp06e4du0atLW18dtvv2XrZrhHjx64dOkS/Pz80K5dOzRs2BCJiYm4fPkyatasKf4ariKTybB8+XIMHjwYCxYswJYtW2BhYYFXr14hODgYgiCgb9++2U5kVaxYEXv37sWECRNw/PhxHD9+HObm5qhcuTIkEgmio6MRHR0N4MOXlUGDBmHAgAHi62vUqIEpU6Zgzpw56NevH6ytrVGxYkVER0fjzp070NbWxqJFi76oG6C7uzuuX7+OQ4cOoWPHjrC1tYWRkRFCQkIQFxeHMmXKYMmSJdkub9asWbh37x4CAgLQqlUr1KlTBwqFApcvX0ZycjI6duwofjkCgJkzZ8LNzQ3z58/Hzp07YW5ujoSEBAQFBeH9+/cYNGgQzMzMMt1etWrVMGXKFMyaNUs8x0qUKIFLly7B2NgYpUuXVvuCm5vz/WucRyEhIXB3dweQfsDdz3n58iXGjx+vtiw1NRUPHz5EaGgoBEGAm5tbli2qVPr164egoCAsX75cHLDz6dOnuH79OnR1dVGxYkU8fPhQPD5WVlZwd3cXW704ODigVKlSuH//Pm7fvg09Pb0Mpz/+UnXq1IGpqSkePXoEqVSabipRIOfn1sSJE3H58mWMGDEiw1lFPrZx40a8evUKOjo6ePDgQbo6UHFwcBCnp27Tpg26dOmCf//9F+3atUODBg3w8uVLBAcHQyqV4s8//8zxmBg5kZt7hczktN5zep2ZNGkSgoKCsGnTJhw/fhxWVlZITk5GcHAwEhIS4OTkJH625VSFChVgZ2cnvm+zSohUrlwZjo6OCAgIQNeuXVGvXj0UL14cYWFhePz4cYbXjIyoxi46cuQIgoKCxC/yqvuTOnXq5FtCrFy5cvjjjz8wduxYjB07FitXrkTVqlXx5MkT3LhxAwAwZcoUtVlhsmPEiBFITk6GmZkZTp8+DX9/f3GA6o8NGzYM5ubmGDduHG7duoVLly6hTZs2sLW1hUwmw7Vr1/Dq1SuYmZmpJbK/9POBco4JEaIColatWjh06BC2bt2KM2fOIDw8HMHBwShRogQcHBzQvn179OzZM92Ng5aWFpYuXYpmzZph165d4qjnVapUwcCBA+Hm5vbZcUnyws8//4w3b95g+/btuHnzJqpUqYIhQ4bA1dVVrSltv379YGxsDG9vb0RERODGjRv47rvv4ObmhkGDBmHt2rXw8fHByZMnszVC/+TJk/H9999jx44dYreRihUrYujQoRgwYAAmTJiAkydP4vTp0+JUsJaWlti7dy9WrVqF7du3Izo6GqVLl0bHjh0xYsQIcbsVK1bErl27sGzZMvz3338ICAiAoaEh6tevj759+8LIyAjOzs44efKkOJBWXtZFrVq1sHPnTqxcuRIXL17EvXv3YGNjgyVLlmDnzp1qCRFtbW14e3vjr7/+wsmTJ3Hu3Dno6urC3Nwco0aNQvfu3dGoUSOEhITg2bNn+TqejJubG6ysrLB+/XoEBwcjLCwM5cuXR9++feHh4YHy5ct/UfllypTB3LlzsWLFCgQFBeHx48eYMmUKgA/TJK9ZswYHDhzAxYsXYWxsjD59+mDEiBHpZjLJTf0CwJw5c1CvXj2sX79ebHljaWmJ0aNHq7XeyM256eDggL1792LNmjU4e/YsTpw4gVKlSsHJyQn9+/fP9i+DEokEixcvRt26dbF9+3acP38ehoaG8PDwgLOzc4ZfohwcHMQmwGfPnsWZM2dgZGSE+vXrw83NLceter777jv4+PiIX6hDQkIQHBwMhUKBMmXKoGXLlmjatCm6dOmSYYLM1dUVVlZW2LhxI4KCgnDnzh2ULVsWTk5OGDRoULopw3NKlRxt3rw5du3ahdu3byMlJQWmpqbo0KEDPDw8Pjsw6KfKlCmDHTt2wNvbG4cPH8aZM2cgk8lgb2+PXr16pZt5wM7ODlu3bsXatWsRHByMgIAA8Vrfq1evTKdf/piLiwvMzMywZs0a3LhxAxKJBM2bN8ekSZPStSTKzfleEM6jzCQmJqabwrlYsWIwNjZG69at4ezsDEdHx2yX98MPP2D9+vVYtWoV7ty5g4iICJQtWxbdunXDoEGDcPr0aSxYsAAnT54Um6xPnjwZVatWxd69e3Hjxg0olUoYGxuje/fuGDp0qFpXlrwikUjQuXNn/P3332jRokWGsx7lxbmVGVUrhZSUFHHK38yoEiIAsGDBAtSsWRM7d+7E2bNnUbJkSTg6OmLkyJE5/jKaG7m5Hn+urOzWe06vM5UrV8b27duxatUqBAYG4tSpU9DV1UX16tXh7OyMHj16fNFxaN++Pa5duwYbG5tsjc+0dOlSrF+/HgcPHsTly5ehra0NMzMz9O7dG3379kWPHj3Ee7nMWiyUKlUKW7duxd9//42zZ8/i3LlzkEqlMDMzg4eHB9zd3fM1IfbDDz9gz549WLduHS5duoRTp06Jn0H9+/fPVRdeVbL53r17uHfvXqbr9ezZE+bm5tDV1cWGDRuwfft27N+/HyEhIQA+XJfd3Nzg7u6u1uUmP9/DlDGJkN+dyonomzVp0iTs3bsXc+bM0fggernRqVMnvH//HlOmTPnsDDdEnzp69ChGjhwJd3d3DB06NF8GiiUiIiKi/MUxRIioyJo+fTpq166NIUOGZDqYJVFGbG1tsWLFCvz7779qXSGIiIiIqPBglxkiKnJevnyJuXPn4v3792K3lo8HMCP6nJ07dyIwMBCxsbF4/fq12oCgRERERFR4MCFCREXOixcvxCkyy5cvj3HjxmU5wBiRSmhoKI4fP47ixYujadOmmDRpkqZDIiIiIqJc4BgiRERERERERFTkcAwRIiIiIiIiIipymBAhIiIiIiIioiKHY4hQvrl27RoEQYBUKtV0KERERERERFQEKJVKSCQS2NvbZ7kuW4hQvhEEARyipuARBAHJycmsm0KEdVb4sM4KF9ZX4cM6K3xYZ4UL66vwYZ39T06+h7KFCOUbVcsQW1tbDUdCH0tMTMStW7dQrVo16OnpaTocygbWWeHDOitcWF+FD+us8GGdFS6sr8KHdfY/oaGh2V6XLUSIiAoBqVQKiUSi6TAomyQSCWQyGeuskGB9FT6ss8KHdVa4sL6oqGALESKiAk4ikcDa2ho6OrxkFxYymQzW1taaDoOyifVV+LDOCh/WWeHyrdZXWloatLTYJoD+h3fXRESFgI6ODnx9fREfH6/pUIiIiIgKHRMTEzg7O2s6DCpgmBAhIiok4uPjERsbq+kwiIiIiIi+CWwvRERERERERERFDluIEBEREREREWVDamoqlEqlpsNI5/379+L/3/I4KVKpFNra2nlWHhMiRERERERERJ8hCAJiY2Px6tUrTYeSobS0NOjo6ODx48ffdEIEAIyMjFC+fPk8mQWJCREiIiIiIiKiz1AlQ8qWLQs9Pb0CNyVxamoq3r9/j+LFi+dpC4qCRBAEJCYmIi4uDgBQoUKFLy6TCZE8lJCQgMaNG6NEiRI4ffo0pFJpnpWtVCrxzz//oF+/fl9UjqOjI7p164aRI0fC19cXkydPRnh4eN4ESURERERE9I1JTU0VkyFlypTRdDgZSk1NBQDo6up+swkR4MOU0AAQFxeHsmXLfvG+ftttab6ygwcPokyZMnj79i2OHTuWp2X7+flh/vz5eVqmk5MTzp07l6dlEhERERERfUtUY4bo6elpOBIC/lcPeTGWCxMieWjPnj1o2rQpGjRogO3bt+dp2YIg5Gl5wIfsoYmJSZ6XS0RERERE9K0paN1kiqq8rAcmRPJIZGQkrl+/jsaNG+OHH35AYGAgoqOjxecdHR3h5eWl9pqPl6WmpmLRokVo3rw5atSogXbt2mHbtm0AIHZtAQALCwsEBgbCy8sLrq6uGDNmDBwcHDB79mwAwK5du9CpUyfUrFkTdnZ2cHFxQWhoaIYx+/r6wsLCQnwcERGBIUOGoG7duqhRowZatWqFDRs25N1BIiIiIiIioq8mP35Y/5YwIZJHdu/eDT09PTRr1gxt2rSBVCrNUSuRrVu34siRI1i6dCn8/f3h6uqK33//HVevXoWTkxOmTJkCADh37hzs7e0BAFeuXIGxsTH+/fdfuLm54dixY5g1axYGDhyIw4cPw9vbG+/fv8fUqVOz3L5CoYCHhweMjIywfft2+Pn5oV27dvjjjz9w69at3B0UIiIiIiIigpubGywsLMR/lpaWsLe3h7OzMzZv3oyUlJQ83d6bN28wYcIEXL16NU/L/dZwUNU8kJKSgv3798PR0RG6urrQ1dVFkyZNsG/fPowdOxbFixfPsoyYmBjo6emhYsWKKFu2LFxdXVG1alV8//330NXVhYGBAQCk6+IyatQo8bn4+HjMnTsXnTt3BgCYmpqiR48emDVrVpbbVygUcHd3R58+fVCiRAmx7HXr1iE8PBxWVlY5OiZERERERET0P9bW1pgxYwaADz0EXr9+jTNnzmD+/Pm4evUqli1blmdT5t66dQv//vsvunfvniflfauYEMkDp0+fxrNnz9ChQwdxWYcOHXDy5EkcPnwYXbt2zbKMPn364Pjx42jevDmsrKzQuHFjdOjQ4bOjGJcpU0ZMhgBA3bp1ERkZiZUrVyIqKgr3799HeHg40tLSstx+6dKl4eLiAj8/P4SFhSEmJga3b98GgGy9noiIiIiIiDKnr68POzs7tWWOjo6oWrUq5s6dCz8/P/HHbfo62GUmD/j6+gIARowYAWtra1hbW2PixIkA8NluMx83izIzM8PRo0exbt06NGjQAKdOnULXrl2xd+/eTF+vq6ur9vjAgQPo3LkzHjx4AAcHB0ycOBGTJk3K1j7Ex8ejc+fO2LVrF8qVKwcXF5fPbpuIiIiIiIi+nKurK8qVK6f23XHXrl3o0KEDatSogRYtWsDLy0ucWhcAXrx4gXHjxqFx48awtbVFt27d4OfnBwAIDAyEu7s7AMDd3R1ubm4APrRKWbNmDTp27CiOOdmrVy9cunRJLZ5Lly7hxx9/RK1atdC+fXucOHECbdq0Ece/DAwMFMe2/Jibm5u4rezuh6axhcgXev78OU6fPg1nZ2f0799f7Tlvb2/s2bMHERERkEqlSEhIEJ9LSEjA8+fPxcebN29GmTJl0KFDBzRu3BgTJkxA//79cejQIXTr1i1bI+muWbMGPXr0wMyZM8VlJ06cAPBhMJ3PleHn54dXr17B398fUqkUABAeHi6+loiIiIiIiPKelpYWGjZsiIMHDyIlJQXr16/H0qVL4erqismTJ+PWrVvw8vLCkydPMG/ePADAr7/+iufPn2PmzJnQ19fH3r17MX36dFSqVAk1a9bE9OnTMWvWLEyfPh3169cHACxevBjbtm3DuHHjYGFhgadPn2LlypX45ZdfcOrUKchkMty8eRMDBw5EvXr1sHz5csTExGDKlClITk7O8X6tXr06y/3QNCZEvtD+/fuRkpKCQYMGoWrVqmrPDR06FHv37sX27dthZ2eHQ4cOoW3btihZsiSWL18ObW1tcd0XL15g5cqV0NXVhaWlJaKionDr1i0xs6eaa/nGjRuoVq1ahrFUqFABwcHBuHnzJgwMDBAQEIAtW7YAAJKTkz87lkn58uWhUChw5MgR1K5dG1FRUZg/f774WiIiIiIiIsofxsbGUCqVePr0Kf766y/89NNP4uQYTZo0gZGREaZOnYr+/fujevXquHz5MoYPH47WrVsDAGrXrg0DAwMUK1YM+vr64nfGatWqiX/HxcVhzJgxaq04ihcvjpEjRyI8PBx2dnZYvXo1SpcujVWrVqFYsWIAAENDQ/z666852p+3b99maz80jQmRL+Tr64tGjRqlS4YAQOXKldG6dWvs378fBw8exKtXr9C/f38YGBjAw8MDb968EdcdMWIElEol5syZg/j4eJiYmKB3794YMmQIAKBBgwaoVasWevXqhUWLFmUYy7Rp0zB9+nS4urqiWLFisLS0xMKFCzFmzBiEhoaiTp06me5Hu3btcPPmTSxYsAAJCQkwNTVFz549ceLECYSGhqJ3795feKSIiIiIiIgoI6pW+VeuXEFSUhIcHR3VhlhwdHQEAJw/fx7Vq1dH/fr14eXlhbCwMDRt2hRNmzbFmDFj0g2r8LE///wTwIcf41VjTp48eRLA/34Ev3r1Klq2bCkmQwDAyckJkydPztH+XLt2LVv7oWlMiHyhAwcOfPZ5VT8r4EOToY95eHiIf+vo6ODXX3/NNPNmaGiInTt3qi0bOXKk2uNKlSph48aN6V7r5OQk/h0QECD+7ezsDGdnZwCARCLB+PHjMX78eLXXftoNiIiIiIiIiPLW06dPoaurK84yM3jw4AzXi4uLAwAsXboUq1atwuHDh+Hv7w8tLS3Ur18fs2bNQuXKlTN8bWhoKGbOnInQ0FDIZDJUq1YN3333HYD/JWRev36N0qVLq71OR0fns5N9ZOTVq1fZ2g9NY0KEiIiIiIiISENSUlIQGBgIBwcHlCxZEsCH8T7MzMzSrWtsbAwAMDAwEH9Qj4qKwvHjx/HXX39h9uzZWLt2bbrXJSQkYODAgbCwsMDBgwdRtWpVaGlp4fTp0/D39xfXK1WqFJ49e5bu9a9fvxb/Vo1N+elspO/evUOJEiUAINv7oWmcZYaIiIiIiIhIQ3bs2IH4+Hj07t0btWrVglQqxdOnT2Frayv+09HRwZIlS/Dw4UM8evQIzZs3x5EjRwAAVatWxYABA9CgQQM8fvwYANTGqwSAqKgovHr1Cu7u7qhWrZrYEuXMmTMA/pfcaNiwIc6cOQOFQiG+9sKFC0hKShIf6+vrAwBiY2PFZa9fv0ZkZKT4ODv7URCwhQgRERERERFRPktISMB///0H4EMC4uXLlzh37hx27NiBzp0744cffgAADBw4EJ6enkhISED9+vXx9OlTeHp6QiKRwNLSEgYGBihfvjzmzJmDhIQEVK5cGSEhITh37hwGDRoE4EMLEgA4deoUDA0N8f3330NfXx+rVq2Cjo4OdHR04O/vj927dwOAmAAZPnw4AgICMGDAAAwcOBCvXr3CkiVL1PbDwsICFSpUwMqVK6Gvrw+JRILVq1dDJpOJ65QqVSrL/SgImBAhIiIiIiIiymdhYWH46aefAHzodlKiRAnI5XL8/vvv6Nmzp7je6NGjYWJigq1bt2LdunUwNDREw4YNMXbsWDHRsWLFCixZsgSenp54+fIlypcvj8GDB2PYsGEAgOrVq6Njx474559/cPbsWfj5+eGvv/7CwoUL8csvv6BEiRKwsrLCli1bMGjQIFy9ehWOjo4wMzPDP//8g/nz54txTJw4UW2sSW1tbSxfvhzz5s3D2LFjYWxsjL59+yIqKgrR0dE52g9Nkwiq0VOI8lhoaCgAwNbWVsOR0McSExNx69YtWFlZidM5U8GmUCggk8ng6+uL+Ph4TYdDREREVOiYmJiIE0rkVFJSEqKjo/H9999/dhYXTUpNTUVSUhJ0dXXTdZfJCxYWFhgxYkS6iT00Iav6yMn3ULYQISIqBFJSUnL9IU5EREREH7qpqMbOIAI4qCoRUYEnCALCwsLUBreigk2hULDOChHWV+HDOit8WGeFy7daX0yG0KfYQoSIqBBQKpVgD8fCQxAEKBQK1lkhwfoqfFhnhQ/rrHBhfdGnwsPDNR1CvmCKjIiIiIiIiIiKHCZEiIgKAalUColEoukwKJskEglkMhnrrJBgfRERERVN7DJDRFTASSQSWFtbQ0eHl+zCQiaTwdraWtNhUDaxvtRx0EEiIioqeHdNRFQI6OjocNpdIsp3XzItJRERUWHDhAgRUSERHx+P2NhYTYdBRERERPRNYHtIIiIiIiIiIipymBAhIiIiIiIioiKHCREiIiIiIiKiXEhLSyt0201NTcXWrVvRo0cP2Nvbo06dOujVqxd2794NQRCyXY5SqYS3t3eu4ygIOIYIERERERERUS5oaWl99YHvv2QAbKVSieHDhyMkJAQjRoxAkyZNkJqairNnz2LBggUICAiAl5cXtLW1syzLz88P8+fPR79+/XIVS0HAhIgGJSQkoHHjxihRogROnz4NqVSaZ2UrlUr8888/nz05fX19MXnyZISHh+fZdomIiIiIiIqSwjTw/erVq3H16lXs3r0bVatWFZebm5ujXr16+PHHH7F+/XoMHjw4y7Jy0pqkoGKXGQ06ePAgypQpg7dv3+LYsWN5WrYqW/c5Tk5OOHfuXJ5ul4iIiIiIiAqetLQ0+Pj4wNnZWS0ZomJtbY0uXbrAx8cHDx48gIWFBQIDA8XnHz58KC5T/bgOQFzm5eUFV1dXjBkzBg4ODpg9ezYA4Nq1a3B3d0ft2rVRv359TJ48GS9fvhTLDQkJgYuLC+zt7VG3bl2MHDkSjx8/zuej8QETIhq0Z88eNG3aFA0aNMD27dvztOzsZOt0dXVhYmKSp9slIiIiIiKigic6OhqvXr2Cg4NDpus0bNgQcXFxWY5R4uTkhClTpgAAzp07B3t7ewDAlStXYGxsjH///Rdubm4ICQmBm5sbqlevjp07d8LT0xPXr1/HgAEDkJqaitTUVAwZMgR169bF/v374e3tjcePH4tl5zcmRDQkMjIS169fR+PGjfHDDz8gMDAQ0dHR4vOOjo7w8vJSe83Hy1JTU7Fo0SI0b94cNWrUQLt27bBt2zYAyHa2ztfXFxYWFmL5ERER4slYo0YNtGrVChs2bMjvQ0FERERERET57PXr1wCAUqVKZbqO6rkXL158tixdXV0YGBgA+DCmSbFixcTnRo0ahUqVKsHMzAwbNmyAhYUFpk2bBnNzczRo0ABLlizBzZs3ce7cOSQkJODly5coW7YsTE1NYWNjg2XLlmH06NFfuLfZw4SIhuzevRt6enpo1qwZ2rRpA6lUmqNWIlu3bsWRI0ewdOlS+Pv7w9XVFb///juuXr2a7WzdxxQKBTw8PGBkZITt27fDz88P7dq1wx9//IFbt27l3Y4TERERERHRV6dKdrx9+zbTdVRJk9KlS+dqG2XKlBETJcCHH90/bZFiaWkJAwMDhIeHw9DQEAMHDsTs2bPRsGFD/PLLL7hy5YraD/f5iQkRDUhJScH+/fvh6OgIXV1dGBkZoUmTJti3bx/ev3+frTJiYmKgp6eHihUrwtTUFK6urti4cSO+//77bGfrPqZQKODu7o7p06fD3NwcZmZmGDVqFABw0FUiIiIiIqJCrnLlyjAxMcGVK1cyXefy5cswMTGBRCJJ91xqamqW29DV1VV7nNlQDoIgiJOKjB8/HgEBARg9ejQEQcDs2bPRvXt3JCcnZ7m9L8WEiAacPn0az549Q4cOHcRlHTp0wKtXr3D48OFsldGnTx8kJCSgefPmcHZ2xp9//onSpUujTJkymb7m02zdx0qXLg0XFxf4+flhxowZ6N+/P1q0aAFAc3NrExERERERUd7Q1tZGv379sHv3bkRGRqZ7/s6dO9i3bx9cXV1RvHhxAB9mRlW5d++e2voZJU0+ZWFhgaCgILVlt2/fRkJCAszNzREVFYUZM2agTJky6N27N5YvX45169YhMjISt2/fzsVe5gyn3dUAX19fAMCIESPSPbd9+3Z07do1w9elpKSIf5uZmeHo0aO4fPkyzp8/j1OnTmHt2rWYP38+unXrluHrP83WfSw+Ph4//fQTSpcuDUdHRzRp0gS2trZo3rx5DvaMiIiIiIiICioPDw+EhoaiT58+GDlyJJo0aQLgw1ALy5cvR4MGDTBo0CBoaWnB1NQUmzZtgpmZGV69egVPT0+1JIienh4A4MaNG6hWrVqG2+vfvz9cXFwwe/ZsuLi44NmzZ5g9ezasra3RsGFDJCQk4ODBg0hKSsLgwYOhpaWFvXv3wtDQMMOZcPIaEyJf2fPnz3H69Gk4Ozujf//+as95e3tjz549iIiIgFQqVcvGJSQk4Pnz5+LjzZs3o0yZMujQoQMaN26MCRMmoH///jh06BC6deuWrWzdx/z8/PDq1Sv4+/uLTZdUXWW+hfmliYiIiIiI8sPXnrnzS7anpaUFT09P7Nu3Dzt27MDSpUshCAKqV6+O8ePHo0ePHuJ3yYULF2LevHno0qULqlSpgsmTJ2Pw4MFiWQ0aNECtWrXQq1cvLFq0KMPt1apVC+vWrcOyZcvQtWtX6Ovro3Xr1hg3bhykUilKlSqFtWvX4s8//8SPP/6I1NRU2NnZYePGjdDX18/1fmYXEyJf2f79+5GSkoJBgwaly3gNHToUe/fuxfbt22FnZ4dDhw6hbdu2KFmyJJYvXw5tbW1x3RcvXmDlypXQ1dWFpaUloqKicOvWLbi7uwPIXrbuY+XLl4dCocCRI0dQu3ZtREVFYf78+QDwVfpuERERERERFTZpaWlwdnbWyHa1tHI/AkbXrl0z7ZmgUqdOHbF3g0pYWJj4t6GhIXbu3Kn2/MiRI9OV07BhQzRs2DDT7djb22PLli3ZiDrvMSHylfn6+qJRo0YZNv+pXLkyWrdujf379+PgwYN49eoV+vfvDwMDA3h4eODNmzfiuiNGjIBSqcScOXMQHx8PExMT9O7dG0OGDAGQvWzdx9q1a4ebN29iwYIFSEhIgKmpKXr27IkTJ04gNDQUvXv3zruDQERERERE9A34kqREYdzut0YisD8E5ZPQ0FAAgK2trYYjoY8lJibi1q1bsLKyElsSUcGmUCggk8mwevVqxMbGajocIvqGlS9fXvxxpSDjZ1nhwzorXFhf6pKSkhAdHS3O6FkQpaamIikpCbq6umo9C75FWdVHTr6HMq1EREREREREREUOEyJEREREREREVOQwIUJERERERERERQ4TIkRERERERERU5HCWGSKiQuJrz3FPREUPrzNERFSUMCFCRFQIpKSkaGSOeyIqetLS0jidIxERFQn8tCMiKuAEQUBYWBgUCoWmQ6FsUigUrLNChPWljskQIiIqKviJR0RUCCiVSgiCoOkwKJsEQYBCoWCdFRKsLyIioqKJCREiIiIiIiKiXEhN1UwyPbfbdXR0hIWFhfivRo0aaNu2LdatW5fHEarz9fWFhYWF+NjCwgK+vr75us3s4BgiRERERERERLmgrS3B6NHxuHtX+dW2Wa2aFMuW5X4QbA8PD3h4eAAAkpKSEBISgqlTp0Imk6FPnz55FaYaJycnNG3aNF/K/hJMiBARFXASiQRSqVTTYRARERFRBu7eVeLmzWRNh5Ftenp6arOKVapUCYGBgdizZ0++JUR0dXWhq6ubL2V/CXaZIaJCLS0tTdMh5DuZTAZra2tIJBJNh0JERERE36CPkxVubm6YNm0aevbsiTp16mD//v1ITk7GH3/8AUdHR9SoUQP16tXDL7/8ghcvXoiv27dvHzp06ABbW1s0bdoUc+fORXLyh0TRp11mCgq2ECGiQk1LSwu+vr6Ij4/XdCj5xsTEBM7OzlAqv15TTCIiIiIqGkJCQuDn54eRI0eKy3bt2oVFixbBwsICJiYmWLhwIU6ePIkFCxbA1NQU4eHhmDx5Mv7++2/89ttvuH37NqZOnYrFixejZs2aiIyMxLhx41CqVCn8/PPPGty7z2NChIgKvfj4eMTGxmo6DCIiIiKiAm/16tXYsGEDgA8zGSqVStSqVQudOnUS17GyslJ7bGtri3bt2qFOnToAAFNTUzRq1AgREREAgIcPH0IikcDU1BTfffcdvvvuO6xfvx76+vpfcc9yjgkRIiIiIiIioiKiV69ecHNzAwCkpKTg/v37WLp0Kfr06YNdu3YBAKpUqaL2mi5duuDChQtYvHgx7t27h6ioKERHR4sJkqZNm8Le3h49evRAxYoV0bhxY7Rq1Qo1atT4ujuXQ0yIEBERERERERURhoaGagkPc3NzGBoawsXFBRcuXACAdAOgTp8+Hf7+/ujatSscHR0xfPhwrF+/Hk+fPgUAFC9eHJs3b0ZYWBjOnTuHc+fOYejQoejatSvmz5//9XYuh5gQISIiIiIiIirCBEEAkPGEBS9fvsSOHTuwdOlSODk5icujoqKgp6cHADh9+jRCQ0MxYsQIWFtbY/Dgwfj777+xatUqJkSKGkdHRzx69Eh8LJVKYWpqip49e2LgwIHZKiMwMBDu7u44ceIEKlasmOfrExERERER0ZerVk1aqLaXmJgoTkggCAJiYmIwb948lC1bFg0bNsTGjRvV1tfX14eBgQFOnDgBGxsbJCUlYcuWLbh58yZq1aoF4MN33pUrV0JfXx+tWrXC69evcerUKdjb239RrPmNCZF84uHhAQ8PDwBAUlISQkJCMHXqVMhksnyZ29ne3h7nzp1D6dKl87xsIiIiIiIiSi81VcCyZSYa2a62tiRXr92wYYM4qKqWlhaMjIxQp04dLF68GDKZLN36UqkUnp6eWLBgATp16gRDQ0PUr18fY8eOxerVq6FQKNCoUSPMnTsXGzZswNKlS6Grq4vmzZtj0qRJX7Sf+Y0JkXyip6cHE5P/vTEqVaqEwMBA7NmzJ18SIsWKFVPbHhEREREREeWv3CYlNLXdgICALNfx8fFJt6xx48Y4cOBAuuVDhgwR/3Z2doazs3OGZX76XHh4eHbCzXdamg6gKPl4YBpBELB27Vq0atUKtWrVQpcuXbB///5MX5uamoqlS5eiSZMmsLOzw6hRozB37lxxdODAwEBYWFjg4cOHAD502/Hy8lIr4+Nlvr6+aNOmDbZv344WLVqgVq1aGDVqFJ4+fYrx48fD3t4ezZo1w+7du/P6MBARERERERFpHBMiX0lISAj8/PzQs2dPAMDSpUuxbds2TJs2DQcOHIC7uzt+//13/PPPPxm+fvHixdixYwdmzJiBPXv2wMTEJMPMXU48fvwYR44cwZo1a7B8+XKcOHECnTp1go2NDfbs2YNmzZrh999/x8uXL79oO0REREREREQFDbvM5JPVq1eL/bKUSiWUSiVq1aqFTp06ITExEd7e3liyZAlatGgBAKhcuTIePXqE9evXp+tSo1AosHXrVkyePBlt2rQBAEydOhXXrl37ohhTUlIwbdo0mJubQy6Xw9LSElKpFP379wcA9O/fH7t27cK9e/dQqlSpL9oWERERERERUUHChEg+6dWrl9idJSUlBffv38fSpUvRp08fzJo1C+/fv8e4ceOgpfW/RjopKSlITk5GUlKSWlmRkZFISkqCnZ2duEwikaB27dq4ffv2F8VZuXJl8W89PT1UqFBBfFy8eHEAQHJy8hdtg4iIiIiIiKigYUIknxgaGqJKlSriY3NzcxgaGsLFxQVnz54FACxbtgxVq1ZN99pixYqpPdbR+VBNqrmhcyslJSXdMqlUfcqmjxM0RERERERE9MGXfh+jvJGX9cBvv1+RquIsLS2ho6ODx48fo0qVKuK/06dPY/369emSElWqVIGuri7+++8/teXXr1/PdFtSqRQJCQni44SEBDx//jzvdoaIiIiIiKgIUP2InJiYqOFICPhfPXz6435usIVIPklMTER8fDyAD4mQmJgYzJs3D2XLlkXjxo3Rq1cveHp6Ql9fHw4ODggMDMSiRYvUpi1SkclkcHNzw/Lly2FiYgJzc3Ps3LkT169fR7169TLcvp2dHQ4dOoS2bduiZMmSWL58ObS1tfN1n4mIiIiIiL412traMDIyQlxcHIAPQw1IJJqZbjczqampeP/+PQB8s9/7BEFAYmIi4uLiYGRklCf7yYRIPtmwYYM4qKqWlhaMjIxQp04dLF68GDKZDJMnT0apUqXg6emJuLg4VKhQAaNGjcLAgQMzLO+XX36BUqnE1KlToVAo0LJlS7Rq1Uo86T81duxYvHr1Cv3794eBgQE8PDzw5s2bfNtfIiIiIiKib1X58uUBQEyKFDRpaWlISUmBjo7ONz8MgpGRkVgfX0oisCNUoXDs2DHUrl0bpUuXFpd5eHigfPnymDdvngYjy1xoaCgAwNbWVsOR0McSExNx69YtWFlZQU9PT9Ph5InVq1cjNjZW02Hkm/Lly2PIkCFQKBSQyWSaDoey4Vt8n33LWF+FD+us8GGdFS6sr8ylpqZCqVRqOox0FAoFoqKiULVq1W/6flEqlWbZMiQn30PZQqSQWL9+PbZu3YoJEyZAX18fJ06cwKVLl8RWKERERERERJS/tLW1C2SXlLS0NAAfZgrV1dXVcDSFx7fdluYbsnjxYpQoUQL9+vVDx44dceDAAXh6eqJBgwaaDo2IiIiIiIio0GELkUKiYsWKWLFihabDICIiIiIiIvomsIUIERERERERERU5bCFCRIWeiYmJpkPIV9/6/hERERERaQITIkRUqKWlpcHZ2VnTYeS7lJQUTYdARERERPRNYZcZIirUvvV51oEP06iFhYWBs6QTEREREeWdb/+bBBFRIScIQoGc756IiIiIqDBjQoSIiIiIiIiIihwmRIiIiIiIiIioyGFChKiIkUgkkMlkkEgkmg6FskkikUAqlWo6DCIiIiKibwpnmSECkJoqQFu7aCQIZDIZrK2tNR0G5YBMJoOVlQ1SUpI1HQoRERER0TeDCREiANraEoweHY+7dzlwJRU81apJsWyZCTjzLhERERFR3mFChOj/3b2rxM2b/AWeiIiIiIioKOAYIkRERERERERU5DAhQkRERERERERFDhMiRERERERERFTkMCFCREREREREREUOEyIFlKOjIywsLMR/NWrUQNu2bbFu3bp83a6XlxccHR0BAA8fPoSFhQUCAwPzdZtEREREREREXxtnmSnAPDw84OHhAQBISkpCSEgIpk6dCplMhj59+uTbNvOrbCIiIiIiIqKCggmRAkxPTw8mJibi40qVKiEwMBB79uzJt6RFiRIlUKJEiXwpm4iIiIiIiKigYEKkkNHV1RX/dnNzg5mZGW7fvo3o6GhMnz4d7dq1w9KlS+Hv74+4uDjo6emhYcOGmDFjBkqXLo1JkyZh79696co1NTVFQEAAvLy8sHfvXgQEBHzN3SIiIiIiIiL6qjiGSCESEhICPz8/9OzZU1y2a9cuuLu7Y+vWrWjatCkWLlyIo0ePYsGCBfD398eCBQtw6dIl/P333wCA3377DefOnRP/eXp6QltbGyNHjtTUbhERERERERF9dWwhUoCtXr0aGzZsAAAolUoolUrUqlULnTp1EtexsrJSe2xra4t27dqhTp06AD60/GjUqBEiIiIAAAYGBjAwMAAAxMTEYMaMGfDw8EC3bt2+1m4RERERERERaRwTIgVYr1694ObmBgBISUnB/fv3sXTpUvTp0we7du0CAFSpUkXtNV26dMGFCxewePFi3Lt3D1FRUYiOjhYTJCqvX7/G4MGDUbduXYwbN+7r7BARERERERFRAcGESAFmaGiolvAwNzeHoaEhXFxccOHCBQDqY4oAwPTp0+Hv74+uXbvC0dERw4cPx/r16/H06VNxHaVSiREjRkAmk2HhwoWQSCRfZ4eIiIiIiIiICggmRAoZQRAAAGlpaemee/nyJXbs2IGlS5fCyclJXB4VFQU9PT3x8fTp0xEdHY3du3erLSciIiIiIiIqKpgQKcASExMRHx8P4EMiJCYmBvPmzUPZsmXRsGFDbNy4UW19fX19GBgY4MSJE7CxsUFSUhK2bNmCmzdvolatWgA+jEty6NAhrF69GlKpVCwfAEqXLv31do6IiIiIiIhIg5gQKcA2bNggDqqqpaUFIyMj1KlTB4sXL4ZMJku3vlQqhaenJxYsWIBOnTrB0NAQ9evXx9ixY7F69WooFArs2LEDSUlJ6Nu3b7rXnzhxIt/3iYiIiIiIiKggYEKkgAoICMhyHR8fn3TLGjdujAMHDqRbPmTIkGyVO3LkSHEK3ooVKyI8PDw74RIREREREREVKlqaDoCIiIiIiIiI6GtjQoSIiIiIiIiIihwmRIiIiIiIiIioyGFChIiIiIiIiIiKHA6qSvT/qlWTajoEogzx3CQiIiIiyntMiBABSE0VsGyZiabDIMqUUpmq6RCIiIiIiL4p7DJDBEBbW6LpEL4ahUKBsLAwKBQKTYdC2aRQKHDr1k0IgqDpUIiIiIiIvhlMiBAVMYIgQKFQ8Mt1ISIIApRKpabDICIiIiL6pjAhQkRERERERERFDhMiRESFgFQqhURSdLp2FXYSiQQymYx1Vkiwvgof1lnhwzojooKIg6oSERVwEokE1tbW0NHhJbuwkMlksLa21nQYlE2sr8KHdVb4FOQ6S0tLg5YWfycmKop4d01EVAjo6OjA19cX8fHxmg6FiIjom2FiYgJnZ2dNh0FEGsKECBFRIREfH4/Y2FhNh0FERERE9E1g2zAiIiIiIiIiKnKYECEiIiIiIiKiIocJESIiIiIiIiIqcpgQISIiIiIiIqIihwmRfJaSkoJNmzbB2dkZ9vb2aNCgATw8PHDp0iVNhwYvLy84OjpqOgwiIiIiIiKir46zzOSj9+/fo3///njy5AlGjRoFe3t7JCUlYc+ePejfvz8WLlyITp06aTpMIiIiIiIioiKHCZF85OnpifDwcPj5+aFChQri8t9++w0JCQmYM2cOHB0dUaJECQ1GSURERERERFT0sMtMPlEqldizZw+cnZ3VkiEqo0ePxtq1a6Grq4uIiAgMGTIEdevWRY0aNdCqVSts2LBBXNfLywv9+vXDmjVr0KxZM9ja2sLV1RWRkZHiOlmVAQA7duxAmzZtULNmTQwdOhSvX79Wez47ZRARERERERF9C5gQyScPHjzAq1ev4ODgkOHz5cqVQ82aNZGcnAwPDw8YGRlh+/bt8PPzQ7t27fDHH3/g1q1b4vpXr15FUFAQ1qxZg61bt+L58+eYOXMmAEChUGRZhp+fH2bNmoV+/frh33//hYODA/755x+x/OyUQURERERERPStYEIkn6haXxgaGn52PYVCAXd3d0yfPh3m5uYwMzPDqFGjAADh4eHieikpKVi4cCEsLS1ha2uLXr16ITg4ONtl+Pj4wMnJCX369MH333+PwYMHo2XLljmOg4iIiIiIiOhbwDFE8knp0qUBAK9evcpyPRcXF/j5+SEsLAwxMTG4ffs2ACAtLU1cz9jYWC25YmBgAKVSme0yIiIi0KFDB7Vt29vbi+tlNw4iIiIiIiKibwFbiOSTSpUqwdjYWGzF8anIyEh4eHggKCgInTt3xq5du1CuXDm4uLhg79696dYvVqxYptuKj4/PVhmfJjakUmmOyyAiIiIiIiL6FrCFSD7R0tJCjx494OPjgwEDBqQbWHXdunUIDQ3F5cuX8erVK/j7+4sJClUXFUEQsrUtPz+/LMuwsrJCcHAw+vXrJ74uNDQ0R2UQERERERERfSvYQiQfDR06FGZmZnBxccG+ffsQExODkJAQTJ48Gfv27cPs2bNhZmYGhUKBI0eO4PHjxzh37hzGjh0LAEhOTs7WdsqXL59lGYMHD8axY8ewbt063Lt3Dz4+PvD3989RGURERERERETfCrYQyUcymQxbtmzBhg0bsHbtWjx+/Bi6urqwtraGj48P6tSpA0EQcPPmTSxYsAAJCQkwNTVFz549ceLECYSGhqJ3795Zbqddu3ZZltGiRQv8+eef8PLygqenJ+zs7ODh4QE/P79sl0FERERERET0rZAI7A9B+UTVJcfW1lbDkdDHEhMTcevWLVhZWUFPT0/T4VA2KBQKyGQyrF69GrGxsZoOh4iI6JtRvnx5DBkyRNNhFDi8Xyx8WGf/k5PvoewyQ0RERERERERFDhMiRERERERERFTkMCFCREREREREREUOEyJEREREREREVORwlhkiokLCxMRE0yEQERF9U/jZSlS0MSFCRFQIpKSkwNnZWdNhEBERfXPS0tKgpcWG80RFEd/5REQFnCAICAsLg0Kh0HQolE0KhYJ1Voiwvgof1lnhU5DrjMkQoqKL734iokJAqVRCEARNh0HZJAgCFAoF66yQYH0VPqyzwod1RkQFERMiRERERERERFTkMCFCREREREREREUOEyJERAWcRCKBVCrVdBhERERERN8UJkSIiAqwtLQ0yGQyWFtbQyKRaDocIiIiIqJvBhMiREQFmJaWFs6cOQMdHc6STkRERESUl5gQISIq4F69eqXpEIiIiIiIvjlMiBARERERERFRkcOECBEREREREREVOUyIEBEREREREVGRw1H6/l9KSgr++ecf/Pvvv4iOjkbx4sVhbW2NwYMHo0GDBnm2ncTEROzduxd9+vTJdRmTJk3C3r171Zbp6OigVKlSaNiwISZPnozSpUsDANzc3GBqaooFCxZ8UdxERERERERE3xImRAC8f/8e/fv3x5MnTzBq1CjY29sjKSkJe/bsQf/+/bFw4UJ06tQpT7a1YcMG+Pr6flFCBADs7e3h5eUlPk5KSsK1a9cwa9YsvHr1CmvXrgUAeHl5QVtb+4u2RURERERERPStYUIEgKenJ8LDw+Hn54cKFSqIy3/77TckJCRgzpw5cHR0RIkSJb54W4IgfHEZACCVSmFiYqK2rFKlSoiJiYGXlxfevn0LAwMDGBkZ5cn2iIiIiIiIiL4lRX4MEaVSiT179sDZ2VktGaIyevRorF27Frq6ugA+TH85c+ZMNG/eHDVr1kSvXr0QGBgorq9QKPDbb7+hcePGsLW1RdeuXXH06FEAH1prrFixAo8ePYKFhQUePnyI5ORk/PHHH3B0dESNGjVQr149/PLLL3jx4kWu9qd48eKQSCRiqxA3NzdMmjRJfD4kJAT9+vWDvb09GjVqhBkzZkChUAAAXr9+jalTp6Jp06awsbFBw4YNMXXqVPF5IiIiIiIiom9FkU+IPHjwAK9evYKDg0OGz5crVw41a9aEtrY2UlNT4eHhgatXr2LRokXw9fWFXC7HgAEDEBISAuB/rU3WrFmDQ4cOoVmzZhgzZgwePnwIDw8PeHh4oHz58jh37hwqVKiAhQsX4ujRo1iwYAH8/f2xYMECXLp0CX///XeO9kMQBAQHB2PTpk344YcfoKenl+G+9u3bF2XLlsWOHTvg5eWF8+fPY+bMmQA+jE0SFhaGFStWwN/fH5MnT8a+ffuwY8eOHB5VIiIiIiIiooKtyHeZef36NQDA0NAwy3XPnTuHmzdv4sCBA5DL5QCAmTNnIjQ0FOvXr4enpydiYmJQokQJVKpUCSVLlsQvv/yCunXrwtDQECVKlICenh60tbXF7i62trZo164d6tSpAwAwNTVFo0aNEBER8dlYrl69Cnt7e/Hx+/fvUbp0aTg5OWH06NEZvmbnzp0wMjLCvHnzoKPzoernzJmDa9euAQAaN26MunXrwsLCAgBQsWJFbNmyJctYiIiIiIiIiAqbIp8QUc3G8urVqyzXjYiIgIGBgZgMAQCJRII6derg3LlzAIBBgwZh6NChaNiwIWrWrInGjRujU6dOMDAwyLDMLl264MKFC1i8eDHu3buHqKgoREdHiwmSzNSoUQOLFy8GAERGRmL27NmwtLTEL7/8kmHrEFX8NjY2YjIEABo0aCDOouPi4oKAgADs3bsX9+7dw927d/Hw4UNUrVo1y2NDREREREREVJgU+S4zlSpVgrGxMYKDgzN8PjIyEh4eHrhz506mA6IKgiAmGezt7XH69GksX74cNjY22LdvH5ycnHDx4sUMXzt9+nSMGTMGSqUSjo6O+PPPP9GhQ4cs49bV1UWVKlVQpUoVODo6Yu3atbh06RLGjh2baZwfJ0I+lZaWhiFDhmDOnDnQ0dGBk5MTVq9enWlXIiIiIiIiIqLCrMgnRLS0tNCjRw/4+vriyZMn6Z5ft24dQkNDYWpqCgsLC7x9+1atC4kgCAgKCkK1atUAAMuXL0dQUBBatWqFqVOnwt/fH5UqVYK/vz+ADy1KVF6+fIkdO3ZgxowZmDx5MpydnWFlZYWoqKgcz0ZTrVo1jB8/HqdOncL27dszXScsLAypqanismPHjsHR0RH//fcfzpw5A09PT4wfPx6dO3dG5cqVERMTk2cz4xAREREREREVFEU+IQIAQ4cOhZmZGVxcXLBv3z7ExMQgJCREHFR09uzZ0NPTQ5MmTWBlZYVx48bh8uXLiIyMxKxZsxAREYG+ffsC+DBw6YwZM3Dx4kU8evQI/v7+ePz4sTjeh56eHl6/fo3o6Gjo6+vDwMAAJ06cwP379xEeHo5p06bh5s2bSE5OzvF+uLi4oE6dOli8eDGePn2a4fMvX77EjBkzEBkZiStXrmDhwoVo0KABTE1NoaOjg8OHD+PBgwcIDQ3F6NGjER8fn6tYiIiIiIiIiAoyJkQAyGQybNmyBd27d8fatWvRpUsXDBkyBHFxcfDx8UG7du0AANra2tiwYQOsra0xYsQIdO/eHXfu3IG3tzfs7OwAADNmzEDDhg3x66+/om3btmKLiy5dugAAfvjhB5iYmKBz584ICwuDp6cnIiIi0KlTJwwcOBAKhQJjx47F3bt3czzdrUQiwZw5c6BUKvH777+ne75cuXLYsGEDoqKi0LVrV4wZMwYtW7bE9OnTUa5cOSxYsAABAQFwcnLCL7/8gnLlyqFfv364cePGFx1fIiIiIiIiooJGIrA/BOWT0NBQAB9m0qGCIzExEbdu3YKVlVWmA/BSwbJ//3507twZCoUCMplM0+FQNvB9Vriwvgof1lnhwzorXFhfhQ/r7H9y8j2ULUSIiIiIiIiIqMhhQoSIiIiIiIiIihwmRIiIiIiIiIioyGFChIiIiIiIiIiKHCZEiIgKOCMjI02HQERERET0zWFChIioAEtLS0OzZs2QkpKi6VCIiIiIiL4pTIgQERVgWlpaUCgUCAsLA2dJJyIiIiLKO0yIEBEVcIIgQKlUajoMIiIiIqJvChMiRERERERERFTkMCFCREREREREREUOEyJERIWAVCqFRCLRdBiUTRKJBDKZjHVWSLC+Ch/WGRER5QUdTQdARESfJ5FIYG1tDR0dXrILC5lMBmtra02HQdnE+ip8CmudpaWlQUuLv0cSERUUvLsmIioEdHR04Ovri/j4eE2HQkREuWBiYgJnZ2dNh0FERB9hQoSIqJCIj49HbGyspsMgIiIiIvomsM0eERERERERERU5TIgQERERERERUZHDhAgRERERERERFTlMiBARERERERFRkcNBVXPJ0dERjx49Eh9LpVIYGxujefPm+OWXX1C6dGkNRpc7bm5uMDU1xYIFCzQdChEREREREVG+YkLkC3h4eMDDwwMAkJSUhIiICCxatAiurq7YsWMHDAwMNBwhEREREREREWWEXWa+gJ6eHkxMTGBiYoJKlSqhVatW2LBhA548eYJ169ZpOjwiIiIiIiIiygQTInnsu+++Q5s2bXDw4EEAwNu3bzFt2jQ0aNAAtWvXhru7O0JDQ8X1vby80K9fP6xZswbNmjWDra0tXF1dERkZKa5jYWGBHTt2wMXFBba2tmjfvj2Cg4OxY8cOtGjRAg4ODhg9ejSSkpLE1+zatQudOnVCzZo1YWdnBxcXF7XtOjo64o8//oCTkxPq16+Py5cvq+1HSkoKRo0ahRYtWiAmJia/DhcRERERERGRRjAhkg/kcjkePHiAhIQEDBo0CA8ePMDq1auxc+dO2NnZoXfv3ggLCxPXv3r1KoKCgrBmzRps3boVz58/x8yZM9XKXLp0KQYOHIh///0XBgYGGDp0KPz9/bFmzRrMnz8fx48fx65duwAAx44dw6xZszBw4EAcPnwY3t7eeP/+PaZOnapW5pYtWzB16lSsW7cOdnZ24vLU1FRMmDABN27cgI+PDypXrpx/B4uIiIiIiIhIA5gQyQclS5YEAAQEBOC///7DsmXLUKtWLZibm2Ps2LGws7PD5s2bxfVTUlKwcOFCWFpawtbWFr169UJwcLBamd27d4ejoyOqVq2KLl264PXr15g+fTrkcjnatm0LKysr3LlzBwBgZGSEuXPnokuXLjA1NYWdnR169OiBiIgItTKbN2+ORo0awdbWFsWKFQMApKWlYfLkybh+/Tp8fHxQqVKl/DxURERERERERBrBQVXzwdu3bwEADx48gCAIaNmypdrzycnJeP/+vfjY2NgYhoaG4mMDAwMolUq111SpUkX8WyaTAYBayw1dXV0kJycDAOrWrYvIyEisXLkSUVFRuH//PsLDw5GWlpZpmSqHDx+GUqmEubk5TExMcrTfRERERERERIUFEyL54ObNmzAzM4NUKoW+vj58fX3TraNqkfHp35nR0UlfVVpaGTfwOXDgACZNmoROnTrBwcEBvXr1QkREBGbNmqW2nq6ubrrXli1bFkuWLIGHhwdWrFiBsWPHZhkbERERERERUWHDLjN5LDY2FidOnECnTp0gl8uRkJAApVKJKlWqiP/Wrl2LEydO5FsMa9asQY8ePbBgwQL06dMHdevWxYMHDwAAgiB89rV169ZFrVq1MH78eKxfvx43btzItziJiIiIiIiINIUJkS+QmJiI+Ph4xMfH48GDBzh+/DgGDhyIihUron///mjatCmsrKwwZswYXLp0Cffv38f8+fPh6+sLc3PzfIurQoUKCA4Oxs2bNxETEwNvb29s2bIFAMRuNVnp1asXatasicmTJ2f7NURERERERESFBRMiX2DDhg1o0qQJmjRpgvbt2+OPP/5Aq1atsHXrVpQoUQLa2trYsGEDatSogdGjR6Nz5864cuUKVqxYgYYNG+ZbXNOmTYOxsTFcXV3Rs2dPnDx5EgsXLgQAtal3P0cikWDOnDmIjo7GX3/9lW+xEhEREREREWmCRMiqDwVRLqmSL7a2thqOhD6WmJiIW7duwcrKCnp6epoOh7JBoVBAJpNh9erViI2N1XQ4RESUC+XLl8eQIUM0HYbG8P6jcGF9FT6ss//JyfdQthAhIiIiIiIioiKHCREiIiIiIiIiKnKYECEiIiIiIiKiIocJESIiIiIiIiIqcnQ0HQAREWWPiYmJpkMgIqJc4jWciKjgYUKEiKgQSElJgbOzs6bDICKiL5CWlgYtLTbQJiIqKHhFJiIq4ARBQFhYGBQKhaZDoWxSKBSss0KE9VX4FNY6YzKEiKhg4VWZiKgQUCqVEARB02FQNgmCAIVCwTorJFhfhQ/rjIiI8gITIkRERERERERU5DAhQkRUCEilUkgkEk2HQURERET0zeCgqkREBZxEIoG1tTV0dDK+ZHOQPiIiIiKinGNChIioENDR0YGvry/i4+PVlpuYmHD2GSIiIiKiXGBChIiokIiPj0dsbKymwyAiIiIi+iawjTURERERERERFTlMiBARERERERFRkcOECBEREREREREVOUyIEBEREREREVGRw0FVvxI3Nzdcvnw5w+c8PDxw48YNmJqaYsGCBdkqz9HREd26dcPIkSPzMkw1vr6+mDx5MsLDw/NtG0RERERERESawITIV9S+fXv89ttv6ZbLZDKkpKRAW1tbA1ERERERERERFT1MiHxFurq6MDEx0XQYREREREREREUexxApINzc3DBp0iQAH7qqtGnTRvy/Ro0acHZ2RlBQUKav37VrFzp16oSaNWvCzs4OLi4uCA0NFZ93dHTE+vXrMXLkSNjb26N+/fqYM2cOUlJSxHWOHTuGTp06wdbWFi4uLnj8+HH+7TARERERERGRBjEhUkA9efIE27dvx6JFi7B3717IZDJMmjQJgiCkW/fYsWOYNWsWBg4ciMOHD8Pb2xvv37/H1KlT1dbz9PRE3bp1sX//fkyYMAFbtmyBn58fACA4OBgjR45E27ZtsX//fnTr1g1r1qz5KvtKRERERERE9LUxIfIVHThwAPb29mr/Bg4cmOG6SqUSM2fOhJ2dHapXr47+/fsjJiYG8fHx6dY1MjLC3Llz0aVLF5iamsLOzg49evRARESE2npNmjSBu7s7KlWqhO7du8PS0hLBwcEAgC1btsDBwQEjRozA999/j549e+Knn37K+4NAREREREREVABwDJGvyNHREePHj1dbpqurm+n65ubm4t8GBgYAPiRKPlW3bl1ERkZi5cqViIqKwv379xEeHo60tLRMy1OVqSovIiICjRs3Vnve3t4emzdvzsaeERERERERERUuTIh8RSVKlECVKlWyvX6xYsXSLcuoy8yBAwcwadIkdOrUCQ4ODujVqxciIiIwa9asbJcnkUjSJVCkUmm2YyUiIiIiIiIqTJgQ+QasWbMGPXr0wMyZM8VlJ06cAPAh4SGRSLIsw9LSEteuXVNbduPGjbwNlIiIiIiIiKiA4Bgi34AKFSogODgYN2/eRExMDLy9vbFlyxYAQHJycrbK8PDwwO3bt/HHH38gOjoa+/fvF8sgIiIiIiIi+tYwIfINmDZtGoyNjeHq6oqePXvi5MmTWLhwIQCoTb37OVZWVli7di0CAwPRuXNneHt7Y+jQofkZNhEREREREZHGsMvMV+Lj45Pt552dneHs7Kz2fP369REeHi4+DggIEP+uVKkSNm7cmK5MJyenDNfPLKaGDRvC19dXbdmgQYM+GzcRERERERFRYcQWIkRERERERERU5DAhQkRERERERERFDhMiRERERERERFTkMCFCREREREREREUOB1UlIiokTExMsrWMiIiIiIiyxoQIEVEhkJKSkm72KZW0tDRoabHBHxERERFRTvAOmoiogBMEAWFhYVAoFBk+z2QIEREREVHO8S6aiKgQUCqVEARB02EQEREREX0zmBAhIiIiIiIioiKHCREiIiIiIiIiKnKYECEiKgSkUikkEommw6BskkgkkMlkrDMiIiKiAoyzzBARFXASiQTW1tbQ0eElu7CQyWSwtrbWdBiFCmdLIiIioq+Nd9dERIWAjo4OfH19ER8fr+lQiPKciYlJptNKExEREeUXJkSIiAqJ+Ph4xMbGajoMIiIiIqJvAtumEhEREREREVGRw4QIERERERERERU5TIgQERERERERUZHDMUS+gCAI2Lt3L/bu3Ys7d+4gISEBFSpUQIsWLTB48GCYmJhoOkQiIiIiIiIiygBbiORSWloahg8fjgULFqBly5bw8fHB0aNHMXXqVISGhqJ79+54/vy5psMkIiIiIiIiogywhUgueXt74/Tp09i5cydsbGzE5d999x3q16+PDh06YP369ZgwYYIGoyQiIiIiIiKijLCFSC4IgoAtW7agc+fOaskQFV1dXWzevBmjR48GADx9+hRjxoxBnTp1UL9+fQwdOhT37t0T1580aRJGjRoFDw8PODg4YO3atfDy8kK/fv2wYsUKNGrUCPb29pg+fTqePHmCIUOGoFatWmjTpg1OnTollvP48WOMGTMGDRs2hI2NDZo1a4ZFixYhLS0NAODr64s2bdqI/9eoUQPOzs4ICgoCAGzatAn29vZQKBRimWlpaWjWrBn++eefvD+QRERERERERBrChEguPHz4EI8ePUKjRo0yXcfU1BTFihVDYmIi3NzcAABbtmyBj48PSpUqhR9//BFPnz4V1/f390ejRo2wZ88edOzYEQBw9epVREdH459//sHUqVOxY8cO9OjRA+3bt4evry/Mzc0xadIkCIIAABg2bBjevn2LjRs34siRI/Dw8MC6desQEBAgbufJkyfYvn07Fi1ahL1790Imk4lldOrUCUqlEkePHhXXv3DhAl6+fCnGRERERERERPQtYEIkF549ewYAKF26tNryoUOHwt7eXvzXoUMHHDx4EG/evMGiRYtgaWkJuVyOuXPnQl9fHzt37hRfa2hoiIEDB+L7779HhQoVAHxonTFz5kx8//336N69O0qVKoUGDRqga9euMDc3R+/evfHy5UvEx8cjKSkJXbp0wezZs2FpaYlKlSqhX79+MDY2Rnh4uLgdpVKJmTNnws7ODtWrV0f//v0RExOD+Ph4lC5dGo6Ojti/f7+4/t69e+Ho6AhDQ8P8PKREREREREREXxXHEMmFUqVKAQBev36ttnzmzJlISkoCAPj4+CAgIABhYWF4/fo16tatq7bu+/fvERkZKT6uUqVKuu2UKVMG+vr64mM9PT1UrlxZfKyrqwsASE5Ohq6uLlxdXXHkyBGEhITg/v37CA8Px7Nnz8QuMyrm5ubi3wYGBgA+JEoAoHv37hg2bBji4uKgp6eH48ePY/ny5dk8MkRERERERESFAxMiuVCpUiWYmJggMDAQTk5O4vJy5cqJf6taVKSlpeH777/H33//na4cPT098W9VcuNjUqk03TItrYwb9SQmJsLV1RVJSUlo164dunXrhpo1a6JPnz7p1i1WrFi6ZapuN02aNIGxsTH8/PxgZGSEkiVLokmTJhluk4iIiIiIiKiwYkIkF7S1teHu7o6VK1eid+/esLS0TLfOkydPAAByuRz//vsvDAwMxC42SqUS48aNQ7t27dQSKl/i3LlzuHnzJs6fPw9jY2MAwKtXr/D8+XMx2ZEd2tra6Nq1K44dO4aSJUuiS5cu0NbWzpMYiYiIiIiIiAoKjiGSSwMHDkTLli3h4uKCVatW4fbt23j48CECAgLg4eGBPXv2oEGDBujcuTMMDQ0xatQoXL9+HZGRkZg0aRLOnDkDCwuLPIunfPnyAID9+/fj0aNHuHr1Kn7++WcolUokJyfnqCxnZ2dcv34dFy5cQLdu3fIsRiIiIiIiIqKCgi1EcklLSwvLli3D4cOHsWfPHmzevBlv3ryBsbEx6tSpgy1btojjhmzZsgULFy7EgAEDkJqaChsbG2zYsEFtLI8vVbNmTUyePBne3t5YtmwZypUrBycnJ1SoUAGhoaE5KsvMzAy1atVCWlpansZIREREREREVFBIhJz0p6AiQRAEtG7dGkOHDkXPnj1zXY4qEWNra5tXoVEeSExMxK1bt2BlZaU2jg0VXAqFAjKZDKtXr0ZsbKymwyHKc+XLl8eQIUM0tn1eFwsf1lnhwzorXFhfhQ/r7H9y8j2ULURIpFQqERAQgEuXLiExMREdOnTQdEhERERERERE+YIJERJJpVLMmTMHALBo0aIin1kkIiIiIiKibxcTIqTm7Nmzmg6BiIiIiIiIKN9xlhkiIiIiIiIiKnLYQoSIqJAwMTHRdAhE+YLnNhEREWkCEyJERIVASkoKnJ2dNR0GUb5JS0uDlhYbrhIREdHXwzsPIqICThAEhIWFQaFQaDoUyiaFQsE6yyEmQ4iIiOhr490HEVEhoFQqIQiCpsOgbBIEAQqFgnVGREREVIAxIUJERERERERERQ4TIkRERERERERU5DAhQkRUCEilUkgkEk2HQdkkkUggk8lYZ4UE66vwYZ0VPqyzwoX1RUUFZ5khIirgJBIJrK2toaPDS3ZhIZPJYG1trekwKJtYX4UP66zwYZ0VLnlZX5xFjAoy3l0TERUCOjo68PX1RXx8vKZDISIiIsoWExMTODs7azoMokwxIUJEVEjEx8cjNjZW02EQEREREX0T2HaJiIiIiIiIiIocJkSIiIiIiIiIqMhhQoSIiIiIiIiIihwmRIiIiIiIiIioyGFC5CuaNGkS3Nzc8qQsNzc3TJo0KU/KykxgYCAsLCzw8OHDfN0OERERERER0dfGhAgRERERERERFTlMiBARERERERFRkcOEiIYolUp4enqiZcuWqFWrFpydnXH+/Hnx+cjISAwaNAj29vZo0qQJxo0bh/j4+EzLO378OHr27Ak7OzvY2trC2dkZZ8+eFZ93c3PD4sWLMWXKFNSpUwcODg4YN24cEhISxHWuXr2Knj17ombNmujcuTNu376dPztPREREREREpGFMiGjI3LlzsX37dkycOBEHDhxA06ZNMXToUERFReHp06dwcXFBlSpVsHv3bqxatQoJCQn46aefkJiYmK6sGzduYOTIkejQoQMOHDiAnTt3onTp0pgwYQKSk5PF9by9vWFsbIzdu3dj0aJFOHHiBLy9vQEADx48gIeHB6ysrLB3714MHz4cf/3119c6HERERERERERflY6mAyiKEhISsHv3bkybNg3t2rUDAIwZMwaCICAhIQH79+9H+fLlMXXqVPE1y5YtQ4MGDXDkyBE4OzurlaetrY1p06bBxcVFXObu7o5Bgwbh+fPnqFChAgCgWrVqGDt2LADAzMwMjRs3xrVr1wAAO3fuhLGxMWbMmAFtbW2Ym5vjyZMnmD9/fr4eCyIiIiIiIiJNYEJEA6Kjo6FUKlGrVi215apkxYoVK3Dnzh3Y29urPf/+/XtERkamK8/KygqGhoZYs2YNoqKicP/+fbG7S2pqqrhe1apV1V5nYGCAN2/eAAAiIiJgbW0NbW1t8XkHB4cv2EsiIiIiIiKigosJEQ2QSqWffT4tLQ0NGjTAjBkz0j1nYGCQbtnly5cxYMAAtGjRArVr10anTp2gUCgwfPhwtfWKFSuW6TYlEgnS0tLUluno8PQgIiIiIiKibxPHENGAKlWqQCqVIjQ0VG35jz/+CG9vb1SvXh2RkZGoUKECqlSpgipVqsDQ0BDz5s1DREREuvI2bNiA+vXrw8vLC/369UPjxo3x5MkTAIAgCNmKydLSEjdu3FAbc+TGjRtfsJdEREREREREBRcTIhogk8ng6uoKT09PnDhxAjExMViyZAkiIiLQrFkzuLi44O3btxg/fjxu376N27dvY8yYMQgNDYVcLk9XXoUKFRAeHo6rV6/i4cOH2LNnDzw9PQFALcHxOb1794ZCocCUKVMQGRmJkydPwsvLK0/3m4iIiIiIiKigYJ8IDRk7diy0tbUxY8YMvH37FpaWllizZo04zseWLVvw559/onfv3tDW1oaDgwM2b96M0qVLpytr1KhRePbsGYYOHQrgw+Cp8+bNw6+//orQ0FCYm5tnGU+5cuWwadMmzJs3D926dUOFChUwbNgwzJw5M293nIiIiIiIiKgAkAjZ7VNBlEOqLkG2trYajoQ+lpiYiFu3bsHKygp6enqaDoeyQaFQQCaTYfXq1YiNjdV0OERERETZUr58eQwZMkTTYRQJvMf/n5x8D2WXGSIiIiIiIiIqcpgQISIiIiIiIvq/9u47rsr6///48yAguDNJza0JioAMF44U1ByZA82NGZlajlwl5so9c6Epjr65Kkc4s9Q0rdwjV2jucoSRJi4UhOv3hz/PxxNoaODxcB73242bnOt6X+/rdZ3XOXB4+X6/L9gdCiIAAAAAAMDuUBABAAAAAAB2h7vMAICNcHNzs3YIAAAAacZnFzzrKIgAgA24e/euQkJCrB0GAADAY0lOTpaDAxMT8GzilQkAzzjDMBQdHa34+Hhrh4I0io+PJ2c2hHzZHnJme8iZbUnPfFEMwbOMVycA2IDExEQZhmHtMJBGhmEoPj6enNkI8mV7yJntIWe2hXzBXlAQAQAAAAAAdueJ1xC5ceOGbt68qfz58ysxMVELFy7UxYsXVa9ePVWsWDE9YwQAu+fk5CSTyWTtMJBGJpNJrq6u5MxGkC/bQ85sDzkD8Cx6ohEiBw8eVFBQkBYtWiRJGjlypMaPH6/Vq1frjTfe0KZNm9I1SACwZyaTSZ6ennJ1dbV2KEgjV1dXcmZDyJftIWe2h5xlrOTkZGuHANikJxohMmXKFJUqVUotW7ZUfHy8Vq1apbZt22rIkCEaMmSIZs2apdq1a6d3rABgtxwdHRUVFaXY2FhrhwIAAJ4hbm5u3IkOeEJPVBA5ePCgJk+erCJFiui7777TnTt31KRJE0lSw4YNtXr16nQNEgAgxcbGKiYmxtphAAAAAJnCE02ZcXBwUNasWSVJP/74o3LlyiUfHx9J99YWcXFxSb8IAQAAAAAA0tkTjRDx8vLSsmXL5OLiom+//Va1atWSyWTS5cuXNWfOHHl5eaV3nAAAAAAAAOnmiUaIvP/++9q+fbtat26tLFmy6J133pEkNWrUSGfPnlWvXr3SM0YAAAAAAIB09UQjRMqVK6eNGzfq1KlTKl26tLJlyyZJ+uijj+Tv7y83N7d0DRIAAAAAACA9pXmEyLhx4ywe58iRQ+XLlzcXQySpXr16FEPSKDw8XKGhoU98/K5du+Th4aHz58+nY1QphYaGKjw8PEPPAQAAAADA05bmESL/93//p7Nnz2rq1KmaPXv2I9uaTCZ169btPwcHAAAAAACQEdJcEPn88881depUnT9/XtOnT39kWwoiAAAAAADgWZbmgoi/v7/mz58vSTp27FiGBWSPgoODVa9ePW3dulWXL19WRESEKlasqLlz5+rLL7/UX3/9peLFi+utt95S48aNU+0jLi5OEyZM0NatW3XlyhXlypVLtWvX1sCBA+Xq6qpdu3bpzTff1MyZMzVhwgSdPXtWhQsXVr9+/VSnTh1JUkJCgiZOnKg1a9YoISFBrVu3VnJy8tN8KgAAAAAAeCqeaFFVpL9FixYpMjJSOXPmlIeHhyZPnqy1a9dqyJAhKlmypPbs2aOPPvpI169fV7t27VIcHx4erkuXLmn69Ol6/vnntX//fn344Yd66aWX1LFjR0lSUlKSJkyYoIEDB6pgwYKaNGmS+vfvrx9++EHZs2fXyJEjtXnzZo0dO1YvvviiZs2apb1796pIkSJP+dkAAAAAACBjPXFB5M6dO/r111+VkJAgwzAkScnJyYqPj9fevXvVr1+/dAvSHtSsWVNVq1aVJN26dUufffaZJk2apFq1akmSihYtqgsXLmjevHmpFkSqVaumihUrysPDQ5JUuHBhLVq0SMePH7do16tXLwUGBkqS3n33Xa1fv17Hjx9X6dKlFRUVpaFDh6pmzZqSpNGjR2vnzp0ZdckAAAAAAFjNExVEdu3apffee09xcXGp7s+ePTsFkcdUrFgx8/cnT57UnTt31LdvXzk4/O9GQHfv3lVCQoJu376d4vi2bdtq8+bNWrFihc6ePauTJ0/q/PnzKlmypEW7Bx/nyJFDkpSYmKgzZ84oMTFR3t7e5v1Zs2aVp6dnul0jAAAAAADPiicqiEyePFnPPfecRowYodWrV8vBwUEhISH64Ycf9MUXX2jOnDnpHWem5+LiYv7+/oibKVOmpChoSJKzs7PF4+TkZHXp0kUnTpxQo0aN1LBhQ5UrV06DBw/+12Pvn89kMlmc+z5HR2ZVAQAAAAAynyf6a/fXX3/VyJEjVbduXV2/fl1ffvmlatasqZo1ayoxMVEzZ87811vz4uFKliwpR0dHXbx4UUFBQebtCxYs0MmTJzV8+HCL9kePHtUPP/ygpUuXqnz58pLujfr4/fff07z+R4kSJZQ1a1bt379fZcuWlXRvRMqxY8dUuXLldLoyAAAAAACeDU9UEElOTlb+/Pkl3ZvqceLECfO+evXqqX///ukTnZ3KmTOnWrduralTpypHjhzy9/fXrl27NGHCBHXp0iVF+3z58snR0VHffPON8ubNq6tXr2rWrFmKjY1VQkJCms6ZPXt2tW/fXtOmTZObm5tKlSqlTz/9VJcuXUrvywMAAAAAwOqeqCBStGhR/frrr6pQoYJKlCih+Ph4nT59WiVLltTdu3d18+bN9I7T7gwYMEDPPfecpk6dqj///FMFCxZUz5491alTpxRt8+fPr7FjxyoiIkKLFy+Wm5ubatWqpY4dO2rz5s1pPmffvn2VNWtWDR8+XDdv3lSDBg0UHBycnpcFAAAAAMAzwWT8c9GINJg9e7Zmzpypvn37qn379mrRooVy5syp0NBQzZo1S4ZhaNmyZRkRL2zI4cOHJclioVZY361bt3T06FGVLVtW2bJls3Y4SIP4+Hi5uroqMjJSMTEx1g4HAAA8QwoUKJDqKPL/gs+Ltoec/c/j/B3q8K8tUtGpUye1bt1aBw8elCQNHTpUR48e1bvvvqvTp0/rgw8+eJJuAQAAAAAAnorHnjJz6NAhXbhwQY0aNVK5cuUk3au8fPfdd+ZpM/dv5woAAAAAAPAsSnNB5Nq1a+rSpYsOHDhgvk2rn5+fPv74YxUsWFA5cuSQj49PRsYKAAAAAACQLtI8ZWbKlCmKjo5Wjx49NHv2bPXv31+nT5/WkCFDMjI+AAAAAACAdJfmESLff/+9+vTpozfeeEOS9PLLLyt//vzq16+fbt26ZfcLtwBARnNzc7N2CAAA4BnD5wPgyaW5IBIbG2teM+S+ypUrKykpSX/88YdKlSqV7sEBAO65e/euQkJCrB0GAAB4BiUnJ8vB4YnulwHYtTS/a+7evStnZ2eLbblz55Yk3blzJ32jAgCYGYah6OhoxcfHWzsUpFF8fDw5syHky/aQM9tDzjIWxRDgyaTLO8cwjPToBgDwEImJifystSGGYSg+Pp6c2QjyZXvIme0hZwCeRelSEDGZTOnRDQAAAAAAwFOR5jVEJOmjjz5Sjhw5zI/vV3gHDx6s7Nmzm7ebTCbNnz8/nUIEAAAAAABIX2kuiFSsWFFSyukxqW1nKBwApC8nJydG49kQk8kkV1dXcmYjyBcAAPYpzQWRhQsXZmQcAICHMJlM8vT0lKPjYw3qgxW5urrK09PT2mEgjTJLvrjLBAAAj4dP1wBgAxwdHRUVFaXY2FhrhwLgGeTm5satuQEAeEwURADARsTGxiomJsbaYQAAAACZAuMqAQAAAACA3aEgAgAAAAAA7A4FEQAAAAAAYHcoiDzDwsPDFRoaatUYPDw8FBUVZdUYAAAAAABIbxREAAAAAACA3aEgAgAAAAAA7A4FERtx6NAhtW3bVn5+fqpYsaJ69OihixcvSpLOnz8vDw8PRUZGqlq1aqpdu7Zu3Lih48ePq0uXLqpYsaK8vLxUu3Ztffrppxb9fv/99woJCZGPj4/q1q2rKVOmKCEhwRqXCAAAAADAU0NBxAYkJSWZCxurV6/WZ599posXL+rDDz+0aLdixQrNnz9fU6ZMUZYsWRQWFqY8efLoyy+/1Nq1a1W/fn2NGzdOR48elST98MMP6tWrl1q2bKm1a9dq6NCh+uabb/T+++9b4zIBAAAAAHhqHK0dAP7djRs39Pfff+uFF15QoUKFVKRIEU2ZMkWXL1+2aNe2bVu99NJLkqQrV66oQ4cOateunbJnzy5J6tmzp+bOnatff/1VZcuW1axZs9SyZUu1bt1aklS0aFENGzZMb7zxhs6fP6/ChQs/3QsFAAAAAOApoSBiA3Lnzq1OnTppxIgRmjZtmqpUqaKaNWuqQYMGFu2KFStm/j5v3rxq27at1q5dq+joaP3+++86duyYJCk5OVmSFB0drUOHDmn58uXm4wzDkCSdOnWKgggAAAAAINOiIGIj+vXrp7Zt22rr1q3asWOHRowYoblz52rlypXmNi4uLubvY2Nj1apVK+XNm1fBwcGqXr26vL29VbNmTXOb5ORkderUSc2aNUtxPjc3twy9HgAAAAAArImCiA04ffq05s+frw8//FBt2rRRmzZttG/fPrVt21bHjh1T3rx5Uxyzdu1aXb16VevXr5eTk5Mk6ddff5X0v1EgpUuX1pkzZyxGluzatUsLFizQRx99pGzZsj2FqwMAAAAA4OljUVUb8Nxzz+nrr7/WkCFDdOrUKZ05c0YrVqxQ7ty5VbJkyVSPKVCggOLj4/Xtt9/q4sWL+umnn9SnTx9JMt9F5u2339b69es1ffp0nTlzRjt27NCAAQN0/fp1RogAAAAAADI1RojYgOeee05z5szRxx9/rJYtWyopKUm+vr76v//7P+XIkUNXr15NcUz9+vX1yy+/aOzYsbpx44YKFSqk119/XZs2bdLhw4fVpk0b1a9fX5MnT1ZkZKRmzZqlPHnyKDg4WP369Xv6FwkAAAAAwFNEQeQZNnbsWPP3fn5+WrRoUartChcubJ4Oc5/JZFK/fv1SFDfefPNNi8cNGjRIsTjrg/7ZLwAAAAAAmQFTZgAAAAAAgN2hIAIAAAAAAOwOBREAAAAAAGB3KIgAAAAAAAC7w6KqAGAjuB02gIfh5wMAAI+PgggA2IC7d+8qJCTE2mEAeIYlJyfLwYHBvwAApBW/NQHgGWcYhqKjoxUfH2/tUJBG8fHx5MyGZJZ8UQwBAODx8JsTAGxAYmKiDMOwdhhII8MwFB8fT85sBPkCAMA+URABAAAAAAB2h4IIAAAAAACwOxREAMAGODk5yWQyWTsMAAAAINPgLjMA8IwzmUzy9PSUoyM/sq2Fu3cAAABkPny6BgAb4OjoqKioKMXGxlo7FLvj5ubGLY8BAAAyIQoiAGAjYmNjFRMTY+0wAAAAgEyB8b8AAAAAAMDuUBABAAAAAAB2h4IIAAAAAACwOxREAAAAAACA3cnUBZHg4GBFRERYO4w0i4qKkoeHh7XDAAAAAAAg08vUBREAAAAAAIDUUBABAAAAAAB2x24LIuHh4QoNDX3otk8//VSenp46dOiQJCk5OVlt27ZVSEiIEhISJEnff/+9QkJC5OPjo7p162rKlCnmfZLk4eGhJUuWqG3btvL29laDBg20f/9+LVmyRLVq1ZK/v7969eql27dvW8SxdOlS1ahRQ+XLl1fXrl114cIF877bt29rypQpql27try9vdWkSROtX7/evD+1aTf/3BYcHKxx48apYcOGqly5snbv3q2kpCRNnjxZ1atXl6+vr3r27KlRo0aleI4AAAAAAMgM7LYg8m86duyogIAADRw4UImJiZozZ46OHj2qSZMmydnZWT/88IN69eqlli1bau3atRo6dKi++eYbvf/++xb9TJ48WZ06ddKqVauUM2dOde3aVevXr9fs2bM1ZswYfffdd1q2bJnFMQsXLtTUqVO1ePFi/f333+rWrZsMw5Ak9enTRytXrtTgwYO1evVq1alTR++9956+++67x7q+RYsWadCgQZo7d658fX01ceJELVmyREOHDtVXX30lNzc3LVy48L89iQAAAAAAPKMcrR3As8rBwUFjx45V48aNNXDgQK1bt04jRoxQ8eLFJUmzZs1Sy5Yt1bp1a0lS0aJFNWzYML3xxhs6f/68ChcuLElq3ry5goODJUlNmjTR8OHDNWTIEBUvXlzu7u6aO3euTpw4YXHuCRMmqEyZMpKkcePGqV69etqxY4fy58+vTZs2adasWapVq5YkqUePHjp27JhmzZqlOnXqpPn6atasqapVq0qS4uPj9fnnn2vAgAGqW7euJGnQoEH6+eefn+zJAwAAAADgGUdB5BEKFSqkDz/8UB9++KHq1KmjZs2amfdFR0fr0KFDWr58uXnb/VEcp06dMhdEihUrZt7v6uoq6V7x5D4XFxeLaTbZs2c3F0MkqXjx4sqdO7eOHz+uq1evSpICAgIs4qxYsaImTZr0WNf2YFynTp3S7du35evra95mMpkUEBCgY8eOPVa/AAAAAADYAgoiD7h7926KbUeOHJGjo6MOHz6suLg45c6dW9K9NUU6depkUSS5z83Nzfy9o2PKp9jB4eEzlbJkyZJiW3JyspydnR96jGEYqZ7nvqSkpBTbXFxcUsR4v6ADAAAAAEBmZ7driDg5OenGjRsW23777TeLxz/++KO++OILzZgxQ66urho2bJh5X+nSpXXmzBkVK1bM/BUTE6Px48fr5s2bTxzXtWvX9Pvvv5sf//rrr7p+/brc3d3NC6Pu27fP4pi9e/fqpZdeMl+XJItrO3v27CPPWaxYMbm4uOjAgQMW2w8ePPiklwEAAAAAwDMt048Q+e233/TDDz9YbHNxcZGvr6+WL1+u1atXy8/PT6tXr9bx48fl4+MjSbp69ao+/PBDtWzZUrVq1VKOHDnUvn17BQcHq1GjRnr77bfVq1cvTZ8+Xa+++qpiYmI0cOBAFS5c2GKEyONycHBQr169NGTIEEnS0KFDValSJVWoUEGSFBQUpGHDhslkMqlYsWL6+uuvtWnTJk2ZMkWS5OvrK5PJpIiICIWGhurw4cNasWLFI8/p6uqq0NBQTZs2TW5ubipVqpSWLl2qgwcPqlKlSk98LQAAAAAAPKsyfUFkzZo1WrNmjcW2QoUKaf369Tp69KhGjhypu3fvqkGDBnrjjTfMC4kOHTpUWbJk0QcffCBJqlChgtq2bavhw4erYsWKql+/viZPnqzIyEjNmjVLefLkUXBwsPr16/ef4s2bN6+aNGmid999V/Hx8QoKCtKgQYPM+ydNmqRJkyZp4MCBunbtmtzd3RUREWFeDLVIkSIaNmyYIiMj9fnnnysgIEAffPCB+vfv/8jzvvfee0pMTNSgQYPM561du7bu3Lnzn64HAAAAAIBnkclg4QhI2rhxowICApQ3b17ztrCwMBUoUECjR49+oj4PHz4sSfL29k6XGJE+bt26paNHj6ps2bLKli2btcNBGsTHx8vV1VWRkZGKiYmxdjh2p0CBAurSpctjHcP7zLaQL9tDzmwPObMt5Mv2kLP/eZy/Q+12DRFYmjdvnvr27aujR4/q3Llz+uyzz7Rz5041btzY2qEBAAAAAJDuKIhAkjRx4kRlz55dHTt2VKNGjbRmzRpNnTpVVapUsXZoAAAAAACku0y/hgjSpnDhwpo+fbq1wwAAAAAA4KlghAgAAAAAALA7jBABABvxX27pjSfH8w4AAJA5URABABtw9+5dhYSEWDsMu5WcnCwHBwZVAgAAZCZ8ugOAZ5xhGIqOjlZ8fLy1Q7FbFEMAAAAyHz7hAYANSExMlGEY1g4DAAAAyDQoiAAAAAAAALtDQQQAbICTk5NMJpO1w0AamUwmubq6kjMbQb5sDzmzPeTMtpAv2AsWVQWAZ5zJZJKnp6ccHfmRbStcXV3l6elp7TCQRuTL9pAz20PObIs95YuF0+0bn64BwAY4OjoqKipKsbGx1g4FAAAgU3Bzc+MufnaOgggA2IjY2FjFxMRYOwwAAAAgU2BsEAAAAAAAsDsURAAAAAAAgN2hIAIAAAAAAOwOBREAAAAAAGB37KYgEhwcrODgYN24cSPFvvDwcIWGhqapH8MwtGLFCl2+fFmSFBUVJQ8Pj3SNVZJ27dolDw8PnT9/Pt37flBoaKjCw8Mz9BwAAAAAADxr7KYgIkkXLlzQ+PHj/1Mfe/bsUXh4uOLj49MpKgAAAAAA8LTZVUGkSJEiWrJkibZv3/7EfRiGkY4RAQAAAAAAa7Crgkjjxo0VGBiogQMHpjp1RpKuX7+uwYMHq0qVKgoICFCHDh10+PBhSfemsXTo0EGSVLt2bUVFRZmPi4qKUp06deTt7a2QkBAdPHjQvC8hIUETJkxQjRo15Ofnp5YtW+qnn36yOLZu3boaOXKkAgIC9O6776aIKy4uToMGDVKNGjVUrlw5BQYGatCgQeaRKrt27ZKnp6e2bt2qRo0aycvLS/Xr19d3331nEcfo0aMVGBiogIAATZgwQcnJyRbnmTdvnurUqSMvLy8FBwdrxowZFIEAAAAAAJmOXRVETCaTRo0apbi4OI0bNy7FfsMw9Pbbb+vcuXOKjIzU0qVL5evrqzZt2ig6Olp+fn6KiIiQJC1btkwNGzY0H7t06VJNmjRJX331lZydndWrVy/zvgEDBmjbtm2aOHGiVqxYoQYNGqhr167asmWLuc3vv/+uP//8UytXrlTv3r1TxBYeHq7o6GhNnz5d69ev14ABA7Ry5UotWbLE3CYpKUkTJkzQwIEDtXbtWrm7u6t///66efOmJGnkyJFat26dxo4dqy+//FIxMTHau3ev+fjNmzcrMjJSw4YN04YNG9SvXz/NnDlTq1evfuLnHAAAAACAZ5GjtQN42goVKqT+/ftryJAhqlevnqpXr27et3PnTh04cEA7d+5Unjx5JEl9+vTR/v37tWDBAo0dO1a5c+eWJOXNm1cuLi7mY0eNGqVSpUpJkt566y11795dly9f1o0bN7R27VqtXLlSZcuWlSS9+eabOnbsmObNm6datWqZ+3j33XdVpEgRSfdGfDyoWrVqqlixonkB18KFC2vRokU6fvy4RbtevXopMDDQ3N/69et1/PhxlS5dWlFRURo6dKhq1qwpSRo9erR27txpPvb333+Xs7OzChUqpBdffFEvvviiXnjhBb344otP9mQDAAAAAPCMsruCiCS1atVK69ev16BBg7R27Vrz9l9++UWGYSgoKMiifUJCgu7cufPIPosXL27+PleuXJKk27dvKzo6WpLUtm1bi/aJiYnmdqn18U9t27bV5s2btWLFCp09e1YnT57U+fPnVbJkSYt2Dz7OkSOH+VxnzpxRYmKivL29zfuzZs0qT09P8+PGjRvrq6++Ur169fTSSy+patWqqlevHgURAAAAAECmY5cFEene9JHXXntNY8aMMW9LTk5Wjhw5LNYGuc/Z2fmR/WXJkiXFNsMwzOtvLF68WNmzZ7fY7+BgOWPpwREnD0pOTlaXLl104sQJNWrUSA0bNlS5cuU0ePDgNMVpGIZMJpP5+wc5Ov7vJZA3b16tWrVKP//8s7Zt26affvpJCxYsUI8ePdS9e/dUYwMAAAAAwBbZ1RoiD3rxxRcVHh6u5cuXm9fRcHd3140bN5SYmKhixYqZv+bMmaNNmzZJkrmwkFalS5eWJMXGxlr0GRUVlWrhJTVHjx7VDz/8oKlTp6pfv35q3LixihYtqt9//z3NC56WKFFCWbNm1f79+83b7t69q2PHjpkfr169Wl988YUCAgLUs2dPLV26VK+//rrWrVv3GFcMAAAAAMCzz24LIpL0+uuvq3r16jp37pwkqUaNGipbtqx69+6tnTt36rffftOYMWMUFRVlXh8kW7ZskqRjx46ZFyt9lNKlSysoKEhDhw7V5s2bde7cOc2ZM0eRkZEqWrRomuLMly+fHB0d9c033+jcuXM6fPiwevXqpdjYWCUkJKSpj+zZs6t9+/aaNm2aNmzYoFOnTmno0KG6dOmSuc2dO3c0btw4rVy5UufPn9fevXu1Z88e+fn5pekcAAAAAADYCrudMnPf/akz0r1pL59++qkmTJigXr16KT4+XqVKldL06dPNC5W6u7urZs2a6tWrl/r06WNefPVRJk+erMmTJ2vIkCGKi4tT0aJFNWrUKDVr1ixNMebPn19jx45VRESEFi9eLDc3N9WqVUsdO3bU5s2b03ytffv2VdasWTV8+HDdvHlTDRo0UHBwsHn/66+/rqtXr+qTTz7RH3/8ody5c6tevXrq169fms8BAAAAAIAtMBlpnXMBPKbDhw9LksVCrrC+W7du6ejRoypbtqx5xBOebfHx8XJ1dVVkZKRiYmKsHQ4AAECmUKBAAXXp0sXaYaQLPuP/z+P8HWrXU2YAAAAAAIB9oiACAAAAAADsDgURAAAAAABgdyiIAAAAAAAAu2P3d5kBAFvh5uZm7RAAAAAyDT5bgYIIANiAu3fvKiQkxNphAAAAZCrJyclycGDihL0i8wDwjDMMQ9HR0YqPj7d2KEij+Ph4cmZDyJftIWe2h5zZFnvKF8UQ+0b2AcAGJCYmyjAMa4eBNDIMQ/Hx8eTMRpAv20PObA85sy3kC/aCgggAAAAAALA7FEQAAAAAAIDdoSACADbAyclJJpPJ2mEgjUwmk1xdXcmZjSBftoec2R5yZlvIF+wFd5kBgGecyWSSp6enHB35kW0rXF1d5enpae0wkEbky/aQM9tDzmzLf8kXd22BLeHTNQDYAEdHR0VFRSk2NtbaoQAAAKTKzc1NISEh1g4DSDMKIgBgI2JjYxUTE2PtMAAAAIBMgbFMAAAAAADA7lAQAQAAAAAAdoeCCAAAAAAAsDuZpiASHBys4OBg3bhxI8W+8PBwhYaGPpU4oqKi5OHh8dCviIiIdDuXh4eHoqKi0q2/1ERERCg4ODhDzwEAAAAAwNOWqRZVvXDhgsaPH6/hw4dbLYaGDRuqRo0aKbaPHTtW69evV82aNa0QFQAAAAAAeFCmGSEiSUWKFNGSJUu0fft2q8Xg4uIiNzc3i69NmzZp7dq1GjBggHx8fKwWGwAAAAAAuCdTFUQaN26swMBADRw4MNWpM/ddv35dgwcPVpUqVRQQEKAOHTro8OHDkqTNmzerTJkyunLlirl906ZN1ahRI/PjuLg4eXp6au/evf8a05EjRzRq1Cg1atRI7dq1S1MMkpScnKzIyEjVq1dPXl5e8vf3V6dOnfT777+nep60tPfw8NDy5cvVsWNH+fj4qHr16po+fbpFP0uWLFHdunXl4+Ojrl27Ki4u7l+vEQAAAAAAW5OpCiImk0mjRo1SXFycxo0bl2obwzD09ttv69y5c4qMjNTSpUvl6+urNm3aKDo6WlWrVlXWrFm1c+dOSdKVK1f066+/6sSJE7p8+bIk6aefflLu3Lnl7+//yHiuXbum9957T0WLFtWIESPSHIMkLViwQPPmzVN4eLjWr1+vGTNm6OzZsxo7dmyq50pr+3HjxqlZs2b6+uuv1b59e0VERGjPnj2SpLVr12r48OHq2LGjVq1aJX9/fy1evDgNzzwAAAAAALYlUxVEJKlQoULq37+/li5dqp9++inF/p07d+rAgQOaMmWKypcvr1KlSqlPnz7y9fXVggUL5OLiosDAQPOx27dvl6enp/Lnz69du3ZJkrZs2aJatWrJweHhT59hGOrfv7/+/vtvTZs2TdmyZUtzDJJUtGhRjRs3TkFBQSpUqJACAwNVv359HT9+PNXzpbV906ZN1aRJExUpUkRdu3ZVrly5tH//fknSwoUL1bBhQ7Vr104lSpRQ586dFRQU9BjPPgAAAAAAtiFTLap6X6tWrbR+/XoNGjRIa9eutdj3yy+/yDCMFH/oJyQk6M6dO5Lu3bHmk08+kSRt27ZNgYGBunDhgnbu3Kn69evrxx9/1MiRIx8Zw7x587R582ZNnjxZpUqVeqIYDh48qKlTp+rMmTM6c+aMTp48qfz586d6vrS2/2csOXPmVGJioiTp+PHjevXVVy32+/n56dixY4+8VgAAAAAAbE2mLIhI0siRI/Xaa69pzJgxFtuTk5OVI0eOVG9X6+zsLEmqVauWhgwZolOnTmnHjh0aNWqULly4oHnz5ungwYOKj49XtWrVHnruffv2afLkyerQoYMaNmyYYn9aYpg9e7ZmzJihZs2aKTAwUB07dtSmTZv09ddfp3rOtLa/3/+DDMOwiO1BTk5OD71OAAAAAABsVaabMnPfiy++qPDwcC1fvtxi8VN3d3fduHFDiYmJKlasmPlrzpw52rRpkyTphRdekJeXl7744gtdvnxZAQEBCgwM1NmzZ7VkyRJVrVpVrq6uqZ73ypUr6t27t7y9vfXBBx+k2iYtMcyaNUvdunXTRx99pFatWsnX11dnz561KF486HHbp6Zs2bLm6TP3PbjQKwAAAAAAmUWmLYhI0uuvv67q1avr3Llz5m01atRQ2bJl1bt3b+3cuVO//fabxowZo6ioKIvpJMHBwVqyZIl8fX3l4uKiIkWKqHDhwlq1apVq16790HP269dPt2/f1uDBg3X16lXFxsZafF2/fj1NMRQsWFDbtm3TyZMndfr0aU2ePFkbNmxQQkJCqud93Pap6dy5szZu3Ki5c+fq7NmzWrhwodavX5/m4wEAAAAAsBWZuiAi3Zs6kzNnTvPjLFmy6NNPP5WXl5d69eqlxo0ba8+ePZo+fboCAwPN7YKDg5WQkKAqVaqYt1WtWlWSHrnQ6LZt2xQXF6eQkBBVr149xdeoUaPSFMP48eN1+/ZtNW/eXO3bt9fx48c1bNgwXb58WRcvXkxx3sdtn5patWrp448/1ldffaXXXntNGzZsUFhYWJqOBQAAAADAlpiMx5lTATyG+9NtvL29rRwJHnTr1i0dPXpUZcuWtbj7EZ5d8fHxcnV1VWRkpGJiYqwdDgAAQKoKFCigLl26WDsMu8Rn/P95nL9DM/0IEQAAAAAAgH+iIAIAAAAAAOwOBREAAAAAAGB3KIgAAAAAAAC742jtAAAAaePm5mbtEAAAAB6KzyqwNRREAMAG3L17VyEhIdYOAwAA4JGSk5Pl4MBEBNgGXqkA8IwzDEPR0dGKj4+3dihIo/j4eHJmQ8iX7SFntoec2Zb/ki+KIbAlvFoBwAYkJibKMAxrh4E0MgxD8fHx5MxGkC/bQ85sDzmzLeQL9oKCCAAAAAAAsDsURAAAAAAAgN2hIAIANsDJyUkmk8naYSCNTCaTXF1dyZmNIF8AANgn7jIDAM84k8kkT09POTryI9tWuLq6ytPT09phII3I13/DHSUAALaKT9cAYAMcHR0VFRWl2NhYa4cCAGZubm7cEhwAYLMoiACAjYiNjVVMTIy1wwAAAAAyBcY3AgAAAAAAu0NBBAAAAAAA2B0KIgAAAAAAwO5QEAEAAAAAAHaHgkgGCw4OVnBwsG7cuJFiX3h4uEJDQ60QFQAAAAAA9o2CyFNw4cIFjR8/3tphAAAAAACA/4+CyFNQpEgRLVmyRNu3b7d2KAAAAAAAQBREnorGjRsrMDBQAwcOTHXqjCRdvXpVw4YNU82aNeXj46PWrVtr165d5v0RERFq3769evfuLX9/f4WGhsrDw0O//PKLuU23bt0UEBCgpKQkSVJycrKqVKmiVatWSZKWLVum1157TT4+PvL19VXbtm11+PBhSdL8+fPl5+en+Ph4c3/Jycl6+eWXtXjx4nR/TgAAAAAAsCYKIk+ByWTSqFGjFBcXp3HjxqXYn5SUpLCwMO3du1cTJkxQVFSU3N3d9dZbb+nQoUPmdnv27FG+fPm0atUqjRgxQoUKFdK2bdvMfezatUs3b940F0kOHTqk69evq1atWtq4caOGDx+uTp066ZtvvtFnn32mO3fuaNCgQZKk1157TYmJidqwYYP5fNu3b9fff/+tRo0aZeTTAwAAAADAU0dB5CkpVKiQ+vfvr6VLl+qnn36y2PfTTz/pl19+0ccff6xKlSrppZde0rBhw1S6dGnNmzfPom3Pnj1VpEgRFS9eXMHBweaCyKFDh+Tk5CRfX1/zyJItW7YoICBAuXPnVp48eTRq1Cg1adJEhQoVkq+vr1q0aKHjx49LkvLmzavg4GCtXr3afK4VK1YoODhYuXPnzsinBgAAAACAp46CyFPUqlUrVatWTYMGDbKYOnP8+HHlzJlT7u7u5m0mk0kVKlQwFywk6fnnn1fOnDnNj4OCgrR//37dvn1b27ZtU5UqVVShQgXt3LlTkrR161bVrl1bklSxYkWVK1dOM2bMUN++fdWiRQuNHj1aycnJ5v6aN2+uHTt26M8//9SNGzf03XffKSQkJMOeDwAAAAAArIWCyFM2cuRIXb9+XWPGjDFvMwwj1baGYcjR0dH82MXFxWJ/pUqV5OzsrN27d2vHjh0KDAxUYGCg9u/frwsXLujo0aPmgsiaNWvUuHFjnTt3Tv7+/urfv7/Cw8Mt+qtevbry5cuntWvXasOGDcqVK5eqV6+eXpcOAAAAAMAzw/HfmyA9vfjiiwoPD9egQYNUpEgRFSxYUB4eHrp+/bqOHz9uHiViGIb27dunl1566aF9OTk5qXr16tq0aZMOHjyosWPHys3NTXfv3lVERITc3d1VuHBhSdLs2bPVokULDRs2zHz8pk2bzOcymUzKkiWLmjZtqo0bNypXrlxq0qSJsmTJkoHPBgAAAAAA1sEIESt4/fXXVb16dZ07d07SvZEZZcuWVd++fbV7926dOnVKw4cP1/Hjx/XGG288sq/g4GBFRUXphRdeUJEiReTi4iI/Pz+tWrXKPDpEkgoWLKj9+/frl19+0e+//67PPvtMixYtkiQlJCSY24WEhOjgwYPavn27mjVrlgFXDwAAAACA9VEQsZKRI0ea1wPJkiWLPv30U3l6eqp79+5q3ry5Tpw4oc8++0y+vr6P7KdmzZpKSkpSlSpVzNuqVq2q5ORki4LI4MGDlS9fPrVv316vv/66vv/+e40fP16SzLfelaTixYurfPny8vT0VKlSpdLxigEAAAAAeHYwZSaDbd68OdXtBQsW1N69e82P8+bNm+otee/r0aOHevTokWJ7njx5FB0dbbGta9eu6tq1q8W2IkWK6P/+7/9SHN+wYUOLx4Zh6M8//0xxPAAAAAAAmQkFEUiSEhMTtXnzZu3cuVO3bt3Sq6++au2QAAAAAADIMBREIOneAq0jR46UJE2YMEHZsmWzckQAAAAAAGQcCiIw+/HHH60dAgAAAAAATwWLqgIAAAAAALvDCBEAsBFubm7WDgEALPBzCQBgyyiIAIANuHv3rkJCQqwdBgCkkJycLAcHBh0DAGwPv70A4BlnGIaio6MVHx9v7VCQRvHx8eTMhpCv/4ZiCADAVvEbDABsQGJiogzDsHYYSCPDMBQfH0/ObAT5AgDAPlEQAQAAAAAAdoeCCADYACcnJ5lMJmuHAQAAAGQaLKoKAM84k8kkT09POTpmvh/ZLMYIAAAAa8l8n64BIBNydHRUVFSUYmNjrR1KunFzc+POOQAAALAaCiIAYCNiY2MVExNj7TAAAACATIFxygAAAAAAwO5QEAEAAAAAAHaHgggAAAAAALA7FEQAAAAAAIDdYVHVZ0xSUpKWLFmiqKgonTp1SlmyZNFLL72kFi1aqHnz5jKZTNYOEQAAAAAAm0dB5BmSmJiobt266dChQ+revbuqV6+upKQk/fjjjxo7dqw2b96siIgIZcmSxdqhAgAAAABg0yiIPEMiIyO1d+9eLV++XCVLljRvL1WqlCpVqqSWLVtq3rx56ty5sxWjBAAAAADA9rGGyDMiOTlZCxcuVEhIiEUx5D5PT081adJECxcu1Llz5+Th4aGvv/5aTZs2lbe3t0JCQnTq1CnNmDFDVatWVaVKlTRs2DAZhmHuPzIyUvXq1ZOXl5f8/f3VqVMn/f777+ZzeHh4aPny5erYsaN8fHxUvXp1TZ8+/ak9BwAAAAAAPC0URJ4RZ86c0dWrV+Xv7//QNoGBgfrzzz+VnJwsSZo8ebI+/PBDLVu2TNeuXVObNm109uxZLVy4UL1799bnn3+u77//XpK0YMECzZs3T+Hh4Vq/fr1mzJihs2fPauzYsRbnGDdunJo1a6avv/5a7du3V0REhPbs2ZNxFw4AAAAAgBVQEHlGxMXFSZKee+65h7a5v+/KlSuSpLCwMFWqVEllypRR3bp1devWLQ0fPlylSpVSmzZt9Pzzz+vEiROSpKJFi2rcuHEKCgpSoUKFFBgYqPr16+v48eMW52jatKmaNGmiIkWKqGvXrsqVK5f279+fEZcMAAAAAIDVsIbIM+J+seP69esPbXO/aJI3b15JUrFixcz7smXLpnz58snV1dW8zcXFRQkJCZKk4OBgHTx4UFOnTtWZM2d05swZnTx5Uvnz57c4R6lSpSwe58yZU4mJif/hygAAAAAAePYwQuQZUbRoUbm5uT1yesru3bvl5uZmvvWuo6NlPcvB4eHpnD17tjp06KC///5bgYGBGjZsmMLCwlK0c3Z2TrHt/jokAAAAAABkFhREnhFZsmRRx44dtXz5cp06dSrF/hMnTmjlypVq3779IwsfDzNr1ix169ZNH330kVq1aiVfX1+dPXuWYgcAAAAAwC4xZeYZEhYWpsOHD6tdu3bq0aOHqlevLkn66aefNG3aNFWpUkVvv/22/vjjj8fuu2DBgtq2bZuCg4Pl4OCgVatWacOGDcqXL196XwYAAAAAAM88Rog8QxwcHDR16lSFh4dr7dq1at68uUJCQrRmzRr169dPM2fOVJYsWZ6o7/Hjx+v27dtq3ry52rdvr+PHj2vYsGG6fPmyLl68mM5XAgAAAADAs40RIs+gpk2bqmnTpg/dX7hwYf36668W23r06KEePXpYbNu8ebP5+3LlymnJkiUp+mrdurX5+3/2+c8+AAAAAADILBghAgAAAAAA7A4FEQAAAAAAYHcoiAAAAAAAALtDQQQAAAAAANgdFlUFABvh5uZm7RDSVWa7HgAAANgWCiIAYAPu3r2rkJAQa4eR7pKTk+XgwGBFAAAAPH18CgWAZ5xhGIqOjlZ8fLy1Q0l3FEMAAABgLXwSBQAbkJiYKMMwrB0GAAAAkGlQEAEAAAAAAHaHgggAAAAAALA7FEQAwAY4OTnJZDJZOwykkclkkqurKzmzEeQLAAD7xF1mAOAZZzKZ5OnpKUdHfmTbCldXV3l6elo7DKQR+bIN3JUKAJDe+HQNADbA0dFRUVFRio2NtXYoAPDUubm5ZcpbjwMArIuCCADYiNjYWMXExFg7DAAAACBTYNwhAAAAAACwOxREAAAAAACA3aEgAgAAAAAA7A4FkQy0evVqtWzZUr6+vvLz81Pz5s315ZdfPrXzR0VFycPD46mdDwAAAAAAW8Giqhlk+fLlGjVqlAYOHKiAgAAZhqFt27Zp5MiR+uuvv9S9e3drhwgAAAAAgN2iIJJBPv/8czVv3lwtWrQwbytZsqQuXbqkBQsWUBABAAAAAMCKmDKTQRwcHPTzzz8rLi7OYnvnzp21ZMkS9ejRQ127djVvP3bsmDw8PDRv3jzztoULF6pu3bqSpISEBE2YMEE1atSQn5+fWrZsqZ9++smi740bN+q1116Tt7e32rZtq4sXL1rs/7c+oqKiVLduXfO/Xl5eCgkJ0b59+9LteQEAAAAA4FlAQSSDdOrUSdHR0Xr55ZfVuXNnzZ49W4cOHVLOnDlVokQJBQUFaffu3bp7964kadu2bTKZTNq1a5e5jy1btqh27dqSpAEDBmjbtm2aOHGiVqxYoQYNGqhr167asmWLJGn//v3q0aOH6tWrp9WrV6tZs2aaPXu2RUz/1ock/fHHH/ryyy81YcIErVixQq6urgoPD5dhGBn7hAEAAAAA8BQxZSaD1K9fXwUKFNCCBQu0bds2bd26VZJUvHhxjR49WrVq1dLAgQN14MABVahQQdu3b1ft2rW1Y8cO3b17VwkJCdq9e7e6du2q3377TWvXrtXKlStVtmxZSdKbb76pY8eOad68eapVq5YWLVokf39/81ScEiVK6Pjx41qwYIEkpakPSUpMTNSwYcMs2nTr1k2xsbF64YUXnuZTCAAAAABAhqEgkoF8fX3l6+ur5ORkHTt2TFu3btWiRYv09ttva+PGjSpfvry2bdsmHx8f7d27VwsXLtSWLVt05MgRXb58WdmyZZO/v782bNggSWrbtq1F/4mJicqVK5ck6fjx46pWrZrFfj8/P3NBJDo6+l/7uK9UqVLm73PmzGluBwAAAABAZkFBJAPExMQoMjJSXbp0UYECBeTg4CBPT095enqqTp06atSokfbs2aPg4GB99913qlSpknLlyiUfHx95e3tr165dunDhgoKCgpQlSxbzdJXFixcre/bsFudycLg368lkMik5Odlin5OTk/n7tPRxn7Ozc4prYsoMAAAAACAzYQ2RDODs7Kxly5Zp9erVKfbdH42RL18+BQcH68iRI9q4caMCAwMlSVWrVtXOnTst1g8pXbq0JCk2NlbFihUzf0VFRSkqKkqSVKZMGf38888W5zpy5Ij5+7T0AQAAAACAvaAgkgHy5s2rTp06aerUqZo8ebKOHj2qc+fO6fvvv1f37t1VuXJlVahQQS+99JIKFSqkZcuWmQsigYGB2rlzp65evWqeAlO6dGkFBQVp6NCh2rx5s86dO6c5c+YoMjJSRYsWlSSFhYXp2LFjGjdunM6cOaPVq1dr0aJF5pjS0gcAAAAAAPaCKTMZpFevXipevLiWLl2qxYsX6/bt23rxxRfVoEEDdenSxdwuKChI8+fPV5UqVSTdW3fExcVFlStXVrZs2cztJk+erMmTJ2vIkCGKi4tT0aJFNWrUKDVr1kySVLZsWc2ZM0cTJkzQokWLVLp0aXXt2lUTJ05Mcx8AAAAAANgLk8HiEMgghw8fliR5e3tbORI86NatWzp69KjKli1rUXTDsys+Pl6urq6KjIxUTEyMtcMBgKeuQIECFv+hxO8y20PObAv5sj3k7H8e5+9QpswAAAAAAAC7Q0EEAAAAAADYHQoiAAAAAADA7lAQAQAAAAAAdoe7zACAjXBzc7N2CABgFfz8AwBkBAoiAGAD7t69q5CQEGuHAQBWk5ycLAcHBjcDANIPv1UA4BlnGIaio6MVHx9v7VCQRvHx8eTMhpAv20AxBACQ3vjNAgA2IDExUYZhWDsMpJFhGIqPjydnNoJ8AQBgnyiIAAAAAAAAu0NBBAAAAAAA2B0KIgBgA5ycnGQymawdBtLIZDLJ1dWVnNkI8gUAgH3iLjMA8IwzmUzy9PSUoyM/sm2Fq6urPD09rR0G0ui/5Is7nwAAYLv4dA0ANsDR0VFRUVGKjY21digA/j83Nzduhw0AgA2jIAIANiI2NlYxMTHWDgMAAADIFBjjCQAAAAAA7A4FEQAAAAAAYHcoiAAAAAAAALtDQQQAAAAAANgduy6IrF69Wi1btpSvr6/8/PzUvHlzffnll0/UV3BwsCIiIiRJhmFoxYoVunz5cnqGa+H8+fPy8PDQrl27MuwckhQeHq7Q0NAMPQcAAAAAAE+b3d5lZvny5Ro1apQGDhyogIAAGYahbdu2aeTIkfrrr7/UvXv3x+4va9askqQ9e/YoPDxcmzZtyojQAQAAAADAf2S3BZHPP/9czZs3V4sWLczbSpYsqUuXLmnBggWPXRDJmzev+XvDMNItTgAAAAAAkP7sdsqMg4ODfv75Z8XFxVls79y5s5YsWaIePXqoa9eu5u3Hjh2Th4eH5s2bZ962cOFC1a1bV9L/pszs2rVLHTp0kCTVrl1bUVFRCg4OloeHR4qvAQMGSLpXQJkzZ45q166t8uXLq0mTJlq9erX5PLt27ZKnp6dmz56typUrKyQkJEXRJSEhQePGjVNwcLC8vLxUqVIlvffee7py5Yqk/02xWb9+vV5//XV5eXkpODhYS5YsMfdhGIY++eQTvfzyy/L19dWAAQN0586d9Hi6AQAAAAB4pthtQaRTp06Kjo7Wyy+/rM6dO2v27Nk6dOiQcubMqRIlSigoKEi7d+/W3bt3JUnbtm2TyWSyWLNjy5Ytql27tkW/fn5+5rVEli1bpoYNG2r58uX66aefzF9hYWHKnj27OnbsKEmaPHmyvvjiCw0ePFhr1qxRhw4d9NFHH2nx4sXmfpOSkrR161YtWbJEo0aNkslksjjv+PHjtWHDBo0dO1br16/X2LFjtXPnTs2cOdOi3ZgxY9S1a1d98803qlWrlj766COdO3dOkjR79mzNnTtXH3zwgaKiopQrVy6tW7cufZ5wAAAAAACeIXY7ZaZ+/foqUKCAFixYoG3btmnr1q2SpOLFi2v06NGqVauWBg4cqAMHDqhChQravn27ateurR07duju3btKSEjQ7t27LUaRSJKzs7Ny584t6d40GhcXF7m4uJj3b9y4UQsWLNCMGTPk4eGhW7du6bPPPtOkSZNUq1YtSVLRokV14cIFzZs3T+3atTMfGxYWpuLFi0u6N+LjQd7e3qpfv74qVKggSSpUqJCqVq2q48ePW7Tr2LGjuYjTu3dvLV68WAcPHlThwoW1cOFCdejQQY0aNZIkDRgwIMMXbQUAAAAAwBrstiAiSb6+vvL19VVycrKOHTumrVu3atGiRXr77be1ceNGlS9fXtu2bZOPj4/27t2rhQsXasuWLTpy5IguX76sbNmyyd/fP83nO3z4sN5//329//775uLHyZMndefOHfXt21cODv8bsHO/6HL79m3ztvvFkNQ0adJE27dv18SJE3X27FmdPn1aZ86cMRdI7itVqpT5+5w5c0qSEhMT9ffffys2Nlbe3t4pnqNTp06l+RoBAAAAALAFdlkQiYmJUWRkpLp06aICBQrIwcFBnp6e8vT0VJ06ddSoUSPt2bNHwcHB+u6771SpUiXlypVLPj4+8vb21q5du3ThwgUFBQUpS5YsaTrnH3/8oXfeeUeNGzc2T5WR/rcA65QpU1SyZMkUxzk7O5u/v38Xm9QMGTJE69evV9OmTRUcHKxu3bpp3rx5unTp0kP7ezCG+1Nw/rk2iaOjXb5EAAAAAACZnF2uIeLs7Kxly5ZZLFx6X65cuSRJ+fLlU3BwsI4cOaKNGzcqMDBQklS1alXt3Lkz1fVD7vvn+h43btxQly5dVKpUKQ0ZMsRiX8mSJeXo6KiLFy+qWLFi5q+tW7dq3rx5FqNGHubvv//WkiVLNHToUA0YMEAhISEqW7asTp8+neY73jz33HMqWLCg9u3bZ7H9yJEjaToeAAAAAABbYpf//Z83b1516tRJU6dO1c2bN1W/fn3lyJFDJ0+e1CeffKLKlStbrMWxbNkyDR8+XJIUGBiomTNnysnJSdWqVUu1/2zZskm6d2ea3Llzq0+fPrpz545Gjhypq1evmosUWbJkUd68edW6dWtNnTpVOXLkkL+/v3bt2qUJEyaoS5cuabqeHDlyKGfOnNq0aZPKlSun27dva9GiRfrll19Uvnz5ND8vb7/9tsaNG6eSJUuqQoUKWrVqlQ4dOqSAgIA09wEAAAAAgC2wy4KIJPXq1UvFixfX0qVLtXjxYt2+fVsvvviiGjRoYFGICAoK0vz581WlShVJ99bUcHFxUeXKlc2Fj39yd3dXzZo11atXL/Xp00c//PCDJKlOnToW7QoVKqTNmzdrwIABeu655zR16lT9+eefKliwoHr27KlOnTql6VqcnJw0depUjR07Vq+99ppy586typUrq0+fPoqMjFR8fHya+mnXrp2Sk5M1c+ZM/fXXX6pRo4ZatGihM2fOpOl4AAAAAABshclI65wK4DEdPnxYklIs1ArrunXrlo4ePaqyZcs+tKiHZ0t8fLxcXV0VGRmpmJgYa4cD4P8rUKBAmkdzIn3xu8z2kDPbQr5sDzn7n8f5O9Qu1xABAAAAAAD2jYIIAAAAAACwOxREAAAAAACA3aEgAgAAAAAA7I7d3mUGAGyNm5ubtUMA8ADekwAA2DYKIgBgA+7evauQkBBrhwHgH5KTk+XgwIBbAABsEb/BAeAZZxiGoqOjFR8fb+1QkEbx8fHkzIb8l3xRDAEAwHbxWxwAbEBiYqIMw7B2GEgjwzAUHx9PzmwE+QIAwD5REAEAAAAAAHaHgggA2AAnJyeZTCZrh4E0MplMcnV1JWc2gnzZHnJme8iZbSFftoecPRmTwfhQZJDDhw9Lkry9va0cCR5069YtHT16VGXLllW2bNmsHQ7SID4+Xk5OTnJ0ZB1sAAAAWI8tLCb+OH+H8ukaAGyAo6OjoqKiFBsba+1QAAAAYIfc3Nwy3V0PKYgAgI2IjY1VTEyMtcMAAAAAMoVne6wLAAAAAABABqAgAgAAAAAA7A4FEQAAAAAAYHcoiAAAAAAAALtj9YJIaGiowsPDU90XHh6u0NDQNPcVERGh4OBg8+MTJ05oy5Yt/ym+f/b5Tw+Lcd26dfL09NTgwYOVnJz82NfyJHbt2iUPDw+dP38+Q88DAAAAAICty1R3mQkLC1O7du3Mj7t06aJmzZqpVq1aTzWOdevW6f3331ebNm00aNAgmUwmDRw4UElJSU81DgAAAAAAkLpMVRDJnj27smfPbtUYvv32W73//vspRr7kzJnTilEBAAAAAIAHWX3KzOPw8PDQ8uXL1bFjR/n4+Kh69eqaPn26ef+D01uCg4N14cIFTZ8+3TxV5fr16xo8eLCqVKmigIAAdejQQYcPH7Y4x5IlS1S3bl35+Pioa9euiouLS3N869evV9++ffXWW2+lmAb04JSZXbt2ydPTU1u3blWjRo3k5eWl+vXr67vvvjO3T0pK0uTJk1W9enX5+vqqZ8+eGjVqlMW0m7179+r111+Xj4+PGjdurGPHjlmcMykpSZ999pnq1asnb29v1atXT1988YV5//04Nm7cqHr16snHx0cdOnTQH3/8oZEjR6pChQoKDAzUzJkz0/wcAAAAAABgC2yqICJJ48aNU7NmzfT111+rffv2ioiI0J49e1K0W758uQoUKKCwsDBFRETIMAy9/fbbOnfunCIjI7V06VL5+vqqTZs2io6OliStXbtWw4cPV8eOHbVq1Sr5+/tr8eLFaYprw4YN6tOnj3x9fdWnT59/bZ+UlKQJEyZo4MCBWrt2rdzd3dW/f3/dvHlTkjRx4kQtWbJEQ4cO1VdffSU3NzctXLjQfPy5c+cUFhamsmXLasWKFerWrZs++eQTi3OMHTtWn3zyibp37641a9aoXbt2GjVqlD777DOLOGbOnKmJEydq/vz5OnbsmJo0aSInJyctW7ZMrVu31pQpU/Trr7+m6XkAAAAAAMAW2FxBpGnTpmrSpImKFCmirl27KleuXNq/f3+Kdnnz5lWWLFmULVs25cmTRzt37tSBAwc0ZcoUlS9fXqVKlTIXMBYsWCBJWrhwoRo2bKh27dqpRIkS6ty5s4KCgv41phMnTqhPnz6qXLmy9u7dazHS41F69eqlwMBAFS9eXO+++65u3Lih48ePKz4+Xp9//rl69eqlunXrqlSpUho0aJA8PT3Nxy5dulT58uXT0KFDVapUKdWrV0/vvPOOef+NGzf0xRdfqGfPnnrttddUvHhxdejQQW3bttXs2bNlGIa57XvvvSdvb2/5+fmpSpUqcnV11QcffKASJUqoS5cu5msEAAAAACCzsHpBxNHRUcnJyanuS05OlqOj5TInpUqVsnicM2dOJSYm/ut5fvnlFxmGoaCgIPn5+Zm/fv75Z506dUqSdPz4cXl7e1sc5+fn9699//333+rVq5c+/fRTvfzyyxo4cKBiYmL+9biSJUuav8+RI4ckKTExUadOndLt27fl6+tr3m8ymRQQEGB+fPz4cXl6eipLlizmbf7+/ubvT58+rcTERItjJKlSpUq6fPmyLl++bN5WrFgx8/fZsmVT4cKFZTKZJEkuLi6SpISEhH+9HgAAAAAAbIXVF1XNlSuXrl27luq+uLg45c6d22Kbs7NzinYPjnZ4mOTkZOXIkUNRUVEp9j3Y5z+LM05OTv/at7+/vzp16iRJGj16tBo1aqR+/fpp/vz5FgWLR533PsMwzEWgR12XyWRKEeuDxaOHHXv/mAfb/rPo5OBg9ToZAAAAAAAZyup/+ZYrV05HjhxJMQIhISFBhw4dSjFi40m5u7vrxo0bSkxMVLFixcxfc+bM0aZNmyRJZcuWTTH95p+LrqbmwYKCm5ubRowYoT179qRY0yOtihUrJhcXFx04cMBi+8GDB83flylTJsXzduTIEfP3pUqVkpOTk/bt22fRx969e+Xm5pai0AQAAAAAgD2xekGkRYsWSk5OVvfu3fXzzz/rwoUL2r17t9599105OjqqRYsWT9x39uzZdfbsWf3111+qUaOGypYtq969e2vnzp367bffNGbMGEVFRZmn4XTu3FkbN27U3LlzdfbsWS1cuFDr169/7PO+8soratasmWbOnJnqgq//xtXVVaGhoZo2bZq+++47nTlzRuPGjbMoiLRp00bx8fH68MMPderUKX3//feKiIgw78+RI4datWqladOmae3atfrtt9+0ePFiff755woLCzNPiQEAAAAAwB5ZvSCSN29eLVmyRLly5VKPHj1Ur1499enTR/ny5dPSpUv/00iG0NBQbdmyRWFhYcqSJYs+/fRTeXl5qVevXmrcuLH27Nmj6dOnKzAwUJJUq1Ytffzxx/rqq6/02muvacOGDQoLC3uicw8aNEgFChRQv379dPXq1cc+/r333lPjxo01aNAgNW3aVH/88Ydq165tnmaTP39+zZ8/XzExMWrWrJnGjh1rsaiqJA0YMEAdOnTQxIkT9eqrr+qLL77QkCFDnviaAAAAAADILExGWhbgwFO3ceNGBQQEKG/evOZtYWFhKlCggEaPHm3FyNLu/nSj9Jr2hPRx69YtHT16VGXLllW2bNmsHQ7SID4+Xq6uroqMjEzTgs0AAABAeitQoID5LqTPssf5O9TqI0SQunnz5qlv3746evSozp07p88++0w7d+5U48aNrR0aAAAAAAA2j4LIM2rixInKnj27OnbsqEaNGmnNmjWaOnWqqlSpYu3QAAAAAACweVa/7S5SV7hwYU2fPt3aYQAAAAAAkCkxQgQAAAAAANgdRogAgI1wc3OzdggAAACwU5nxsygFEQCwAXfv3lVISIi1wwAAAIAdS05OloND5ploknmuBAAyKcMwFB0drfj4eGuHgjSKj48nZzaEfNkecmZ7yJltIV+252nlLDMVQyQKIgBgExITE2UYhrXDQBoZhqH4+HhyZiPIl+0hZ7aHnNkW8mV7yNmToSACAAAAAADsDgURAAAAAABgdyiIAIANcHJykslksnYYSCOTySRXV1dyZiPIl+0hZwCA9MBdZgDgGWcymeTp6SlHR35k2wpXV1d5enpaOwykEfmyPeQsfWS2u0UAwOPi0zUA2ABHR0dFRUUpNjbW2qEAADIBNzc3bucOwO5REAEAGxEbG6uYmBhrhwEAAABkCoyRAwAAAAAAdoeCCAAAAAAAsDsURAAAAAAAgN3JFAWR0NBQhYeHp7ovPDxcoaGhae4rIiJCwcHB5scnTpzQli1b/lN8/+wzNYcOHVKXLl1UqVIleXt7q169evr4449148YNc5vExER99tlnj3Xuixcv6uuvv36SsAEAAAAAyLQyRUEkPYWFhWn58uXmx126dNHhw4cz9JwnTpxQaGioXnrpJS1cuFDr1q1T3759tXbtWr377rvmdmvXrtWYMWMeq+/+/fvrxx9/TO+QAQAAAACwadxl5h+yZ8+u7NmzP9VzRkVFqVixYnr//ffN24oUKSIXFxe9/fbbOnbsmMqUKSPDMJ5qXAAAAAAAZFZ2N0LEw8NDy5cvV8eOHeXj46Pq1atr+vTp5v0PTm8JDg7WhQsXNH36dPO0m+vXr2vw4MGqUqWKAgIC1KFDhxQjSJYsWaK6devKx8dHXbt2VVxc3CNjMplMunDhgk6ePGmxvWrVqvr6669VokQJRUVFacCAAeZr2LVrl5KTkxUZGal69erJy8tL/v7+6tSpk37//XdJ96YS7d69WytWrDBfU0JCgiZMmKAaNWrIz89PLVu21E8//WQ+Z1JSkiZMmKCaNWvKy8tL9evX1xdffPEkTzUAAAAAAM8suyuISNK4cePUrFkzff3112rfvr0iIiK0Z8+eFO2WL1+uAgUKKCwsTBERETIMQ2+//bbOnTunyMhILV26VL6+vmrTpo2io6Ml3ZvWMnz4cHXs2FGrVq2Sv7+/Fi9e/Mh4WrVqJUdHRzVq1EitW7fWpEmT9OOPPyopKUkvvfSSsmbNqoYNG+rDDz+UJP3000/y8/PTggULNG/ePIWHh2v9+vWaMWOGzp49q7Fjx0q6V9zx8/NTgwYNzNOABgwYoG3btmnixIlasWKFGjRooK5du5rXSfn888/17bffavLkyVq/fr3at2+vjz76SHv37k2vpx8AAAAAAKuzy4JI06ZN1aRJExUpUkRdu3ZVrly5tH///hTt8ubNqyxZsihbtmzKkyePdu7cqQMHDmjKlCkqX768SpUqpT59+sjX11cLFiyQJC1cuFANGzZUu3btVKJECXXu3FlBQUGPjKdYsWJauXKlQkND9eeffyoyMlKdOnVS9erVtXTpUkmSi4uLcubMKUlyc3OTs7OzihYtqnHjxikoKEiFChVSYGCg6tevr+PHj0uS8uTJIycnJ7m4uChv3rz67bffzOuQVK5cWcWLF9ebb76pV199VfPmzZMk/f7778qWLZsKFy6sQoUKqX379vq///s/lShRIt2efwAAAAAArC1TrCHi6Oio5OTkVPclJyfL0dHyMkuVKmXxOGfOnEpMTPzX8/zyyy8yDCNFgSMhIUF37tyRJB0/flyvvvqqxX4/Pz8dO3bskX0XLFhQAwcO1MCBA3Xu3Dlt375dn3/+uQYPHqz8+fOrZs2aKY4JDg7WwYMHNXXqVJ05c0ZnzpzRyZMnlT9//lTPcX8US9u2bS22JyYmKleuXJKkdu3a6bvvvlPNmjVVtmxZVatWTa+++qqef/75R8YPAAAAAIAtyRQFkVy5cunatWup7ouLi1Pu3Lkttjk7O6dol5YFS5OTk5UjRw5FRUWl2Pdgn/8szjg5OT2y3/Hjx6tGjRoKDAyUdG9B1VatWqlZs2aqW7eutm7dmmpBZPbs2ZoxY4aaNWumwMBAdezYUZs2bXrobXbvX+PixYtTLBzr4HBvsFDx4sW1YcMG7d69W9u2bdOWLVs0Z84cjRkzRs2aNXvkdQAAAAAAYCsyxZSZcuXK6ciRI0pISLDYnpCQoEOHDsnb2ztdzuPu7q4bN24oMTFRxYoVM3/NmTNHmzZtkiSVLVs2xfSbf7tt744dO/Tpp5+m2O7s7CwXFxfz6AyTyWSxf9asWerWrZs++ugjtWrVSr6+vjp79uxDizulS5eWJMXGxlrEHxUVZS7yLFiwQBs2bFC1atX0wQcfaM2aNQoMDNS6devS8AwBAAAAAGAbMkVBpEWLFkpOTlb37t31888/68KFC9q9e7feffddOTo6qkWLFk/cd/bs2XX27Fn99ddfqlGjhsqWLavevXtr586d+u233zRmzBhFRUWZp+F07txZGzdu1Ny5c3X27FktXLhQ69evf+Q5evfure3bt+u9997Tnj17zPF/8MEHunnzplq1aiVJypYtmyTpyJEjun37tgoWLKht27bp5MmTOn36tCZPnqwNGzZYFIayZ8+uCxcuKCYmRqVLl1ZQUJCGDh2qzZs369y5c5ozZ44iIyNVtGhRSdKVK1c0fPhwbdq0SRcuXNCPP/6oo0ePys/P74mfQwAAAAAAnjWZoiCSN29eLVmyRLly5VKPHj1Ur1499enTR/ny5dPSpUtTTJl5HKGhodqyZYvCwsKUJUsWffrpp/Ly8lKvXr3UuHFj7dmzR9OnTzdPd6lVq5Y+/vhjffXVV3rttde0YcMGhYWFPfIcL7/8shYuXKiEhAS99957qlevnnr37i2TyaQvv/xS+fLlkyRVqVJF5cuXV+vWrfX9999r/Pjxun37tpo3b6727dvr+PHjGjZsmC5fvqyLFy9Kklq3bq3jx4+rcePGSkpK0uTJk/XKK69oyJAhatiwoVauXKlRo0aZp8N0795dLVq00MiRI1WvXj0NGTJEbdq0UZcuXZ74OQQAAAAA4FljMtKyeAbwBO5PFUqvKUtIH7du3dLRo0dVtmxZ86gjPNvi4+Pl6uqqyMhIxcTEWDscAEAmUKBAgaf6H158/rAt5Mv2kLP/eZy/QzPFCBEAAAAAAIDHQUEEAAAAAADYHQoiAAAAAADA7lAQAQAAAAAAdsfR2gEAANLGzc3N2iEAADIJfqcAAAURALAJd+/eVUhIiLXDAABkIsnJyXJwYMA4APvFT0AAeMYZhqHo6GjFx8dbOxSkUXx8PDmzIeTL9pCz9EExBIC9MxmGYVg7CGRO+/fvl2EYcnZ2tnYoeIBhGEpMTJSTk5NMJpO1w0EakDPbQ85sC/myPeTM9pAz20K+bA85+5+EhASZTCb5+/v/a1umzCDD2Psb8VllMpkoUtkYcmZ7yJltIV+2h5zZHnJmW8iX7SFn/2MymdL8tygjRAAAAAAAgN1h4iAAAAAAALA7FEQAAAAAAIDdoSACAAAAAADsDgURAAAAAABgdyiIAAAAAAAAu0NBBAAAAAAA2B0KIgAAAAAAwO5QEAEAAAAAAHaHgggAAAAAALA7FEQAAAAAAIDdoSACAAAAAADsDgURAAAAAABgdyiIAJnMnTt3NGzYMAUGBsrPz099+/bVlStXHnnM+fPn1aVLF/n7+6t69eqaMmWKkpKSzPuTkpLk4+MjDw8Pi6+IiIiMvpxMKTk5WdOmTVONGjXk6+urt99+W+fOnXto+7///lt9+/ZVxYoVValSJQ0bNkzx8fEWbb755hs1bNhQPj4+atq0qXbs2JHRl2E3MiJfr7zySor3U3h4eEZfit143Jw9eFynTp1S/dnGeyxjZUTOeJ9lnMfN14kTJ9S5c2dVrlxZgYGB6tmzpy5evGjRZvHixapdu7Z8fHzUtm1bRUdHZ/Rl2JX0zhmfDTPe4+bsl19+0RtvvCE/Pz9VqVJFQ4YM0fXr1y3a8LssFQaATCU8PNyoU6eOsWfPHuPgwYNG06ZNjXbt2j20fUJCgvHKK68YnTt3Nn799Vdj48aNRqVKlYypU6ea25w8edJwd3c3jh49avz555/mrxs3bjyNS8p0IiIijMqVKxvff/+9cfToUSMsLMx45ZVXjDt37qTavn379kbz5s2NI0eOGNu3bzeCgoKMDz74wLx/x44dRrly5Yz58+cbJ0+eNMaOHWt4eXkZJ0+efFqXlKmld75u3rxplClTxvj+++8t3k/Xrl17WpeU6T1uzgzDMO7cuWP079/fcHd3N6ZNm2axj/dYxkvvnPE+y1iPk68rV64Y1apVM3r06GH8+uuvxuHDh4127doZDRo0MG7fvm0YhmFERUUZPj4+xqpVq4wTJ04Y77//vlGpUiXj8uXLT/vSMq30zhmfDTPe4+QsNjbWqFixojFgwADj9OnTxr59+4yGDRsa7777rrkNv8tSR0EEyERiYmKMMmXKGFu2bDFvO336tOHu7m7s378/1WPWrFljeHl5GVevXjVv+/LLLw1/f3/zD9yvv/7a8Pf3z9jg7cSdO3cMPz8/Y/HixeZtcXFxho+Pj7FmzZoU7ffv32+4u7tb/LL68ccfDQ8PDyMmJsYwDMMICwsz3nvvPYvjWrVqZQwePDhjLsKOZES+Dh48aLi7u1u855B+HjdnhmEY+/btM1599VWjdu3aRoUKFVL8cc17LGNlRM54n2Wcx83X0qVLDT8/PyM+Pt687eLFi4a7u7uxfft2wzAM45VXXjHGjx9v3p+YmGjUrFnTmDVrVgZeif3IiJzx2TBjPW7ODhw4YPTu3dtITEw0b/vss8+M8uXLmx/zuyx1TJkBMpF9+/ZJkqpUqWLeVqJECeXPn1979uxJ9Zi9e/eqXLlyyp07t3lblSpVdOPGDR09elSS9Ouvv6pUqVIZGLn9OHbsmG7evKnAwEDztly5csnT0zPVHO3du1dubm4Wz3+lSpVkMpm0b98+JScna//+/Rb9SVLlypUfmnOkXXrnS7r3fsqXL5/Few7p53FzJklbt25VjRo1tHLlSuXMmdNiH++xjJfeOZN4n2Wkx81XYGCgPvnkE7m4uJi3OTjc+xPk2rVrunz5ss6ePWvRn6OjoypUqMB7LJ2kd84kPhtmtMfNWfny5TVp0iQ5OjpKkk6dOqVVq1apWrVqkvhd9iiO1g4AQPq5dOmSnnvuOWXNmtVi+wsvvKCYmJhUj4mJiVGBAgVStJekP/74Q+XLl9fx48d19+5dvfXWWzp27Jjy58+vN954Q02aNMmYC8nE7uehYMGCFtsflqNLly6laOvs7Kw8efLojz/+0LVr13Tr1q1Uc/iwnCPt0jtf0r0PkdmyZVPPnj21f/9+Pffcc2revLk6dOhg/sCJJ/e4OZOk3r17P7Q/3mMZL71zJvE+y0iPm6/ChQurcOHCFttmz54tFxcXVaxY0fyzMbX+jh07lp6h2630zpkkPhtmsCf5uXhfvXr1dPbsWRUqVEjTp0+XxO+yR6EgAtiQ8+fPq3bt2g/d/95778nZ2TnF9qxZs+rOnTupHnP79m3lypUrRXtJ5mNOnDih5ORk9ezZUwUKFNDWrVs1YMAAJSYmqkWLFk96OXbp/uKa/8xT1qxZFRcXl2r7R+X09u3bD+3vYTlH2qV3vqR776dr166pXr166tatm/bt26cJEyYoLi5O7733XgZchX153Jz9G95jGS+9cybxPstI/zVfCxcu1KJFizRo0CDlzZtXp0+ffmh/vMfSR3rnTOKzYUb7LzmbOHGi4uPjNWHCBHXo0EGrVq3id9kjUBABbEj+/Pm1bt26h+7funWrEhISUmy/c+eOXF1dUz3GxcUlxTH3fzBmy5ZNkrR27VolJSUpe/bskqQyZcro4sWLmjdvHr/0HtP94acJCQkWQ1EflqPU8nO/fbZs2czFq9Ry+LCcI+3SO1+SNGfOHN25c8c8zN/Dw0M3btzQzJkz1aNHD/73+j963Jz9G95jGS+9cybxPstIT5ovwzA0depUzZw5U++8845CQ0NT9Pcg3mPpJ71zJvHZMKP9l5+L3t7ekqTp06erZs2a2rhxo2rWrGnu70G8z7jtLmBTnJycVKpUqYd+FShQQFevXk3xw+7PP/9U/vz5U+2zQIEC+vPPP1O0l2Q+xsXFxfwL7z53d3e7H2L3JO4PfUztOU8tR6nlJyEhQVevXtULL7ygPHnyKFu2bGnuD48nvfMl3fvfmX+ueeDu7q5bt2498f+G438eN2f/hvdYxkvvnEm8zzLSk+QrMTFR77//vmbNmqUBAwaoV69e/6k/PJ70zpnEZ8OM9rg5O336tLZs2WKxLX/+/MqTJ48uXbrE77JHoCACZCIBAQFKTk42L94oSWfOnNGlS5fMcz7/qWLFioqOjtaNGzfM23bu3Kns2bOrTJkyunbtmipVqqSoqCiL4w4fPqzSpUtnzIVkYmXKlFGOHDm0a9cu87Zr164pOjo61RxVrFhRMTEx+u2338zbdu/eLelevk0mk/z9/c3b7tu1a5cqVKiQQVdhP9I7X4ZhqE6dOuY5vfcdPnxYbm5ueu655zLoSuzH4+bs3/Aey3jpnTPeZxnrSfL1wQcf6Ntvv9XHH3+sjh07Wux7/vnnVaJECYv+7t69q7179z5R/pFSeueMz4YZ73Fztn37dvXs2dO86K0k/f777/r7779VqlQpfpc9AlNmgEwkf/78evXVVzVo0CCNHj1arq6uGjp0qCpVqiRfX19J9/63Oi4uTrlz55azs7Pq1KmjKVOmqFevXurXr5/Onz+vSZMmKSwsTM7OznJ2dlaVKlU0efJkPf/88ypWrJg2bNig1atXKzIy0roXbIOcnZ3Vvn17TZw4UXnz5lWhQoU0YcIEFShQQK+88oqSkpJ05coV5cyZUy4uLipfvrz8/f3Vu3dvffTRR7p165aGDBmipk2bmiv6b775pjp37ixPT0+9/PLL+uqrr3T06FGNGjXKyldr+zIiX3Xr1tW8efNUsmRJeXl5aceOHZo7d64GDhxo5avNHB43Z2nBeyxjpXfOTCYT77MM9Lj5ioqK0rp16/TBBx+oUqVKio2NNfd1v01YWJhGjRqlYsWKydvbW7Nnz9bt27eZepFO0jtnuXLl4rNhBnvcnDVq1EizZ8/W+++/r379+ikuLk4jR46Uj4+PgoKCJPG77KGse9dfAOnt5s2bxsCBA40KFSoYFSpUMPr06WNcuXLFvH/nzp2Gu7u7sXPnTvO2s2fPGm+++abh7e1tVK9e3ZgyZYqRlJRk3n/9+nVj9OjRRs2aNQ0vLy+jSZMmxsaNG5/qdWUmd+/eNcaPH29UqVLF8PX1Nd5++23j3LlzhmEYxrlz5wx3d3fjq6++Mrf/66+/jB49ehi+vr5G5cqVjaFDhxq3b9+26HPFihVG3bp1DW9vb6NZs2bG9u3bn+o1ZWbpna/ExERj+vTpRu3atY1y5coZ9erVM5YsWfLUrysze9ycPSgoKMiYNm1aiu28xzJWeueM91nGepx8vfnmm4a7u3uqXw/mdO7cucbLL79s+Pj4GG3btjWio6Otcm2ZVXrnjM+GGe9xfy6ePn3a6Ny5sxEQEGBUqlTJGDBggBEXF2fRJ7/LUjIZhmFYuygDAAAAAADwNLGGCAAAAAAAsDsURAAAAAAAgN2hIAIAAAAAAOwOBREAAAAAAGB3KIgAAAAAAAC7Q0EEAAAAAADYHQoiAADAzDAMa4cAAADwVFAQAQDgGRMaGioPDw+LLy8vL9WqVUvDhg1TXFxcup8zISFBo0eP1po1a9K977Se/4033lC5cuVUtmxZeXp6ys/PT2fOnPnPfYeHh8vDw0Mvv/zyQws+EydOlIeHh0JDQ1Pd//nnn8vDw0Nr16791/OdP39eHh4eioqKkiTt2rVLHh4e2rVr15NfRBrP9V8lJSVp2bJlCg0NVbVq1cyvuwEDBujUqVPpco5nUXh4uIKDgx/ZJioqSh4eHjp//vx/Pl9oaOhDX2sAgKfH0doBAACAlDw9PTV06FDz48TERP3yyy+aNGmSjh49qi+++EImkyndzvfnn39q/vz5GjNmTLr1+bgGDhyo27dvK0uWLHJ2dlbBggWVI0eOdOnbwcFBly5d0v79+xUQEJBi/7p16x56bEJCgiIjI/XOO++oUaNGj33ucuXKacmSJXrppZce+9in6caNG3rnnXd08OBBtWzZUm+++aZy5syps2fPauHChWrevLnmzp2rChUqWDvUdPfuu++qQ4cO1g4DAPCUURABAOAZlCNHDvn6+lpsq1ixom7evKlp06bp4MGDKfbbMmdnZ7m7u2dY/wULFpRhGPrmm29SFEQOHDigS5cuPfT8hmHo//7v/1SiRIknOndquXwWjRo1Sj///LMWLVpkEW/FihXVuHFjtW3bVoMHD9Y333xjvSAzSNGiRa0dAgDACpgyAwCADfHy8pIkXbx40bxt3bp1CgkJkZ+fn6pVq6YhQ4ZYTKuJiIhQ3bp1tWXLFr322mvy8vJSvXr1tHLlSkn3pl3Url1bkjRgwADz1IHUhvWnNv1jz549euutt1SxYkV5eXkpODhYERERSk5ONre5ceOGRowYoRo1asjX11fNmzfXli1bzPtv376tjz/+WK+88oq8vLzk7++vN998U0ePHrU4/7Zt29S2bVsFBASocuXK6tu3r/744480PXf169fXhg0bUkybWbdunapWrao8efKkOGbZsmUKCQlR48aNFRQUpIiICCUlJVm02bBhgxo3biwfHx81a9ZMx44d+9fn7LvvvlPbtm3l5+cnLy8v1a9fX4sXL/7Xa/i3c0nS1atXNWTIEFWtWlXe3t5q2bKlduzY8ch+L1y4oBUrVqhDhw6pFm+yZs2q3r17y8vLS9evXzdvP378uLp06SJ/f3/5+/urW7duOnfuXIpr37Fjh8LCwlS+fHlVq1ZNEyZMsHgek5OTNXv2bNWtW9f8+ly4cKFFDKGhoerXr5969uwpX19fvfnmm5Kk69eva8yYMapTp468vb3VqFEjLV++3OLYI0eO6I033lBAQID8/PzUsWNHHThwwLz/n1NmkpOT9cknn6hWrVoqX7683n333VSnqqUljxcvXlT37t0VEBCgatWq6f/+7/9SzcGyZcv06quvmqcppfZaAwCkLwoiAADYkPtrahQpUkSS9Mknn6hPnz7y9fXVtGnT1K1bN61fv16hoaG6ffu2+bjY2FgNHz5cHTp00OzZs1W4cGH1799fp06d0gsvvKDp06dLkt555x3z92lx7NgxdezYUXny5NHkyZM1c+ZMVahQQdOnTzePJEhKSlJYWJjWrFmjrl27aubMmSpZsqS6deum/fv3S5I++OADffXVV+rcubM+/fRTDRgwQCdOnFDfvn3NBYyVK1cqLCxMBQsW1KRJkzRgwAD9/PPPatWqlS5fvvyvsTZs2NA8bea+5ORkffvtt3r11VdTtI+MjNTgwYMVGBioWbNmqV27dpozZ44GDx5sbrN582b17NlTHh4emjFjhho0aKD333//kXFs2bJF3bp1U7ly5fTJJ58oIiJCRYoU0fDhw3Xw4MGHHpeWc925c0dvvPGGNm3apN69e2v69OkqUKCAOnXq9MiiyKZNm2QYxiOnBFWvXl0TJkxQzpw5Jd17LbZu3VqXL1/WuHHjNGrUKJ07d05t2rRJkY9+/fopICBAs2bNUqNGjTR37lwtW7bMvP+jjz7StGnT1LhxY82aNUv169fX6NGjNWPGDIt+vvnmG2XPnl0zZ85Up06ddPv2bbVt21Zr1qxRp06d9MknnyggIEADBw7UrFmzJN0rxnXq1EnPPfecIiIiNHnyZMXHx+utt96yKO48aMKECZoxY4ZatGih6dOnK0+ePPr4448t2qQlj7du3VL79u11/PhxjRgxQoMHD9ayZcv0888/W/SVltcaACADGAAA4JnSvn17o127dkZiYqL566+//jLWrVtnVKpUyWjVqpWRnJxsXL161fDy8jIGDx5scfyePXsMd3d3Y9GiRYZhGMa0adMMd3d3Y/v27eY2Fy5cMNzd3Y158+YZhmEY586dM9zd3Y2vvvrKIo727dtb9L1z507D3d3d2Llzp2EYhrFixQqjU6dORlJSkrlNUlKSERAQYI5r8+bNhru7u7Fx40aLNq1atTJmzJhh3LlzxwgLCzO+/vpri3N9+umnhru7u/Hnn38aSUlJRrVq1YywsDCLNr/99ptRrlw5Y9y4cQ99Pvv3728EBQUZhmEYtWvXNkaMGGHet2vXLsPb29u4fv26xfVeu3bN8PHxMYYMGWLR19KlSw13d3fj+PHjhmEYRkhIiPH6669btImMjLR4Lv/5nM2ZM8fo37+/xTF///234e7ubkRGRj70OtJyriVLlhju7u7GgQMHzG2Sk5ONdu3aGSEhIQ/te8SIEYa7u7tx8+ZNi+1JSUkWr8PExEQjOTnZMAzD6NOnj1G1alXj+vXrFtcREBBgjB071uLaJ0+ebNFvcHCw0aVLF8MwDOP06dOGh4dHimufPHmy4e3tbVy5csUwjHuvx/Llyxt37twxt1m8eLHh7u5u7N+/3+LYDz/80PD29jb+/vtv4+effzbc3d2Nffv2mff/9ttvxvjx440//vjDMAzL10hcXJxRrlw5Y8KECRZ9vvXWW4a7u7tx7tw5wzDSlsdFixYZHh4exokTJ8xtLl68aJQrV+6xX2sAgPTHGiIAADyD9uzZo3Llyllsc3BwUNWqVTV8+HCZTCYdOHBACQkJKf5Xv0KFCipUqJB2796tdu3ambc/OBWiQIECku79D/Z/0bRpUzVt2lR37tzRmTNn9Ntvv+no0aNKSkpSYmKiJGnfvn1ycnKymJLg4OCgL7/80vx43rx5kqRLly7pzJkzOnv2rL7//ntJ9xY1PXPmjGJjY9W3b1+L8xctWlR+fn7avXt3muJt2LChVq5cqYEDB8pkMunrr79WrVq1Uize+vPPP+v27dsKDg7W3bt3zdvvX8O2bdtUpEgR/fLLL3rvvfcsjm3QoEGK0QQP6tSpkyTp5s2bOnPmjH7//XcdPnzYfK2puX37dprOtWPHDrm5ualcuXIWcQcFBWn8+PGKi4tT7ty5U/RvPOTuOx988EGKOw+NGTNGISEh2rlzpypVqiQXFxfzuXLkyKEKFSpo+/btFsf4+flZPC5QoID5tbdz504ZhpHqcz1z5kzt27dPderUkSSVLFlSzs7O5ja7d+9WoUKFUvTfuHFjLV++XAcPHlSFChWUN29ede3aVfXr11eNGjVUrVq1h47kOXDggBITExUUFGSxvUGDBvrxxx/Nj9OSx71796po0aIWC+oWLFjQ4r2Yltda6dKlU40VAPDfUBABAOAZVK5cOQ0bNkySZDKZlDVr1hR3Xbm/pkG+fPlSHJ8vX74U0wFcXV3N3zs43Js1+7A/hNPq9u3bGjFihFatWqW7d++qcOHC8vPzk6Ojo7nvq1evKk+ePOZzpubHH3/U6NGjdfr0aWXPnl1lypRRtmzZzDFevXr1kdcaHR2dpngbNmyoyMhI7d+/X76+vtqwYYM++uijFO3un69z586p9vPnn38qLi5OhmHoueees9j3wgsvPDKGK1euaOjQofruu+9kMplUrFgx851bHpaPtJ7r6tWrio2NTVFMuy82NjbVgsiLL74o6d5aIg/+8f3ee+/pjTfeMB/7zjvvWJxr3bp1qd6hJ2/evBaPXVxcLB47ODhYvD4kpTptSbpXJLsve/bsFvvi4uLk5uaW4pj7r5Nr164pe/bsWrx4sWbOnKlvvvlGS5YskYuLi5o0aaJBgwZZFFju9ykpxXP9z/OkJY9xcXEp+rnf119//WVx/Y96rQEAMgYFEQAAnkHZs2eXt7f3I9vc/8P2r7/+UsmSJS32xcbGmtcZ+S/+uajjP0eUjBo1SuvXr9eUKVNUtWpVcxEjMDDQ3CZnzpy6evWqDMOwuFXwL7/8IpPJpBw5cqhbt26qU6eOIiMjVaRIEZlMJi1evNj8P/L3Fzy9/0fkP681tT86U1OmTBmVKFFC3377rW7fvq07d+6oVq1aKdrlypVLkjRx4kQVL148xf58+fKZizz/jOn+H7gP069fP50+fVqfffaZ/Pz85OzsrPj4eC1duvShx6T1XDlz5lTx4sU1ceLEVPspXLhwqtuDg4M1fvx4ffvttxYFkSJFiphfR+fPn09xrqpVq5oXN32Qo2PaP2Lef67nz5+fouAh/a9Yk5rcuXPrt99+S7E9NjZW0v+KGiVLljQv5Hro0CGtWrVKX3zxhYoWLWoe6XHf/WMuX75s8b7653Odljw+99xzqcb3YF9pea0BADIGi6oCAGCjypcvL2dnZ61du9Zi+969e3Xx4kX5+/unua8sWbKk2JYjRw7FxMRYbNu3b1+Kx5UrV1adOnXMxZAjR47oypUr5rvMVKhQQYmJifrhhx/MxxmGoQEDBmj27Nk6cuSI7ty5o86dO6to0aLmosn9YohhGCpRooTc3NxSXOu5c+d04MCBx7rWhg0basOGDVq3bp3q1q2rrFmzpmhTvnx5OTk56dKlS/L29jZ/OTo6atKkSTp//ryyZs0qPz+/FHeu2bx58yPPv2/fPr3yyiuqXLmyeXTC/efmwTvzPCit56pUqZL++OMPPf/88xZxb9u2TXPnzk01z5JUokQJ82KnD9595UEnTpxIca6TJ0+qbNmy5vN4eXnps88+08aNGx/5HDzo/qiKv//+2yLmK1euaOrUqY8sMFWsWFEXLlxIsUjp6tWr5eTkJB8fH3377beqUqWKYmNjlSVLFvn5+emjjz5Srly5LO7WdJ+fn59cXFz07bffWmy/P4XrvrTksUqVKjp//rx5Ko10b2TJg89xWl5rAICMwQgRAABsVJ48edS5c2fNmDFDTk5OCgoK0vnz5zV16lS99NJLatasWZr7un/nkB07dqhUqVIqX768goKCtHnzZo0ZM0bBwcHau3ev+Va99/n4+Oibb77RF198oVKlSunYsWOaOXOmTCaT4uPjJUm1atWSn5+fwsPD1atXLxUpUkSrVq3S6dOnNXLkSOXOnVuOjo6aMGGCwsLClJCQoKioKPNteW/duiUHBwf16dNHAwYMUN++fdW4cWP9/fffmj59unLnzp3qKIWHadiwoWbMmKFVq1bpk08+SbXNc889p06dOmnq1Km6ceOGKleurEuXLmnq1KkymUwqU6aMJKlPnz5644031L17d7Vq1Upnzpwx393kYXx8fLRmzRqVK1dOBQoU0P79+zV79myL5yw1aTlXSEiIFi1apDfffFNdu3ZVwYIFtX37ds2ZM0ft27eXk5PTQ/sfNmyY4uLi1L59ezVp0kQvv/yy8ubNqwsXLmj9+vX6/vvvVbJkSfn4+EiS3n33XbVu3VpdunRRmzZtlDVrVi1ZskTfffedpk2b9sjn4EEeHh5q3LixBg8erAsXLsjLy0tnzpzR5MmTVbhw4VRHTTx4vZ9//rm6deumnj17qnDhwtq8ebO++uorde/eXbly5ZK/v7+Sk5PVrVs3de7cWdmzZ9c333yj69ev65VXXknRZ/bs2fXuu+9qypQpcnV1VZUqVbR169YUBZG05LFJkyZasGCBunfvrt69eytHjhyaOXOmReErra81AED6oyACAIAN69Gjh/Lly6dFixZpyZIlypMnj+rXr69evXqZR2ykRY4cOfTmm29qyZIl2rp1q7Zt26bmzZvr999/14oVK/Tll1+qYsWKmjZtmtq0aWM+Ljw8XImJiZoyZYoSEhJUuHBhvfPOOzp58qQ2b96spKQkZcmSRXPmzNHEiRM1ZMgQSff+F/7TTz81/3H98ccfa/r06XrnnXeUO3du+fr6auHChQoNDdXevXvl4eGhkJAQZc+eXZGRkerWrZty5MihGjVqqE+fPqmuI/EwL730ktzd3RUbG6uqVas+tF2vXr3k5uamzz//XHPnzlXu3LkVGBioPn36mAtIFSpU0Jw5czRp0iR1795dhQsX1ujRo9W1a9eH9jt27FiNGDFCI0aMkCQVL15cw4YN0+rVq7V3796HHpeWc2XLlk2LFy/Wxx9/rAkTJuj69esqVKiQ+vbtq7CwsEc+Lzly5NCcOXP0zTffaOXKlRoxYoSuXr2q3Llzy9vbW+PHj1eDBg3MRZUyZcpo8eLFmjx5sj744AMZhiF3d3fNmDFDtWvXfuS5/mnMmDGKjIzUl19+qZiYGD3//PNq2LChevXq9dBRLdK9dXEWLlyojz/+2FxQKFmypEaNGqUWLVpIurfOyty5czV16lQNHDhQ8fHxKl26tCIiIlSlSpVU++3SpYuyZcum+fPna/78+fLz81P//v0t1ptJSx6dnZ01f/58jR49WqNGjZLJZFLLli1VpEgRi1sTp+W1BgBIfybjv66mBgAAkEZt2rRRtmzZNHToUBUtWtTa4QAAADvGGiIAAOCpOHXqlHr27KmzZ89q5syZ1g4HAADYOabMAACAp2LEiBHat2+fSpcurQ4dOlg7HAAAYOeYMgMAAAAAAOwOU2YAAAAAAIDdoSACAAAAAADsDgURAAAAAABgdyiIAAAAAAAAu0NBBAAAAAAA2B0KIgAAAAAAwO5QEAEAAAAAAHaHgggAAAAAALA7FEQAAAAAAIDd+X+M+FhDQwl02wAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1200x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dados_comparacao = df[df['Nome do País'].isin(media_felicidade_paises_top20.index.tolist() + ['Brazil'])]\n",
+    "\n",
+    "media_generosidade = dados_comparacao.groupby('Nome do País')['Generosidade'].mean()\n",
+    "\n",
+    "media_generosidade = media_generosidade.reset_index()\n",
+    "\n",
+    "# Destacar o Brasil no gráfico\n",
+    "media_generosidade['Destaque'] = media_generosidade['Nome do País'].apply(lambda x: 'Brasil' if x == 'Brazil' else 'Outros')\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "sns.barplot(x='Generosidade', y='Nome do País', data=media_generosidade, hue='Destaque', dodge=False, palette={'Brasil': 'blue', 'Outros': 'gray'})\n",
+    "\n",
+    "plt.title('Comparação da Pontuação de Generosidade: Brasil vs. 20 Países Mais Felizes', fontsize=16)\n",
+    "plt.xlabel('Pontuação Média de Generosidade')\n",
+    "plt.ylabel('País')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Teste de Hipótese\n",
+    "\n",
+    "Hipótese Nula (H₀): O Brasil é um dos países mais felizes do mundo.\n",
+    "Hipótese Alternativa (H₁): O Brasil não é um dos países mais felizes do mundo."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Posição do Brasil: 25\n",
+      "Rejeitamos a hipótese nula: O Brasil não é um dos países mais felizes do mundo.\n"
+     ]
+    }
+   ],
+   "source": [
+    "corte = 20\n",
+    "\n",
+    "if posicao_brasil <= corte:\n",
+    "    resultado = \"Não rejeitamos a hipótese nula: O Brasil é um dos países mais felizes do mundo.\"\n",
+    "else:\n",
+    "    resultado = \"Rejeitamos a hipótese nula: O Brasil não é um dos países mais felizes do mundo.\"\n",
+    "\n",
+    "print(f\"Posição do Brasil: {posicao_brasil}\")\n",
+    "print(resultado)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# SQL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3\n",
+    "import pandas as pd\n",
+    "\n",
+    "conexao = sqlite3.connect('felicidade_mundial.db')\n",
+    "cursor = conexao.cursor()\n",
+    "\n",
+    "cursor.execute('''\n",
+    "CREATE TABLE IF NOT EXISTS tabela_felicidade (\n",
+    "    id INTEGER PRIMARY KEY,\n",
+    "    \"Nome do País\" TEXT,\n",
+    "    \"Ano\" INTEGER,\n",
+    "    \"Índice de Felicidade\" REAL,\n",
+    "    \"PIB per capita (logaritmo)\" REAL,\n",
+    "    \"Apoio Social\" REAL,\n",
+    "    \"Expectativa de vida saudável ao nascer\" REAL,\n",
+    "    \"Liberdade para fazer escolhas\" REAL,\n",
+    "    \"Generosidade\" REAL,\n",
+    "    \"Percepções de corrupção\" REAL,\n",
+    "    \"Afeto positivo\" REAL,\n",
+    "    \"Afeto negativo\" REAL\n",
+    ")\n",
+    "''')\n",
+    "\n",
+    "df = pd.read_csv('world-happiness-report.csv')\n",
+    "\n",
+    "df.to_sql('tabela_felicidade', conexao, if_exists='replace', index=False)\n",
+    "conexao.commit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Os 20 países mais felizes de 2020:\n",
+      "Empty DataFrame\n",
+      "Columns: [\"Nome do País\", \"Índice de Felicidade\"]\n",
+      "Index: []\n"
+     ]
+    },
+    {
+     "ename": "KeyError",
+     "evalue": "'Nome do País'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "File \u001b[1;32mc:\\Users\\sukzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m-> 3805\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_engine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcasted_key\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n",
+      "File \u001b[1;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mindex.pyx:196\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7081\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "File \u001b[1;32mpandas\\\\_libs\\\\hashtable_class_helper.pxi:7089\u001b[0m, in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Nome do País'",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[134], line 24\u001b[0m\n\u001b[0;32m     21\u001b[0m todos_paises_2020 \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mread_sql_query(consulta_posicao_brasil, conexao)\n\u001b[0;32m     23\u001b[0m \u001b[38;5;66;03m# posição do Brasil\u001b[39;00m\n\u001b[1;32m---> 24\u001b[0m posicao_brasil_2020 \u001b[38;5;241m=\u001b[39m todos_paises_2020[\u001b[43mtodos_paises_2020\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mNome do País\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mBrazil\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mindex[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m     25\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mO Brasil está na posição \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mposicao_brasil_2020\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m no índice de felicidade em 2020.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m     27\u001b[0m conexao\u001b[38;5;241m.\u001b[39mclose()\n",
+      "File \u001b[1;32mc:\\Users\\sukzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   4101\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[1;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_loc\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[0;32m   4104\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m [indexer]\n",
+      "File \u001b[1;32mc:\\Users\\sukzw\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3807\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[0;32m   3808\u001b[0m         \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[0;32m   3809\u001b[0m         \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[0;32m   3810\u001b[0m     ):\n\u001b[0;32m   3811\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[1;32m-> 3812\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[0;32m   3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[0;32m   3814\u001b[0m     \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[0;32m   3815\u001b[0m     \u001b[38;5;66;03m#  InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[0;32m   3816\u001b[0m     \u001b[38;5;66;03m#  the TypeError.\u001b[39;00m\n\u001b[0;32m   3817\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n",
+      "\u001b[1;31mKeyError\u001b[0m: 'Nome do País'"
+     ]
+    }
+   ],
+   "source": [
+    "# 20 países mais felizes de 2020\n",
+    "\n",
+    "consulta_20_mais_felizes = '''\n",
+    "SELECT \"Nome do País\", \"Índice de Felicidade\"\n",
+    "FROM tabela_felicidade\n",
+    "WHERE \"Ano\" = 2020\n",
+    "ORDER BY \"Índice de Felicidade\" DESC\n",
+    "LIMIT 20;\n",
+    "'''\n",
+    "top_20_felizes = pd.read_sql_query(consulta_20_mais_felizes, conexao)\n",
+    "print(\"Os 20 países mais felizes de 2020:\")\n",
+    "print(top_20_felizes)\n",
+    "\n",
+    "# posição do Brasil em 2020\n",
+    "consulta_posicao_brasil = '''\n",
+    "SELECT \"Nome do País\", \"Índice de Felicidade\"\n",
+    "FROM tabela_felicidade\n",
+    "WHERE \"Ano\" = 2020\n",
+    "ORDER BY \"Índice de Felicidade\" DESC;\n",
+    "'''\n",
+    "todos_paises_2020 = pd.read_sql_query(consulta_posicao_brasil, conexao)\n",
+    "\n",
+    "# posição do Brasil\n",
+    "posicao_brasil_2020 = todos_paises_2020[todos_paises_2020['Nome do País'] == 'Brazil'].index[0] + 1\n",
+    "print(f\"O Brasil está na posição {posicao_brasil_2020} no índice de felicidade em 2020.\")\n",
+    "\n",
+    "conexao.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/felicidade_mundial.db b/exercicios/para-casa/felicidade_mundial.db
new file mode 100644
index 0000000..abf9fad
Binary files /dev/null and b/exercicios/para-casa/felicidade_mundial.db differ
diff --git a/exercicios/para-casa/world-happiness-report.csv b/exercicios/para-casa/world-happiness-report.csv
new file mode 100644
index 0000000..98129dd
--- /dev/null
+++ b/exercicios/para-casa/world-happiness-report.csv
@@ -0,0 +1,1950 @@
+Country name,year,Life Ladder,Log GDP per capita,Social support,Healthy life expectancy at birth,Freedom to make life choices,Generosity,Perceptions of corruption,Positive affect,Negative affect
+Afghanistan,2008,3.724,7.370,0.451,50.800,0.718,0.168,0.882,0.518,0.258
+Afghanistan,2009,4.402,7.540,0.552,51.200,0.679,0.190,0.850,0.584,0.237
+Afghanistan,2010,4.758,7.647,0.539,51.600,0.600,0.121,0.707,0.618,0.275
+Afghanistan,2011,3.832,7.620,0.521,51.920,0.496,0.162,0.731,0.611,0.267
+Afghanistan,2012,3.783,7.705,0.521,52.240,0.531,0.236,0.776,0.710,0.268
+Afghanistan,2013,3.572,7.725,0.484,52.560,0.578,0.061,0.823,0.621,0.273
+Afghanistan,2014,3.131,7.718,0.526,52.880,0.509,0.104,0.871,0.532,0.375
+Afghanistan,2015,3.983,7.702,0.529,53.200,0.389,0.080,0.881,0.554,0.339
+Afghanistan,2016,4.220,7.697,0.559,53.000,0.523,0.042,0.793,0.565,0.348
+Afghanistan,2017,2.662,7.697,0.491,52.800,0.427,-0.121,0.954,0.496,0.371
+Afghanistan,2018,2.694,7.692,0.508,52.600,0.374,-0.094,0.928,0.424,0.405
+Afghanistan,2019,2.375,7.697,0.420,52.400,0.394,-0.108,0.924,0.351,0.502
+Albania,2007,4.634,9.142,0.821,65.800,0.529,-0.009,0.875,0.553,0.246
+Albania,2009,5.485,9.262,0.833,66.200,0.525,-0.158,0.864,0.640,0.279
+Albania,2010,5.269,9.303,0.733,66.400,0.569,-0.172,0.726,0.648,0.300
+Albania,2011,5.867,9.331,0.759,66.680,0.487,-0.205,0.877,0.628,0.257
+Albania,2012,5.510,9.347,0.785,66.960,0.602,-0.169,0.848,0.607,0.271
+Albania,2013,4.551,9.359,0.759,67.240,0.632,-0.127,0.863,0.634,0.338
+Albania,2014,4.814,9.378,0.626,67.520,0.735,-0.025,0.883,0.685,0.335
+Albania,2015,4.607,9.403,0.639,67.800,0.704,-0.081,0.885,0.688,0.350
+Albania,2016,4.511,9.437,0.638,68.100,0.730,-0.017,0.901,0.675,0.322
+Albania,2017,4.640,9.476,0.638,68.400,0.750,-0.029,0.876,0.669,0.334
+Albania,2018,5.004,9.518,0.684,68.700,0.824,0.009,0.899,0.713,0.319
+Albania,2019,4.995,9.544,0.686,69.000,0.777,-0.099,0.914,0.681,0.274
+Albania,2020,5.365,9.497,0.710,69.300,0.754,0.007,0.891,0.679,0.265
+Algeria,2010,5.464,9.287,,64.500,0.593,-0.205,0.618,,
+Algeria,2011,5.317,9.297,0.810,64.660,0.530,-0.181,0.638,0.550,0.255
+Algeria,2012,5.605,9.311,0.839,64.820,0.587,-0.172,0.690,0.604,0.230
+Algeria,2014,6.355,9.335,0.818,65.140,,,,0.626,0.177
+Algeria,2016,5.341,9.362,0.749,65.500,,,,0.661,0.377
+Algeria,2017,5.249,9.354,0.807,65.700,0.437,-0.167,0.700,0.642,0.289
+Algeria,2018,5.043,9.348,0.799,65.900,0.583,-0.146,0.759,0.591,0.293
+Algeria,2019,4.745,9.337,0.803,66.100,0.385,0.005,0.741,0.585,0.215
+Angola,2011,5.589,8.946,0.723,52.500,0.584,0.055,0.911,0.659,0.361
+Angola,2012,4.360,8.992,0.753,53.200,0.456,-0.136,0.906,0.558,0.305
+Angola,2013,3.937,9.005,0.722,53.900,0.410,-0.104,0.816,0.658,0.371
+Angola,2014,3.795,9.017,0.755,54.600,0.375,-0.168,0.834,0.579,0.368
+Argentina,2006,6.313,9.942,0.938,66.820,0.733,-0.157,0.852,0.825,0.328
+Argentina,2007,6.073,10.018,0.862,66.940,0.653,-0.141,0.881,0.828,0.279
+Argentina,2008,5.961,10.048,0.892,67.060,0.678,-0.132,0.865,0.823,0.318
+Argentina,2009,6.424,9.977,0.919,67.180,0.637,-0.130,0.885,0.864,0.237
+Argentina,2010,6.441,10.066,0.927,67.300,0.730,-0.126,0.855,0.846,0.211
+Argentina,2011,6.776,10.112,0.889,67.480,0.816,-0.174,0.755,0.840,0.232
+Argentina,2012,6.468,10.091,0.902,67.660,0.747,-0.148,0.817,0.857,0.272
+Argentina,2013,6.582,10.103,0.910,67.840,0.737,-0.130,0.823,0.842,0.254
+Argentina,2014,6.671,10.067,0.918,68.020,0.745,-0.164,0.854,0.857,0.238
+Argentina,2015,6.697,10.083,0.926,68.200,0.881,-0.174,0.851,0.859,0.305
+Argentina,2016,6.427,10.051,0.883,68.400,0.848,-0.192,0.851,0.842,0.312
+Argentina,2017,6.039,10.067,0.907,68.600,0.832,-0.186,0.841,0.809,0.292
+Argentina,2018,5.793,10.032,0.900,68.800,0.846,-0.211,0.855,0.820,0.321
+Argentina,2019,6.086,10.000,0.896,69.000,0.817,-0.211,0.830,0.826,0.319
+Argentina,2020,5.901,9.850,0.897,69.200,0.823,-0.122,0.816,0.764,0.342
+Armenia,2006,4.289,9.044,0.682,64.800,0.520,-0.231,0.850,0.494,0.469
+Armenia,2007,4.882,9.181,0.760,64.900,0.605,-0.251,0.817,0.507,0.412
+Armenia,2008,4.652,9.256,0.709,65.000,0.462,-0.215,0.876,0.521,0.385
+Armenia,2009,4.178,9.111,0.680,65.100,0.441,-0.214,0.882,0.543,0.411
+Armenia,2010,4.368,9.136,0.660,65.200,0.459,-0.176,0.891,0.510,0.426
+Armenia,2011,4.260,9.182,0.705,65.360,0.465,-0.225,0.875,0.475,0.459
+Armenia,2012,4.320,9.249,0.676,65.520,0.502,-0.215,0.893,0.518,0.464
+Armenia,2013,4.277,9.277,0.723,65.680,0.504,-0.195,0.900,0.562,0.450
+Armenia,2014,4.453,9.307,0.739,65.840,0.506,-0.218,0.920,0.581,0.404
+Armenia,2015,4.348,9.334,0.723,66.000,0.551,-0.203,0.901,0.594,0.438
+Armenia,2016,4.325,9.333,0.709,66.300,0.611,-0.170,0.921,0.594,0.437
+Armenia,2017,4.288,9.402,0.698,66.600,0.614,-0.147,0.865,0.625,0.437
+Armenia,2018,5.062,9.451,0.814,66.900,0.808,-0.163,0.677,0.581,0.455
+Armenia,2019,5.488,9.522,0.782,67.200,0.844,-0.172,0.583,0.598,0.430
+Australia,2005,7.341,10.659,0.968,71.400,0.935,,0.390,0.843,0.238
+Australia,2007,7.285,10.703,0.965,71.720,0.891,0.347,0.513,0.826,0.215
+Australia,2008,7.254,10.719,0.947,71.880,0.916,0.305,0.431,0.826,0.218
+Australia,2010,7.450,10.722,0.955,72.200,0.932,0.317,0.366,0.834,0.220
+Australia,2011,7.406,10.733,0.967,72.300,0.945,0.369,0.382,0.816,0.195
+Australia,2012,7.196,10.754,0.945,72.400,0.935,0.274,0.368,0.811,0.214
+Australia,2013,7.364,10.762,0.928,72.500,0.933,0.269,0.432,0.819,0.177
+Australia,2014,7.289,10.772,0.924,72.600,0.923,0.319,0.442,0.775,0.245
+Australia,2015,7.309,10.779,0.952,72.700,0.922,0.332,0.357,0.790,0.210
+Australia,2016,7.250,10.791,0.942,73.000,0.922,0.239,0.399,0.791,0.236
+Australia,2017,7.257,10.798,0.950,73.300,0.911,0.317,0.411,0.780,0.225
+Australia,2018,7.177,10.811,0.940,73.600,0.916,0.146,0.405,0.759,0.187
+Australia,2019,7.234,10.815,0.943,73.900,0.918,0.121,0.430,0.770,0.202
+Australia,2020,7.137,10.760,0.937,74.200,0.905,0.210,0.491,0.769,0.205
+Austria,2006,7.122,10.842,0.936,70.760,0.941,0.302,0.490,0.823,0.174
+Austria,2008,7.181,10.887,0.935,71.080,0.879,0.291,0.614,0.832,0.173
+Austria,2010,7.303,10.861,0.914,71.400,0.896,0.131,0.546,0.815,0.156
+Austria,2011,7.471,10.887,0.944,71.540,0.939,0.132,0.703,0.789,0.145
+Austria,2012,7.401,10.889,0.945,71.680,0.920,0.118,0.771,0.822,0.157
+Austria,2013,7.499,10.883,0.950,71.820,0.922,0.168,0.679,0.787,0.163
+Austria,2014,6.950,10.882,0.899,71.960,0.885,0.118,0.567,0.780,0.170
+Austria,2015,7.076,10.881,0.928,72.100,0.900,0.099,0.557,0.798,0.164
+Austria,2016,7.048,10.891,0.926,72.400,0.889,0.080,0.524,0.756,0.197
+Austria,2017,7.294,10.908,0.906,72.700,0.890,0.133,0.518,0.748,0.180
+Austria,2018,7.396,10.928,0.912,73.000,0.904,0.053,0.523,0.752,0.226
+Austria,2019,7.195,10.939,0.964,73.300,0.903,0.060,0.457,0.774,0.205
+Austria,2020,7.213,10.851,0.925,73.600,0.912,0.011,0.464,0.769,0.206
+Azerbaijan,2006,4.728,9.170,0.854,61.880,0.772,-0.235,0.774,0.512,0.276
+Azerbaijan,2007,4.568,9.386,0.753,62.260,0.522,-0.207,0.871,0.521,0.284
+Azerbaijan,2008,4.817,9.465,0.684,62.640,0.601,-0.029,0.715,0.578,0.227
+Azerbaijan,2009,4.574,9.534,0.736,63.020,0.498,-0.087,0.754,0.544,0.234
+Azerbaijan,2010,4.219,9.569,0.687,63.400,0.501,-0.123,0.858,0.527,0.272
+Azerbaijan,2011,4.680,9.540,0.725,63.640,0.537,-0.105,0.795,0.536,0.258
+Azerbaijan,2012,4.911,9.549,0.762,63.880,0.599,-0.140,0.763,0.554,0.266
+Azerbaijan,2013,5.481,9.592,0.770,64.120,0.672,-0.168,0.699,0.619,0.242
+Azerbaijan,2014,5.252,9.607,0.799,64.360,0.733,-0.208,0.654,0.598,0.220
+Azerbaijan,2015,5.147,9.606,0.786,64.600,0.764,-0.198,0.616,0.607,0.206
+Azerbaijan,2016,5.304,9.564,0.777,64.900,0.713,-0.204,0.607,0.598,0.191
+Azerbaijan,2017,5.152,9.555,0.787,65.200,0.731,-0.225,0.653,0.592,0.198
+Azerbaijan,2018,5.168,9.562,0.781,65.500,0.772,-0.232,0.561,0.593,0.191
+Azerbaijan,2019,5.173,9.575,0.887,65.800,0.854,-0.214,0.457,0.643,0.164
+Bahrain,2009,5.701,10.709,0.904,65.940,0.896,0.037,0.506,0.764,0.422
+Bahrain,2010,5.937,10.706,0.877,66.300,0.862,-0.001,0.715,0.685,0.423
+Bahrain,2011,4.824,10.696,0.908,66.580,0.870,-0.051,0.583,0.544,0.514
+Bahrain,2012,5.027,10.716,0.911,66.860,0.682,,0.438,0.589,0.381
+Bahrain,2013,6.690,10.757,0.884,67.140,0.809,,0.525,0.768,0.306
+Bahrain,2014,6.165,10.783,,67.420,,,,,
+Bahrain,2015,6.007,10.785,0.853,67.700,0.850,0.112,,0.716,0.303
+Bahrain,2016,6.170,10.781,0.863,68.100,0.889,0.088,,0.787,0.283
+Bahrain,2017,6.227,10.771,0.876,68.500,0.906,0.136,,0.814,0.290
+Bahrain,2019,7.098,10.715,0.878,69.300,0.907,0.048,,0.762,0.317
+Bahrain,2020,6.173,10.620,0.848,69.700,0.945,0.132,,0.790,0.297
+Bangladesh,2006,4.319,7.783,0.672,59.020,0.612,0.068,0.786,0.600,0.321
+Bangladesh,2007,4.607,7.839,0.514,59.440,0.605,0.040,0.806,0.635,0.313
+Bangladesh,2008,5.052,7.886,0.467,59.860,0.606,-0.044,0.802,0.725,0.232
+Bangladesh,2009,5.083,7.924,0.528,60.280,0.631,-0.075,0.776,0.670,0.223
+Bangladesh,2010,4.858,7.967,0.549,60.700,0.659,-0.016,0.774,0.629,0.292
+Bangladesh,2011,4.986,8.018,0.606,61.120,0.838,-0.069,0.757,0.685,0.235
+Bangladesh,2012,4.724,8.070,0.582,61.540,0.668,-0.034,0.765,0.714,0.183
+Bangladesh,2013,4.660,8.116,0.530,61.960,0.742,-0.016,0.743,0.619,0.246
+Bangladesh,2014,4.636,8.164,0.577,62.380,0.736,-0.098,0.789,,0.231
+Bangladesh,2015,4.633,8.216,0.601,62.800,0.815,-0.068,0.721,0.635,0.226
+Bangladesh,2016,4.556,8.274,0.649,63.300,0.875,-0.089,0.688,0.560,0.235
+Bangladesh,2017,4.310,8.334,0.713,63.800,0.896,0.012,0.635,0.569,0.214
+Bangladesh,2018,4.499,8.399,0.706,64.300,0.901,-0.043,0.701,0.541,0.361
+Bangladesh,2019,5.114,8.467,0.673,64.800,0.902,-0.051,0.656,0.537,0.369
+Bangladesh,2020,5.280,8.472,0.739,65.300,0.777,-0.009,0.742,0.582,0.332
+Belarus,2006,5.658,9.489,0.918,61.100,0.707,-0.246,0.708,0.605,0.269
+Belarus,2007,5.617,9.576,0.858,61.400,0.667,-0.225,0.695,0.596,0.235
+Belarus,2008,5.463,9.677,0.904,61.700,0.640,-0.220,0.696,,0.246
+Belarus,2009,5.564,9.681,0.908,62.000,0.679,-0.203,0.676,0.566,0.223
+Belarus,2010,5.526,9.758,0.918,62.300,0.700,-0.163,0.706,0.567,0.208
+Belarus,2011,5.225,9.812,0.910,62.880,0.656,-0.168,0.672,0.521,0.249
+Belarus,2012,5.749,9.830,0.902,63.460,0.645,-0.217,0.657,0.523,0.181
+Belarus,2013,5.876,9.839,0.923,64.040,0.723,-0.177,0.653,0.609,0.206
+Belarus,2014,5.812,9.856,0.880,64.620,0.647,-0.048,0.682,0.619,0.209
+Belarus,2015,5.719,9.815,0.924,65.200,0.623,-0.091,0.669,0.584,0.184
+Belarus,2016,5.178,9.788,0.927,65.500,0.658,-0.125,0.664,0.554,0.182
+Belarus,2017,5.553,9.814,0.900,65.800,0.621,-0.122,0.654,0.541,0.233
+Belarus,2018,5.234,9.846,0.905,66.100,0.644,-0.174,0.718,0.450,0.236
+Belarus,2019,5.821,9.860,0.917,66.400,0.657,-0.186,0.546,0.591,0.190
+Belgium,2005,7.262,10.745,0.935,69.900,0.924,,0.598,0.796,0.260
+Belgium,2007,7.219,10.792,0.922,70.260,0.901,0.069,0.721,0.813,0.218
+Belgium,2008,7.117,10.789,0.923,70.440,0.887,0.007,0.652,0.813,0.242
+Belgium,2010,6.854,10.779,0.931,70.800,0.807,0.022,0.697,0.828,0.240
+Belgium,2011,7.111,10.783,0.937,70.920,0.880,-0.014,0.711,0.835,0.225
+Belgium,2012,6.935,10.784,0.927,71.040,0.855,-0.050,0.758,0.820,0.238
+Belgium,2013,7.104,10.784,0.909,71.160,0.891,0.017,0.574,0.797,0.217
+Belgium,2014,6.855,10.795,0.944,71.280,0.861,0.001,0.512,0.798,0.252
+Belgium,2015,6.904,10.810,0.885,71.400,0.869,0.062,0.469,0.805,0.240
+Belgium,2016,6.949,10.819,0.929,71.600,0.866,-0.056,0.497,0.765,0.260
+Belgium,2017,6.928,10.834,0.922,71.800,0.857,0.054,0.543,0.786,0.234
+Belgium,2018,6.892,10.844,0.930,72.000,0.808,-0.125,0.630,0.750,0.250
+Belgium,2019,6.772,10.853,0.884,72.200,0.776,-0.172,0.672,0.733,0.244
+Belgium,2020,6.839,10.771,0.904,72.400,0.767,-0.164,0.634,0.647,0.260
+Belize,2007,6.451,8.892,0.872,61.600,0.705,0.033,0.769,0.759,0.251
+Belize,2014,5.956,8.883,0.757,62.220,0.874,0.022,0.782,0.755,0.282
+Benin,2006,3.330,7.866,0.445,50.100,0.580,-0.011,0.790,0.587,0.309
+Benin,2008,3.667,7.915,0.382,50.900,0.709,-0.004,0.825,0.584,0.303
+Benin,2011,3.870,7.904,0.477,51.980,0.773,-0.142,0.849,0.626,0.219
+Benin,2012,3.193,7.923,0.523,52.260,0.769,-0.111,0.806,0.583,0.231
+Benin,2013,3.479,7.964,0.577,52.540,0.783,-0.085,0.856,0.702,0.216
+Benin,2014,3.347,7.998,0.506,52.820,0.776,-0.096,0.855,0.590,0.273
+Benin,2015,3.625,7.988,0.434,53.100,0.733,-0.027,0.850,0.592,0.373
+Benin,2016,4.007,7.993,0.493,53.500,0.780,-0.065,0.838,0.608,0.456
+Benin,2017,4.853,8.021,0.436,53.900,0.727,-0.065,0.767,0.615,0.458
+Benin,2018,5.820,8.059,0.504,54.300,0.713,0.002,0.747,0.647,0.468
+Benin,2019,4.976,8.098,0.442,54.700,0.770,-0.016,0.698,0.659,0.441
+Benin,2020,4.408,8.102,0.507,55.100,0.783,-0.083,0.532,0.609,0.305
+Bhutan,2013,5.569,9.123,0.819,59.600,0.810,0.353,0.802,0.779,0.217
+Bhutan,2014,4.939,9.167,0.880,59.900,0.834,0.268,0.650,0.859,0.324
+Bhutan,2015,5.082,9.219,0.848,60.200,0.830,0.277,0.634,0.810,0.312
+Bolivia,2006,5.374,8.686,0.834,59.000,0.770,-0.044,0.794,0.739,0.432
+Bolivia,2007,5.628,8.714,0.796,59.500,0.780,0.001,0.817,0.771,0.388
+Bolivia,2008,5.298,8.756,0.785,60.000,0.726,-0.092,0.801,0.781,0.392
+Bolivia,2009,6.086,8.773,0.831,60.500,0.779,-0.036,0.763,0.797,0.372
+Bolivia,2010,5.781,8.797,0.807,61.000,0.703,-0.068,0.781,0.766,0.350
+Bolivia,2011,5.779,8.831,0.817,61.340,0.782,-0.039,0.825,0.761,0.361
+Bolivia,2012,6.019,8.865,0.781,61.680,0.862,-0.015,0.840,0.782,0.409
+Bolivia,2013,5.767,8.915,0.803,62.020,0.846,-0.067,0.812,0.759,0.410
+Bolivia,2014,5.865,8.953,0.821,62.360,0.881,0.018,0.832,0.809,0.398
+Bolivia,2015,5.834,8.985,0.829,62.700,0.884,-0.030,0.862,0.786,0.393
+Bolivia,2016,5.770,9.012,0.796,63.000,0.882,-0.047,0.853,0.783,0.376
+Bolivia,2017,5.651,9.039,0.779,63.300,0.884,-0.120,0.819,0.698,0.434
+Bolivia,2018,5.916,9.066,0.827,63.600,0.863,-0.093,0.786,0.742,0.387
+Bolivia,2019,5.674,9.074,0.784,63.900,0.881,-0.086,0.857,0.751,0.419
+Bolivia,2020,5.559,8.998,0.805,64.200,0.877,-0.054,0.868,0.790,0.382
+Bosnia and Herzegovina,2007,4.900,9.267,0.766,66.040,0.342,0.006,0.926,0.613,0.296
+Bosnia and Herzegovina,2009,4.963,9.296,0.735,66.480,0.258,-0.026,0.959,0.572,0.390
+Bosnia and Herzegovina,2010,4.669,9.312,0.773,66.700,0.365,-0.128,0.933,0.517,0.409
+Bosnia and Herzegovina,2011,4.995,9.333,0.725,66.740,0.333,-0.035,0.925,0.596,0.326
+Bosnia and Herzegovina,2012,4.773,9.342,0.779,66.780,0.420,-0.013,0.953,0.548,0.338
+Bosnia and Herzegovina,2013,5.124,9.382,0.767,66.820,0.390,0.042,0.970,0.544,0.315
+Bosnia and Herzegovina,2014,5.249,9.411,0.788,66.860,0.412,0.232,0.976,0.531,0.262
+Bosnia and Herzegovina,2015,5.117,9.457,0.656,66.900,0.631,-0.055,0.960,0.534,0.286
+Bosnia and Herzegovina,2016,5.181,9.500,0.808,67.200,0.633,0.134,0.957,0.641,0.304
+Bosnia and Herzegovina,2017,5.090,9.532,0.775,67.500,0.564,0.092,0.923,0.597,0.271
+Bosnia and Herzegovina,2018,5.887,9.576,0.836,67.800,0.659,0.123,0.913,0.643,0.277
+Bosnia and Herzegovina,2019,6.016,9.609,0.873,68.100,0.722,0.079,0.963,0.633,0.238
+Bosnia and Herzegovina,2020,5.516,9.583,0.899,68.400,0.740,0.138,0.916,0.644,0.325
+Botswana,2006,4.739,9.492,0.883,46.820,0.824,-0.195,0.723,0.688,0.226
+Botswana,2008,5.451,9.590,0.832,49.860,0.858,-0.164,0.806,0.731,0.218
+Botswana,2010,3.553,9.556,0.866,52.900,0.826,-0.143,0.814,0.690,0.172
+Botswana,2011,3.520,9.600,0.860,53.680,0.813,-0.250,0.816,0.739,0.160
+Botswana,2012,4.836,9.632,0.837,54.460,0.799,-0.203,0.814,0.773,0.171
+Botswana,2013,4.128,9.728,0.856,55.240,0.767,-0.154,0.749,0.698,0.244
+Botswana,2014,4.031,9.756,0.859,56.020,0.791,-0.105,0.743,0.674,0.245
+Botswana,2015,3.762,9.724,0.816,56.800,0.857,-0.116,0.860,0.746,0.261
+Botswana,2016,3.499,9.748,0.768,57.500,0.852,-0.253,0.729,0.686,0.252
+Botswana,2017,3.505,9.756,0.768,58.200,0.817,-0.248,0.731,0.656,0.276
+Botswana,2018,3.461,9.778,0.795,58.900,0.818,-0.254,0.807,0.730,0.267
+Botswana,2019,3.471,9.785,0.774,59.600,0.833,-0.239,0.792,0.712,0.273
+Brazil,2005,6.637,9.438,0.883,63.300,0.882,,0.745,0.818,0.302
+Brazil,2007,6.321,9.515,0.886,63.780,0.777,-0.016,0.728,0.859,0.299
+Brazil,2008,6.691,9.555,0.878,64.020,0.782,-0.078,0.688,0.820,0.265
+Brazil,2009,7.001,9.544,0.913,64.260,0.767,-0.055,0.723,0.833,0.274
+Brazil,2010,6.837,9.607,0.906,64.500,0.806,-0.054,0.656,0.817,0.250
+Brazil,2011,7.038,9.637,0.916,64.760,0.834,-0.072,0.662,0.807,0.268
+Brazil,2012,6.660,9.647,0.890,65.020,0.849,,0.623,0.755,0.350
+Brazil,2013,7.140,9.668,0.910,65.280,0.785,-0.095,0.707,0.818,0.276
+Brazil,2014,6.981,9.664,0.898,65.540,0.714,-0.115,0.710,0.788,0.274
+Brazil,2015,6.547,9.620,0.907,65.800,0.799,-0.016,0.771,0.755,0.325
+Brazil,2016,6.375,9.578,0.912,66.000,0.807,-0.100,0.781,0.763,0.302
+Brazil,2017,6.333,9.583,0.905,66.200,0.765,-0.175,0.794,0.716,0.308
+Brazil,2018,6.191,9.589,0.882,66.400,0.751,-0.117,0.763,0.750,0.350
+Brazil,2019,6.451,9.592,0.899,66.600,0.830,-0.062,0.762,0.761,0.337
+Brazil,2020,6.110,9.522,0.831,66.800,0.786,-0.053,0.729,0.692,0.389
+Bulgaria,2007,3.844,9.715,0.832,65.100,0.566,-0.138,0.976,0.594,0.226
+Bulgaria,2010,3.912,9.765,0.843,65.700,0.545,-0.144,0.941,0.546,0.238
+Bulgaria,2011,3.875,9.795,0.860,65.800,0.664,-0.228,0.948,0.534,0.271
+Bulgaria,2012,4.222,9.804,0.838,65.900,0.641,-0.172,0.938,0.573,0.237
+Bulgaria,2013,3.993,9.813,0.829,66.000,0.603,-0.191,0.962,0.623,0.278
+Bulgaria,2014,4.438,9.838,0.886,66.100,0.576,-0.055,0.955,0.628,0.236
+Bulgaria,2015,4.865,9.883,0.908,66.200,0.637,-0.200,0.941,0.643,0.214
+Bulgaria,2016,4.838,9.928,0.926,66.400,0.700,-0.170,0.936,0.622,0.172
+Bulgaria,2017,5.097,9.969,0.942,66.600,0.689,-0.154,0.911,0.614,0.189
+Bulgaria,2018,5.099,10.007,0.924,66.800,0.724,-0.176,0.952,0.639,0.189
+Bulgaria,2019,5.108,10.047,0.948,67.000,0.822,-0.109,0.943,0.663,0.200
+Bulgaria,2020,5.598,9.991,0.916,67.200,0.818,-0.004,0.901,0.706,0.221
+Burkina Faso,2006,3.801,7.367,0.796,46.660,0.588,0.028,0.798,0.716,0.266
+Burkina Faso,2007,4.017,7.377,0.771,47.420,0.582,-0.060,0.833,0.651,0.281
+Burkina Faso,2008,3.846,7.403,0.727,48.180,0.612,-0.101,0.887,0.523,0.304
+Burkina Faso,2010,4.036,7.453,0.773,49.700,0.587,-0.036,0.767,0.590,0.217
+Burkina Faso,2011,4.785,7.487,0.710,50.240,0.725,-0.105,0.707,0.579,0.205
+Burkina Faso,2012,3.955,7.520,0.744,50.780,0.622,-0.070,0.726,0.545,0.300
+Burkina Faso,2013,3.326,7.546,0.745,51.320,0.741,-0.016,0.765,0.630,0.287
+Burkina Faso,2014,3.481,7.559,0.742,51.860,0.710,-0.004,0.801,0.614,0.256
+Burkina Faso,2015,4.419,7.568,0.705,52.400,0.659,0.004,0.693,0.579,0.359
+Burkina Faso,2016,4.206,7.596,0.764,52.900,0.645,-0.001,0.721,0.616,0.337
+Burkina Faso,2017,4.647,7.627,0.785,53.400,0.614,-0.063,0.727,0.585,0.354
+Burkina Faso,2018,4.927,7.665,0.665,53.900,0.721,-0.013,0.757,0.711,0.343
+Burkina Faso,2019,4.741,7.691,0.683,54.400,0.678,-0.004,0.729,0.691,0.365
+Burundi,2008,3.563,6.719,0.291,49.020,0.260,-0.019,0.860,0.440,0.253
+Burundi,2009,3.792,6.723,0.326,49.660,0.427,-0.019,0.718,0.641,0.164
+Burundi,2011,3.706,6.748,0.422,50.680,0.490,-0.062,0.677,0.689,0.190
+Burundi,2014,2.905,6.787,0.565,51.820,0.431,-0.059,0.808,0.656,0.251
+Burundi,2018,3.775,6.635,0.485,53.400,0.646,-0.024,0.599,0.666,0.363
+Cambodia,2006,3.569,7.746,0.793,55.300,,0.255,0.829,0.719,0.341
+Cambodia,2007,4.156,7.829,0.675,56.100,0.819,0.116,0.879,,0.320
+Cambodia,2008,4.462,7.879,0.619,56.900,0.914,0.045,0.888,0.739,0.335
+Cambodia,2009,4.111,7.865,0.818,57.700,0.937,0.152,0.965,0.796,0.188
+Cambodia,2010,4.141,7.907,0.697,58.500,0.940,0.350,0.896,0.774,0.422
+Cambodia,2011,4.161,7.960,0.716,58.880,0.927,0.418,0.775,0.799,0.308
+Cambodia,2012,3.899,8.014,0.606,59.260,0.956,0.247,0.890,0.820,0.352
+Cambodia,2013,3.674,8.068,0.651,59.640,0.941,0.164,0.812,0.792,0.440
+Cambodia,2014,3.883,8.121,0.693,60.020,0.938,0.239,0.843,0.783,0.482
+Cambodia,2015,4.162,8.173,0.729,60.400,0.956,0.210,0.825,0.813,0.399
+Cambodia,2016,4.461,8.225,0.746,60.800,0.958,0.076,0.840,0.839,0.398
+Cambodia,2017,4.586,8.276,0.765,61.200,0.964,0.088,0.821,0.799,0.408
+Cambodia,2018,5.122,8.333,0.795,61.600,0.958,0.036,,0.845,0.414
+Cambodia,2019,4.998,8.387,0.759,62.000,0.957,0.013,0.828,0.844,0.390
+Cambodia,2020,4.377,8.362,0.724,62.400,0.963,0.052,0.863,0.878,0.390
+Cameroon,2006,3.851,8.007,0.690,45.980,0.653,-0.009,0.907,0.606,0.271
+Cameroon,2007,4.350,8.028,0.717,46.560,0.644,-0.031,0.910,0.635,0.249
+Cameroon,2008,4.292,8.034,0.697,47.140,0.580,-0.069,0.945,0.600,0.312
+Cameroon,2009,4.741,8.029,0.729,47.720,0.698,-0.017,0.925,0.593,0.250
+Cameroon,2010,4.554,8.035,0.759,48.300,0.792,0.002,0.875,0.606,0.274
+Cameroon,2011,4.434,8.048,0.738,48.700,0.817,-0.029,0.870,0.598,0.272
+Cameroon,2012,4.245,8.065,0.743,49.100,0.766,-0.032,0.898,0.618,0.284
+Cameroon,2013,4.271,8.090,0.760,49.500,0.794,-0.030,0.867,0.681,0.268
+Cameroon,2014,4.240,8.120,0.778,49.900,0.795,-0.071,0.856,0.623,0.216
+Cameroon,2015,5.038,8.149,0.646,50.300,0.791,0.049,0.868,0.651,0.346
+Cameroon,2016,4.816,8.167,0.659,51.100,0.713,-0.003,0.879,0.662,0.367
+Cameroon,2017,5.074,8.176,0.695,51.900,0.767,-0.028,0.844,0.632,0.377
+Cameroon,2018,5.251,8.190,0.677,52.700,0.816,0.036,0.884,0.642,0.356
+Cameroon,2019,4.937,8.203,0.711,53.500,0.712,-0.008,0.817,0.629,0.326
+Cameroon,2020,5.241,8.175,0.720,54.300,0.675,0.049,0.837,0.630,0.386
+Canada,2005,7.418,10.652,0.962,71.300,0.957,0.256,0.503,0.839,0.233
+Canada,2007,7.482,10.739,,71.660,0.930,0.249,0.406,0.872,0.257
+Canada,2008,7.486,10.738,0.939,71.840,0.926,0.262,0.370,0.890,0.202
+Canada,2009,7.488,10.697,0.943,72.020,0.915,0.246,0.413,0.867,0.248
+Canada,2010,7.650,10.717,0.954,72.200,0.934,0.230,0.413,0.879,0.233
+Canada,2011,7.426,10.738,0.922,72.360,0.951,0.253,0.433,0.881,0.248
+Canada,2012,7.415,10.744,0.948,72.520,0.918,0.290,0.466,0.857,0.229
+Canada,2013,7.594,10.757,0.936,72.680,0.916,0.316,0.406,0.851,0.263
+Canada,2014,7.304,10.775,0.918,72.840,0.939,0.270,0.442,0.833,0.259
+Canada,2015,7.413,10.774,0.939,73.000,0.931,0.253,0.427,0.845,0.286
+Canada,2016,7.245,10.773,0.924,73.200,0.912,0.211,0.385,0.825,0.237
+Canada,2017,7.415,10.792,0.934,73.400,0.945,0.163,0.362,0.863,0.218
+Canada,2018,7.175,10.798,0.923,73.600,0.946,0.106,0.372,0.824,0.259
+Canada,2019,7.109,10.800,0.925,73.800,0.912,0.112,0.436,0.822,0.285
+Canada,2020,7.025,10.730,0.931,74.000,0.887,0.050,0.434,0.796,0.307
+Central African Republic,2007,4.160,6.987,0.532,40.900,0.663,0.081,0.782,0.568,0.330
+Central African Republic,2010,3.568,7.091,0.483,42.700,0.690,-0.036,0.845,0.523,0.257
+Central African Republic,2011,3.678,7.125,0.387,43.080,0.780,-0.016,0.834,0.524,0.277
+Central African Republic,2016,2.693,6.785,0.290,44.900,0.624,0.033,0.859,0.579,0.494
+Central African Republic,2017,3.476,6.817,0.320,45.200,0.645,0.073,0.890,0.614,0.599
+Chad,2006,3.435,7.360,0.724,43.180,0.306,0.028,0.961,0.580,0.263
+Chad,2007,4.141,7.359,0.479,43.660,0.295,-0.011,0.874,0.614,0.245
+Chad,2008,4.632,7.356,0.571,44.140,0.527,0.063,0.944,0.569,0.225
+Chad,2009,3.639,7.364,0.646,44.620,0.401,0.021,0.931,0.557,0.221
+Chad,2010,3.743,7.457,0.734,45.100,0.505,0.025,0.858,0.545,0.287
+Chad,2011,4.393,7.425,0.819,45.420,0.540,0.031,0.876,0.591,0.289
+Chad,2012,4.033,7.476,0.673,45.740,0.563,-0.034,0.884,0.527,0.316
+Chad,2013,3.508,7.498,0.714,46.060,0.488,-0.045,0.882,0.462,0.314
+Chad,2014,3.460,7.532,0.733,46.380,0.567,-0.070,0.881,0.536,0.329
+Chad,2015,4.323,7.527,0.751,46.700,0.474,-0.029,0.889,0.607,0.358
+Chad,2016,4.029,7.431,0.616,47.200,0.525,0.052,0.820,0.582,0.468
+Chad,2017,4.559,7.370,0.661,47.700,0.615,0.008,0.792,0.628,0.538
+Chad,2018,4.486,7.363,0.577,48.200,0.650,0.024,0.763,0.553,0.544
+Chad,2019,4.251,7.365,0.640,48.700,0.537,0.055,0.832,0.587,0.460
+Chile,2006,6.063,9.850,0.836,68.660,0.744,0.168,0.634,0.804,0.348
+Chile,2007,5.698,9.887,0.815,68.720,0.662,0.244,0.723,0.767,0.342
+Chile,2008,5.789,9.911,0.804,68.780,0.640,0.084,0.741,0.757,0.330
+Chile,2009,6.494,9.885,0.832,68.840,0.747,0.149,0.734,0.808,0.300
+Chile,2010,6.636,9.931,0.857,68.900,0.786,0.108,0.702,0.809,0.300
+Chile,2011,6.526,9.980,0.819,69.040,0.701,0.112,0.753,0.804,0.317
+Chile,2012,6.599,10.023,0.855,69.180,0.734,0.195,0.782,0.815,0.288
+Chile,2013,6.740,10.053,0.862,69.320,0.737,0.085,0.741,0.855,0.285
+Chile,2014,6.844,10.059,0.862,69.460,0.733,0.217,0.758,0.870,0.276
+Chile,2015,6.533,10.070,0.827,69.600,0.769,0.041,0.812,0.803,0.333
+Chile,2016,6.579,10.074,0.841,69.700,0.652,0.102,0.858,0.869,0.283
+Chile,2017,6.320,10.072,0.880,69.800,0.790,-0.020,0.836,0.838,0.291
+Chile,2018,6.436,10.097,0.890,69.900,0.789,-0.060,0.816,0.833,0.276
+Chile,2019,5.942,10.095,0.869,70.000,0.659,-0.103,0.860,0.809,0.337
+Chile,2020,6.151,10.020,0.888,70.100,0.781,0.033,0.812,0.815,0.336
+China,2006,4.560,8.696,0.747,66.880,,,,0.809,0.170
+China,2007,4.863,8.824,0.811,67.060,,-0.176,,0.817,0.159
+China,2008,4.846,8.911,0.748,67.240,0.853,-0.092,,0.817,0.147
+China,2009,4.454,8.996,0.798,67.420,0.771,-0.160,,0.786,0.162
+China,2010,4.653,9.092,0.768,67.600,0.805,-0.133,,0.765,0.158
+China,2011,5.037,9.179,0.787,67.760,0.824,-0.186,,0.820,0.134
+China,2012,5.095,9.249,0.788,67.920,0.808,-0.185,,0.821,0.159
+China,2013,5.241,9.319,0.778,68.080,0.805,-0.158,,0.836,0.142
+China,2014,5.196,9.386,0.820,68.240,,-0.217,,0.854,0.112
+China,2015,5.304,9.449,0.794,68.400,,-0.244,,0.809,0.171
+China,2016,5.325,9.510,0.742,68.700,,-0.228,,0.826,0.146
+China,2017,5.099,9.571,0.772,69.000,0.878,-0.175,,0.821,0.214
+China,2018,5.131,9.632,0.788,69.300,0.895,-0.159,,0.856,0.190
+China,2019,5.144,9.688,0.822,69.600,0.927,-0.173,,0.891,0.147
+China,2020,5.771,9.702,0.808,69.900,0.891,-0.103,,0.789,0.245
+Colombia,2006,6.025,9.277,0.910,65.220,0.805,-0.015,0.808,0.800,0.326
+Colombia,2007,6.138,9.330,0.894,65.340,0.786,-0.040,0.860,0.808,0.287
+Colombia,2008,6.168,9.351,0.880,65.460,0.795,-0.042,0.763,0.803,0.307
+Colombia,2009,6.272,9.351,0.886,65.580,0.757,-0.055,0.837,0.843,0.273
+Colombia,2010,6.408,9.384,0.893,65.700,0.816,-0.050,0.815,0.831,0.265
+Colombia,2011,6.464,9.442,0.904,65.920,0.811,-0.073,0.847,0.832,0.286
+Colombia,2012,6.375,9.471,0.914,66.140,0.828,-0.009,0.868,0.846,0.294
+Colombia,2013,6.607,9.512,0.901,66.360,0.841,-0.071,0.898,0.851,0.278
+Colombia,2014,6.449,9.546,0.907,66.580,0.801,-0.090,0.887,0.847,0.278
+Colombia,2015,6.388,9.564,0.890,66.800,0.791,-0.100,0.843,0.839,0.292
+Colombia,2016,6.234,9.571,0.882,67.100,0.835,-0.100,0.898,0.794,0.294
+Colombia,2017,6.157,9.569,0.909,67.400,0.838,-0.157,0.875,0.837,0.299
+Colombia,2018,5.984,9.579,0.871,67.700,0.851,-0.148,0.855,0.825,0.301
+Colombia,2019,6.350,9.598,0.873,68.000,0.822,-0.172,0.854,0.822,0.322
+Colombia,2020,5.709,9.495,0.797,68.300,0.840,-0.085,0.808,0.795,0.340
+Comoros,2009,3.476,7.952,0.629,54.360,0.508,-0.074,0.838,0.672,0.167
+Comoros,2010,3.812,7.965,0.721,54.700,0.529,0.005,0.741,0.728,0.178
+Comoros,2011,3.838,7.981,0.722,55.020,0.500,-0.075,0.732,0.667,0.173
+Comoros,2012,3.956,7.988,0.719,55.340,0.534,-0.121,0.651,0.612,0.212
+Comoros,2018,3.973,8.028,0.621,57.200,0.560,0.086,0.794,0.748,0.337
+Comoros,2019,4.609,8.033,0.632,57.500,0.538,0.077,0.762,0.736,0.336
+Congo (Brazzaville),2008,3.820,8.082,0.555,52.200,0.526,-0.098,,0.573,0.298
+Congo (Brazzaville),2011,4.510,8.180,0.637,54.580,0.745,-0.109,0.833,0.621,0.288
+Congo (Brazzaville),2012,3.919,8.192,0.622,54.960,0.773,-0.112,0.800,0.564,0.323
+Congo (Brazzaville),2013,3.955,8.201,0.680,55.340,0.726,-0.078,0.752,0.610,0.291
+Congo (Brazzaville),2014,4.056,8.242,0.686,55.720,0.662,-0.110,0.808,0.595,0.400
+Congo (Brazzaville),2015,4.691,8.243,0.642,56.100,0.850,-0.104,0.841,0.606,0.261
+Congo (Brazzaville),2016,4.119,8.190,0.615,56.700,0.786,-0.072,0.790,0.610,0.304
+Congo (Brazzaville),2017,4.884,8.146,0.655,57.300,0.778,-0.131,0.763,0.599,0.382
+Congo (Brazzaville),2018,5.490,8.136,0.621,57.900,0.699,-0.092,0.738,0.588,0.448
+Congo (Brazzaville),2019,5.213,8.101,0.625,58.500,0.686,-0.046,0.741,0.645,0.405
+Congo (Kinshasa),2009,3.984,6.728,0.733,49.340,0.556,-0.022,0.824,0.491,0.283
+Congo (Kinshasa),2011,4.517,6.797,0.744,50.340,0.631,-0.025,0.856,0.617,0.208
+Congo (Kinshasa),2012,4.639,6.832,0.770,50.780,0.557,-0.035,0.807,0.634,0.230
+Congo (Kinshasa),2013,4.497,6.880,0.830,51.220,0.480,0.012,0.913,0.589,0.187
+Congo (Kinshasa),2014,4.414,6.937,0.822,51.660,0.556,0.009,0.814,0.559,0.305
+Congo (Kinshasa),2015,3.903,6.971,0.767,52.100,0.574,-0.048,0.866,0.589,0.301
+Congo (Kinshasa),2016,4.522,6.962,0.864,52.500,0.637,-0.024,0.875,0.646,0.222
+Congo (Kinshasa),2017,4.311,6.966,0.670,52.900,0.704,0.068,0.809,0.551,0.404
+Costa Rica,2006,7.082,9.565,0.937,69.940,0.882,0.060,0.798,0.868,0.236
+Costa Rica,2007,7.432,9.630,0.918,69.880,0.923,0.098,0.820,0.875,0.240
+Costa Rica,2008,6.851,9.662,0.916,69.820,0.912,0.096,0.816,0.844,0.233
+Costa Rica,2009,7.615,9.639,0.900,69.760,0.886,0.065,0.787,0.876,0.217
+Costa Rica,2010,7.271,9.675,0.915,69.700,0.881,0.047,0.763,0.886,0.221
+Costa Rica,2011,7.229,9.705,0.892,69.900,0.926,-0.033,0.837,0.876,0.269
+Costa Rica,2012,7.272,9.740,0.902,70.100,0.929,0.046,0.794,0.897,0.263
+Costa Rica,2013,7.158,9.751,0.902,70.300,0.898,0.018,0.813,0.850,0.278
+Costa Rica,2014,7.247,9.775,0.914,70.500,0.927,0.010,0.788,0.837,0.290
+Costa Rica,2015,6.854,9.799,0.878,70.700,0.907,-0.059,0.761,0.850,0.286
+Costa Rica,2016,7.136,9.830,0.901,70.900,0.873,-0.032,0.781,0.874,0.281
+Costa Rica,2017,7.225,9.858,0.922,71.100,0.936,-0.076,0.742,0.874,0.275
+Costa Rica,2018,7.141,9.874,0.876,71.300,0.942,-0.107,0.781,0.870,0.326
+Costa Rica,2019,6.998,9.885,0.906,71.500,0.927,-0.146,0.836,0.848,0.303
+Croatia,2007,5.821,10.162,0.910,67.120,0.662,-0.092,0.934,0.586,0.337
+Croatia,2009,5.433,10.104,0.861,67.640,0.549,-0.271,0.958,0.637,0.272
+Croatia,2010,5.596,10.091,0.796,67.900,0.564,-0.237,0.973,0.607,0.259
+Croatia,2011,5.385,10.091,0.790,68.000,0.517,-0.198,0.977,0.598,0.273
+Croatia,2012,6.028,10.072,0.776,68.100,0.542,-0.242,0.924,0.622,0.271
+Croatia,2013,5.885,10.069,0.751,68.200,0.627,-0.204,0.936,0.590,0.285
+Croatia,2014,5.381,10.072,0.646,68.300,0.519,0.132,0.918,0.596,0.286
+Croatia,2015,5.205,10.104,0.768,68.400,0.694,-0.096,0.849,0.609,0.294
+Croatia,2016,5.417,10.146,0.798,69.000,0.672,-0.065,0.884,0.614,0.337
+Croatia,2017,5.343,10.189,0.770,69.600,0.716,-0.104,0.892,0.655,0.316
+Croatia,2018,5.536,10.224,0.910,70.200,0.691,-0.151,0.925,0.582,0.290
+Croatia,2019,5.626,10.258,0.936,70.800,0.739,-0.137,0.932,0.551,0.269
+Croatia,2020,6.508,10.166,0.923,71.400,0.837,-0.063,0.961,0.743,0.286
+Cuba,2006,5.418,,0.970,68.440,0.281,,,0.647,0.277
+Cyprus,2006,6.238,10.566,0.878,71.440,0.836,0.018,0.712,0.829,0.253
+Cyprus,2009,6.833,10.558,0.812,72.160,0.775,0.054,0.801,0.746,0.329
+Cyprus,2010,6.387,10.551,0.822,72.400,0.755,0.073,0.833,0.786,0.296
+Cyprus,2011,6.690,10.530,0.844,72.540,0.745,0.180,0.841,0.763,0.272
+Cyprus,2012,6.181,10.479,0.767,72.680,0.725,0.098,0.871,0.755,0.369
+Cyprus,2013,5.439,10.414,0.744,72.820,0.656,0.102,0.867,0.748,0.420
+Cyprus,2014,5.627,10.406,0.770,72.960,0.715,0.060,0.868,0.737,0.397
+Cyprus,2015,5.439,10.445,0.770,73.100,0.628,0.114,0.893,0.747,0.383
+Cyprus,2016,5.795,10.506,0.786,73.300,0.756,-0.030,0.898,0.742,0.336
+Cyprus,2017,6.062,10.539,0.819,73.500,0.812,0.043,0.851,0.784,0.301
+Cyprus,2018,6.276,10.567,0.826,73.700,0.794,-0.022,0.848,0.750,0.298
+Cyprus,2019,6.137,10.585,0.776,73.900,0.740,-0.008,0.865,0.763,0.290
+Cyprus,2020,6.260,,0.806,74.100,0.763,,0.816,0.759,0.284
+Czech Republic,2005,6.439,10.324,0.919,67.000,0.865,,0.901,0.723,0.258
+Czech Republic,2007,6.500,10.437,0.900,67.440,0.799,-0.063,0.928,0.736,0.277
+Czech Republic,2010,6.250,10.419,0.934,68.100,0.779,-0.042,0.926,0.641,0.244
+Czech Republic,2011,6.331,10.435,0.914,68.240,0.787,-0.106,0.950,0.601,0.253
+Czech Republic,2012,6.334,10.426,0.912,68.380,0.740,-0.154,0.957,0.609,0.257
+Czech Republic,2013,6.698,10.420,0.888,68.520,0.726,-0.156,0.916,0.720,0.253
+Czech Republic,2014,6.484,10.446,0.878,68.660,0.800,-0.168,0.897,0.678,0.235
+Czech Republic,2015,6.608,10.496,0.911,68.800,0.808,-0.146,0.886,0.751,0.206
+Czech Republic,2016,6.736,10.518,0.931,69.300,0.850,-0.197,0.900,0.756,0.201
+Czech Republic,2017,6.790,10.558,0.901,69.800,0.832,-0.177,0.867,0.739,0.227
+Czech Republic,2018,7.034,10.583,0.929,70.300,0.790,-0.292,0.851,0.714,0.178
+Czech Republic,2020,6.897,10.530,0.964,71.300,0.906,-0.127,0.884,0.832,0.290
+Denmark,2005,8.019,10.851,0.972,69.600,0.971,,0.237,0.860,0.154
+Denmark,2007,7.834,10.891,0.954,69.920,0.932,0.240,0.206,0.828,0.194
+Denmark,2008,7.971,10.880,0.954,70.080,0.970,0.272,0.248,0.757,0.163
+Denmark,2009,7.683,10.824,0.939,70.240,0.949,0.264,0.206,0.749,0.234
+Denmark,2010,7.771,10.839,0.975,70.400,0.944,0.242,0.175,0.785,0.155
+Denmark,2011,7.788,10.848,0.962,70.620,0.935,0.298,0.220,0.769,0.175
+Denmark,2012,7.520,10.846,0.951,70.840,0.933,0.139,0.187,0.774,0.209
+Denmark,2013,7.589,10.851,0.965,71.060,0.920,0.215,0.170,0.862,0.195
+Denmark,2014,7.508,10.862,0.956,71.280,0.942,0.118,0.237,0.832,0.233
+Denmark,2015,7.514,10.878,0.960,71.500,0.941,0.222,0.191,0.829,0.218
+Denmark,2016,7.558,10.903,0.954,71.800,0.948,0.138,0.210,0.836,0.208
+Denmark,2017,7.594,10.916,0.952,72.100,0.955,0.155,0.181,0.824,0.206
+Denmark,2018,7.649,10.935,0.958,72.400,0.935,0.018,0.151,0.821,0.206
+Denmark,2019,7.693,10.954,0.958,72.700,0.963,0.020,0.174,0.862,0.181
+Denmark,2020,7.515,10.910,0.947,73.000,0.938,0.052,0.214,0.818,0.227
+Djibouti,2008,5.009,8.111,0.690,53.260,0.773,0.129,0.576,0.755,0.120
+Djibouti,2009,4.906,7.927,0.901,53.780,0.649,0.005,0.634,0.662,0.232
+Djibouti,2010,5.006,7.812,,54.300,0.764,-0.058,0.597,,
+Djibouti,2011,4.369,7.880,0.633,54.700,0.746,-0.057,0.519,0.579,0.181
+Dominican Republic,2006,5.088,9.314,0.919,62.680,0.858,0.038,0.755,0.748,0.274
+Dominican Republic,2007,5.081,9.372,0.848,62.960,0.886,-0.008,0.772,0.767,0.260
+Dominican Republic,2008,4.842,9.391,0.850,63.240,0.848,-0.045,0.728,0.732,0.329
+Dominican Republic,2009,5.432,9.388,0.878,63.520,0.863,-0.053,0.806,0.785,0.280
+Dominican Republic,2010,4.735,9.456,0.860,63.800,0.824,-0.075,0.780,0.787,0.282
+Dominican Republic,2011,5.397,9.475,0.872,64.020,0.848,0.014,0.788,0.809,0.300
+Dominican Republic,2012,4.753,9.489,0.879,64.240,0.840,-0.062,0.727,0.797,0.297
+Dominican Republic,2013,5.016,9.525,0.878,64.460,0.889,0.021,0.752,0.793,0.295
+Dominican Republic,2014,5.387,9.582,0.891,64.680,0.905,-0.020,0.760,0.798,0.300
+Dominican Republic,2015,5.062,9.637,0.893,64.900,0.856,-0.065,0.755,0.714,0.295
+Dominican Republic,2016,5.239,9.691,0.895,65.200,0.873,-0.080,0.737,0.760,0.278
+Dominican Republic,2017,5.605,9.725,0.894,65.500,0.855,-0.121,0.760,0.739,0.275
+Dominican Republic,2018,5.433,9.782,0.862,65.800,0.867,-0.150,0.762,0.745,0.291
+Dominican Republic,2019,6.004,9.821,0.884,66.100,0.877,-0.123,0.746,0.784,0.264
+Dominican Republic,2020,5.168,9.802,0.806,66.400,0.835,-0.128,0.636,0.734,0.314
+Ecuador,2006,5.024,9.186,0.910,66.080,0.671,-0.091,0.901,0.825,0.357
+Ecuador,2007,4.996,9.191,0.839,66.260,0.670,-0.063,0.830,0.833,0.286
+Ecuador,2008,5.297,9.236,0.829,66.440,0.640,-0.094,0.801,0.843,0.283
+Ecuador,2009,6.022,9.225,0.779,66.620,0.737,-0.108,0.774,0.840,0.256
+Ecuador,2010,5.838,9.244,0.839,66.800,0.723,-0.063,0.806,0.826,0.220
+Ecuador,2011,5.795,9.304,0.818,66.960,0.788,-0.155,0.702,0.862,0.271
+Ecuador,2012,5.961,9.344,0.785,67.120,0.825,-0.084,0.730,0.847,0.333
+Ecuador,2013,6.019,9.377,0.801,67.280,0.787,-0.191,0.646,0.851,0.267
+Ecuador,2014,5.946,9.399,0.831,67.440,0.719,-0.167,0.661,0.859,0.306
+Ecuador,2015,5.964,9.384,0.856,67.600,0.801,-0.114,0.666,0.851,0.323
+Ecuador,2016,6.115,9.355,0.842,67.900,0.846,-0.015,0.774,0.846,0.365
+Ecuador,2017,5.840,9.360,0.849,68.200,0.879,-0.167,0.734,0.829,0.314
+Ecuador,2018,6.128,9.355,0.851,68.500,0.869,-0.099,0.831,0.876,0.328
+Ecuador,2019,5.809,9.339,0.808,68.800,0.830,-0.115,0.839,0.811,0.374
+Ecuador,2020,5.354,9.244,0.804,69.100,0.829,-0.157,0.855,0.790,0.416
+Egypt,2005,5.168,9.036,0.848,59.700,0.817,,,0.735,0.346
+Egypt,2007,5.541,9.135,0.686,59.820,0.609,-0.121,,0.665,0.355
+Egypt,2008,4.632,9.186,0.738,59.880,,-0.087,0.914,0.683,0.301
+Egypt,2009,5.066,9.213,0.744,59.940,0.611,-0.100,0.801,0.642,0.339
+Egypt,2010,4.669,9.244,0.769,60.000,0.486,-0.076,0.826,0.567,0.276
+Egypt,2011,4.174,9.240,0.753,60.160,0.590,-0.151,0.859,0.529,0.353
+Egypt,2012,4.204,9.240,0.737,60.320,0.452,-0.138,0.880,0.527,0.398
+Egypt,2013,3.559,9.239,0.675,60.480,0.474,-0.141,0.913,0.551,0.483
+Egypt,2014,4.885,9.245,0.619,60.640,0.578,-0.126,0.749,0.543,0.327
+Egypt,2015,4.763,9.266,0.730,60.800,0.659,-0.089,0.684,0.610,0.344
+Egypt,2016,4.557,9.287,0.809,61.100,0.656,-0.141,0.818,0.611,0.370
+Egypt,2017,3.929,9.307,0.638,61.400,0.593,-0.152,,0.539,0.414
+Egypt,2018,4.005,9.338,0.759,61.700,0.682,-0.215,,0.492,0.285
+Egypt,2019,4.328,9.373,0.772,62.000,0.774,-0.199,,0.517,0.313
+Egypt,2020,4.472,9.383,0.673,62.300,0.770,-0.112,,0.599,0.442
+El Salvador,2006,5.701,8.873,0.878,62.920,0.683,-0.056,0.807,0.864,0.233
+El Salvador,2007,5.296,8.887,0.717,63.240,0.639,-0.015,0.785,0.869,0.220
+El Salvador,2008,5.191,8.908,0.747,63.560,0.636,-0.078,0.735,0.842,0.232
+El Salvador,2009,6.839,8.883,0.734,63.880,0.671,-0.103,0.648,0.850,0.243
+El Salvador,2010,6.740,8.900,0.757,64.200,0.669,-0.064,0.694,0.814,0.302
+El Salvador,2011,4.741,8.933,0.731,64.400,0.747,-0.126,0.707,0.875,0.336
+El Salvador,2012,5.934,8.956,0.806,64.600,0.683,-0.155,0.786,0.831,0.365
+El Salvador,2013,6.325,8.974,0.827,64.800,0.716,-0.150,0.772,0.828,0.317
+El Salvador,2014,5.857,8.986,0.798,65.000,0.778,-0.194,0.781,0.837,0.330
+El Salvador,2015,6.018,9.005,0.791,65.200,0.733,-0.156,0.805,0.826,0.333
+El Salvador,2016,6.140,9.025,0.794,65.500,0.800,-0.185,0.797,0.761,0.346
+El Salvador,2017,6.339,9.042,0.829,65.800,0.758,-0.172,0.778,0.849,0.268
+El Salvador,2018,6.241,9.061,0.820,66.100,0.863,-0.095,0.801,0.860,0.270
+El Salvador,2019,6.455,9.080,0.764,66.400,0.877,-0.109,0.682,0.871,0.271
+El Salvador,2020,5.462,9.019,0.696,66.700,0.924,-0.126,0.583,0.839,0.329
+Estonia,2006,5.371,10.270,0.910,64.860,0.749,-0.264,0.797,0.655,0.215
+Estonia,2007,5.332,10.347,0.896,65.320,0.712,-0.246,0.743,0.666,0.176
+Estonia,2008,5.452,10.298,0.904,65.780,0.642,-0.217,0.663,0.597,0.218
+Estonia,2009,5.138,10.144,0.874,66.240,0.611,-0.230,0.793,0.598,0.243
+Estonia,2011,5.487,10.248,0.909,66.960,0.735,-0.168,0.687,0.651,0.205
+Estonia,2012,5.364,10.282,0.889,67.220,0.697,-0.192,0.793,0.647,0.199
+Estonia,2013,5.367,10.299,0.901,67.480,0.754,-0.201,0.726,0.702,0.199
+Estonia,2014,5.556,10.331,0.917,67.740,0.773,-0.153,0.652,0.680,0.203
+Estonia,2015,5.629,10.348,0.918,68.000,0.815,-0.164,0.569,0.723,0.183
+Estonia,2016,5.650,10.374,0.938,68.200,0.843,-0.149,0.639,0.726,0.177
+Estonia,2017,5.938,10.429,0.936,68.400,0.862,-0.101,0.668,0.805,0.160
+Estonia,2018,6.091,10.472,0.933,68.600,0.886,-0.141,0.621,0.795,0.163
+Estonia,2019,6.035,10.511,0.934,68.800,0.887,-0.096,0.576,0.804,0.156
+Estonia,2020,6.453,10.459,0.958,69.000,0.954,-0.082,0.398,0.807,0.188
+Ethiopia,2012,4.561,7.271,0.659,55.200,0.776,-0.044,,0.668,0.137
+Ethiopia,2013,4.445,7.343,0.602,55.800,0.707,-0.008,0.750,0.643,0.213
+Ethiopia,2014,4.507,7.413,0.640,56.400,0.694,0.080,0.702,0.738,0.303
+Ethiopia,2015,4.573,7.484,0.626,57.000,0.803,0.113,0.567,0.714,0.237
+Ethiopia,2016,4.298,7.547,0.719,57.500,0.744,0.038,0.703,0.727,0.254
+Ethiopia,2017,4.180,7.612,0.734,58.000,0.717,0.001,0.757,0.609,0.304
+Ethiopia,2018,4.379,7.651,0.740,58.500,0.740,0.039,0.799,0.660,0.272
+Ethiopia,2019,4.100,7.705,0.748,59.000,0.754,0.053,0.732,0.631,0.283
+Ethiopia,2020,4.549,7.711,0.823,59.500,0.769,0.188,0.784,0.669,0.252
+Finland,2006,7.672,10.745,0.965,69.760,0.969,-0.005,0.132,0.722,0.172
+Finland,2008,7.671,10.796,0.951,70.080,0.934,0.028,0.217,0.773,0.144
+Finland,2010,7.393,10.734,0.935,70.400,0.916,0.091,0.413,0.832,0.202
+Finland,2011,7.354,10.754,0.938,70.640,0.936,0.101,0.320,0.773,0.205
+Finland,2012,7.420,10.735,0.928,70.880,0.921,-0.001,0.361,0.796,0.202
+Finland,2013,7.445,10.722,0.941,71.120,0.919,0.040,0.306,0.769,0.195
+Finland,2014,7.385,10.714,0.952,71.360,0.933,-0.001,0.265,0.784,0.199
+Finland,2015,7.448,10.716,0.948,71.600,0.930,0.111,0.223,0.751,0.191
+Finland,2016,7.660,10.740,0.954,71.700,0.948,-0.027,0.250,0.797,0.182
+Finland,2017,7.788,10.768,0.964,71.800,0.962,-0.002,0.192,0.787,0.176
+Finland,2018,7.858,10.783,0.962,71.900,0.938,-0.127,0.199,0.782,0.182
+Finland,2019,7.780,10.792,0.937,72.000,0.948,-0.052,0.195,0.755,0.181
+Finland,2020,7.889,10.750,0.962,72.100,0.962,-0.116,0.164,0.744,0.193
+France,2005,7.093,10.642,0.940,71.300,0.895,,0.688,0.769,0.225
+France,2006,6.583,10.659,0.944,71.480,0.789,0.126,0.699,0.777,0.289
+France,2008,7.008,10.674,0.935,71.840,0.833,-0.031,0.669,0.746,0.281
+France,2009,6.283,10.639,0.918,72.020,0.798,-0.082,0.654,0.763,0.303
+France,2010,6.798,10.654,0.943,72.200,0.850,-0.104,0.623,0.790,0.261
+France,2011,6.959,10.671,0.921,72.400,0.903,-0.102,0.627,0.781,0.281
+France,2012,6.649,10.669,0.937,72.600,0.841,-0.149,0.608,0.754,0.253
+France,2013,6.667,10.669,0.908,72.800,0.878,-0.125,0.699,0.800,0.205
+France,2014,6.467,10.674,0.878,73.000,0.803,-0.118,0.656,0.811,0.216
+France,2015,6.358,10.682,0.896,73.200,0.817,-0.139,0.641,0.786,0.215
+France,2016,6.475,10.690,0.885,73.400,0.787,-0.091,0.623,0.773,0.270
+France,2017,6.635,10.711,0.931,73.600,0.834,-0.123,0.601,0.762,0.242
+France,2018,6.666,10.727,0.921,73.800,0.816,-0.138,0.582,0.767,0.282
+France,2019,6.690,10.740,0.958,74.000,0.827,-0.133,0.568,0.735,0.250
+France,2020,6.714,10.643,0.947,74.200,0.823,-0.169,0.565,0.732,0.231
+Gabon,2011,4.255,9.608,0.653,55.480,0.772,-0.211,0.851,0.591,0.264
+Gabon,2012,3.972,9.621,0.736,56.160,0.566,-0.195,0.810,0.470,0.266
+Gabon,2013,3.800,9.638,0.733,56.840,0.682,-0.146,0.780,0.510,0.287
+Gabon,2014,3.918,9.644,0.829,57.520,0.607,-0.198,0.782,0.539,0.293
+Gabon,2015,4.661,9.649,0.756,58.200,0.671,-0.194,0.867,0.626,0.372
+Gabon,2016,4.832,9.639,0.780,58.700,0.699,-0.204,0.817,0.640,0.432
+Gabon,2017,4.782,9.616,0.807,59.200,0.652,-0.228,0.868,0.634,0.446
+Gabon,2018,4.783,9.599,0.785,59.700,0.719,-0.197,0.823,0.641,0.418
+Gabon,2019,4.914,9.607,0.763,60.200,0.736,-0.203,0.846,0.693,0.413
+Gambia,2017,4.118,7.637,0.697,54.700,0.812,0.111,0.572,0.838,0.277
+Gambia,2018,4.922,7.671,0.685,55.000,0.719,0.440,0.691,0.804,0.379
+Gambia,2019,5.164,7.699,0.694,55.300,0.677,0.410,0.798,0.773,0.401
+Georgia,2006,3.675,8.993,0.647,65.120,0.553,-0.267,0.752,0.433,0.269
+Georgia,2007,3.707,9.117,0.548,65.040,0.464,-0.267,0.697,0.427,0.236
+Georgia,2008,4.156,9.144,0.608,64.960,0.614,-0.224,0.498,0.441,0.262
+Georgia,2009,3.801,9.116,0.544,64.880,0.495,-0.233,0.535,0.492,0.242
+Georgia,2010,4.102,9.184,0.540,64.800,0.558,-0.248,0.460,0.502,0.243
+Georgia,2011,4.203,9.263,0.503,64.860,0.632,-0.255,0.353,0.515,0.247
+Georgia,2012,4.254,9.332,0.533,64.920,0.659,-0.269,0.321,0.559,0.250
+Georgia,2013,4.349,9.371,0.559,64.980,0.722,-0.254,0.349,0.595,0.200
+Georgia,2014,4.288,9.414,0.558,65.040,0.720,-0.233,0.416,0.570,0.204
+Georgia,2015,4.122,9.442,0.517,65.100,0.640,-0.205,0.502,0.547,0.233
+Georgia,2016,4.448,9.470,0.533,64.900,0.606,-0.249,0.561,0.564,0.223
+Georgia,2017,4.451,9.517,0.590,64.700,0.821,-0.244,0.590,0.581,0.210
+Georgia,2018,4.659,9.565,0.617,64.500,0.775,-0.233,0.755,0.573,0.244
+Georgia,2019,4.892,9.617,0.675,64.300,0.811,-0.260,0.647,0.604,0.244
+Georgia,2020,5.123,9.569,0.718,64.100,0.764,-0.221,0.583,0.611,0.295
+Germany,2005,6.620,10.689,0.963,70.200,0.847,,0.781,0.776,0.197
+Germany,2007,6.417,10.759,0.926,70.480,0.801,0.167,0.792,0.732,0.231
+Germany,2008,6.522,10.770,0.923,70.620,0.766,,0.758,0.787,0.220
+Germany,2009,6.641,10.714,0.935,70.760,0.844,0.127,0.690,0.792,0.206
+Germany,2010,6.725,10.756,0.939,70.900,0.843,0.095,0.688,0.794,0.182
+Germany,2011,6.621,10.813,0.947,70.980,0.906,0.033,0.677,0.794,0.165
+Germany,2012,6.702,10.816,0.926,71.060,0.904,0.071,0.679,0.804,0.170
+Germany,2013,6.965,10.817,0.931,71.140,0.894,0.024,0.566,0.743,0.205
+Germany,2014,6.984,10.835,0.938,71.220,0.899,0.088,0.474,0.785,0.188
+Germany,2015,7.037,10.844,0.926,71.300,0.889,0.178,0.412,0.765,0.203
+Germany,2016,6.874,10.858,0.906,71.600,0.871,0.148,0.446,0.738,0.187
+Germany,2017,7.074,10.878,0.892,71.900,0.841,0.145,0.414,0.737,0.196
+Germany,2018,7.118,10.890,0.920,72.200,0.877,0.034,0.496,0.780,0.243
+Germany,2019,7.035,10.893,0.886,72.500,0.885,0.057,0.462,0.751,0.226
+Germany,2020,7.312,10.833,0.905,72.800,0.864,-0.060,0.424,0.760,0.206
+Ghana,2006,4.535,8.073,0.728,52.340,0.849,0.213,0.814,0.671,0.198
+Ghana,2007,5.220,8.090,0.730,52.780,0.891,0.138,0.771,0.686,0.217
+Ghana,2008,4.965,8.152,0.622,53.220,0.838,0.120,0.863,0.717,0.172
+Ghana,2009,4.198,8.174,0.633,53.660,0.757,0.005,0.890,0.774,0.198
+Ghana,2010,4.606,8.225,0.739,54.100,0.891,0.074,0.875,0.783,0.184
+Ghana,2011,5.608,8.332,0.724,54.480,0.852,0.011,0.790,0.744,0.209
+Ghana,2012,5.057,8.397,0.685,54.860,0.679,0.040,0.898,0.760,0.152
+Ghana,2013,4.965,8.445,0.676,55.240,0.794,-0.065,0.880,0.691,0.211
+Ghana,2014,3.860,8.450,0.651,55.620,0.677,0.001,0.913,0.696,0.280
+Ghana,2015,3.986,8.449,0.687,56.000,0.852,-0.038,0.945,0.690,0.265
+Ghana,2016,4.514,8.460,0.647,56.400,0.751,0.090,0.894,0.668,0.305
+Ghana,2017,5.481,8.517,0.669,56.800,0.783,0.079,0.839,0.703,0.248
+Ghana,2018,5.004,8.555,0.761,57.200,0.817,0.062,0.846,0.747,0.250
+Ghana,2019,4.967,8.596,0.746,57.600,0.787,0.116,0.857,0.682,0.270
+Ghana,2020,5.319,8.590,0.643,58.000,0.824,0.200,0.847,0.713,0.253
+Greece,2005,6.006,10.462,0.837,70.500,0.734,,0.861,0.692,0.264
+Greece,2007,6.647,10.543,0.808,70.900,0.575,-0.190,0.845,0.738,0.222
+Greece,2009,6.039,10.491,0.793,71.300,0.443,-0.293,0.959,0.649,0.254
+Greece,2010,5.840,10.433,0.868,71.500,0.484,-0.303,0.954,0.634,0.292
+Greece,2011,5.372,10.339,0.852,71.560,0.528,-0.316,0.941,0.591,0.323
+Greece,2012,5.096,10.268,0.812,71.620,0.373,-0.305,0.959,0.581,0.352
+Greece,2013,4.720,10.243,0.687,71.680,0.426,-0.272,0.941,0.689,0.482
+Greece,2014,4.756,10.257,0.832,71.740,0.369,-0.288,0.930,0.695,0.385
+Greece,2015,5.623,10.259,0.835,71.800,0.532,-0.272,0.824,0.740,0.277
+Greece,2016,5.303,10.261,0.803,72.000,0.482,-0.260,0.898,0.701,0.336
+Greece,2017,5.148,10.278,0.753,72.200,0.438,-0.290,0.872,0.603,0.333
+Greece,2018,5.409,10.299,0.794,72.400,0.564,-0.335,0.860,0.666,0.255
+Greece,2019,5.952,10.319,0.891,72.600,0.614,-0.289,0.848,0.668,0.236
+Greece,2020,5.788,10.215,0.779,72.800,0.565,-0.241,0.764,0.684,0.322
+Guatemala,2006,5.901,8.850,0.830,60.740,0.663,0.172,0.706,0.818,0.287
+Guatemala,2007,6.330,8.891,0.866,61.080,0.628,0.136,0.810,0.819,0.224
+Guatemala,2008,6.414,8.904,0.866,61.420,0.630,0.206,0.796,0.834,0.234
+Guatemala,2009,6.452,8.891,0.834,61.760,0.643,0.197,0.755,0.829,0.240
+Guatemala,2010,6.290,8.901,0.859,62.100,0.696,0.166,0.795,0.850,0.236
+Guatemala,2011,5.743,8.923,0.768,62.460,0.763,0.009,0.863,0.844,0.289
+Guatemala,2012,5.856,8.935,0.802,62.820,0.865,0.020,0.821,0.863,0.349
+Guatemala,2013,5.985,8.953,0.830,63.180,0.884,0.045,0.817,0.867,0.333
+Guatemala,2014,6.536,8.980,0.834,63.540,0.843,0.108,0.804,0.835,0.305
+Guatemala,2015,6.465,9.003,0.823,63.900,0.869,0.051,0.822,0.851,0.311
+Guatemala,2016,6.359,9.013,0.811,64.200,0.863,0.011,0.812,0.846,0.321
+Guatemala,2017,6.325,9.026,0.826,64.500,0.915,-0.059,0.800,0.846,0.308
+Guatemala,2018,6.627,9.042,0.841,64.800,0.910,-0.010,0.765,0.871,0.262
+Guatemala,2019,6.262,9.064,0.774,65.100,0.901,-0.062,0.773,0.859,0.311
+Guinea,2011,4.045,7.567,0.598,50.220,0.797,0.041,0.743,0.701,0.260
+Guinea,2012,3.652,7.603,0.542,50.440,0.646,0.001,0.794,0.677,0.285
+Guinea,2013,3.902,7.619,0.567,50.660,0.693,0.091,0.815,0.600,0.348
+Guinea,2014,3.412,7.632,0.638,50.880,0.684,0.006,0.705,0.629,0.351
+Guinea,2015,3.505,7.645,0.579,51.100,0.666,0.007,0.762,0.667,0.268
+Guinea,2016,3.603,7.721,0.675,52.200,0.726,-0.056,0.803,0.687,0.374
+Guinea,2017,4.874,7.792,0.634,53.300,0.738,0.038,0.750,0.704,0.422
+Guinea,2018,5.252,7.823,0.630,54.400,0.731,0.092,0.778,0.744,0.440
+Guinea,2019,4.768,7.849,0.655,55.500,0.691,0.097,0.756,0.685,0.473
+Guyana,2007,5.993,8.773,0.849,57.260,0.694,0.110,0.836,0.768,0.296
+Haiti,2006,3.754,7.407,0.694,48.460,0.449,0.401,0.854,0.613,0.332
+Haiti,2008,3.846,7.417,0.679,40.380,0.465,0.261,0.812,0.608,0.256
+Haiti,2010,3.766,7.384,0.554,32.300,0.373,0.216,0.848,0.555,0.293
+Haiti,2011,4.845,7.423,0.567,36.860,0.413,0.243,0.682,0.625,0.245
+Haiti,2012,4.413,7.437,0.749,41.420,0.482,0.289,0.717,0.593,0.284
+Haiti,2013,4.622,7.464,0.648,45.980,0.610,0.289,0.669,0.538,0.327
+Haiti,2014,3.889,7.477,0.554,50.540,0.509,0.285,0.708,0.593,0.327
+Haiti,2015,3.570,7.476,0.564,55.100,0.398,0.306,0.777,0.619,0.333
+Haiti,2016,3.352,7.477,0.584,55.300,0.304,0.291,0.839,0.553,0.367
+Haiti,2017,3.824,7.475,0.647,55.500,0.484,0.381,0.647,0.573,0.322
+Haiti,2018,3.615,7.477,0.538,55.700,0.591,0.422,0.720,0.584,0.359
+Honduras,2006,5.397,8.462,0.933,64.540,0.650,0.089,0.844,0.858,0.155
+Honduras,2007,5.097,8.500,0.819,64.780,0.676,0.230,0.826,0.759,0.199
+Honduras,2008,5.420,8.520,0.828,65.020,0.687,0.223,0.863,0.789,0.206
+Honduras,2009,6.033,8.474,0.824,65.260,0.661,0.119,0.857,0.803,0.261
+Honduras,2010,5.866,8.490,0.803,65.500,0.646,0.105,0.820,0.797,0.260
+Honduras,2011,4.961,8.508,0.766,65.720,0.783,0.095,0.884,0.816,0.307
+Honduras,2012,4.602,8.530,0.779,65.940,0.700,-0.003,0.871,0.847,0.294
+Honduras,2013,4.713,8.540,0.792,66.160,0.698,-0.027,0.868,0.817,0.283
+Honduras,2014,5.056,8.552,0.790,66.380,0.696,0.015,0.834,0.820,0.299
+Honduras,2015,4.845,8.572,0.772,66.600,0.534,-0.097,0.848,0.863,0.311
+Honduras,2016,5.648,8.593,0.774,66.800,0.850,0.080,0.793,0.832,0.297
+Honduras,2017,6.020,8.624,0.843,67.000,0.898,0.072,0.783,0.842,0.248
+Honduras,2018,5.908,8.643,0.827,67.200,0.872,0.099,0.804,0.872,0.287
+Honduras,2019,5.930,8.653,0.797,67.400,0.846,0.063,0.815,0.850,0.279
+Hong Kong S.A.R. of China,2006,5.511,10.746,0.812,,0.910,0.156,0.356,0.723,0.236
+Hong Kong S.A.R. of China,2008,5.137,10.816,0.840,,0.922,0.296,0.274,0.719,0.237
+Hong Kong S.A.R. of China,2009,5.397,10.788,0.835,,0.918,0.308,0.272,0.762,0.210
+Hong Kong S.A.R. of China,2010,5.643,10.847,0.857,,0.890,0.332,0.256,0.710,0.183
+Hong Kong S.A.R. of China,2011,5.474,10.887,0.846,,0.894,0.235,0.245,0.734,0.196
+Hong Kong S.A.R. of China,2012,5.484,10.893,0.826,,0.880,0.222,0.380,0.715,0.183
+Hong Kong S.A.R. of China,2014,5.458,10.940,0.834,,0.843,0.224,0.423,0.684,0.243
+Hong Kong S.A.R. of China,2016,5.498,10.970,0.832,,0.800,0.100,0.403,0.664,0.213
+Hong Kong S.A.R. of China,2017,5.362,11.000,0.831,,0.831,0.140,0.416,0.640,0.201
+Hong Kong S.A.R. of China,2019,5.659,11.000,0.856,,0.727,0.067,0.432,0.599,0.358
+Hong Kong S.A.R. of China,2020,5.295,,0.813,,0.705,,0.380,0.609,0.210
+Hungary,2005,5.194,10.108,0.930,64.600,0.697,,0.903,0.675,0.290
+Hungary,2007,4.954,10.153,0.931,65.000,0.538,-0.161,0.895,0.701,0.230
+Hungary,2009,4.895,10.097,0.901,65.400,0.464,-0.125,0.915,0.664,0.228
+Hungary,2010,4.725,10.106,0.896,65.600,0.514,-0.145,0.983,0.656,0.235
+Hungary,2011,4.918,10.127,0.894,65.760,0.631,-0.089,0.940,0.642,0.305
+Hungary,2012,4.683,10.117,0.906,65.920,0.569,-0.136,0.930,0.652,0.315
+Hungary,2013,4.914,10.140,0.877,66.080,0.674,-0.113,0.912,0.706,0.307
+Hungary,2014,5.181,10.183,0.845,66.240,0.494,-0.150,0.855,0.651,0.238
+Hungary,2015,5.344,10.223,0.859,66.400,0.558,-0.198,0.908,0.707,0.245
+Hungary,2016,5.449,10.248,0.900,66.800,0.554,-0.187,0.924,0.666,0.243
+Hungary,2017,6.065,10.293,0.877,67.200,0.661,-0.139,0.886,0.735,0.181
+Hungary,2018,5.936,10.344,0.941,67.600,0.693,-0.243,0.911,0.676,0.201
+Hungary,2019,6.000,10.393,0.947,68.000,0.798,-0.195,0.884,0.743,0.180
+Hungary,2020,6.038,10.335,0.943,68.400,0.771,-0.120,0.836,0.735,0.240
+Iceland,2008,6.888,10.861,0.977,72.320,0.885,0.272,0.708,0.880,0.153
+Iceland,2012,7.591,10.777,0.979,72.760,0.905,0.241,0.759,0.900,0.157
+Iceland,2013,7.501,10.809,0.967,72.840,0.923,0.306,0.713,0.870,0.156
+Iceland,2015,7.498,10.854,0.980,73.000,0.940,0.301,0.639,0.849,0.180
+Iceland,2016,7.510,10.904,0.985,73.000,0.952,0.281,0.719,0.874,0.158
+Iceland,2017,7.476,10.925,0.967,73.000,0.939,0.246,0.727,0.895,0.148
+Iceland,2019,7.533,10.931,0.982,73.000,0.959,,0.699,0.836,0.178
+Iceland,2020,7.575,10.824,0.983,73.000,0.949,0.160,0.644,0.863,0.172
+India,2006,5.348,8.145,0.707,55.720,0.774,,0.855,0.687,0.199
+India,2007,5.027,8.204,0.569,56.140,0.729,-0.051,0.862,0.668,0.253
+India,2008,5.146,8.220,0.684,56.560,0.756,-0.072,0.891,0.674,0.259
+India,2009,4.522,8.281,0.653,56.980,0.679,-0.026,0.895,0.771,0.301
+India,2010,4.989,8.349,0.605,57.400,0.783,0.058,0.863,0.697,0.267
+India,2011,4.635,8.387,0.553,57.700,0.838,-0.038,0.908,0.648,0.232
+India,2012,4.720,8.428,0.511,58.000,0.609,0.067,0.830,0.629,0.295
+India,2013,4.428,8.478,0.553,58.300,0.740,0.084,0.832,0.680,0.330
+India,2014,4.424,8.538,0.621,58.600,0.809,-0.026,0.832,0.711,0.285
+India,2015,4.342,8.604,0.610,58.900,0.777,-0.005,0.776,0.701,0.322
+India,2016,4.179,8.673,0.614,59.300,0.820,0.046,0.765,0.695,0.346
+India,2017,4.046,8.730,0.607,59.700,0.886,-0.042,0.781,0.682,0.318
+India,2018,3.818,8.779,0.638,60.100,0.890,0.085,0.805,0.657,0.357
+India,2019,3.249,8.818,0.561,60.500,0.876,0.112,0.752,0.648,0.466
+India,2020,4.225,8.703,0.617,60.900,0.906,0.075,0.780,0.752,0.383
+Indonesia,2006,4.947,8.850,0.771,59.840,0.713,0.347,0.915,0.825,0.266
+Indonesia,2007,5.101,8.898,0.704,59.980,0.603,0.311,0.960,0.812,0.242
+Indonesia,2008,4.815,8.943,0.675,60.120,0.596,0.164,0.968,0.774,0.239
+Indonesia,2009,5.472,8.975,0.779,60.260,0.784,0.191,0.911,0.865,0.193
+Indonesia,2010,5.457,9.022,0.816,60.400,0.700,0.448,0.954,0.837,0.218
+Indonesia,2011,5.173,9.069,0.825,60.620,0.878,0.438,0.962,0.864,0.273
+Indonesia,2012,5.368,9.114,0.834,60.840,0.770,0.354,0.962,0.897,0.229
+Indonesia,2013,5.292,9.155,0.794,61.060,0.781,0.376,0.973,0.893,0.249
+Indonesia,2014,5.597,9.190,0.905,61.280,0.719,0.408,0.970,0.852,0.242
+Indonesia,2015,5.043,9.225,0.809,61.500,0.779,0.471,0.946,0.876,0.274
+Indonesia,2016,5.136,9.262,0.792,61.700,0.830,0.500,0.890,0.833,0.342
+Indonesia,2017,5.098,9.300,0.796,61.900,0.865,0.488,0.900,0.863,0.319
+Indonesia,2018,5.340,9.339,0.809,62.100,0.879,0.512,0.868,0.864,0.296
+Indonesia,2019,5.347,9.377,0.802,62.300,0.866,0.555,0.861,0.877,0.302
+Iran,2005,5.308,9.393,0.766,62.000,0.651,,0.636,0.608,0.456
+Iran,2007,5.336,9.497,0.718,62.760,0.533,0.056,0.872,0.626,0.361
+Iran,2008,5.129,9.489,0.633,63.140,0.601,0.052,0.868,0.624,0.345
+Iran,2011,4.768,9.547,0.582,64.140,0.798,0.200,0.665,0.578,0.359
+Iran,2012,4.609,9.458,0.600,64.380,0.764,,0.678,0.609,0.525
+Iran,2013,5.140,9.443,0.664,64.620,0.730,0.216,0.685,0.659,0.552
+Iran,2014,4.682,9.476,0.644,64.860,0.767,0.241,0.640,0.618,0.512
+Iran,2015,4.750,9.449,0.572,65.100,0.780,0.176,0.699,0.645,0.520
+Iran,2016,4.653,9.561,0.566,65.400,0.773,0.186,0.713,0.687,0.526
+Iran,2017,4.717,9.584,0.714,65.700,0.731,0.218,0.715,0.694,0.439
+Iran,2018,4.278,,0.674,66.000,0.603,,0.703,0.553,0.493
+Iran,2019,5.006,,0.698,66.300,0.623,,0.728,0.600,0.449
+Iran,2020,4.865,,0.757,66.600,0.600,,0.710,0.582,0.470
+Iraq,2008,4.590,9.063,0.744,58.320,0.386,-0.061,0.910,0.525,0.448
+Iraq,2009,4.775,9.076,0.862,58.960,0.431,-0.199,0.854,0.523,0.404
+Iraq,2010,5.065,9.112,0.854,59.600,0.419,-0.125,0.859,0.542,0.431
+Iraq,2011,4.725,9.152,0.751,59.360,0.347,-0.070,0.780,0.488,0.557
+Iraq,2012,4.660,9.246,0.730,59.120,0.315,-0.020,0.789,0.423,0.449
+Iraq,2013,4.725,9.280,0.728,58.880,,-0.050,0.710,,0.554
+Iraq,2014,4.542,9.250,0.725,58.640,0.646,-0.001,0.726,0.574,0.564
+Iraq,2015,4.493,9.241,0.684,58.400,0.599,0.019,0.762,0.490,0.581
+Iraq,2016,4.413,9.354,0.719,59.000,0.666,-0.052,0.799,0.489,0.570
+Iraq,2017,4.462,9.303,0.695,59.600,0.628,0.000,0.757,0.505,0.591
+Iraq,2018,4.886,9.274,0.764,60.200,0.598,-0.068,0.887,0.605,0.482
+Iraq,2020,4.785,9.167,0.708,61.400,0.700,-0.021,0.849,0.644,0.532
+Ireland,2006,7.144,10.972,0.967,70.140,0.943,0.242,0.473,0.878,0.209
+Ireland,2008,7.568,10.929,0.983,70.820,0.894,0.322,0.487,0.875,0.148
+Ireland,2009,7.046,10.866,0.959,71.160,0.835,0.315,0.580,0.862,0.233
+Ireland,2010,7.257,10.879,0.973,71.500,0.856,0.348,0.618,0.876,0.201
+Ireland,2011,7.007,10.878,0.977,71.600,0.952,0.383,0.590,0.865,0.190
+Ireland,2012,6.965,10.876,0.962,71.700,0.902,0.302,0.573,0.835,0.237
+Ireland,2013,6.760,10.884,0.955,71.800,0.884,0.331,0.558,0.814,0.245
+Ireland,2014,7.018,10.959,0.968,71.900,0.922,0.264,0.406,0.784,0.229
+Ireland,2015,6.830,11.174,0.953,72.000,0.892,0.233,0.409,0.799,0.225
+Ireland,2016,7.041,11.199,0.958,72.100,0.875,0.174,0.399,0.809,0.211
+Ireland,2017,7.060,11.266,0.943,72.200,0.905,0.216,0.337,0.833,0.213
+Ireland,2018,6.962,11.332,0.938,72.300,0.861,0.144,0.362,0.811,0.213
+Ireland,2019,7.255,11.371,0.944,72.400,0.892,0.074,0.373,0.807,0.223
+Ireland,2020,7.035,11.323,0.960,72.500,0.882,0.014,0.356,0.797,0.246
+Israel,2006,7.173,10.389,0.927,71.120,0.817,,0.905,0.696,0.308
+Israel,2007,6.841,10.428,0.868,71.440,0.683,0.219,0.868,0.696,0.320
+Israel,2008,7.261,10.440,0.859,71.760,0.663,0.139,0.898,0.710,0.349
+Israel,2009,7.353,10.425,0.937,72.080,0.593,0.172,0.923,0.695,0.327
+Israel,2010,7.359,10.461,0.882,72.400,0.561,0.150,0.902,0.679,0.362
+Israel,2011,7.433,10.489,0.893,72.460,0.722,0.141,0.891,0.738,0.384
+Israel,2012,7.111,10.493,0.903,72.520,0.681,0.153,0.862,0.665,0.319
+Israel,2013,7.321,10.515,0.909,72.580,0.739,0.150,0.849,0.698,0.409
+Israel,2014,7.401,10.533,0.889,72.640,0.707,0.094,0.818,0.604,0.271
+Israel,2015,7.079,10.536,0.864,72.700,0.753,0.108,0.789,0.697,0.256
+Israel,2016,7.159,10.555,0.890,72.900,0.772,0.153,0.804,0.629,0.263
+Israel,2017,7.331,10.570,0.916,73.100,0.768,0.145,0.793,0.674,0.276
+Israel,2018,6.927,10.585,0.910,73.300,0.725,0.055,0.770,0.663,0.282
+Israel,2019,7.332,10.601,0.946,73.500,0.834,0.085,0.743,0.635,0.266
+Israel,2020,7.195,10.538,0.959,73.700,0.831,-0.049,0.748,0.621,0.243
+Italy,2005,6.854,10.703,0.928,71.900,0.802,,0.944,0.679,0.295
+Italy,2007,6.574,10.727,0.912,72.260,0.684,0.113,0.922,0.716,0.303
+Italy,2008,6.780,10.711,0.880,72.440,0.543,0.049,0.946,0.637,0.268
+Italy,2009,6.334,10.652,0.880,72.620,0.701,0.240,0.890,0.775,0.279
+Italy,2010,6.354,10.666,0.872,72.800,0.738,-0.060,0.921,0.596,0.236
+Italy,2011,6.057,10.671,0.913,72.840,0.568,-0.018,0.933,0.658,0.266
+Italy,2012,5.839,10.638,0.869,72.880,0.570,0.113,0.908,0.670,0.388
+Italy,2013,6.009,10.608,0.916,72.920,0.499,-0.102,0.943,0.779,0.357
+Italy,2014,6.027,10.599,0.898,72.960,0.624,-0.065,0.920,0.716,0.356
+Italy,2015,5.848,10.608,0.909,73.000,0.575,-0.064,0.913,0.692,0.329
+Italy,2016,5.955,10.622,0.927,73.200,0.624,-0.081,0.903,0.685,0.339
+Italy,2017,6.199,10.640,0.920,73.400,0.633,-0.035,0.867,0.661,0.323
+Italy,2018,6.517,10.650,0.913,73.600,0.650,-0.021,0.888,0.649,0.403
+Italy,2019,6.445,10.655,0.838,73.800,0.709,-0.082,0.866,0.631,0.328
+Italy,2020,6.488,10.563,0.890,74.000,0.718,-0.150,0.844,0.670,0.311
+Ivory Coast,2009,4.197,8.209,0.667,45.780,0.760,-0.153,0.902,0.604,0.186
+Ivory Coast,2013,3.739,8.274,0.709,47.100,0.739,-0.031,0.691,0.743,0.306
+Ivory Coast,2014,3.570,8.334,0.711,47.400,0.781,-0.080,0.671,0.647,0.291
+Ivory Coast,2015,4.445,8.393,0.704,47.700,0.800,-0.053,0.744,0.664,0.347
+Ivory Coast,2016,4.543,8.437,0.617,48.300,0.769,-0.043,0.757,0.704,0.378
+Ivory Coast,2017,5.038,8.483,0.661,48.900,0.732,-0.111,0.771,0.698,0.357
+Ivory Coast,2018,5.268,8.523,0.621,49.500,0.713,-0.050,0.791,0.682,0.386
+Ivory Coast,2019,5.392,8.564,0.679,50.100,0.736,-0.017,0.799,0.674,0.425
+Ivory Coast,2020,5.257,8.565,0.613,50.700,0.770,0.016,0.777,0.693,0.340
+Jamaica,2006,6.208,9.225,0.909,64.900,0.738,-0.004,0.946,0.788,0.201
+Jamaica,2011,5.374,9.164,0.855,66.220,0.796,-0.064,0.909,0.836,0.237
+Jamaica,2013,5.709,9.151,0.865,66.460,0.793,-0.021,0.931,0.734,0.312
+Jamaica,2014,5.311,9.152,0.874,66.580,0.809,-0.001,0.861,0.737,0.310
+Jamaica,2017,5.890,9.169,0.913,67.100,0.861,-0.130,0.883,0.769,0.243
+Jamaica,2019,6.309,9.186,0.878,67.500,0.891,-0.137,0.885,0.752,0.195
+Japan,2005,6.516,10.529,0.928,73.200,0.868,,0.699,0.739,0.153
+Japan,2007,6.238,10.558,0.938,73.440,0.796,-0.090,0.809,0.731,0.207
+Japan,2008,5.911,10.546,0.887,73.560,0.772,-0.135,0.816,0.780,0.191
+Japan,2009,5.845,10.491,0.888,73.680,0.730,-0.210,0.740,0.785,0.169
+Japan,2010,6.057,10.532,0.902,73.800,0.772,-0.140,0.770,0.827,0.188
+Japan,2011,6.263,10.532,0.917,73.980,0.814,-0.052,0.734,0.776,0.181
+Japan,2012,5.968,10.549,0.905,74.160,0.753,,0.692,0.777,0.171
+Japan,2013,5.959,10.570,0.924,74.340,0.821,-0.147,0.650,0.794,0.175
+Japan,2014,5.923,10.575,0.900,74.520,0.838,-0.139,0.617,0.742,0.189
+Japan,2015,5.880,10.588,0.923,74.700,0.832,-0.155,0.654,0.768,0.176
+Japan,2016,5.955,10.595,0.900,74.800,0.836,-0.062,0.698,0.760,0.192
+Japan,2017,5.911,10.618,0.882,74.900,0.849,-0.206,0.659,0.740,0.176
+Japan,2018,5.794,10.623,0.886,75.000,0.773,-0.261,0.687,0.703,0.185
+Japan,2019,5.908,10.632,0.878,75.100,0.806,-0.255,0.617,0.743,0.194
+Japan,2020,6.118,10.580,0.887,75.200,0.806,-0.259,0.609,0.742,0.186
+Jordan,2005,6.295,9.246,0.920,63.500,,,0.670,0.696,0.240
+Jordan,2007,5.598,9.321,0.841,63.980,0.646,-0.112,0.664,0.683,0.240
+Jordan,2008,4.930,9.343,0.766,64.220,,-0.127,0.709,0.669,0.331
+Jordan,2009,6.000,9.347,0.899,64.460,0.771,-0.075,0.739,0.645,0.265
+Jordan,2010,5.570,9.317,0.918,64.700,0.788,-0.046,,0.643,0.343
+Jordan,2011,5.539,9.289,0.878,65.000,0.760,-0.143,,0.612,0.260
+Jordan,2012,5.132,9.261,0.829,65.300,0.693,-0.160,,0.565,0.345
+Jordan,2013,5.172,9.237,0.840,65.600,0.692,-0.117,,0.684,0.286
+Jordan,2014,5.333,9.222,0.816,65.900,0.729,-0.104,,0.660,0.313
+Jordan,2015,5.405,9.207,0.830,66.200,0.767,-0.046,,0.690,0.305
+Jordan,2016,5.271,9.197,0.820,66.400,0.771,-0.038,,0.641,0.312
+Jordan,2017,4.808,9.194,0.815,66.600,0.766,-0.152,,0.628,0.392
+Jordan,2018,4.639,9.196,0.800,66.800,0.762,-0.186,,,
+Jordan,2019,4.453,9.201,0.793,67.000,0.726,-0.165,,,
+Jordan,2020,4.094,9.150,0.709,67.200,0.779,-0.150,,,
+Kazakhstan,2006,5.476,9.804,0.872,58.200,0.731,-0.274,0.865,0.669,0.185
+Kazakhstan,2007,5.719,9.878,0.861,58.700,0.806,-0.246,0.865,0.651,0.179
+Kazakhstan,2008,5.886,9.898,0.839,59.200,0.727,-0.221,0.899,0.675,0.160
+Kazakhstan,2009,5.383,9.884,0.893,59.700,0.856,-0.249,0.845,0.679,0.129
+Kazakhstan,2010,5.514,9.940,0.904,60.200,0.785,-0.215,0.823,0.692,0.149
+Kazakhstan,2011,5.736,9.997,0.905,60.720,0.878,-0.235,0.802,0.695,0.154
+Kazakhstan,2012,5.759,10.030,0.892,61.240,0.840,-0.171,0.877,0.740,0.184
+Kazakhstan,2013,5.835,10.074,0.889,61.760,0.782,-0.229,0.820,0.674,0.164
+Kazakhstan,2014,5.970,10.101,0.795,62.280,0.799,0.004,0.805,0.718,0.169
+Kazakhstan,2015,5.950,10.098,0.931,62.800,0.740,-0.037,0.714,0.730,0.174
+Kazakhstan,2016,5.534,10.095,0.928,63.400,0.783,-0.036,0.702,0.702,0.155
+Kazakhstan,2017,5.882,10.121,0.914,64.000,0.745,-0.035,0.755,0.757,0.171
+Kazakhstan,2018,6.008,10.148,0.937,64.600,0.840,-0.098,0.824,0.693,0.162
+Kazakhstan,2019,6.272,10.179,0.951,65.200,0.852,-0.055,0.708,0.787,0.139
+Kazakhstan,2020,6.168,10.135,0.966,65.800,0.872,-0.056,0.661,0.684,0.150
+Kenya,2006,4.223,8.039,0.909,50.220,0.616,-0.020,0.860,0.705,0.198
+Kenya,2007,4.576,8.078,0.841,51.540,0.750,0.054,0.799,0.725,0.162
+Kenya,2008,4.015,8.052,0.827,52.860,0.620,-0.012,0.909,0.772,0.149
+Kenya,2009,4.270,8.057,0.789,54.180,0.584,0.100,0.913,0.772,0.183
+Kenya,2010,4.256,8.111,0.805,55.500,0.635,0.019,0.918,0.819,0.123
+Kenya,2011,4.405,8.143,0.846,56.060,0.709,0.022,0.923,0.760,0.228
+Kenya,2012,4.547,8.161,0.831,56.620,0.628,0.066,0.911,0.707,0.194
+Kenya,2013,3.795,8.192,0.825,57.180,0.708,0.212,0.861,0.765,0.161
+Kenya,2014,4.905,8.219,0.765,57.740,0.819,0.172,0.849,0.814,0.221
+Kenya,2015,4.358,8.249,0.777,58.300,0.793,0.221,0.853,0.702,0.172
+Kenya,2016,4.396,8.282,0.706,58.900,0.749,0.298,0.828,0.743,0.226
+Kenya,2017,4.476,8.306,0.715,59.500,0.853,0.234,0.854,0.788,0.230
+Kenya,2018,4.656,8.344,0.707,60.100,0.821,0.291,0.844,0.759,0.237
+Kenya,2019,4.619,8.373,0.676,60.700,0.818,0.310,0.794,0.751,0.251
+Kenya,2020,4.547,8.365,0.674,61.300,0.702,0.260,0.837,0.733,0.297
+Kosovo,2007,5.104,8.928,0.848,,0.381,0.144,0.894,0.655,0.237
+Kosovo,2008,5.522,8.981,0.884,,,0.090,0.849,,0.318
+Kosovo,2009,5.891,9.008,0.830,,0.506,0.201,0.968,0.598,0.169
+Kosovo,2010,5.177,9.033,0.708,,0.451,0.170,0.967,0.695,0.118
+Kosovo,2011,4.860,9.067,0.759,,0.589,0.004,0.919,0.696,0.124
+Kosovo,2012,5.640,9.086,0.757,,0.636,0.027,0.950,0.596,0.100
+Kosovo,2013,6.126,9.113,0.721,,0.568,0.115,0.935,0.692,0.203
+Kosovo,2014,5.000,9.129,0.706,,0.441,0.012,0.775,0.636,0.206
+Kosovo,2015,5.077,9.182,0.805,,0.561,0.181,0.851,0.753,0.180
+Kosovo,2016,5.759,9.228,0.824,,0.827,0.125,0.941,0.704,0.150
+Kosovo,2017,6.149,9.262,0.792,,0.858,0.117,0.925,0.738,0.186
+Kosovo,2018,6.392,9.296,0.822,,0.890,0.269,0.922,0.778,0.170
+Kosovo,2019,6.425,9.339,0.843,,0.841,0.247,0.920,0.749,0.141
+Kosovo,2020,6.294,,0.792,,0.880,,0.910,0.726,0.201
+Kuwait,2006,6.076,11.228,0.919,63.960,0.769,-0.236,0.328,0.846,0.182
+Kuwait,2009,6.585,11.065,0.926,64.440,0.819,0.007,0.675,0.718,0.252
+Kuwait,2010,6.798,10.982,0.893,64.600,0.703,-0.031,0.486,0.718,0.203
+Kuwait,2011,6.378,11.017,0.882,64.900,0.769,,0.560,0.793,0.177
+Kuwait,2012,6.221,11.025,0.889,65.200,0.934,,,0.821,0.095
+Kuwait,2013,6.480,10.985,0.862,65.500,0.751,,,0.752,0.283
+Kuwait,2014,6.180,10.945,,65.800,,,,,
+Kuwait,2015,6.146,10.912,0.823,66.100,0.822,0.082,,0.723,0.324
+Kuwait,2016,5.947,10.910,0.845,66.300,0.841,-0.075,,0.688,0.315
+Kuwait,2017,6.094,10.837,0.853,66.500,0.884,-0.005,,0.692,0.307
+Kuwait,2019,6.106,10.817,0.842,66.900,0.867,-0.104,,0.695,0.303
+Kyrgyzstan,2006,4.641,8.185,0.844,59.980,0.678,-0.140,0.879,0.655,0.159
+Kyrgyzstan,2007,4.698,8.258,0.833,60.260,0.684,-0.091,0.929,0.655,0.130
+Kyrgyzstan,2008,4.737,8.329,0.792,60.540,0.719,-0.100,0.923,0.623,0.147
+Kyrgyzstan,2009,5.069,8.345,0.855,60.820,0.699,-0.140,0.896,0.607,0.165
+Kyrgyzstan,2010,4.996,8.329,0.885,61.100,0.720,-0.072,0.926,0.650,0.123
+Kyrgyzstan,2011,4.921,8.374,0.891,61.520,0.748,-0.155,0.932,0.681,0.151
+Kyrgyzstan,2012,5.208,8.357,0.856,61.940,0.703,-0.079,0.892,0.691,0.182
+Kyrgyzstan,2013,5.402,8.441,0.851,62.360,0.755,-0.085,0.900,0.722,0.135
+Kyrgyzstan,2014,5.252,8.460,0.898,62.780,0.736,0.355,0.897,0.725,0.185
+Kyrgyzstan,2015,4.905,8.477,0.857,63.200,0.813,0.200,0.858,0.767,0.173
+Kyrgyzstan,2016,4.857,8.500,0.914,63.500,0.814,0.056,0.917,0.778,0.126
+Kyrgyzstan,2017,5.630,8.526,0.883,63.800,0.859,0.143,0.874,0.755,0.160
+Kyrgyzstan,2018,5.297,8.543,0.898,64.100,0.945,0.267,0.907,0.763,0.203
+Kyrgyzstan,2019,5.685,8.567,0.877,64.400,0.920,-0.002,0.885,0.766,0.207
+Kyrgyzstan,2020,6.250,8.503,0.902,64.700,0.935,0.103,0.931,0.803,0.258
+Laos,2006,5.076,8.251,0.807,53.920,0.925,0.439,0.688,0.886,0.163
+Laos,2007,5.364,8.307,0.790,54.440,0.867,0.478,0.580,0.861,0.136
+Laos,2008,5.044,8.366,0.807,54.960,0.886,0.416,0.637,0.829,0.202
+Laos,2011,4.704,8.548,0.691,56.300,0.882,0.459,0.587,0.900,0.225
+Laos,2012,4.876,8.611,0.693,56.600,,0.232,,0.917,0.387
+Laos,2017,4.623,8.890,0.707,58.300,0.891,0.073,0.592,0.873,0.344
+Laos,2018,4.859,8.935,0.705,58.700,0.907,0.141,0.634,0.852,0.332
+Laos,2019,5.197,8.965,0.729,59.100,0.906,0.061,0.620,0.878,0.306
+Laos,2020,5.284,8.960,0.660,59.500,0.915,0.141,0.748,0.822,0.358
+Latvia,2006,4.710,10.032,0.884,63.160,0.641,-0.229,0.937,0.654,0.234
+Latvia,2007,4.667,10.136,0.836,63.520,0.700,-0.167,0.924,0.673,0.247
+Latvia,2008,5.145,10.112,0.855,63.880,0.630,-0.203,0.926,0.639,0.215
+Latvia,2009,4.669,9.975,0.807,64.240,0.437,-0.180,0.942,0.525,0.242
+Latvia,2011,4.967,10.029,0.836,64.860,0.564,-0.002,0.934,0.563,0.222
+Latvia,2012,5.125,10.082,0.851,65.120,0.564,-0.038,0.895,0.560,0.232
+Latvia,2013,5.070,10.116,0.834,65.380,0.631,-0.073,0.837,0.642,0.227
+Latvia,2014,5.729,10.144,0.881,65.640,0.671,-0.043,0.804,0.652,0.226
+Latvia,2015,5.881,10.185,0.879,65.900,0.656,-0.077,0.808,0.608,0.228
+Latvia,2016,5.940,10.211,0.917,66.200,0.685,-0.156,0.868,0.654,0.231
+Latvia,2017,5.978,10.257,0.895,66.500,0.700,-0.154,0.798,0.623,0.232
+Latvia,2018,5.901,10.307,0.913,66.800,0.608,-0.212,0.799,0.585,0.192
+Latvia,2019,5.970,10.336,0.936,67.100,0.698,-0.194,0.789,0.575,0.212
+Latvia,2020,6.229,10.300,0.928,67.400,0.820,-0.078,0.809,0.714,0.202
+Lebanon,2005,5.491,9.565,0.796,64.600,0.703,,0.945,0.584,0.292
+Lebanon,2006,4.653,9.568,0.853,64.720,0.670,0.069,0.902,0.548,0.320
+Lebanon,2008,4.595,9.743,0.717,64.960,0.524,0.035,0.927,0.527,0.365
+Lebanon,2009,5.206,9.830,0.736,65.080,0.665,0.071,0.937,0.528,0.401
+Lebanon,2010,5.032,9.878,0.721,65.200,0.678,0.073,0.949,0.525,0.341
+Lebanon,2011,5.188,9.838,0.733,65.280,0.657,0.006,0.911,0.578,0.320
+Lebanon,2012,4.573,9.800,0.713,65.360,0.621,-0.006,0.856,0.499,0.339
+Lebanon,2013,4.983,9.772,0.708,65.440,0.655,-0.004,0.921,0.499,0.409
+Lebanon,2014,5.233,9.739,0.759,65.520,0.657,-0.012,0.939,0.559,0.267
+Lebanon,2015,5.172,9.699,0.742,65.600,0.597,0.073,0.889,0.568,0.243
+Lebanon,2016,5.271,9.687,0.828,66.100,0.657,0.031,0.853,0.553,0.263
+Lebanon,2017,5.154,9.681,0.777,66.600,0.605,-0.074,0.911,0.515,0.244
+Lebanon,2018,5.167,9.656,0.829,67.100,0.607,-0.066,0.907,0.464,0.271
+Lebanon,2019,4.024,9.597,0.866,67.600,0.447,-0.081,0.890,0.322,0.494
+Lesotho,2011,4.898,7.777,0.824,45.740,0.618,-0.087,0.768,0.793,0.170
+Lesotho,2016,3.808,7.953,0.798,46.600,0.729,-0.099,0.743,0.732,0.270
+Lesotho,2017,3.795,7.931,0.769,47.300,0.757,-0.145,0.797,0.746,0.255
+Lesotho,2019,3.512,7.926,0.790,48.700,0.716,-0.131,0.915,0.735,0.273
+Liberia,2007,3.701,7.196,0.594,49.140,0.790,0.115,0.776,0.613,0.435
+Liberia,2008,4.221,7.223,0.619,49.960,0.724,-0.035,0.840,0.585,0.261
+Liberia,2010,4.196,7.258,0.827,51.600,0.819,-0.038,0.818,0.595,0.217
+Liberia,2014,4.571,7.391,0.708,53.280,0.590,-0.030,0.869,0.543,0.443
+Liberia,2015,2.702,7.365,0.638,53.700,0.671,-0.061,0.903,0.505,0.388
+Liberia,2016,3.355,7.324,0.643,54.500,0.763,0.033,0.901,0.636,0.509
+Liberia,2017,4.424,7.324,0.685,55.300,0.733,-0.012,0.867,0.668,0.391
+Liberia,2018,4.135,7.311,0.727,56.100,0.766,0.050,0.868,0.660,0.436
+Liberia,2019,5.121,7.264,0.712,56.900,0.706,0.051,0.828,0.636,0.389
+Libya,2012,5.754,9.842,0.855,62.660,0.712,-0.032,0.791,0.695,0.316
+Libya,2015,5.615,9.308,0.868,62.300,0.775,-0.044,,0.704,0.369
+Libya,2016,5.434,9.268,0.876,62.300,0.822,-0.089,,0.718,0.383
+Libya,2017,5.647,9.491,0.823,62.300,0.779,-0.019,0.673,0.697,0.379
+Libya,2018,5.494,9.617,0.824,62.300,0.781,-0.101,0.646,0.706,0.399
+Libya,2019,5.330,9.627,0.827,62.300,0.762,-0.073,0.686,0.709,0.401
+Lithuania,2006,5.954,10.046,0.930,63.140,0.567,-0.295,0.967,0.621,0.254
+Lithuania,2007,5.808,10.163,0.941,63.480,0.590,-0.282,0.966,0.589,0.279
+Lithuania,2008,5.554,10.199,0.914,63.820,0.621,-0.259,0.961,0.533,0.276
+Lithuania,2009,5.467,10.050,0.933,64.160,0.496,-0.303,0.979,0.526,0.271
+Lithuania,2010,5.066,10.085,0.882,64.500,0.519,-0.275,0.962,0.473,0.272
+Lithuania,2011,5.432,10.167,0.911,64.700,0.566,-0.148,0.964,0.570,0.275
+Lithuania,2012,5.771,10.218,0.919,64.900,0.503,-0.273,0.957,0.581,0.277
+Lithuania,2013,5.596,10.263,0.913,65.100,0.556,-0.237,0.936,0.581,0.294
+Lithuania,2014,6.126,10.306,0.908,65.300,0.508,-0.263,0.956,0.619,0.287
+Lithuania,2015,5.711,10.335,0.929,65.500,0.641,-0.254,0.924,0.595,0.276
+Lithuania,2016,5.866,10.373,0.938,66.100,0.614,-0.266,0.949,0.594,0.250
+Lithuania,2017,6.273,10.429,0.926,66.700,0.749,-0.174,0.790,0.608,0.195
+Lithuania,2018,6.309,10.474,0.929,67.300,0.699,-0.237,0.852,0.517,0.214
+Lithuania,2019,6.064,10.518,0.918,67.900,0.780,-0.251,0.783,0.566,0.276
+Lithuania,2020,6.391,10.504,0.953,68.500,0.824,-0.122,0.829,0.660,0.202
+Luxembourg,2009,6.958,11.562,0.939,71.440,0.939,0.127,0.432,0.799,0.238
+Luxembourg,2010,7.097,11.592,0.952,71.700,0.908,0.096,0.423,0.809,0.216
+Luxembourg,2011,7.101,11.595,0.934,71.880,0.962,0.106,0.388,0.836,0.200
+Luxembourg,2012,6.964,11.567,0.914,72.060,0.917,0.059,0.403,0.815,0.227
+Luxembourg,2013,7.131,11.580,0.917,72.240,0.790,-0.054,0.301,0.640,0.185
+Luxembourg,2014,6.891,11.598,0.875,72.420,0.938,0.106,0.366,0.803,0.170
+Luxembourg,2015,6.702,11.617,0.934,72.600,0.932,0.052,0.375,0.757,0.193
+Luxembourg,2016,6.967,11.640,0.941,72.600,0.882,0.019,0.356,0.758,0.192
+Luxembourg,2017,7.061,11.634,0.905,72.600,0.903,0.044,0.330,0.766,0.184
+Luxembourg,2018,7.243,11.645,0.902,72.600,0.884,-0.022,0.385,0.750,0.202
+Luxembourg,2019,7.404,11.648,0.912,72.600,0.930,-0.045,0.390,0.789,0.212
+Madagascar,2006,3.980,7.376,0.711,54.040,,-0.039,,0.702,0.161
+Madagascar,2008,4.640,7.439,0.776,54.920,0.332,-0.099,0.773,0.614,0.215
+Madagascar,2011,4.381,7.336,0.818,56.220,0.546,-0.062,0.897,0.510,0.235
+Madagascar,2012,3.551,7.339,0.673,56.640,0.487,-0.055,0.854,0.690,0.194
+Madagascar,2013,3.816,7.334,0.673,57.060,0.480,-0.019,0.868,0.734,0.241
+Madagascar,2014,3.676,7.340,0.655,57.480,0.529,-0.023,0.791,0.748,0.192
+Madagascar,2015,3.593,7.344,0.647,57.900,0.545,-0.041,0.861,0.802,0.226
+Madagascar,2016,3.663,7.356,0.746,58.300,0.570,-0.069,0.864,0.813,0.204
+Madagascar,2017,4.079,7.368,0.626,58.700,0.570,-0.033,0.847,0.752,0.375
+Madagascar,2018,4.071,7.386,0.666,59.100,0.551,0.003,0.889,0.752,0.362
+Madagascar,2019,4.339,7.406,0.701,59.500,0.550,-0.012,0.720,0.723,0.304
+Malawi,2006,3.830,6.678,0.554,44.880,0.767,0.200,0.676,0.670,0.222
+Malawi,2007,4.891,6.742,0.600,46.260,0.910,0.202,0.691,0.727,0.176
+Malawi,2009,5.148,6.838,0.718,49.020,0.879,0.176,0.689,0.765,0.130
+Malawi,2011,3.946,6.895,0.613,51.420,0.733,0.099,0.853,0.713,0.268
+Malawi,2012,4.279,6.885,0.604,52.440,0.637,0.169,0.886,0.816,0.200
+Malawi,2013,4.035,6.907,0.563,53.460,0.752,0.078,0.857,0.808,0.248
+Malawi,2014,4.563,6.935,0.512,54.480,0.786,0.061,0.824,0.704,0.263
+Malawi,2015,3.868,6.935,0.494,55.500,0.801,0.058,0.835,0.633,0.260
+Malawi,2016,3.476,6.932,0.524,56.200,0.810,0.066,0.824,0.603,0.325
+Malawi,2017,3.417,6.945,0.555,56.900,0.848,0.025,0.735,0.609,0.312
+Malawi,2018,3.335,6.949,0.528,57.600,0.799,0.073,0.766,0.586,0.365
+Malawi,2019,3.869,6.966,0.549,58.300,0.765,0.004,0.680,0.537,0.348
+Malaysia,2006,6.012,9.839,0.866,64.960,0.837,0.201,0.740,0.750,0.243
+Malaysia,2007,6.239,9.881,0.871,65.120,0.844,0.089,0.799,0.775,0.162
+Malaysia,2008,5.807,9.909,0.803,65.280,0.780,0.044,0.884,0.815,0.186
+Malaysia,2009,5.385,9.875,0.792,65.440,0.874,-0.009,0.858,0.822,0.164
+Malaysia,2010,5.580,9.930,0.839,65.600,0.769,0.032,0.844,0.832,0.192
+Malaysia,2011,5.786,9.966,0.770,65.760,0.840,-0.016,0.842,0.887,0.155
+Malaysia,2012,5.914,10.005,0.841,65.920,0.848,0.017,0.847,0.867,0.177
+Malaysia,2013,5.770,10.037,0.831,66.080,0.791,0.264,0.755,0.736,0.317
+Malaysia,2014,5.963,10.082,0.863,66.240,0.808,0.239,0.845,0.770,0.261
+Malaysia,2015,6.322,10.118,0.818,66.400,0.675,0.222,0.838,0.775,0.314
+Malaysia,2018,5.339,10.223,0.789,67.000,0.875,0.127,0.894,0.824,0.200
+Malaysia,2019,5.428,10.252,0.842,67.200,0.916,0.123,0.782,0.834,0.176
+Maldives,2018,5.198,9.826,0.913,70.600,0.855,0.024,,,
+Mali,2006,4.014,7.593,0.761,45.920,0.555,-0.072,0.761,0.767,0.209
+Mali,2008,4.115,7.607,0.747,47.160,0.495,-0.012,0.918,0.682,0.164
+Mali,2009,3.977,7.622,0.733,47.780,0.634,0.008,0.819,0.760,0.150
+Mali,2010,3.762,7.642,0.751,48.400,0.749,-0.028,0.811,0.797,0.162
+Mali,2011,4.667,7.643,0.796,48.760,0.823,-0.101,0.726,0.758,0.132
+Mali,2012,4.313,7.605,0.823,49.120,0.704,-0.088,0.787,0.681,0.109
+Mali,2013,3.676,7.599,0.820,49.480,0.665,-0.053,0.755,0.724,0.193
+Mali,2014,3.975,7.638,0.843,49.840,0.652,-0.037,0.658,0.741,0.186
+Mali,2015,4.582,7.669,0.830,50.200,0.634,-0.068,0.800,0.709,0.243
+Mali,2016,4.016,7.695,0.836,50.700,0.696,-0.070,0.862,0.807,0.305
+Mali,2017,4.742,7.718,0.741,51.200,0.753,-0.069,0.863,0.742,0.393
+Mali,2018,4.416,7.733,0.692,51.700,0.737,-0.034,0.793,0.770,0.370
+Mali,2019,4.988,7.752,0.755,52.200,0.670,-0.038,0.846,0.712,0.358
+Malta,2009,6.328,10.331,0.916,71.380,0.803,0.464,,0.715,0.358
+Malta,2010,5.774,10.361,0.908,71.600,0.802,0.287,,0.697,0.375
+Malta,2011,6.155,10.370,0.923,71.720,0.882,0.296,,0.736,0.340
+Malta,2012,5.963,10.389,0.922,71.840,0.861,0.352,,0.744,0.391
+Malta,2013,6.380,10.422,0.942,71.960,0.909,0.410,,0.660,0.370
+Malta,2014,6.452,10.486,0.941,72.080,0.904,0.404,0.670,0.652,0.352
+Malta,2015,6.613,10.566,0.919,72.200,0.912,0.347,0.664,0.680,0.355
+Malta,2016,6.591,10.599,0.930,72.200,0.916,0.345,0.696,0.687,0.355
+Malta,2017,6.676,10.635,0.937,72.200,0.924,0.253,0.690,0.721,0.302
+Malta,2018,6.910,10.670,0.932,72.200,0.927,0.179,0.595,0.721,0.296
+Malta,2019,6.733,10.677,0.922,72.200,0.924,0.087,0.689,0.707,0.356
+Malta,2020,6.157,,0.938,72.200,0.931,,0.675,0.601,0.411
+Mauritania,2007,4.149,8.533,0.682,53.660,0.573,-0.072,0.586,0.733,0.174
+Mauritania,2008,4.248,8.501,0.670,53.940,0.593,-0.018,0.841,0.732,0.176
+Mauritania,2009,4.500,8.473,0.819,54.220,0.735,0.039,0.848,0.738,0.170
+Mauritania,2010,4.772,8.470,0.857,54.500,0.669,0.055,0.727,0.778,0.129
+Mauritania,2011,4.785,8.481,0.750,54.820,0.567,0.052,0.747,0.762,0.175
+Mauritania,2012,4.673,8.495,0.763,55.140,0.487,-0.021,0.707,0.782,0.164
+Mauritania,2013,4.199,8.506,0.741,55.460,0.603,-0.079,0.676,0.793,0.196
+Mauritania,2014,4.483,8.519,0.853,55.780,0.468,-0.054,0.589,0.755,0.163
+Mauritania,2015,3.923,8.542,0.875,56.100,0.447,0.055,0.715,0.820,0.194
+Mauritania,2016,4.472,8.526,0.785,56.400,0.467,-0.175,0.842,0.735,0.222
+Mauritania,2017,4.678,8.533,0.779,56.700,0.527,-0.153,0.777,0.637,0.272
+Mauritania,2018,4.314,8.526,0.802,57.000,0.467,-0.112,0.711,0.663,0.276
+Mauritania,2019,4.153,8.556,0.798,57.300,0.628,-0.102,0.743,0.692,0.260
+Mauritius,2011,5.477,9.767,0.800,64.700,0.848,0.191,0.847,0.738,0.253
+Mauritius,2014,5.648,9.865,0.785,65.300,0.824,0.176,0.879,0.808,0.222
+Mauritius,2016,5.610,9.935,0.836,65.800,0.819,0.139,0.891,0.785,0.246
+Mauritius,2017,6.174,9.972,0.910,66.100,0.912,0.087,0.818,0.748,0.169
+Mauritius,2018,5.882,10.008,0.909,66.400,0.867,-0.073,0.785,0.774,0.158
+Mauritius,2019,6.241,10.043,0.913,66.700,0.893,-0.053,0.810,0.808,0.149
+Mauritius,2020,6.015,9.972,0.893,67.000,0.843,-0.037,0.772,0.767,0.138
+Mexico,2005,6.581,9.788,0.903,66.200,0.814,,0.764,0.820,0.219
+Mexico,2007,6.525,9.825,0.879,66.320,0.670,-0.095,0.747,0.816,0.248
+Mexico,2008,6.829,9.821,0.876,66.380,0.677,-0.128,0.785,0.825,0.201
+Mexico,2009,6.963,9.752,0.868,66.440,0.682,-0.076,0.764,0.849,0.196
+Mexico,2010,6.802,9.788,0.876,66.500,0.778,-0.048,0.693,0.840,0.215
+Mexico,2011,6.910,9.810,0.824,66.680,0.831,-0.099,0.698,0.790,0.228
+Mexico,2012,7.320,9.832,0.767,66.860,0.788,-0.093,0.633,0.784,0.278
+Mexico,2013,7.443,9.832,0.759,67.040,0.739,-0.165,0.615,0.790,0.223
+Mexico,2014,6.680,9.847,0.782,67.220,0.779,-0.094,0.630,0.802,0.229
+Mexico,2015,6.236,9.867,0.761,67.400,0.719,-0.152,0.708,0.745,0.237
+Mexico,2016,6.824,9.884,0.893,67.700,0.752,-0.153,0.809,0.859,0.220
+Mexico,2017,6.410,9.893,0.800,68.000,0.861,-0.202,0.801,0.843,0.231
+Mexico,2018,6.550,9.903,0.858,68.300,0.816,-0.179,0.809,0.882,0.213
+Mexico,2019,6.432,9.891,0.852,68.600,0.903,-0.141,0.809,0.864,0.252
+Mexico,2020,5.964,9.782,0.779,68.900,0.873,-0.119,0.778,0.810,0.292
+Moldova,2006,5.102,8.936,0.812,60.580,0.554,-0.164,0.926,0.619,0.255
+Moldova,2007,4.775,8.968,0.804,60.760,0.696,-0.186,0.930,0.571,0.306
+Moldova,2008,5.503,9.045,0.872,60.940,0.641,-0.056,0.926,0.584,0.284
+Moldova,2009,5.554,8.984,0.856,61.120,0.551,-0.099,0.925,0.562,0.306
+Moldova,2010,5.590,9.054,0.847,61.300,0.598,-0.088,0.929,0.584,0.278
+Moldova,2011,5.792,9.111,0.869,61.620,0.628,-0.082,0.957,0.568,0.285
+Moldova,2012,5.996,9.105,0.826,61.940,0.602,-0.050,0.955,0.568,0.314
+Moldova,2013,5.756,9.192,0.803,62.260,0.658,-0.069,0.941,0.582,0.261
+Moldova,2014,5.917,9.241,0.805,62.580,0.623,-0.113,0.925,0.583,0.260
+Moldova,2015,6.017,9.246,0.840,62.900,0.595,-0.090,0.943,0.590,0.281
+Moldova,2016,5.578,9.300,0.837,63.600,0.557,-0.047,0.969,0.621,0.275
+Moldova,2017,5.326,9.363,0.831,64.300,0.553,-0.053,0.926,0.581,0.259
+Moldova,2018,5.682,9.423,0.892,65.000,0.824,-0.084,0.929,0.582,0.270
+Moldova,2019,5.803,9.475,0.809,65.700,0.784,-0.092,0.884,0.631,0.262
+Moldova,2020,5.812,9.462,0.874,66.400,0.859,-0.058,0.941,0.727,0.268
+Mongolia,2007,4.609,8.833,0.881,58.820,0.781,0.064,0.918,0.571,0.203
+Mongolia,2008,4.493,8.904,0.920,59.280,0.484,0.068,0.962,0.586,0.173
+Mongolia,2010,4.586,8.920,0.904,60.200,0.631,0.099,0.928,0.712,0.150
+Mongolia,2011,5.031,9.061,0.948,60.500,0.700,0.151,0.931,0.692,0.153
+Mongolia,2012,4.885,9.158,0.919,60.800,0.688,0.107,0.932,0.689,0.181
+Mongolia,2013,4.913,9.248,0.935,61.100,0.748,0.136,0.928,0.649,0.179
+Mongolia,2014,4.825,9.304,0.943,61.400,0.752,0.146,0.909,0.627,0.170
+Mongolia,2015,4.983,9.308,0.906,61.700,0.686,0.173,0.900,0.653,0.208
+Mongolia,2016,5.057,9.300,0.947,61.900,0.760,0.090,0.900,0.694,0.171
+Mongolia,2017,5.334,9.334,0.924,62.100,0.675,0.119,0.865,0.675,0.214
+Mongolia,2018,5.465,9.386,0.942,62.300,0.696,0.054,0.849,0.654,0.192
+Mongolia,2019,5.563,9.418,0.946,62.500,0.711,0.149,0.873,0.707,0.167
+Mongolia,2020,6.011,9.396,0.918,62.700,0.718,0.141,0.843,0.636,0.260
+Montenegro,2007,5.196,9.693,0.832,66.200,0.512,-0.134,0.815,0.579,0.340
+Montenegro,2009,4.801,9.699,0.816,66.800,0.556,-0.101,0.838,0.623,0.423
+Montenegro,2010,5.455,9.724,0.805,67.100,0.552,-0.206,0.757,0.595,0.410
+Montenegro,2011,5.223,9.755,0.818,67.260,0.546,-0.226,0.762,0.603,0.378
+Montenegro,2012,5.219,9.726,0.704,67.420,0.462,-0.192,0.755,0.574,0.379
+Montenegro,2013,5.074,9.760,0.736,67.580,0.502,-0.176,0.693,0.539,0.331
+Montenegro,2014,5.283,9.777,0.863,67.740,0.503,0.097,0.768,0.587,0.368
+Montenegro,2015,5.125,9.810,0.740,67.900,0.583,-0.144,0.781,0.580,0.337
+Montenegro,2016,5.304,9.839,0.866,68.100,0.569,-0.087,0.849,0.591,0.337
+Montenegro,2017,5.615,9.885,0.881,68.300,0.626,-0.083,0.756,0.519,0.350
+Montenegro,2018,5.650,9.934,0.856,68.500,0.626,-0.051,0.769,0.590,0.355
+Montenegro,2019,5.386,9.970,0.832,68.700,0.694,-0.105,0.820,0.591,0.366
+Montenegro,2020,5.722,9.913,0.887,68.900,0.802,0.060,0.845,0.603,0.411
+Morocco,2010,4.383,8.746,,63.500,0.663,-0.162,0.900,,
+Morocco,2011,5.085,8.783,0.833,63.800,0.579,-0.218,0.875,0.736,0.187
+Morocco,2012,4.970,8.799,0.676,64.100,0.757,-0.187,0.845,0.687,0.281
+Morocco,2013,5.142,8.829,0.597,64.400,0.572,-0.210,0.771,0.784,0.239
+Morocco,2015,5.163,8.872,0.606,65.000,0.713,-0.228,0.842,0.661,0.262
+Morocco,2016,5.386,8.869,0.655,65.300,0.817,-0.237,0.717,0.713,0.205
+Morocco,2017,5.312,8.898,0.641,65.600,0.814,-0.216,0.841,0.559,0.323
+Morocco,2018,4.897,8.914,0.554,65.900,0.773,-0.234,0.843,0.638,0.416
+Morocco,2019,5.057,8.925,0.535,66.200,0.757,-0.244,0.757,0.589,0.410
+Morocco,2020,4.803,8.871,0.553,66.500,0.819,-0.229,0.803,0.587,0.256
+Mozambique,2006,4.595,6.776,0.879,44.800,0.684,0.041,0.758,0.623,0.327
+Mozambique,2007,4.833,6.823,0.748,45.500,0.643,0.074,0.854,0.634,0.240
+Mozambique,2008,4.654,6.865,0.756,46.200,0.514,0.006,0.864,0.623,0.280
+Mozambique,2011,4.971,6.979,0.818,48.320,0.639,-0.024,0.719,0.592,0.243
+Mozambique,2015,4.550,7.141,0.666,51.200,0.813,0.089,0.632,0.564,0.340
+Mozambique,2017,4.280,7.157,0.678,53.200,0.823,-0.030,0.682,0.648,0.353
+Mozambique,2018,4.654,7.162,0.738,54.200,0.897,0.049,0.691,0.640,0.397
+Mozambique,2019,4.932,7.155,0.742,55.200,0.870,0.073,0.682,0.587,0.384
+Myanmar,2012,4.439,8.158,0.612,57.020,0.691,0.645,0.695,0.764,0.205
+Myanmar,2013,4.176,8.230,0.757,57.380,0.775,0.689,0.638,0.803,0.217
+Myanmar,2014,4.786,8.299,0.774,57.740,,0.698,0.592,0.858,0.112
+Myanmar,2015,4.224,8.359,0.752,58.100,0.808,0.688,0.633,0.866,0.272
+Myanmar,2016,4.623,8.408,0.793,58.400,0.877,0.679,0.607,0.804,0.302
+Myanmar,2017,4.154,8.464,0.795,58.700,0.886,0.650,0.619,0.746,0.282
+Myanmar,2018,4.411,8.523,0.774,59.000,0.906,0.490,0.647,0.777,0.300
+Myanmar,2019,4.434,8.545,0.763,59.300,0.899,0.561,0.682,0.755,0.286
+Myanmar,2020,4.431,8.554,0.796,59.600,0.825,0.470,0.647,0.800,0.289
+Namibia,2007,4.886,9.059,0.828,49.680,0.781,-0.101,0.839,0.811,0.160
+Namibia,2014,4.574,9.232,0.763,55.160,0.849,-0.183,0.790,0.749,0.239
+Namibia,2017,4.441,9.215,0.828,56.200,0.810,-0.190,0.831,0.721,0.277
+Namibia,2018,4.834,9.204,0.864,56.500,0.754,-0.169,0.846,0.739,0.240
+Namibia,2019,4.436,9.173,0.845,56.800,0.739,-0.174,0.879,0.672,0.256
+Namibia,2020,4.451,9.104,0.741,57.100,0.666,-0.104,0.810,0.648,0.248
+Nepal,2006,4.567,7.616,0.874,57.200,0.689,,0.897,0.717,0.171
+Nepal,2007,4.748,7.638,0.787,57.700,0.413,0.317,0.891,0.643,0.152
+Nepal,2008,4.441,7.686,0.818,58.200,0.618,0.291,0.900,0.745,0.153
+Nepal,2009,4.917,7.723,0.813,58.700,0.616,0.044,0.950,0.570,0.215
+Nepal,2010,4.350,7.765,0.779,59.200,0.519,0.092,0.911,0.672,0.226
+Nepal,2011,3.809,7.797,0.741,59.400,0.525,-0.010,0.935,0.699,0.207
+Nepal,2012,4.233,7.846,0.734,59.600,0.638,0.070,0.883,0.736,0.231
+Nepal,2013,4.605,7.889,0.740,59.800,0.722,0.151,0.877,0.629,0.279
+Nepal,2014,4.975,7.948,0.786,60.000,0.712,0.121,0.841,0.614,0.287
+Nepal,2015,4.812,7.976,0.748,60.200,0.763,0.227,0.824,0.543,0.358
+Nepal,2016,5.100,7.973,0.837,61.300,0.839,0.168,0.817,0.627,0.370
+Nepal,2017,4.737,8.039,0.816,62.400,0.845,0.134,0.770,0.571,0.376
+Nepal,2018,4.910,8.087,0.768,63.500,0.770,0.122,0.742,0.537,0.387
+Nepal,2019,5.449,8.136,0.772,64.600,0.790,0.167,0.712,0.536,0.357
+Netherlands,2005,7.464,10.814,0.947,70.400,0.901,,0.571,0.869,0.233
+Netherlands,2007,7.452,10.881,0.944,70.800,0.896,0.344,0.445,0.818,0.213
+Netherlands,2008,7.631,10.899,0.944,71.000,0.883,0.365,0.419,0.788,0.182
+Netherlands,2010,7.502,10.864,0.957,71.400,0.921,0.349,0.399,0.853,0.206
+Netherlands,2011,7.564,10.875,0.938,71.520,0.925,0.336,0.359,0.863,0.181
+Netherlands,2012,7.471,10.861,0.939,71.640,0.877,0.288,0.434,0.861,0.226
+Netherlands,2013,7.407,10.857,0.925,71.760,0.919,0.305,0.505,0.867,0.235
+Netherlands,2014,7.321,10.867,0.909,71.880,0.910,0.331,0.457,0.868,0.221
+Netherlands,2015,7.324,10.882,0.879,72.000,0.904,0.261,0.412,0.834,0.202
+Netherlands,2016,7.541,10.899,0.926,72.100,0.907,0.239,0.433,0.838,0.215
+Netherlands,2017,7.459,10.921,0.937,72.200,0.920,0.250,0.363,0.852,0.185
+Netherlands,2018,7.463,10.941,0.939,72.300,0.920,0.161,0.371,0.862,0.205
+Netherlands,2019,7.425,10.953,0.941,72.400,0.886,0.213,0.360,0.838,0.231
+Netherlands,2020,7.504,10.901,0.944,72.500,0.935,0.151,0.281,0.784,0.247
+New Zealand,2006,7.305,10.526,0.946,71.200,0.932,0.312,0.224,0.880,0.219
+New Zealand,2007,7.604,10.546,0.967,71.400,0.878,0.279,0.295,0.854,0.238
+New Zealand,2008,7.381,10.528,0.944,71.600,0.893,0.298,0.334,0.854,0.232
+New Zealand,2010,7.224,10.520,0.976,72.000,0.918,0.254,0.321,0.847,0.235
+New Zealand,2011,7.191,10.536,0.954,72.120,0.935,0.284,0.269,0.864,0.210
+New Zealand,2012,7.250,10.553,0.930,72.240,0.902,0.287,0.289,0.866,0.207
+New Zealand,2013,7.280,10.571,0.958,72.360,0.944,0.237,0.312,0.835,0.151
+New Zealand,2014,7.306,10.592,0.942,72.480,0.932,0.348,0.273,0.848,0.199
+New Zealand,2015,7.418,10.608,0.987,72.600,0.942,0.329,0.186,0.834,0.160
+New Zealand,2016,7.226,10.623,0.937,72.800,0.927,0.266,0.278,0.833,0.207
+New Zealand,2017,7.327,10.633,0.955,73.000,0.942,0.294,0.222,0.817,0.172
+New Zealand,2018,7.370,10.660,0.954,73.200,0.949,0.120,0.207,0.845,0.168
+New Zealand,2019,7.205,10.666,0.939,73.400,0.912,0.157,0.234,0.816,0.191
+New Zealand,2020,7.257,10.600,0.952,73.600,0.918,0.125,0.283,0.849,0.209
+Nicaragua,2006,4.460,8.398,0.877,64.140,0.745,0.010,0.844,0.779,0.294
+Nicaragua,2007,4.944,8.434,0.866,64.480,0.836,0.140,0.826,0.810,0.287
+Nicaragua,2008,5.104,8.454,0.857,64.820,0.791,0.076,0.819,0.784,0.289
+Nicaragua,2009,5.353,8.407,0.835,65.160,0.746,0.070,0.794,0.781,0.299
+Nicaragua,2010,5.687,8.436,0.863,65.500,0.792,0.018,0.802,0.805,0.268
+Nicaragua,2011,5.386,8.484,0.800,65.720,0.779,-0.020,0.760,0.791,0.309
+Nicaragua,2012,5.448,8.534,0.894,65.940,0.850,0.017,0.644,0.803,0.255
+Nicaragua,2013,5.772,8.569,0.868,66.160,0.859,0.039,0.636,0.839,0.271
+Nicaragua,2014,6.275,8.602,0.839,66.380,0.817,0.104,0.699,0.813,0.334
+Nicaragua,2015,5.924,8.636,0.827,66.600,0.809,0.077,0.728,0.797,0.346
+Nicaragua,2016,6.013,8.668,0.853,66.900,0.717,0.039,0.731,0.805,0.380
+Nicaragua,2017,6.476,8.700,0.838,67.200,0.922,0.010,0.673,0.850,0.308
+Nicaragua,2018,5.819,8.647,0.854,67.500,0.797,0.009,0.713,0.793,0.408
+Nicaragua,2019,6.113,8.595,0.874,67.800,0.883,0.029,0.622,0.835,0.337
+Niger,2006,3.737,6.888,0.677,46.360,0.750,0.076,0.755,0.755,0.179
+Niger,2007,4.277,6.881,0.726,47.120,0.584,-0.056,0.748,0.706,0.158
+Niger,2008,4.236,6.918,0.607,47.880,0.649,-0.055,0.749,0.650,0.194
+Niger,2009,4.267,6.898,0.771,48.640,0.880,-0.009,0.483,0.730,0.115
+Niger,2010,4.101,6.941,0.655,49.400,0.817,-0.023,0.529,0.745,0.126
+Niger,2011,4.556,6.925,0.818,49.920,0.780,-0.055,0.549,0.700,0.166
+Niger,2012,3.798,6.987,0.700,50.440,0.734,-0.064,0.777,0.603,0.142
+Niger,2013,3.716,7.002,0.696,50.960,0.825,-0.077,0.711,0.650,0.208
+Niger,2014,4.181,7.027,0.753,51.480,0.688,-0.046,0.605,0.678,0.205
+Niger,2015,3.671,7.030,0.713,52.000,0.728,-0.032,0.703,0.682,0.218
+Niger,2016,4.235,7.047,0.683,52.500,0.702,-0.016,0.814,0.675,0.325
+Niger,2017,4.616,7.058,0.582,53.000,0.684,-0.030,0.778,0.731,0.427
+Niger,2018,5.164,7.087,0.612,53.500,0.791,0.009,0.637,0.771,0.503
+Niger,2019,5.004,7.106,0.677,54.000,0.831,0.026,0.729,0.816,0.304
+Nigeria,2006,4.710,8.326,0.735,44.120,0.649,0.085,0.871,0.781,0.178
+Nigeria,2007,4.890,8.364,0.718,44.640,0.635,0.136,0.918,0.826,0.141
+Nigeria,2008,4.939,8.403,0.780,45.160,0.584,0.119,0.892,0.740,0.244
+Nigeria,2009,4.980,8.453,0.722,45.680,0.537,0.067,0.913,0.745,0.225
+Nigeria,2010,4.760,8.504,0.824,46.200,0.565,0.067,0.911,0.782,0.190
+Nigeria,2012,5.493,8.543,0.818,47.120,0.652,0.066,0.900,0.811,0.209
+Nigeria,2013,4.818,8.581,0.663,47.580,0.622,0.051,0.905,0.638,0.286
+Nigeria,2015,4.933,8.615,0.812,48.500,0.680,-0.035,0.926,0.717,0.251
+Nigeria,2016,5.220,8.573,0.805,48.900,0.798,0.043,0.905,0.732,0.252
+Nigeria,2017,5.322,8.555,0.733,49.300,0.826,0.124,0.835,0.725,0.236
+Nigeria,2018,5.252,8.548,0.741,49.700,0.790,-0.010,0.866,0.805,0.256
+Nigeria,2019,4.356,8.544,0.734,50.100,0.729,0.032,0.873,0.715,0.245
+Nigeria,2020,5.503,8.484,0.739,50.500,0.713,0.099,0.913,0.744,0.316
+North Cyprus,2012,5.463,,0.871,,0.693,,0.855,0.709,0.405
+North Cyprus,2013,5.567,,0.869,,0.775,,0.715,0.622,0.443
+North Cyprus,2014,5.786,,0.802,,0.830,,0.692,0.724,0.311
+North Cyprus,2015,5.843,,0.791,,0.785,,0.659,0.702,0.319
+North Cyprus,2016,5.827,,0.808,,0.796,,0.670,0.644,0.346
+North Cyprus,2018,5.608,,0.837,,0.797,,0.614,0.480,0.262
+North Cyprus,2019,5.467,,0.803,,0.793,,0.640,0.494,0.296
+North Macedonia,2007,4.494,9.416,0.811,64.095,0.439,0.080,0.870,0.603,0.251
+North Macedonia,2009,4.428,9.464,0.734,64.349,0.552,-0.042,0.844,0.576,0.370
+North Macedonia,2010,4.180,9.496,0.687,64.502,0.513,-0.058,0.856,0.567,0.314
+North Macedonia,2011,4.898,9.518,0.784,64.661,0.607,-0.087,0.865,0.588,0.363
+North Macedonia,2012,4.640,9.513,0.798,64.811,0.613,-0.084,0.920,0.642,0.422
+North Macedonia,2013,5.186,9.541,0.832,64.942,0.641,0.025,0.861,0.578,0.331
+North Macedonia,2014,5.204,9.576,0.793,65.053,0.645,0.035,0.861,0.637,0.307
+North Macedonia,2015,4.976,9.613,0.766,65.145,0.660,-0.047,0.824,0.620,0.299
+North Macedonia,2016,5.346,9.640,0.871,65.225,0.706,0.080,0.870,0.639,0.292
+North Macedonia,2017,5.234,9.650,0.800,65.303,0.752,-0.059,0.856,0.502,0.299
+North Macedonia,2018,5.240,9.677,0.849,65.389,0.745,-0.041,0.910,0.590,0.298
+North Macedonia,2019,5.015,9.711,0.815,65.474,0.725,0.024,0.923,0.576,0.304
+North Macedonia,2020,5.054,9.690,0.750,65.560,0.787,0.131,0.877,0.605,0.365
+Norway,2006,7.416,11.031,0.959,71.320,0.960,0.109,0.397,0.832,0.197
+Norway,2008,7.632,11.042,0.936,71.560,0.947,0.018,0.503,0.792,0.155
+Norway,2012,7.678,11.017,0.948,72.240,0.947,0.147,0.368,0.823,0.213
+Norway,2014,7.444,11.024,0.941,72.680,0.956,0.181,0.405,0.834,0.194
+Norway,2015,7.603,11.033,0.947,72.900,0.948,0.257,0.299,0.843,0.209
+Norway,2016,7.596,11.035,0.960,73.000,0.954,0.133,0.410,0.850,0.209
+Norway,2017,7.579,11.050,0.950,73.100,0.953,0.236,0.250,0.849,0.203
+Norway,2018,7.444,11.056,0.966,73.200,0.960,0.094,0.268,0.827,0.212
+Norway,2019,7.442,11.061,0.942,73.300,0.954,0.111,0.271,0.823,0.195
+Norway,2020,7.290,11.042,0.956,73.400,0.965,0.075,0.271,0.823,0.216
+Oman,2011,6.853,10.382,,65.500,0.916,0.025,,,0.295
+Pakistan,2005,5.225,8.218,0.591,54.200,0.630,,0.844,,0.237
+Pakistan,2007,5.671,8.277,0.479,55.000,0.396,0.089,0.794,0.683,0.310
+Pakistan,2008,4.414,8.271,0.373,55.400,0.335,0.100,0.848,0.655,0.321
+Pakistan,2009,5.208,8.277,0.522,55.800,0.388,0.077,0.874,0.639,0.349
+Pakistan,2010,5.786,8.270,0.571,56.200,0.364,0.300,0.852,0.651,0.372
+Pakistan,2011,5.267,8.276,0.510,56.420,0.376,0.030,0.857,0.628,0.358
+Pakistan,2012,5.132,8.289,0.542,56.640,0.367,0.165,0.842,0.665,0.332
+Pakistan,2013,5.138,8.311,0.607,56.860,0.448,0.100,0.792,0.598,0.274
+Pakistan,2014,5.436,8.336,0.552,57.080,0.543,0.140,0.677,0.585,0.295
+Pakistan,2015,4.823,8.361,0.562,57.300,0.587,0.085,0.717,0.575,0.329
+Pakistan,2016,5.549,8.394,0.627,57.700,0.634,0.095,0.793,0.648,0.332
+Pakistan,2017,5.831,8.428,0.690,58.100,0.713,0.045,0.714,0.586,0.308
+Pakistan,2018,5.472,8.464,0.685,58.500,0.773,0.069,0.799,0.567,0.377
+Pakistan,2019,4.443,8.453,0.617,58.900,0.685,0.124,0.776,0.581,0.424
+Palestinian Territories,2006,4.716,8.213,0.818,61.780,0.547,,0.858,0.497,0.431
+Palestinian Territories,2007,4.151,8.218,0.712,61.897,0.365,-0.080,0.844,0.566,0.412
+Palestinian Territories,2008,4.386,8.276,0.666,62.015,0.358,-0.070,0.753,0.571,0.403
+Palestinian Territories,2009,4.470,8.329,0.738,62.132,0.468,-0.085,0.797,0.544,0.466
+Palestinian Territories,2010,4.703,8.383,0.822,62.250,0.504,-0.117,0.752,0.628,0.381
+Palestinian Territories,2011,4.751,8.474,0.751,,0.522,-0.127,0.750,0.567,0.388
+Palestinian Territories,2012,4.647,8.531,0.782,,0.542,-0.153,0.730,0.616,0.379
+Palestinian Territories,2013,4.844,8.489,0.761,,0.454,-0.150,0.780,0.594,0.365
+Palestinian Territories,2014,4.722,8.457,0.775,,0.657,-0.147,0.804,0.565,0.380
+Palestinian Territories,2015,4.695,8.480,0.766,,0.556,-0.153,0.774,0.594,0.369
+Palestinian Territories,2016,4.907,8.498,0.818,,0.608,-0.129,0.812,0.593,0.378
+Palestinian Territories,2017,4.628,8.485,0.824,,0.632,-0.163,0.831,0.597,0.416
+Palestinian Territories,2018,4.554,,0.819,,0.655,,0.814,0.610,0.419
+Palestinian Territories,2019,4.483,,0.833,,0.653,,0.829,0.625,0.400
+Panama,2006,6.128,9.764,0.951,67.900,0.882,-0.047,0.912,0.845,0.232
+Panama,2007,6.894,9.859,0.937,68.000,0.640,0.083,0.915,0.820,0.149
+Panama,2008,6.931,9.935,0.922,68.100,0.707,0.060,0.881,0.819,0.150
+Panama,2009,7.034,9.930,0.905,68.200,0.721,0.014,0.889,0.883,0.144
+Panama,2010,7.321,9.969,0.928,68.300,0.755,-0.009,0.880,0.888,0.146
+Panama,2011,7.248,10.059,0.876,68.500,0.829,0.009,0.840,0.885,0.180
+Panama,2012,6.860,10.135,0.897,68.700,0.783,-0.002,0.796,0.869,0.207
+Panama,2013,6.866,10.184,0.896,68.900,0.811,0.018,0.814,0.869,0.226
+Panama,2014,6.631,10.217,0.873,69.100,0.894,0.002,0.847,0.808,0.254
+Panama,2015,6.606,10.255,0.883,69.300,0.847,-0.007,0.810,0.801,0.264
+Panama,2016,6.118,10.287,0.882,69.400,0.884,-0.102,0.837,0.858,0.244
+Panama,2017,6.568,10.324,0.912,69.500,0.900,-0.170,0.841,0.833,0.242
+Panama,2018,6.281,10.343,0.904,69.600,0.861,-0.131,0.837,0.884,0.223
+Panama,2019,6.086,10.356,0.886,69.700,0.883,-0.199,0.869,0.878,0.244
+Paraguay,2006,4.730,9.088,0.895,63.620,0.691,0.066,0.841,0.816,0.303
+Paraguay,2007,5.272,9.126,0.863,63.840,0.699,0.132,0.930,0.867,0.219
+Paraguay,2008,5.570,9.174,0.889,64.060,0.649,0.057,0.891,0.849,0.259
+Paraguay,2009,5.576,9.158,0.900,64.280,0.718,0.027,0.857,0.832,0.186
+Paraguay,2010,5.841,9.250,0.889,64.500,0.726,0.076,0.780,0.855,0.176
+Paraguay,2011,5.677,9.278,0.869,64.620,0.666,0.191,0.756,0.810,0.190
+Paraguay,2012,5.820,9.259,0.931,64.740,0.748,0.200,0.774,0.837,0.213
+Paraguay,2013,5.936,9.326,0.939,64.860,0.909,0.046,0.903,0.919,0.224
+Paraguay,2014,5.119,9.360,0.959,64.980,0.759,-0.002,0.762,0.944,0.216
+Paraguay,2015,5.560,9.377,0.914,65.100,0.806,-0.008,0.863,0.866,0.219
+Paraguay,2016,5.801,9.406,0.940,65.300,0.854,-0.071,0.756,0.925,0.197
+Paraguay,2017,5.713,9.441,0.902,65.500,0.891,0.003,0.810,0.903,0.232
+Paraguay,2019,5.653,9.448,0.892,65.900,0.876,0.028,0.882,0.858,0.275
+Peru,2006,4.811,8.989,0.875,65.340,0.668,-0.071,0.895,0.697,0.420
+Peru,2007,5.214,9.063,0.756,65.580,0.638,-0.078,0.931,0.758,0.361
+Peru,2008,5.129,9.142,0.777,65.820,0.638,-0.067,0.896,0.763,0.354
+Peru,2009,5.519,9.145,0.799,66.060,0.638,-0.079,0.880,0.811,0.320
+Peru,2010,5.613,9.217,0.812,66.300,0.757,-0.060,0.881,0.800,0.330
+Peru,2011,5.892,9.270,0.756,66.480,0.773,-0.123,0.824,0.780,0.331
+Peru,2012,5.825,9.322,0.764,66.660,0.703,-0.079,0.867,0.757,0.398
+Peru,2013,5.783,9.369,0.797,66.840,0.703,-0.066,0.870,0.778,0.390
+Peru,2014,5.866,9.382,0.819,67.020,0.722,-0.136,0.878,0.759,0.319
+Peru,2015,5.577,9.402,0.798,67.200,0.802,-0.090,0.884,0.754,0.378
+Peru,2016,5.701,9.426,0.803,67.500,0.830,-0.134,0.866,0.822,0.338
+Peru,2017,5.711,9.434,0.830,67.800,0.827,-0.154,0.895,0.789,0.394
+Peru,2018,5.680,9.456,0.845,68.100,0.830,-0.178,0.906,0.809,0.380
+Peru,2019,5.999,9.461,0.809,68.400,0.815,-0.130,0.874,0.820,0.375
+Philippines,2006,4.670,8.562,0.795,59.800,0.828,0.063,0.841,0.832,
+Philippines,2007,5.074,8.608,0.801,60.000,0.852,-0.022,0.880,0.784,0.378
+Philippines,2008,4.589,8.634,0.798,60.200,0.861,0.083,0.817,0.805,0.384
+Philippines,2009,4.880,8.632,0.775,60.400,0.874,0.004,0.805,0.846,0.311
+Philippines,2010,4.942,8.686,0.805,60.600,0.893,0.033,0.812,0.876,0.294
+Philippines,2011,4.994,8.707,0.789,60.800,0.883,0.073,0.783,0.851,0.358
+Philippines,2012,5.002,8.756,0.813,61.000,0.914,0.053,0.771,0.865,0.351
+Philippines,2013,4.977,8.805,0.846,61.200,0.907,0.021,0.756,0.799,0.332
+Philippines,2014,5.313,8.850,0.813,61.400,0.902,-0.015,0.787,0.813,0.334
+Philippines,2015,5.547,8.896,0.854,61.600,0.912,-0.051,0.755,0.805,0.351
+Philippines,2016,5.431,8.950,0.821,61.700,0.908,-0.071,0.792,0.821,0.290
+Philippines,2017,5.594,9.002,0.851,61.800,0.926,-0.141,0.711,0.769,0.341
+Philippines,2018,5.869,9.050,0.846,61.900,0.918,-0.108,0.726,0.773,0.393
+Philippines,2019,6.268,9.095,0.845,62.000,0.910,-0.083,0.748,0.781,0.341
+Philippines,2020,5.080,9.061,0.781,62.100,0.932,-0.116,0.744,0.804,0.327
+Poland,2005,5.587,9.849,0.922,66.300,0.782,,0.983,0.715,0.282
+Poland,2007,5.886,9.978,0.913,66.700,0.772,-0.047,0.925,0.760,0.238
+Poland,2009,5.772,10.047,0.917,67.100,0.821,0.073,0.898,0.690,0.246
+Poland,2010,5.887,10.085,0.955,67.300,0.795,0.002,0.905,0.737,0.234
+Poland,2011,5.646,10.133,0.905,67.460,0.868,-0.067,0.908,0.725,0.224
+Poland,2012,5.876,10.149,0.936,67.620,0.811,-0.027,0.888,0.787,0.267
+Poland,2013,5.746,10.164,0.912,67.780,0.776,-0.137,0.916,0.784,0.242
+Poland,2014,5.750,10.197,0.924,67.940,0.875,-0.064,0.898,0.777,0.223
+Poland,2015,6.007,10.235,0.893,68.100,0.793,-0.093,0.810,0.734,0.240
+Poland,2016,6.162,10.266,0.917,68.500,0.871,-0.091,0.848,0.777,0.224
+Poland,2017,6.201,10.314,0.882,68.900,0.831,-0.122,0.639,0.677,0.203
+Poland,2018,6.111,10.366,0.863,69.300,0.870,-0.254,0.720,0.742,0.176
+Poland,2019,6.242,10.407,0.878,69.700,0.883,-0.231,0.696,0.725,0.168
+Poland,2020,6.139,10.371,0.953,70.100,0.767,-0.007,0.787,0.760,0.329
+Portugal,2006,5.405,10.360,0.905,69.840,0.882,-0.179,0.880,0.709,0.333
+Portugal,2008,5.717,10.384,0.886,70.320,0.646,-0.217,0.933,0.703,0.309
+Portugal,2010,5.095,10.368,0.864,70.800,0.721,-0.106,0.948,0.742,0.265
+Portugal,2011,5.220,10.353,0.856,71.000,0.875,-0.173,0.962,0.725,0.279
+Portugal,2012,4.994,10.315,0.866,71.200,0.774,-0.097,0.959,0.729,0.370
+Portugal,2013,5.158,10.312,0.867,71.400,0.788,-0.118,0.946,0.700,0.348
+Portugal,2014,5.127,10.325,0.862,71.600,0.847,-0.126,0.941,0.705,0.358
+Portugal,2015,5.081,10.347,0.866,71.800,0.800,-0.163,0.941,0.657,0.371
+Portugal,2016,5.447,10.370,0.905,72.000,0.838,-0.225,0.922,0.684,0.326
+Portugal,2017,5.711,10.407,0.900,72.200,0.905,-0.176,0.881,0.649,0.294
+Portugal,2018,5.920,10.434,0.887,72.400,0.877,-0.261,0.880,0.679,0.318
+Portugal,2019,6.095,10.457,0.876,72.600,0.882,-0.234,0.915,0.710,0.300
+Portugal,2020,5.768,10.371,0.875,72.800,0.913,-0.238,0.867,0.648,0.383
+Qatar,2009,6.418,11.456,0.894,66.580,0.865,0.235,0.184,0.678,0.258
+Qatar,2010,6.850,11.520,,66.700,,0.104,,,
+Qatar,2011,6.592,11.553,0.857,67.020,0.905,0.012,,0.761,0.328
+Qatar,2012,6.611,11.523,0.838,67.340,0.924,0.162,,0.766,0.322
+Qatar,2015,6.375,11.486,,68.300,,,,,
+Romania,2005,5.049,9.724,0.838,64.000,0.800,,0.957,0.642,0.346
+Romania,2007,5.394,9.892,0.736,64.480,0.686,-0.188,0.949,0.644,0.277
+Romania,2009,5.368,9.949,0.812,64.960,0.606,-0.196,0.967,0.548,0.270
+Romania,2010,4.909,9.915,0.689,65.200,0.566,-0.085,0.974,0.596,0.344
+Romania,2011,5.023,9.940,0.753,65.420,0.650,-0.140,0.964,0.543,0.294
+Romania,2012,5.167,9.965,0.740,65.640,0.645,-0.112,0.959,0.556,0.343
+Romania,2013,5.082,10.004,0.778,65.860,0.655,-0.129,0.952,0.640,0.329
+Romania,2014,5.727,10.041,0.753,66.080,0.754,-0.100,0.958,0.654,0.331
+Romania,2015,5.777,10.083,0.787,66.300,0.796,-0.141,0.962,0.714,0.312
+Romania,2016,5.969,10.136,0.809,66.600,0.822,-0.115,0.949,0.694,0.258
+Romania,2017,6.090,10.211,0.811,66.900,0.839,-0.160,0.926,0.734,0.231
+Romania,2018,6.151,10.260,0.818,67.200,0.845,-0.217,0.921,0.735,0.298
+Romania,2019,6.130,10.306,0.842,67.500,0.848,-0.221,0.954,0.697,0.244
+Russia,2006,4.964,9.991,0.895,58.680,0.643,-0.307,0.935,0.611,0.232
+Russia,2007,5.223,10.074,0.885,59.260,0.593,-0.284,0.933,0.623,0.193
+Russia,2008,5.619,10.125,0.882,59.840,0.643,-0.305,0.924,0.594,0.166
+Russia,2009,5.158,10.044,0.908,60.420,0.617,-0.283,0.954,0.566,0.169
+Russia,2010,5.385,10.087,0.909,61.000,0.613,-0.296,0.937,0.589,0.171
+Russia,2011,5.389,10.129,0.883,61.420,0.626,-0.279,0.935,0.601,0.165
+Russia,2012,5.621,10.166,0.901,61.840,0.609,-0.293,0.938,0.611,0.174
+Russia,2013,5.537,10.182,0.881,62.260,0.661,-0.289,0.934,0.680,0.180
+Russia,2014,6.037,10.171,0.932,62.680,0.744,-0.265,0.869,0.688,0.151
+Russia,2015,5.996,10.149,0.924,63.100,0.685,-0.171,0.913,0.679,0.130
+Russia,2016,5.855,10.149,0.911,63.500,0.714,-0.181,0.925,0.636,0.142
+Russia,2017,5.579,10.166,0.896,63.900,0.731,-0.145,0.862,0.710,0.195
+Russia,2018,5.514,10.191,0.909,64.300,0.729,-0.147,0.865,0.673,0.199
+Russia,2019,5.441,10.205,0.910,64.700,0.715,-0.116,0.848,0.691,0.200
+Russia,2020,5.495,10.162,0.887,65.100,0.714,-0.071,0.823,0.645,0.190
+Rwanda,2006,4.215,7.111,0.718,49.880,0.915,,0.299,0.735,0.189
+Rwanda,2008,4.363,7.239,0.486,53.040,0.752,0.017,0.286,0.643,0.221
+Rwanda,2009,4.030,7.273,0.559,54.620,0.766,-0.001,0.410,0.678,0.112
+Rwanda,2011,4.097,7.369,0.570,56.820,0.829,-0.039,0.161,0.665,0.154
+Rwanda,2012,3.333,7.428,0.637,57.440,0.835,-0.012,0.081,0.703,0.132
+Rwanda,2013,3.466,7.449,0.750,58.060,0.904,-0.028,0.117,0.760,0.167
+Rwanda,2014,3.596,7.484,0.748,58.680,0.894,-0.023,0.078,0.763,0.134
+Rwanda,2015,3.483,7.544,0.678,59.300,0.908,0.025,0.095,0.721,0.206
+Rwanda,2016,3.333,7.576,0.665,59.900,0.911,0.025,0.159,0.752,0.285
+Rwanda,2017,3.108,7.588,0.517,60.500,0.908,0.051,0.214,0.762,0.358
+Rwanda,2018,3.561,7.644,0.616,61.100,0.924,0.057,0.164,0.793,0.308
+Rwanda,2019,3.268,7.708,0.489,61.700,0.869,0.064,0.168,0.736,0.418
+Saudi Arabia,2005,7.080,10.699,0.868,63.500,,,0.505,0.730,0.243
+Saudi Arabia,2007,7.267,10.689,0.892,63.860,0.622,0.005,,0.772,0.232
+Saudi Arabia,2008,6.811,10.722,0.823,64.040,0.532,-0.022,0.508,0.710,0.202
+Saudi Arabia,2009,6.148,10.673,0.921,64.220,0.639,-0.110,0.445,0.742,0.319
+Saudi Arabia,2010,6.307,10.693,0.880,64.400,0.678,-0.033,,0.645,0.297
+Saudi Arabia,2011,6.700,10.758,0.830,64.600,0.603,-0.142,,0.726,0.240
+Saudi Arabia,2012,6.396,10.779,0.867,64.800,0.560,-0.120,,0.715,0.225
+Saudi Arabia,2013,6.495,10.776,0.827,65.000,0.661,-0.081,,0.744,0.276
+Saudi Arabia,2014,6.278,10.783,0.818,65.200,0.762,-0.073,,0.705,0.313
+Saudi Arabia,2015,6.345,10.798,0.820,65.400,0.820,-0.045,,0.724,0.327
+Saudi Arabia,2016,6.474,10.792,0.890,65.700,0.774,-0.132,,0.793,0.266
+Saudi Arabia,2017,6.294,10.764,0.840,66.000,0.814,-0.131,,0.775,0.306
+Saudi Arabia,2018,6.356,10.771,0.868,66.300,0.855,-0.192,,0.764,0.288
+Saudi Arabia,2019,6.561,10.757,0.912,66.600,0.891,-0.147,,0.732,0.238
+Saudi Arabia,2020,6.560,10.701,0.890,66.900,0.884,-0.111,,0.754,0.251
+Senegal,2006,4.417,7.881,0.760,53.380,0.736,-0.051,0.805,0.740,0.225
+Senegal,2007,4.680,7.903,0.718,54.060,0.698,-0.002,0.827,0.714,0.199
+Senegal,2008,4.683,7.916,0.756,54.740,0.612,-0.030,0.879,0.673,0.252
+Senegal,2009,4.335,7.909,0.810,55.420,0.557,-0.036,0.918,0.757,0.228
+Senegal,2010,4.372,7.917,0.760,56.100,0.777,-0.077,0.851,0.769,0.143
+Senegal,2011,3.834,7.904,0.602,56.560,0.641,-0.160,0.870,0.752,0.180
+Senegal,2012,3.669,7.926,0.711,57.020,0.668,-0.036,0.852,0.771,0.214
+Senegal,2013,3.647,7.926,0.823,57.480,0.636,-0.052,0.837,0.680,0.165
+Senegal,2014,4.395,7.961,0.856,57.940,0.692,-0.045,0.700,0.725,0.157
+Senegal,2015,4.617,7.995,0.702,58.400,0.720,-0.111,0.765,0.711,0.208
+Senegal,2016,4.595,8.029,0.839,58.800,0.744,-0.086,0.794,0.784,0.245
+Senegal,2017,4.683,8.072,0.744,59.200,0.687,-0.044,0.825,0.746,0.291
+Senegal,2018,4.769,8.106,0.739,59.600,0.629,-0.074,0.805,0.714,0.247
+Senegal,2019,5.489,8.130,0.688,60.000,0.759,-0.019,0.796,0.789,0.332
+Serbia,2007,4.750,9.532,0.844,65.600,0.453,-0.165,0.905,0.576,0.334
+Serbia,2009,4.380,9.568,0.770,66.000,0.373,-0.178,0.961,0.544,0.435
+Serbia,2010,4.461,9.579,0.726,66.200,0.463,-0.170,0.965,0.532,0.415
+Serbia,2011,4.815,9.607,0.773,66.360,0.440,-0.185,0.977,0.545,0.410
+Serbia,2012,5.155,9.605,0.819,66.520,0.461,-0.130,0.952,0.514,0.371
+Serbia,2013,5.102,9.638,0.828,66.680,0.533,-0.100,0.908,0.529,0.403
+Serbia,2014,5.113,9.627,0.783,66.840,0.532,0.072,0.912,0.498,0.326
+Serbia,2015,5.318,9.649,0.816,67.000,0.546,-0.062,0.859,0.496,0.303
+Serbia,2016,5.753,9.688,0.895,67.400,0.614,-0.068,0.890,0.535,0.298
+Serbia,2017,5.122,9.713,0.884,67.800,0.685,-0.077,0.851,0.510,0.326
+Serbia,2018,5.936,9.762,0.853,68.200,0.740,-0.100,0.864,0.559,0.296
+Serbia,2019,6.241,9.808,0.903,68.600,0.753,-0.040,0.813,0.509,0.242
+Serbia,2020,6.042,9.788,0.852,69.000,0.843,0.149,0.824,0.603,0.358
+Sierra Leone,2006,3.628,7.136,0.561,40.300,0.679,0.101,0.836,0.505,0.381
+Sierra Leone,2007,3.585,7.187,0.686,41.200,0.720,0.248,0.830,0.582,0.290
+Sierra Leone,2008,2.997,7.215,0.591,42.100,0.716,0.148,0.925,0.534,0.370
+Sierra Leone,2010,4.134,7.254,0.812,43.900,0.726,0.012,0.910,0.514,0.290
+Sierra Leone,2011,4.502,7.292,0.782,44.320,0.770,0.005,0.855,0.446,0.300
+Sierra Leone,2013,4.514,7.577,0.708,45.160,0.720,-0.071,0.856,0.521,0.423
+Sierra Leone,2014,4.500,7.600,0.869,45.580,0.681,0.034,0.786,0.570,0.334
+Sierra Leone,2015,4.909,7.347,0.611,46.000,0.624,0.050,0.825,0.625,0.414
+Sierra Leone,2016,4.733,7.384,0.657,47.600,0.681,0.106,0.863,0.584,0.456
+Sierra Leone,2017,4.090,7.404,0.652,49.200,0.711,0.079,0.848,0.600,0.495
+Sierra Leone,2018,4.306,7.417,0.650,50.800,0.716,0.095,0.856,0.552,0.466
+Sierra Leone,2019,3.447,7.449,0.611,52.400,0.718,0.074,0.874,0.513,0.438
+Singapore,2006,6.463,11.168,0.904,73.600,0.757,0.138,,0.751,0.267
+Singapore,2007,6.834,11.212,0.921,73.900,0.867,0.293,0.064,0.700,0.114
+Singapore,2008,6.642,11.178,0.845,74.200,0.661,0.046,0.066,0.721,0.256
+Singapore,2009,6.145,11.149,0.866,74.500,0.776,-0.075,0.035,0.500,0.208
+Singapore,2010,6.531,11.267,0.864,74.800,0.846,-0.018,0.060,0.602,0.131
+Singapore,2011,6.561,11.307,0.904,75.020,0.822,-0.149,0.099,0.483,0.144
+Singapore,2013,6.533,11.357,0.808,75.460,0.827,0.115,0.242,0.770,0.148
+Singapore,2014,7.062,11.383,0.822,75.680,0.835,0.154,0.133,0.841,0.180
+Singapore,2015,6.620,11.400,0.866,75.900,0.887,0.150,0.099,0.803,0.142
+Singapore,2016,6.033,11.419,0.925,76.200,0.904,0.143,0.047,0.824,0.111
+Singapore,2017,6.378,11.461,0.897,76.500,0.926,0.136,0.162,0.800,0.179
+Singapore,2018,6.375,11.490,0.903,76.800,0.916,-0.066,0.097,0.787,0.107
+Singapore,2019,6.378,11.486,0.925,77.100,0.938,0.027,0.070,0.723,0.138
+Slovakia,2006,5.265,10.015,0.954,66.000,0.542,-0.050,0.946,0.678,0.308
+Slovakia,2010,6.052,10.169,0.920,66.800,0.636,-0.101,0.907,0.666,0.277
+Slovakia,2011,5.945,10.196,0.917,67.040,0.727,0.010,0.907,0.637,0.287
+Slovakia,2012,5.911,10.213,0.926,67.280,0.620,-0.028,0.907,0.656,0.302
+Slovakia,2013,5.937,10.218,0.909,67.520,0.598,-0.051,0.915,0.698,0.277
+Slovakia,2014,6.139,10.244,0.924,67.760,0.635,-0.126,0.914,0.703,0.267
+Slovakia,2015,6.162,10.291,0.943,68.000,0.587,-0.128,0.928,0.714,0.269
+Slovakia,2016,5.993,10.310,0.945,68.300,0.700,-0.061,0.917,0.774,0.232
+Slovakia,2017,6.366,10.339,0.913,68.600,0.714,-0.055,0.920,0.788,0.213
+Slovakia,2018,6.235,10.376,0.922,68.900,0.758,-0.167,0.910,0.754,0.253
+Slovakia,2019,6.243,10.398,0.933,69.200,0.771,-0.129,0.926,0.750,0.252
+Slovakia,2020,6.519,10.332,0.954,69.500,0.762,-0.075,0.901,0.764,0.274
+Slovenia,2006,5.811,10.403,0.936,68.000,0.936,0.043,0.708,0.652,0.307
+Slovenia,2009,5.830,10.410,0.919,68.900,0.896,-0.019,0.804,0.641,0.303
+Slovenia,2010,6.083,10.419,0.917,69.200,0.896,0.029,0.845,0.671,0.295
+Slovenia,2011,6.036,10.426,0.931,69.400,0.907,-0.025,0.893,0.652,0.285
+Slovenia,2012,6.063,10.397,0.925,69.600,0.904,-0.020,0.891,0.656,0.284
+Slovenia,2013,5.975,10.385,0.932,69.800,0.890,0.036,0.918,0.635,0.274
+Slovenia,2014,5.678,10.412,0.908,70.000,0.888,0.052,0.909,0.620,0.291
+Slovenia,2015,5.741,10.433,0.901,70.200,0.896,0.008,0.892,0.659,0.261
+Slovenia,2016,5.937,10.463,0.934,70.500,0.904,-0.055,0.838,0.626,0.272
+Slovenia,2017,6.167,10.509,0.928,70.800,0.921,-0.025,0.829,0.615,0.286
+Slovenia,2018,6.249,10.546,0.941,71.100,0.942,-0.119,0.839,0.644,0.275
+Slovenia,2019,6.665,10.563,0.949,71.400,0.945,-0.102,0.785,0.679,0.228
+Slovenia,2020,6.462,10.478,0.953,71.700,0.958,-0.081,0.797,0.610,0.314
+Somalia,2014,5.528,,0.611,49.600,0.874,,0.456,0.834,0.207
+Somalia,2015,5.354,,0.599,50.100,0.968,,0.410,0.901,0.187
+Somalia,2016,4.668,,0.594,50.000,0.917,,0.441,0.891,0.193
+Somaliland region,2009,4.991,,0.880,,0.746,,0.513,0.819,0.112
+Somaliland region,2010,4.657,,0.829,,0.820,,0.471,0.769,0.083
+Somaliland region,2011,4.931,,0.788,,0.858,,0.357,0.749,0.122
+Somaliland region,2012,5.057,,0.786,,0.758,,0.334,0.735,0.152
+South Africa,2006,5.084,9.386,0.913,48.020,0.649,-0.084,,0.802,0.223
+South Africa,2007,5.204,9.426,0.788,48.640,0.690,-0.158,0.859,0.735,0.210
+South Africa,2008,5.346,9.444,0.810,49.260,0.749,-0.095,0.866,0.773,0.206
+South Africa,2009,5.218,9.414,0.877,49.880,0.739,-0.154,0.904,0.727,0.231
+South Africa,2010,4.652,9.430,0.917,50.500,0.739,-0.202,0.791,0.794,0.124
+South Africa,2011,4.931,9.447,0.858,51.460,0.835,-0.154,0.819,0.763,0.230
+South Africa,2012,5.134,9.453,0.907,52.420,0.590,-0.163,0.838,0.761,0.178
+South Africa,2013,3.661,9.461,0.839,53.380,0.714,-0.077,0.800,0.773,0.167
+South Africa,2014,4.828,9.464,0.881,54.340,0.794,-0.117,0.820,0.798,0.243
+South Africa,2015,4.887,9.460,0.898,55.300,0.862,-0.127,0.853,0.781,0.161
+South Africa,2016,4.770,9.450,0.875,55.700,0.774,-0.070,0.813,0.786,0.301
+South Africa,2017,4.514,9.450,0.870,56.100,0.787,-0.129,0.865,0.785,0.268
+South Africa,2018,4.884,9.444,0.841,56.500,0.753,-0.050,0.841,0.812,0.283
+South Africa,2019,5.035,9.432,0.848,56.900,0.738,-0.134,0.820,0.801,0.268
+South Africa,2020,4.947,9.332,0.891,57.300,0.757,-0.015,0.912,0.820,0.294
+South Korea,2006,5.332,10.310,0.775,70.200,0.715,-0.052,0.799,0.651,0.338
+South Korea,2007,5.767,10.361,0.827,70.500,0.656,-0.059,0.803,0.690,0.226
+South Korea,2008,5.390,10.383,0.754,70.800,0.524,-0.102,0.771,0.643,0.239
+South Korea,2009,5.648,10.386,0.811,71.100,0.600,-0.096,0.787,0.697,0.209
+South Korea,2010,6.116,10.447,0.816,71.400,0.677,-0.033,0.752,0.662,0.130
+South Korea,2011,6.947,10.475,0.809,71.660,0.682,-0.048,0.827,0.656,0.168
+South Korea,2012,6.003,10.494,0.775,71.920,0.618,,0.844,0.664,0.206
+South Korea,2013,5.959,10.520,0.797,72.180,0.642,-0.050,0.832,0.676,0.189
+South Korea,2014,5.801,10.546,0.738,72.440,0.623,-0.043,0.834,0.653,0.283
+South Korea,2015,5.780,10.568,0.768,72.700,0.616,-0.036,0.841,0.650,0.244
+South Korea,2016,5.971,10.593,0.811,73.000,0.591,0.026,0.862,0.676,0.233
+South Korea,2017,5.874,10.621,0.807,73.300,0.538,0.014,0.851,0.623,0.235
+South Korea,2018,5.840,10.643,0.798,73.600,0.600,-0.089,0.797,0.661,0.217
+South Korea,2019,5.903,10.661,0.783,73.900,0.706,-0.055,0.718,0.684,0.236
+South Korea,2020,5.793,10.648,0.808,74.200,0.711,-0.106,0.665,0.640,0.247
+South Sudan,2014,3.832,,0.545,49.840,0.567,,0.742,0.614,0.428
+South Sudan,2015,4.071,,0.585,50.200,0.512,,0.710,0.586,0.450
+South Sudan,2016,2.888,,0.532,50.600,0.440,,0.785,0.615,0.549
+South Sudan,2017,2.817,,0.557,51.000,0.456,,0.761,0.586,0.517
+Spain,2005,7.153,10.546,0.961,71.500,0.916,,0.777,0.776,0.241
+Spain,2007,6.995,10.587,0.957,72.060,0.782,-0.093,0.784,0.763,0.264
+Spain,2008,7.294,10.579,0.948,72.340,0.834,-0.150,0.683,0.772,0.260
+Spain,2009,6.199,10.532,0.929,72.620,0.749,-0.127,0.798,0.752,0.336
+Spain,2010,6.188,10.529,0.950,72.900,0.796,-0.138,0.840,0.724,0.322
+Spain,2011,6.518,10.518,0.944,73.020,0.819,-0.122,0.846,0.737,0.356
+Spain,2012,6.291,10.487,0.937,73.140,0.755,-0.059,0.844,0.749,0.366
+Spain,2013,6.150,10.476,0.929,73.260,0.759,-0.101,0.916,0.696,0.372
+Spain,2014,6.456,10.492,0.948,73.380,0.738,-0.028,0.854,0.716,0.335
+Spain,2015,6.381,10.531,0.956,73.500,0.732,-0.072,0.822,0.732,0.285
+Spain,2016,6.319,10.560,0.942,73.800,0.768,-0.048,0.819,0.653,0.301
+Spain,2017,6.230,10.586,0.903,74.100,0.756,-0.032,0.791,0.625,0.302
+Spain,2018,6.513,10.605,0.910,74.400,0.722,-0.075,0.777,0.659,0.357
+Spain,2019,6.457,10.618,0.949,74.700,0.778,-0.049,0.730,0.663,0.316
+Spain,2020,6.502,10.488,0.935,75.000,0.783,-0.121,0.730,0.686,0.317
+Sri Lanka,2006,4.345,8.912,0.864,65.780,0.724,0.062,0.838,0.757,0.216
+Sri Lanka,2007,4.415,8.970,0.838,65.860,0.736,0.110,0.847,0.709,0.220
+Sri Lanka,2008,4.431,9.021,0.816,65.940,0.834,0.163,0.861,0.790,0.153
+Sri Lanka,2009,4.212,9.049,0.830,66.020,0.799,0.306,0.690,0.770,0.172
+Sri Lanka,2010,3.977,9.119,0.814,66.100,0.738,0.259,0.769,0.823,0.163
+Sri Lanka,2011,4.181,9.193,0.842,66.200,0.823,0.145,0.760,0.825,0.175
+Sri Lanka,2012,4.225,9.279,0.824,66.300,0.800,0.161,0.823,0.864,0.197
+Sri Lanka,2013,4.365,9.305,0.809,66.400,0.834,0.268,0.842,0.860,0.208
+Sri Lanka,2014,4.268,9.344,0.805,66.500,0.868,0.299,0.791,0.843,0.187
+Sri Lanka,2015,4.612,9.383,0.863,66.600,0.902,0.319,0.859,0.848,0.235
+Sri Lanka,2017,4.331,9.440,0.823,67.000,0.827,0.094,0.844,0.795,0.270
+Sri Lanka,2018,4.435,9.462,0.833,67.200,0.859,0.106,0.856,0.831,0.302
+Sri Lanka,2019,4.213,9.479,0.815,67.400,0.824,0.051,0.863,0.816,0.315
+Sudan,2009,4.455,8.106,0.911,53.700,0.710,0.077,0.701,0.734,0.245
+Sudan,2010,4.435,8.076,0.855,54.000,0.648,-0.040,0.737,0.669,0.221
+Sudan,2011,4.314,8.204,0.818,54.280,0.583,-0.024,0.663,0.586,0.249
+Sudan,2012,4.550,8.296,0.813,54.560,0.412,-0.056,0.734,0.576,0.242
+Sudan,2014,4.139,8.317,0.811,55.120,0.390,-0.063,0.794,0.541,0.303
+Suriname,2012,6.269,9.797,0.797,62.240,0.885,-0.077,0.751,0.764,0.250
+Swaziland,2011,4.867,8.940,0.837,40.808,0.607,-0.067,0.917,0.821,0.251
+Swaziland,2018,4.212,9.060,0.779,50.353,0.710,-0.178,0.692,0.824,0.252
+Swaziland,2019,4.396,9.070,0.759,51.270,0.597,-0.191,0.724,0.778,0.280
+Sweden,2005,7.376,10.739,0.951,71.200,0.964,,,0.840,0.151
+Sweden,2007,7.241,10.806,0.917,71.480,0.910,0.146,0.289,0.796,0.177
+Sweden,2008,7.516,10.793,0.923,71.620,0.912,0.125,0.314,0.804,0.134
+Sweden,2009,7.266,10.740,0.903,71.760,0.864,0.221,0.292,0.820,0.151
+Sweden,2010,7.496,10.790,0.970,71.900,0.905,0.141,0.253,0.833,0.200
+Sweden,2011,7.382,10.814,0.921,71.980,0.941,0.161,0.269,0.815,0.179
+Sweden,2012,7.560,10.800,0.929,72.060,0.944,0.132,0.254,0.855,0.170
+Sweden,2013,7.434,10.804,0.916,72.140,0.936,0.159,0.324,0.829,0.184
+Sweden,2014,7.239,10.820,0.933,72.220,0.945,0.202,0.250,0.836,0.208
+Sweden,2015,7.289,10.853,0.929,72.300,0.935,0.211,0.232,0.818,0.191
+Sweden,2016,7.369,10.861,0.912,72.400,0.918,0.146,0.246,0.816,0.201
+Sweden,2017,7.287,10.873,0.914,72.500,0.935,0.170,0.239,0.814,0.175
+Sweden,2018,7.375,10.881,0.931,72.600,0.942,0.077,0.263,0.823,0.161
+Sweden,2019,7.398,10.882,0.934,72.700,0.942,0.091,0.250,0.826,0.202
+Sweden,2020,7.314,10.838,0.936,72.800,0.951,0.091,0.203,0.766,0.222
+Switzerland,2006,7.473,11.050,0.951,71.540,0.919,0.290,0.408,0.821,0.212
+Switzerland,2009,7.525,11.055,0.938,72.260,0.891,0.125,0.342,0.814,0.202
+Switzerland,2012,7.776,11.079,0.947,72.780,0.945,0.139,0.323,0.859,0.176
+Switzerland,2014,7.493,11.098,0.959,73.060,0.949,0.060,0.283,0.823,0.189
+Switzerland,2015,7.572,11.100,0.938,73.200,0.928,0.109,0.210,0.809,0.166
+Switzerland,2016,7.459,11.106,0.928,73.500,0.934,0.088,0.302,0.779,0.206
+Switzerland,2017,7.474,11.115,0.950,73.800,0.925,0.180,0.316,0.774,0.196
+Switzerland,2018,7.509,11.134,0.930,74.100,0.926,0.101,0.301,0.792,0.192
+Switzerland,2019,7.694,11.136,0.949,74.400,0.913,0.036,0.294,0.798,0.171
+Switzerland,2020,7.508,11.081,0.946,74.700,0.917,-0.064,0.280,0.769,0.193
+Syria,2008,5.323,8.652,0.712,63.900,0.661,0.122,0.680,0.609,0.338
+Syria,2009,4.979,8.654,0.842,64.000,0.748,0.082,0.688,0.574,0.292
+Syria,2010,4.465,8.729,0.934,64.100,0.647,0.008,0.743,0.558,0.225
+Syria,2011,4.038,8.727,0.576,62.320,0.530,0.131,0.741,0.599,0.496
+Syria,2012,3.164,8.563,0.588,60.540,0.467,0.316,0.673,0.464,0.705
+Syria,2013,2.688,8.396,0.585,58.760,0.455,0.225,0.663,0.387,0.622
+Syria,2015,3.462,8.442,0.464,55.200,0.448,0.045,0.685,0.369,0.643
+Taiwan Province of China,2006,6.189,10.613,0.882,68.680,0.630,-0.030,0.846,0.814,0.094
+Taiwan Province of China,2008,5.548,10.606,0.830,69.140,0.642,-0.017,0.785,0.794,0.169
+Taiwan Province of China,2010,6.229,10.691,0.831,69.600,0.677,0.005,0.821,0.845,0.136
+Taiwan Province of China,2011,6.309,10.705,0.863,,0.761,0.035,0.755,0.827,0.112
+Taiwan Province of China,2012,6.126,10.716,0.825,,0.698,0.022,0.803,0.821,0.140
+Taiwan Province of China,2013,6.340,10.750,0.817,,0.690,0.002,0.841,0.846,0.124
+Taiwan Province of China,2014,6.363,10.798,0.870,,0.693,0.092,0.866,0.849,0.108
+Taiwan Province of China,2015,6.450,10.842,0.885,,0.701,0.019,0.857,0.832,0.129
+Taiwan Province of China,2016,6.513,10.855,0.895,,0.719,-0.049,0.811,0.833,0.108
+Taiwan Province of China,2017,6.359,10.871,0.891,,0.760,-0.070,0.743,0.837,0.114
+Taiwan Province of China,2018,6.467,,0.896,,0.741,,0.736,0.848,0.093
+Taiwan Province of China,2019,6.537,,0.893,,0.814,,0.718,0.860,0.093
+Taiwan Province of China,2020,6.751,,0.901,,0.799,,0.711,0.845,0.083
+Tajikistan,2006,4.613,7.554,0.724,60.640,0.702,-0.088,0.768,0.566,0.195
+Tajikistan,2007,4.432,7.609,0.727,61.080,0.818,0.000,0.659,0.694,0.133
+Tajikistan,2008,5.064,7.665,0.701,61.520,0.816,0.018,0.723,0.606,0.160
+Tajikistan,2009,4.575,7.682,0.676,61.960,0.744,0.001,0.792,0.605,0.203
+Tajikistan,2010,4.381,7.723,0.759,62.400,0.784,0.062,0.679,0.643,0.192
+Tajikistan,2011,4.263,7.772,0.751,62.560,0.776,-0.119,0.672,0.698,0.166
+Tajikistan,2012,4.497,7.821,0.729,62.720,0.749,-0.073,0.717,0.714,0.198
+Tajikistan,2013,4.967,7.870,0.701,62.880,0.693,0.063,0.764,0.677,0.170
+Tajikistan,2014,4.896,7.911,0.810,63.040,0.853,0.002,0.698,0.656,0.196
+Tajikistan,2015,5.124,7.945,0.844,63.200,0.847,0.022,0.742,0.689,0.196
+Tajikistan,2016,5.104,7.987,0.857,63.500,0.703,0.010,0.632,0.644,0.220
+Tajikistan,2017,5.829,8.036,0.663,63.800,0.832,0.124,0.718,0.603,0.278
+Tajikistan,2018,5.497,8.082,0.875,64.100,,-0.065,0.578,0.695,0.220
+Tajikistan,2019,5.464,8.126,0.880,64.400,,-0.045,0.490,0.729,0.178
+Tajikistan,2020,5.373,8.080,0.790,64.700,,-0.040,0.550,0.749,0.344
+Tanzania,2006,3.922,7.485,0.783,48.700,0.787,-0.027,0.649,0.748,0.209
+Tanzania,2007,4.318,7.522,0.708,49.600,0.716,-0.013,0.707,0.755,0.220
+Tanzania,2008,4.385,7.549,0.774,50.500,0.562,0.256,0.930,0.744,0.178
+Tanzania,2009,3.408,7.572,0.837,51.400,0.607,0.308,0.903,0.778,0.161
+Tanzania,2010,3.229,7.604,0.813,52.300,0.597,0.139,0.866,0.717,0.146
+Tanzania,2011,4.074,7.649,0.883,53.040,0.736,-0.046,0.816,0.765,0.145
+Tanzania,2012,4.007,7.663,0.832,53.780,0.577,0.213,0.887,0.679,0.195
+Tanzania,2013,3.852,7.699,0.803,54.520,0.654,0.054,0.859,0.738,0.191
+Tanzania,2014,3.483,7.734,0.789,55.260,0.654,0.110,0.878,0.731,0.241
+Tanzania,2015,3.661,7.764,0.790,56.000,0.759,0.149,0.906,0.619,0.192
+Tanzania,2016,2.903,7.800,0.638,56.500,0.775,0.179,0.739,0.694,0.246
+Tanzania,2017,3.347,7.836,0.705,57.000,0.800,0.116,0.654,0.715,0.255
+Tanzania,2018,3.445,7.859,0.675,57.500,0.807,0.153,0.612,0.762,0.221
+Tanzania,2019,3.640,7.886,0.687,58.000,0.850,0.100,0.589,0.726,0.243
+Tanzania,2020,3.786,7.881,0.740,58.500,0.830,0.295,0.521,0.686,0.271
+Thailand,2006,5.885,9.461,0.894,64.140,0.863,0.331,0.935,0.814,0.164
+Thailand,2007,5.784,9.508,0.889,64.480,0.870,0.391,0.898,0.832,0.180
+Thailand,2008,5.636,9.520,0.832,64.820,0.868,0.425,0.933,0.819,0.145
+Thailand,2009,5.476,9.508,0.893,65.160,0.868,0.525,0.904,0.898,0.166
+Thailand,2010,6.217,9.576,0.898,65.500,0.860,0.536,0.917,0.901,0.182
+Thailand,2011,6.664,9.579,0.884,65.720,0.927,0.400,0.923,0.934,0.117
+Thailand,2012,6.300,9.645,0.906,65.940,0.847,0.380,0.909,0.855,0.138
+Thailand,2013,6.231,9.667,0.926,66.160,0.781,0.456,0.925,0.846,0.141
+Thailand,2014,6.985,9.672,0.933,66.380,0.900,0.553,0.920,0.811,0.169
+Thailand,2015,6.202,9.699,0.866,66.600,0.885,0.316,0.914,0.910,0.174
+Thailand,2016,6.074,9.729,0.908,66.800,0.924,0.356,0.878,0.835,0.218
+Thailand,2017,5.939,9.765,0.877,67.000,0.923,0.212,0.884,0.816,0.232
+Thailand,2018,6.012,9.803,0.873,67.200,0.905,0.259,0.907,0.843,0.198
+Thailand,2019,6.022,9.824,0.903,67.400,0.898,0.309,0.877,0.843,0.208
+Thailand,2020,5.885,9.769,0.867,67.600,0.840,0.273,0.918,0.783,0.326
+Togo,2006,3.202,7.078,0.435,49.260,0.628,-0.007,0.850,0.615,0.348
+Togo,2008,2.808,7.052,0.291,50.180,0.287,-0.055,0.932,0.362,0.379
+Togo,2011,2.936,7.146,0.303,51.580,0.584,-0.070,0.832,0.480,0.395
+Togo,2014,2.839,7.247,0.444,53.020,0.663,-0.085,0.795,0.583,0.443
+Togo,2015,3.768,7.277,0.479,53.500,0.772,-0.069,0.733,0.599,0.416
+Togo,2016,3.879,7.306,0.509,53.900,0.730,-0.007,0.815,0.604,0.483
+Togo,2017,4.361,7.324,0.508,54.300,0.717,-0.042,0.726,0.614,0.426
+Togo,2018,4.023,7.348,0.596,54.700,0.612,-0.007,0.809,0.608,0.446
+Togo,2019,4.179,7.375,0.539,55.100,0.617,0.065,0.737,0.590,0.444
+Trinidad and Tobago,2006,5.832,10.224,0.887,61.760,0.840,0.141,0.917,0.798,0.229
+Trinidad and Tobago,2008,6.696,10.295,0.858,62.080,0.838,0.087,0.959,0.817,0.184
+Trinidad and Tobago,2011,6.519,10.263,0.863,62.540,0.775,0.078,0.900,0.906,0.134
+Trinidad and Tobago,2013,6.168,10.285,0.883,62.820,0.847,0.128,0.948,0.833,0.286
+Trinidad and Tobago,2017,6.192,10.183,0.916,63.500,0.859,0.015,0.911,0.846,0.248
+Tunisia,2009,5.025,9.197,,64.960,0.781,-0.119,0.722,,
+Tunisia,2010,5.131,9.222,0.863,65.100,0.624,-0.135,0.732,0.725,0.249
+Tunisia,2011,4.876,9.192,0.715,65.280,0.603,-0.199,0.913,0.588,0.248
+Tunisia,2012,4.464,9.222,0.614,65.460,0.568,-0.176,0.899,0.521,0.327
+Tunisia,2013,5.246,9.240,0.648,65.640,0.536,-0.207,0.886,0.517,0.239
+Tunisia,2014,4.764,9.260,0.680,65.820,0.589,-0.232,0.783,0.503,0.321
+Tunisia,2015,5.132,9.261,0.609,66.000,0.711,-0.226,0.815,0.573,0.320
+Tunisia,2016,4.521,9.262,0.702,66.300,0.614,-0.165,0.811,0.612,0.378
+Tunisia,2017,4.124,9.269,0.717,66.600,0.478,-0.219,0.869,0.421,0.377
+Tunisia,2018,4.741,9.284,0.733,66.900,0.650,-0.191,0.840,0.592,0.365
+Tunisia,2019,4.315,9.283,0.610,67.200,0.659,-0.209,0.889,0.539,0.433
+Tunisia,2020,4.731,9.231,0.719,67.500,0.668,-0.202,0.877,0.585,0.439
+Turkey,2005,4.719,9.809,0.820,62.600,0.623,,0.877,0.557,
+Turkey,2007,5.623,9.903,0.792,63.320,0.459,-0.178,0.800,0.651,0.395
+Turkey,2008,5.118,9.899,0.645,63.680,0.415,-0.189,0.785,0.614,0.345
+Turkey,2009,5.213,9.838,0.755,64.040,0.456,-0.227,0.853,0.598,0.316
+Turkey,2010,5.490,9.906,0.795,64.400,0.515,-0.187,0.811,0.652,0.327
+Turkey,2011,5.272,9.996,0.692,64.640,0.446,-0.242,0.649,0.621,0.380
+Turkey,2012,5.309,10.026,0.739,64.880,0.471,-0.216,0.702,0.645,0.335
+Turkey,2013,4.888,10.091,0.795,65.120,0.541,-0.229,0.698,0.635,0.392
+Turkey,2014,5.580,10.124,0.863,65.360,0.649,-0.024,0.764,0.483,0.377
+Turkey,2015,5.514,10.166,0.851,65.600,0.653,-0.016,0.806,0.460,0.382
+Turkey,2016,5.326,10.181,0.880,66.000,0.644,-0.065,0.764,0.465,0.390
+Turkey,2017,5.607,10.238,0.876,66.400,0.644,-0.237,0.671,0.450,0.313
+Turkey,2018,5.186,10.251,0.847,66.800,0.529,-0.176,0.805,0.435,0.351
+Turkey,2019,4.872,10.246,0.792,67.200,0.631,-0.136,0.760,0.422,0.368
+Turkey,2020,4.862,10.219,0.857,67.600,0.510,-0.111,0.774,0.384,0.440
+Turkmenistan,2009,6.568,8.989,0.924,59.440,,-0.102,,0.781,0.152
+Turkmenistan,2011,5.792,9.182,0.964,60.040,,0.018,,0.639,0.122
+Turkmenistan,2012,5.464,9.269,0.946,60.280,0.786,-0.123,,0.584,0.117
+Turkmenistan,2013,5.392,9.348,0.846,60.520,0.705,-0.071,,0.599,0.160
+Turkmenistan,2014,5.787,9.427,0.909,60.760,0.805,0.032,,0.695,0.154
+Turkmenistan,2015,5.791,9.472,0.960,61.000,0.701,0.093,,0.705,0.301
+Turkmenistan,2016,5.887,9.515,0.929,61.400,0.749,0.005,,0.636,0.255
+Turkmenistan,2017,5.229,9.561,0.908,61.800,0.720,0.066,,0.521,0.350
+Turkmenistan,2018,4.621,9.605,0.984,62.200,0.858,0.260,,0.612,0.189
+Turkmenistan,2019,5.474,9.651,0.982,62.600,0.892,0.285,,0.510,0.183
+Uganda,2006,3.734,7.370,0.760,46.480,0.747,-0.041,0.807,0.590,0.254
+Uganda,2007,4.456,7.419,0.845,47.460,0.708,-0.001,0.881,0.708,0.228
+Uganda,2008,4.569,7.471,0.813,48.440,0.578,-0.055,0.848,0.641,0.240
+Uganda,2009,4.612,7.505,0.852,49.420,0.760,-0.037,0.840,0.640,0.296
+Uganda,2010,4.193,7.528,0.830,50.400,0.801,-0.015,0.855,0.648,0.251
+Uganda,2011,4.826,7.586,0.882,51.220,0.733,0.032,0.830,0.678,0.254
+Uganda,2012,4.309,7.592,0.885,52.040,0.649,0.081,0.838,0.754,0.265
+Uganda,2013,3.710,7.595,0.878,52.860,0.763,0.053,0.820,0.676,0.346
+Uganda,2014,3.770,7.611,0.821,53.680,0.834,0.009,0.898,0.681,0.397
+Uganda,2015,4.238,7.627,0.747,54.500,0.758,0.135,0.873,0.703,0.353
+Uganda,2016,4.233,7.637,0.754,54.900,0.739,0.132,0.811,0.668,0.410
+Uganda,2017,4.001,7.638,0.740,55.300,0.772,0.060,0.816,0.703,0.400
+Uganda,2018,4.322,7.660,0.740,55.700,0.729,0.079,0.856,0.685,0.390
+Uganda,2019,4.948,7.688,0.805,56.100,0.704,0.139,0.826,0.693,0.385
+Uganda,2020,4.641,7.684,0.800,56.500,0.687,0.147,0.878,0.699,0.425
+Ukraine,2006,4.804,9.380,0.852,60.120,0.624,-0.257,0.929,0.622,0.249
+Ukraine,2007,5.252,9.459,0.820,60.640,0.494,-0.241,0.968,0.636,0.208
+Ukraine,2008,5.172,9.488,0.860,61.160,0.487,-0.265,0.929,0.573,0.186
+Ukraine,2009,5.166,9.332,0.845,61.680,0.460,-0.241,0.962,0.583,0.189
+Ukraine,2010,5.058,9.374,0.884,62.200,0.484,-0.189,0.954,0.513,0.227
+Ukraine,2011,5.083,9.431,0.859,62.500,0.579,-0.228,0.933,0.590,0.220
+Ukraine,2012,5.030,9.436,0.898,62.800,0.564,-0.223,0.896,0.570,0.193
+Ukraine,2013,4.711,9.438,0.897,63.100,0.569,-0.216,0.937,0.643,0.225
+Ukraine,2014,4.297,9.426,0.877,63.400,0.533,0.084,0.927,0.594,0.249
+Ukraine,2015,3.965,9.327,0.909,63.700,0.431,-0.033,0.952,0.574,0.241
+Ukraine,2016,4.029,9.353,0.885,64.000,0.503,0.011,0.891,0.589,0.220
+Ukraine,2017,4.311,9.382,0.858,64.300,0.599,-0.002,0.937,0.597,0.235
+Ukraine,2018,4.662,9.420,0.901,64.600,0.663,-0.074,0.943,0.609,0.222
+Ukraine,2019,4.702,9.458,0.883,64.900,0.715,-0.081,0.885,0.634,0.201
+Ukraine,2020,5.270,9.428,0.885,65.200,0.784,0.126,0.946,0.688,0.285
+United Arab Emirates,2006,6.734,11.367,0.903,65.920,0.898,-0.033,0.203,0.746,0.275
+United Arab Emirates,2009,6.866,10.975,0.885,66.280,0.849,0.019,0.339,0.770,0.287
+United Arab Emirates,2010,7.097,10.914,0.912,66.400,0.878,0.057,0.355,0.763,0.233
+United Arab Emirates,2011,7.119,10.935,0.881,66.420,0.889,0.071,,0.763,0.216
+United Arab Emirates,2012,7.218,10.958,0.856,66.440,0.920,,,0.768,0.224
+United Arab Emirates,2013,6.621,11.001,0.864,66.460,0.936,,,,0.291
+United Arab Emirates,2014,6.540,11.041,,66.480,,,,,
+United Arab Emirates,2015,6.568,11.086,0.824,66.500,0.915,0.201,,0.761,0.296
+United Arab Emirates,2016,6.831,11.105,0.849,66.700,0.949,0.131,,0.775,0.245
+United Arab Emirates,2017,7.039,11.115,0.836,66.900,0.962,0.216,,0.795,0.208
+United Arab Emirates,2018,6.604,11.112,0.851,67.100,0.944,0.054,,0.787,0.302
+United Arab Emirates,2019,6.711,11.114,0.862,67.300,0.911,0.129,,0.793,0.284
+United Arab Emirates,2020,6.458,11.053,0.827,67.500,0.942,0.060,,0.752,0.298
+United Kingdom,2005,6.984,10.663,0.979,69.900,0.922,,0.398,0.864,0.262
+United Kingdom,2007,6.802,10.699,0.970,70.460,0.838,0.336,0.498,0.782,0.241
+United Kingdom,2008,6.986,10.689,0.954,70.740,0.759,0.331,0.548,0.819,0.218
+United Kingdom,2009,6.907,10.638,0.964,71.020,0.816,0.341,0.559,0.846,0.231
+United Kingdom,2010,7.029,10.649,0.955,71.300,0.841,0.403,0.587,0.863,0.176
+United Kingdom,2011,6.869,10.657,0.949,71.380,0.900,0.336,0.438,0.844,0.174
+United Kingdom,2012,6.881,10.664,0.935,71.460,0.889,0.371,0.425,0.844,0.184
+United Kingdom,2013,6.918,10.679,0.937,71.540,0.905,0.346,0.568,0.776,0.252
+United Kingdom,2014,6.758,10.697,0.910,71.620,0.857,0.355,0.484,0.794,0.251
+United Kingdom,2015,6.515,10.712,0.936,71.700,0.833,0.300,0.456,0.798,0.219
+United Kingdom,2016,6.824,10.724,0.954,71.900,0.821,0.250,0.458,0.776,0.230
+United Kingdom,2017,7.103,10.736,0.937,72.100,0.813,0.291,0.419,0.759,0.210
+United Kingdom,2018,7.233,10.743,0.928,72.300,0.838,0.226,0.404,0.783,0.228
+United Kingdom,2019,7.157,10.751,0.943,72.500,0.854,0.271,0.485,0.775,0.251
+United Kingdom,2020,6.798,10.626,0.929,72.700,0.885,0.203,0.490,0.758,0.225
+United States,2006,7.182,10.924,0.965,68.060,0.911,,0.600,0.827,0.261
+United States,2007,7.513,10.933,,68.220,0.872,0.197,0.633,0.829,0.232
+United States,2008,7.280,10.922,0.953,68.380,0.878,0.255,0.668,0.872,0.227
+United States,2009,7.158,10.888,0.912,68.540,0.831,0.201,0.665,0.843,0.262
+United States,2010,7.164,10.905,0.926,68.700,0.828,0.244,0.690,0.861,0.231
+United States,2011,7.115,10.913,0.922,68.680,0.863,0.161,0.697,0.836,0.273
+United States,2012,7.026,10.928,0.903,68.660,0.823,0.215,0.710,0.834,0.260
+United States,2013,7.249,10.939,0.925,68.640,0.792,0.274,0.747,0.814,0.260
+United States,2014,7.151,10.956,0.902,68.620,0.866,0.221,0.702,0.834,0.281
+United States,2015,6.864,10.977,0.904,68.600,0.849,0.219,0.698,0.814,0.275
+United States,2016,6.804,10.986,0.897,68.500,0.758,0.144,0.739,0.806,0.264
+United States,2017,6.992,11.001,0.921,68.400,0.868,0.197,0.681,0.827,0.268
+United States,2018,6.883,11.025,0.904,68.300,0.825,0.116,0.710,0.815,0.292
+United States,2019,6.944,11.043,0.917,68.200,0.836,0.144,0.707,0.815,0.244
+United States,2020,7.028,11.001,0.937,68.100,0.850,0.034,0.678,0.787,0.295
+Uruguay,2006,5.786,9.543,0.912,67.440,0.807,-0.113,0.477,0.784,0.306
+Uruguay,2007,5.694,9.604,0.875,67.580,0.786,-0.165,0.614,0.777,0.274
+Uruguay,2008,5.664,9.671,0.879,67.720,0.808,-0.143,0.597,0.751,0.264
+Uruguay,2009,6.296,9.710,0.924,67.860,0.825,-0.118,0.544,0.793,0.255
+Uruguay,2010,6.062,9.782,0.893,68.000,0.832,-0.158,0.471,0.807,0.231
+Uruguay,2011,6.554,9.830,0.891,68.140,0.851,-0.080,0.556,0.805,0.252
+Uruguay,2012,6.450,9.861,0.865,68.280,0.871,0.067,0.615,0.788,0.214
+Uruguay,2013,6.444,9.904,0.917,68.420,0.888,-0.043,0.586,0.826,0.253
+Uruguay,2014,6.561,9.932,0.902,68.560,0.904,-0.073,0.533,0.869,0.251
+Uruguay,2015,6.628,9.932,0.891,68.700,0.917,-0.032,0.673,0.893,0.300
+Uruguay,2016,6.171,9.946,0.900,68.800,0.886,-0.072,0.676,0.842,0.283
+Uruguay,2017,6.336,9.968,0.914,68.900,0.898,-0.091,0.627,0.836,0.280
+Uruguay,2018,6.372,9.980,0.917,69.000,0.876,-0.097,0.683,0.877,0.275
+Uruguay,2019,6.600,9.979,0.933,69.100,0.903,-0.095,0.599,0.889,0.222
+Uruguay,2020,6.310,9.937,0.921,69.200,0.908,-0.084,0.491,0.807,0.265
+Uzbekistan,2006,5.232,8.193,0.903,61.440,0.784,-0.115,0.609,0.728,0.195
+Uzbekistan,2008,5.311,8.339,0.894,62.320,0.831,-0.023,,0.714,0.187
+Uzbekistan,2009,5.261,8.400,0.905,62.760,,0.013,0.610,0.736,0.159
+Uzbekistan,2010,5.095,8.445,0.903,63.200,,-0.030,0.519,0.776,0.152
+Uzbekistan,2011,5.739,8.493,0.924,63.400,0.934,0.042,0.522,0.787,0.123
+Uzbekistan,2012,6.019,8.550,0.933,63.600,0.914,-0.037,0.463,0.786,0.118
+Uzbekistan,2013,5.940,8.607,0.963,63.800,0.950,-0.034,0.434,0.749,0.130
+Uzbekistan,2014,6.049,8.659,0.952,64.000,0.954,0.061,0.536,0.805,0.106
+Uzbekistan,2015,5.972,8.714,0.968,64.200,0.980,0.375,0.471,0.840,0.103
+Uzbekistan,2016,5.893,8.756,0.945,64.500,0.984,0.208,,0.842,0.147
+Uzbekistan,2017,6.421,8.782,0.942,64.800,0.985,0.123,0.465,0.839,0.203
+Uzbekistan,2018,6.205,8.818,0.921,65.100,0.970,0.318,0.520,0.825,0.209
+Uzbekistan,2019,6.154,8.853,0.915,65.400,0.970,0.304,0.511,0.845,0.220
+Venezuela,2005,7.170,9.313,0.955,65.400,0.838,,0.720,0.819,0.233
+Venezuela,2006,6.525,9.460,0.946,65.460,0.798,-0.031,0.646,0.859,0.178
+Venezuela,2008,6.258,9.701,0.922,65.580,0.678,-0.225,0.776,0.802,0.224
+Venezuela,2009,7.189,9.542,0.945,65.640,0.677,-0.116,0.828,0.825,0.180
+Venezuela,2010,7.478,9.717,0.932,65.700,0.768,-0.155,0.754,0.862,0.130
+Venezuela,2011,6.580,9.822,0.931,65.740,0.766,-0.226,0.772,0.828,0.199
+Venezuela,2012,7.067,9.826,0.932,65.780,0.804,-0.193,0.743,0.858,0.176
+Venezuela,2013,6.553,9.739,0.896,65.820,0.642,-0.220,0.837,0.840,0.238
+Venezuela,2014,6.136,9.557,0.904,65.860,0.570,-0.199,0.827,0.811,0.244
+Venezuela,2015,5.569,9.001,0.911,65.900,0.512,-0.117,0.813,0.867,0.223
+Venezuela,2016,4.041,9.010,0.902,66.100,0.458,-0.155,0.890,0.688,0.392
+Venezuela,2017,5.071,9.073,0.896,66.300,0.636,-0.169,0.844,0.726,0.363
+Venezuela,2018,5.006,,0.887,66.500,0.611,,0.828,0.759,0.374
+Venezuela,2019,5.081,,0.888,66.700,0.626,,0.839,0.761,0.351
+Venezuela,2020,4.574,,0.805,66.900,0.612,,0.811,0.722,0.396
+Vietnam,2006,5.294,8.335,0.888,65.860,0.886,0.015,,0.682,0.204
+Vietnam,2007,5.422,8.394,0.856,66.020,0.918,0.089,0.754,0.588,0.206
+Vietnam,2008,5.480,8.440,0.805,66.180,0.889,0.201,0.789,0.665,0.218
+Vietnam,2009,5.304,8.483,0.815,66.340,0.834,-0.062,0.838,0.583,0.190
+Vietnam,2010,5.296,8.535,0.787,66.500,0.831,-0.006,0.743,0.685,0.216
+Vietnam,2011,5.767,8.585,0.898,66.660,0.818,0.105,0.742,0.532,0.193
+Vietnam,2012,5.535,8.626,0.775,66.820,0.856,-0.110,0.815,0.615,0.221
+Vietnam,2013,5.023,8.668,0.759,66.980,0.920,-0.027,0.771,0.718,0.165
+Vietnam,2014,5.085,8.716,0.792,67.140,,0.000,,0.701,0.241
+Vietnam,2015,5.076,8.770,0.849,67.300,,0.086,,0.642,0.232
+Vietnam,2016,5.062,8.820,0.876,67.500,0.894,-0.090,0.799,0.536,0.223
+Vietnam,2017,5.175,8.876,,67.700,,,,,
+Vietnam,2018,5.296,8.934,0.832,67.900,0.909,-0.041,0.808,0.692,0.191
+Vietnam,2019,5.467,8.992,0.848,68.100,0.952,-0.126,0.788,0.751,0.186
+Yemen,2007,4.477,8.214,0.825,53.400,0.673,0.011,,0.592,0.379
+Yemen,2009,4.809,8.278,0.756,54.000,0.644,-0.052,0.832,0.583,0.374
+Yemen,2010,4.350,8.453,0.727,54.300,0.659,-0.104,0.853,0.582,0.308
+Yemen,2011,3.746,8.336,0.663,54.300,0.638,-0.173,0.754,0.503,0.285
+Yemen,2012,4.061,8.236,0.682,54.300,0.706,-0.171,0.793,0.502,0.263
+Yemen,2013,4.218,8.242,0.694,54.300,0.543,-0.179,0.885,0.558,0.266
+Yemen,2014,3.968,8.117,0.638,54.300,0.664,-0.157,0.885,0.611,0.276
+Yemen,2015,2.983,7.858,0.669,54.300,0.610,-0.139,0.829,0.507,0.321
+Yemen,2016,3.826,7.715,0.775,55.100,0.533,-0.151,,0.469,0.228
+Yemen,2017,3.254,7.578,0.790,55.900,0.595,-0.147,,0.455,0.295
+Yemen,2018,3.058,,0.789,56.700,0.553,,0.793,0.461,0.315
+Yemen,2019,4.197,,0.870,57.500,0.651,,0.798,0.543,0.213
+Zambia,2006,4.824,7.817,0.798,44.260,0.721,-0.006,0.785,0.701,0.226
+Zambia,2007,3.998,7.871,0.688,45.720,0.682,-0.067,0.948,0.687,0.246
+Zambia,2008,4.730,7.918,0.624,47.180,0.717,0.056,0.890,0.744,0.206
+Zambia,2009,5.260,7.978,0.782,48.640,0.696,-0.096,0.917,0.728,0.123
+Zambia,2011,4.999,8.071,0.864,50.840,0.663,0.003,0.882,0.833,0.204
+Zambia,2012,5.013,8.114,0.780,51.580,0.788,0.008,0.806,0.726,0.250
+Zambia,2013,5.244,8.131,0.761,52.320,0.770,-0.104,0.732,0.735,0.308
+Zambia,2014,4.346,8.146,0.706,53.060,0.812,-0.011,0.809,0.692,0.327
+Zambia,2015,4.843,8.144,0.691,53.800,0.759,-0.039,0.871,0.690,0.382
+Zambia,2016,4.348,8.151,0.767,54.300,0.812,0.122,0.771,0.731,0.372
+Zambia,2017,3.933,8.156,0.744,54.800,0.823,0.140,0.740,0.685,0.387
+Zambia,2018,4.041,8.167,0.718,55.300,0.791,0.048,0.811,0.703,0.351
+Zambia,2019,3.307,8.155,0.638,55.800,0.811,0.077,0.832,0.743,0.394
+Zambia,2020,4.838,8.117,0.767,56.300,0.750,0.056,0.810,0.691,0.345
+Zimbabwe,2006,3.826,7.711,0.822,41.580,0.431,-0.076,0.905,0.715,0.297
+Zimbabwe,2007,3.280,7.666,0.828,42.860,0.456,-0.082,0.946,0.661,0.265
+Zimbabwe,2008,3.174,7.461,0.843,44.140,0.344,-0.090,0.964,0.631,0.250
+Zimbabwe,2009,4.056,7.563,0.806,45.420,0.411,-0.078,0.931,0.736,0.218
+Zimbabwe,2010,4.682,7.729,0.857,46.700,0.665,-0.093,0.828,0.748,0.122
+Zimbabwe,2011,4.846,7.846,0.865,48.120,0.633,-0.088,0.830,0.781,0.211
+Zimbabwe,2012,4.955,7.983,0.896,49.540,0.470,-0.103,0.859,0.669,0.177
+Zimbabwe,2013,4.690,7.985,0.799,50.960,0.576,-0.104,0.831,0.712,0.182
+Zimbabwe,2014,4.184,7.991,0.766,52.380,0.642,-0.074,0.820,0.725,0.239
+Zimbabwe,2015,3.703,7.992,0.736,53.800,0.667,-0.123,0.810,0.715,0.179
+Zimbabwe,2016,3.735,7.984,0.768,54.400,0.733,-0.095,0.724,0.738,0.209
+Zimbabwe,2017,3.638,8.016,0.754,55.000,0.753,-0.098,0.751,0.806,0.224
+Zimbabwe,2018,3.616,8.049,0.775,55.600,0.763,-0.068,0.844,0.710,0.212
+Zimbabwe,2019,2.694,7.950,0.759,56.200,0.632,-0.064,0.831,0.716,0.235
+Zimbabwe,2020,3.160,7.829,0.717,56.800,0.643,-0.009,0.789,0.703,0.346
diff --git a/exercicios/para-sala/exercicio_sala_de_aula.ipynb b/exercicios/para-sala/exercicio_sala_de_aula.ipynb
new file mode 100644
index 0000000..6546153
--- /dev/null
+++ b/exercicios/para-sala/exercicio_sala_de_aula.ipynb
@@ -0,0 +1,1099 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ETL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Leitura do nosso arquivo csv\n",
+    "\n",
+    "df = pd.read_csv(\"titanic.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Moran, Mr. James</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>McCarthy, Mr. Timothy J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>17463</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>E46</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Palsson, Master. Gosta Leonard</td>\n",
+       "      <td>male</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>347742</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Nasser, Mrs. Nicholas (Adele Achem)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>237736</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "5            6         0       3   \n",
+       "6            7         0       1   \n",
+       "7            8         0       3   \n",
+       "8            9         1       3   \n",
+       "9           10         1       2   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "5                                   Moran, Mr. James    male   NaN      0   \n",
+       "6                            McCarthy, Mr. Timothy J    male  54.0      0   \n",
+       "7                     Palsson, Master. Gosta Leonard    male   2.0      3   \n",
+       "8  Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)  female  27.0      0   \n",
+       "9                Nasser, Mrs. Nicholas (Adele Achem)  female  14.0      1   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  \n",
+       "5      0            330877   8.4583   NaN        Q  \n",
+       "6      0             17463  51.8625   E46        S  \n",
+       "7      1            349909  21.0750   NaN        S  \n",
+       "8      2            347742  11.1333   NaN        S  \n",
+       "9      0            237736  30.0708   NaN        C  "
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(891, 12)"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Quantidades de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Backup\n",
+    "df_backup = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PassengerId      0\n",
+      "Survived         0\n",
+      "Pclass           0\n",
+      "Name             0\n",
+      "Sex              0\n",
+      "Age            177\n",
+      "SibSp            0\n",
+      "Parch            0\n",
+      "Ticket           0\n",
+      "Fare             0\n",
+      "Cabin          687\n",
+      "Embarked         2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Contas dados nulos em cada coluna\n",
+    "nulos_por_colunas = df.isnull().sum()\n",
+    "print(nulos_por_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      1\n",
+      "1      0\n",
+      "2      1\n",
+      "3      0\n",
+      "4      1\n",
+      "      ..\n",
+      "886    1\n",
+      "887    0\n",
+      "888    2\n",
+      "889    0\n",
+      "890    1\n",
+      "Length: 891, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Contar dados nulos por linhas\n",
+    "nulos_por_linhas = df.isnull().sum(axis=1)\n",
+    "print(nulos_por_linhas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Descrição dos dados\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Verificar as infomações\n",
+    "info_df = df.info()\n",
+    "print(info_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remover linhas duplicadas\n",
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Remover linhas duplicadas de uma coluna específica\n",
+    "df = df.drop_duplicates([\"PassengerId\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Função das linhas duplicadas (apenas explicação)\n",
+    "def visulizar_linhas_duplicadas(df):\n",
+    "  duplicados = df[df.duplicated(keep=False)] # colocar o keep para trazer apenas as duplicadas, o padrão ele traz as linhas que NÃO são duplicadas\n",
+    "  return duplicados\n",
+    "\n",
+    "linhas_duplicadas = visulizar_linhas_duplicadas(df)\n",
+    "print(linhas_duplicadas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apagar colunas do df\n",
+    "df = df.drop(columns=[\"SibSp\",\"Parch\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Apagar as informações NAN (na são os valores nulos), utilizar em uma df teste para não afetar a original\n",
+    "df_teste = df.dropna(subset=[\"Cabin\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(204, 10)"
+      ]
+     },
+     "execution_count": 254,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_teste.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Resetar o index (para apagar o número de índice caso tenha)\n",
+    "df = df.reset_index(drop=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Ticket',\n",
+       "       'Fare', 'Cabin', 'Embarked'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Renomear as colunas\n",
+    "df.rename(columns={\n",
+    "  'PassengerId': 'IdPassageiro', \n",
+    "  'Survived': 'Sobreviveu', \n",
+    "  'Pclass': 'Classe', \n",
+    "  'Name': 'Nome', \n",
+    "  'Sex': 'Genero', \n",
+    "  'Age': 'Idade', \n",
+    "  'Ticket': 'Bilhete',       \n",
+    "  'Fare': 'Tarifa', \n",
+    "  'Cabin': 'Cabine', \n",
+    "  'Embarked': 'Embarque',\n",
+    "}, inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['IdPassageiro', 'Sobreviveu', 'Classe', 'Nome', 'Genero', 'Idade',\n",
+       "       'Bilhete', 'Tarifa', 'Cabine', 'Embarque'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Salvar no csv\n",
+    "df.to_csv('titanic_tratado.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Análises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Quantos passageiros estavam em casa classe do Titanic?\n",
+    "\n",
+    "# Contagem do número de passageiros\n",
+    "contagem_passageiros = df[\"Classe\"].value_counts()\n",
+    "\n",
+    "# Criação do gráfico\n",
+    "contagem_passageiros.plot(kind=\"bar\", edgecolor=\"blue\", color=\"pink\")\n",
+    "\n",
+    "# Configurações\n",
+    "plt.xlabel(\"Classe do Passageiro\")\n",
+    "plt.ylabel(\"Quantidade\")\n",
+    "plt.title(\"Número de passageiros por Classe\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Qual é a taxa de sobreviventes por gênero?\n",
+    "\n",
+    "# Agrupamento de gênero por sobreviventes\n",
+    "taxa_sob_genero = df.groupby(\"Genero\")[\"Sobreviveu\"].mean()\n",
+    "\n",
+    "# Cores para barras\n",
+    "cores = [\"lightpink\", \"lightblue\"]\n",
+    "\n",
+    "# Plotagem\n",
+    "barras = taxa_sob_genero.plot.bar(edgecolor = \"blue\", color= cores)\n",
+    "\n",
+    "# Os rótulos\n",
+    "plt.xlabel(\"Gênero\")\n",
+    "plt.ylabel(\"Taxa Sobreviventes\")\n",
+    "plt.title(\"Taxa de Sobreviventes por Gênero\")\n",
+    "\n",
+    "# Adicionar Legendas\n",
+    "plt.legend([\"Taxa de Sobrevivência\"])\n",
+    "\n",
+    "# Adicionar rótulos nos gráficos\n",
+    "for i, v in enumerate(taxa_sob_genero):\n",
+    "  barras.text(i, v + 0.01, f\"{v:.2f}\", color = \"black\", ha = \"center\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribuição de idades\n",
+    "\n",
+    "# Plotagem\n",
+    "df[\"Idade\"].plot.hist(bins=10, edgecolor=\"blue\", color=\"pink\")\n",
+    "\n",
+    "plt.xlabel(\"Idade\")\n",
+    "plt.ylabel(\"Quantidade\")\n",
+    "plt.title(\"Distruição de idade\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Teste Hipóteses "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teste de Classe e Sobrevivência\n",
+    "\n",
+    "Hipótese Nula H0: Os sobreviventes não dependem da idade dos passageiros.<p>\n",
+    "Hipótese Alternativa h1: Sobreviventes dependem da idade dos passageiros."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: seaborn in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (0.13.2)\n",
+      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from seaborn) (2.0.1)\n",
+      "Requirement already satisfied: pandas>=1.2 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from seaborn) (2.2.2)\n",
+      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from seaborn) (3.9.2)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.2.1)\n",
+      "Requirement already satisfied: cycler>=0.10 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.53.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.5)\n",
+      "Requirement already satisfied: packaging>=20.0 in c:\\users\\sukzw\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (23.1)\n",
+      "Requirement already satisfied: pillow>=8 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (10.4.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (3.1.2)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\sukzw\\appdata\\roaming\\python\\python311\\site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
+      "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\sukzw\\appdata\\local\\programs\\python\\python311\\lib\\site-packages (from pandas>=1.2->seaborn) (2024.1)\n",
+      "Requirement already satisfied: six>=1.5 in c:\\users\\sukzw\\appdata\\roaming\\python\\python311\\site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.16.0)\n",
+      "Note: you may need to restart the kernel to use updated packages.\n"
+     ]
+    }
+   ],
+   "source": [
+    "pip install seaborn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import ttest_ind\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Teste T de idade\n",
+      "Estatísticaa T: -2.06668694625381\n",
+      "Valor P: 0.03912465401348249\n",
+      "Rejeitamos a hipótese nula\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Amostras\n",
+    "idade_sobreviventes = df[df['Sobreviveu']== 1][\"Idade\"].dropna()\n",
+    "idade_nao_sobreviveu = df[df['Sobreviveu']== 0][\"Idade\"].dropna()\n",
+    "\n",
+    "# Teste t\n",
+    "estatistica_t, valor_p = ttest_ind(idade_sobreviventes, idade_nao_sobreviveu)\n",
+    "\n",
+    "print(\"Teste T de idade\")\n",
+    "print(f\"Estatísticaa T: {estatistica_t}\")\n",
+    "print(f\"Valor P: {valor_p}\")\n",
+    "\n",
+    "# Gráfico\n",
+    "sns.histplot(idade_sobreviventes, color= 'blue', label='Sobreviventes', kde=True, bins= 20)\n",
+    "sns.histplot(idade_nao_sobreviveu, color= 'red', label='Não Sobreviveu', kde=True, bins= 20)\n",
+    "\n",
+    "# Rótulos\n",
+    "plt.legend()\n",
+    "plt.title(\"Distribuição de Idade dos sobreviventes\")\n",
+    "plt.xlabel(\"Idade\")\n",
+    "plt.ylabel(\"Contagem\")\n",
+    "plt.show\n",
+    "\n",
+    "# Interpretação\n",
+    "if valor_p < 0.05:\n",
+    "  print(\"Rejeitamos a hipótese nula\")\n",
+    "else: \n",
+    "  print(\"Não rejeitamos a hipótese nula\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Amostra e SQL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Amostra\n",
+    "baby_df = df.sample(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "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",
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+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>IdPassageiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Bilhete</th>\n",
+       "      <th>Tarifa</th>\n",
+       "      <th>Cabine</th>\n",
+       "      <th>Embarque</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Fortune, Mr. Charles Alexander</td>\n",
+       "      <td>male</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>19950</td>\n",
+       "      <td>263.0</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>63</th>\n",
+       "      <td>64</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Skoog, Master. Harald</td>\n",
+       "      <td>male</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>347088</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>427</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Clarke, Mrs. Charles V (Ada Maria Winfield)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>2003</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     IdPassageiro  Sobreviveu  Classe  \\\n",
+       "27             28           0       1   \n",
+       "63             64           0       3   \n",
+       "426           427           1       2   \n",
+       "\n",
+       "                                            Nome  Genero  Idade Bilhete  \\\n",
+       "27                Fortune, Mr. Charles Alexander    male   19.0   19950   \n",
+       "63                         Skoog, Master. Harald    male    4.0  347088   \n",
+       "426  Clarke, Mrs. Charles V (Ada Maria Winfield)  female   28.0    2003   \n",
+       "\n",
+       "     Tarifa       Cabine Embarque  \n",
+       "27    263.0  C23 C25 C27        S  \n",
+       "63     27.9          NaN        S  \n",
+       "426    26.0          NaN        S  "
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "baby_df.head(3)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/titanic_tratado.csv b/exercicios/para-sala/titanic_tratado.csv
new file mode 100644
index 0000000..3a992a0
--- /dev/null
+++ b/exercicios/para-sala/titanic_tratado.csv
@@ -0,0 +1,892 @@
+IdPassageiro,Sobreviveu,Classe,Nome,Genero,Idade,Bilhete,Tarifa,Cabine,Embarque
+1,0,3,"Braund, Mr. Owen Harris",male,22.0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26.0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35.0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54.0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4.0,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58.0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20.0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39.0,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14.0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,248706,16.0,,S
+17,0,3,"Rice, Master. Eugene",male,2.0,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,244373,13.0,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31.0,345763,18.0,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35.0,239865,26.0,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34.0,248698,13.0,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15.0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28.0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8.0,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38.0,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19.0,19950,263.0,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40.0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66.0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28.0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42.0,113789,52.0,,S
+37,1,3,"Mamee, Mr. Hanna",male,,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21.0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18.0,345764,18.0,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14.0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40.0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27.0,11668,21.0,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3.0,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19.0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18.0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7.0,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21.0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49.0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29.0,2926,26.0,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65.0,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21.0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5.0,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11.0,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22.0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38.0,113572,80.0,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45.0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4.0,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19.0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17.0,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26.0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32.0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16.0,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21.0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26.0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32.0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25.0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,248738,29.0,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30.0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22.0,345767,9.0,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29.0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28.0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17.0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16.0,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23.0,19950,263.0,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24.0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29.0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20.0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46.0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26.0,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59.0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71.0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23.0,PC 17759,63.3583,D10 D12,C
+99,1,2,"Doling, Mrs. John T (Ada Julia Bone)",female,34.0,231919,23.0,,S
+100,0,2,"Kantor, Mr. Sinai",male,34.0,244367,26.0,,S
+101,0,3,"Petranec, Miss. Matilda",female,28.0,349245,7.8958,,S
+102,0,3,"Petroff, Mr. Pastcho (""Pentcho"")",male,,349215,7.8958,,S
+103,0,1,"White, Mr. Richard Frasar",male,21.0,35281,77.2875,D26,S
+104,0,3,"Johansson, Mr. Gustaf Joel",male,33.0,7540,8.6542,,S
+105,0,3,"Gustafsson, Mr. Anders Vilhelm",male,37.0,3101276,7.925,,S
+106,0,3,"Mionoff, Mr. Stoytcho",male,28.0,349207,7.8958,,S
+107,1,3,"Salkjelsvik, Miss. Anna Kristine",female,21.0,343120,7.65,,S
+108,1,3,"Moss, Mr. Albert Johan",male,,312991,7.775,,S
+109,0,3,"Rekic, Mr. Tido",male,38.0,349249,7.8958,,S
+110,1,3,"Moran, Miss. Bertha",female,,371110,24.15,,Q
+111,0,1,"Porter, Mr. Walter Chamberlain",male,47.0,110465,52.0,C110,S
+112,0,3,"Zabour, Miss. Hileni",female,14.5,2665,14.4542,,C
+113,0,3,"Barton, Mr. David John",male,22.0,324669,8.05,,S
+114,0,3,"Jussila, Miss. Katriina",female,20.0,4136,9.825,,S
+115,0,3,"Attalah, Miss. Malake",female,17.0,2627,14.4583,,C
+116,0,3,"Pekoniemi, Mr. Edvard",male,21.0,STON/O 2. 3101294,7.925,,S
+117,0,3,"Connors, Mr. Patrick",male,70.5,370369,7.75,,Q
+118,0,2,"Turpin, Mr. William John Robert",male,29.0,11668,21.0,,S
+119,0,1,"Baxter, Mr. Quigg Edmond",male,24.0,PC 17558,247.5208,B58 B60,C
+120,0,3,"Andersson, Miss. Ellis Anna Maria",female,2.0,347082,31.275,,S
+121,0,2,"Hickman, Mr. Stanley George",male,21.0,S.O.C. 14879,73.5,,S
+122,0,3,"Moore, Mr. Leonard Charles",male,,A4. 54510,8.05,,S
+123,0,2,"Nasser, Mr. Nicholas",male,32.5,237736,30.0708,,C
+124,1,2,"Webber, Miss. Susan",female,32.5,27267,13.0,E101,S
+125,0,1,"White, Mr. Percival Wayland",male,54.0,35281,77.2875,D26,S
+126,1,3,"Nicola-Yarred, Master. Elias",male,12.0,2651,11.2417,,C
+127,0,3,"McMahon, Mr. Martin",male,,370372,7.75,,Q
+128,1,3,"Madsen, Mr. Fridtjof Arne",male,24.0,C 17369,7.1417,,S
+129,1,3,"Peter, Miss. Anna",female,,2668,22.3583,F E69,C
+130,0,3,"Ekstrom, Mr. Johan",male,45.0,347061,6.975,,S
+131,0,3,"Drazenoic, Mr. Jozef",male,33.0,349241,7.8958,,C
+132,0,3,"Coelho, Mr. Domingos Fernandeo",male,20.0,SOTON/O.Q. 3101307,7.05,,S
+133,0,3,"Robins, Mrs. Alexander A (Grace Charity Laury)",female,47.0,A/5. 3337,14.5,,S
+134,1,2,"Weisz, Mrs. Leopold (Mathilde Francoise Pede)",female,29.0,228414,26.0,,S
+135,0,2,"Sobey, Mr. Samuel James Hayden",male,25.0,C.A. 29178,13.0,,S
+136,0,2,"Richard, Mr. Emile",male,23.0,SC/PARIS 2133,15.0458,,C
+137,1,1,"Newsom, Miss. Helen Monypeny",female,19.0,11752,26.2833,D47,S
+138,0,1,"Futrelle, Mr. Jacques Heath",male,37.0,113803,53.1,C123,S
+139,0,3,"Osen, Mr. Olaf Elon",male,16.0,7534,9.2167,,S
+140,0,1,"Giglio, Mr. Victor",male,24.0,PC 17593,79.2,B86,C
+141,0,3,"Boulos, Mrs. Joseph (Sultana)",female,,2678,15.2458,,C
+142,1,3,"Nysten, Miss. Anna Sofia",female,22.0,347081,7.75,,S
+143,1,3,"Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck)",female,24.0,STON/O2. 3101279,15.85,,S
+144,0,3,"Burke, Mr. Jeremiah",male,19.0,365222,6.75,,Q
+145,0,2,"Andrew, Mr. Edgardo Samuel",male,18.0,231945,11.5,,S
+146,0,2,"Nicholls, Mr. Joseph Charles",male,19.0,C.A. 33112,36.75,,S
+147,1,3,"Andersson, Mr. August Edvard (""Wennerstrom"")",male,27.0,350043,7.7958,,S
+148,0,3,"Ford, Miss. Robina Maggie ""Ruby""",female,9.0,W./C. 6608,34.375,,S
+149,0,2,"Navratil, Mr. Michel (""Louis M Hoffman"")",male,36.5,230080,26.0,F2,S
+150,0,2,"Byles, Rev. Thomas Roussel Davids",male,42.0,244310,13.0,,S
+151,0,2,"Bateman, Rev. Robert James",male,51.0,S.O.P. 1166,12.525,,S
+152,1,1,"Pears, Mrs. Thomas (Edith Wearne)",female,22.0,113776,66.6,C2,S
+153,0,3,"Meo, Mr. Alfonzo",male,55.5,A.5. 11206,8.05,,S
+154,0,3,"van Billiard, Mr. Austin Blyler",male,40.5,A/5. 851,14.5,,S
+155,0,3,"Olsen, Mr. Ole Martin",male,,Fa 265302,7.3125,,S
+156,0,1,"Williams, Mr. Charles Duane",male,51.0,PC 17597,61.3792,,C
+157,1,3,"Gilnagh, Miss. Katherine ""Katie""",female,16.0,35851,7.7333,,Q
+158,0,3,"Corn, Mr. Harry",male,30.0,SOTON/OQ 392090,8.05,,S
+159,0,3,"Smiljanic, Mr. Mile",male,,315037,8.6625,,S
+160,0,3,"Sage, Master. Thomas Henry",male,,CA. 2343,69.55,,S
+161,0,3,"Cribb, Mr. John Hatfield",male,44.0,371362,16.1,,S
+162,1,2,"Watt, Mrs. James (Elizabeth ""Bessie"" Inglis Milne)",female,40.0,C.A. 33595,15.75,,S
+163,0,3,"Bengtsson, Mr. John Viktor",male,26.0,347068,7.775,,S
+164,0,3,"Calic, Mr. Jovo",male,17.0,315093,8.6625,,S
+165,0,3,"Panula, Master. Eino Viljami",male,1.0,3101295,39.6875,,S
+166,1,3,"Goldsmith, Master. Frank John William ""Frankie""",male,9.0,363291,20.525,,S
+167,1,1,"Chibnall, Mrs. (Edith Martha Bowerman)",female,,113505,55.0,E33,S
+168,0,3,"Skoog, Mrs. William (Anna Bernhardina Karlsson)",female,45.0,347088,27.9,,S
+169,0,1,"Baumann, Mr. John D",male,,PC 17318,25.925,,S
+170,0,3,"Ling, Mr. Lee",male,28.0,1601,56.4958,,S
+171,0,1,"Van der hoef, Mr. Wyckoff",male,61.0,111240,33.5,B19,S
+172,0,3,"Rice, Master. Arthur",male,4.0,382652,29.125,,Q
+173,1,3,"Johnson, Miss. Eleanor Ileen",female,1.0,347742,11.1333,,S
+174,0,3,"Sivola, Mr. Antti Wilhelm",male,21.0,STON/O 2. 3101280,7.925,,S
+175,0,1,"Smith, Mr. James Clinch",male,56.0,17764,30.6958,A7,C
+176,0,3,"Klasen, Mr. Klas Albin",male,18.0,350404,7.8542,,S
+177,0,3,"Lefebre, Master. Henry Forbes",male,,4133,25.4667,,S
+178,0,1,"Isham, Miss. Ann Elizabeth",female,50.0,PC 17595,28.7125,C49,C
+179,0,2,"Hale, Mr. Reginald",male,30.0,250653,13.0,,S
+180,0,3,"Leonard, Mr. Lionel",male,36.0,LINE,0.0,,S
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