statistical_thinking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import statsmodels as sm\n",
    "from scipy import stats\n",
    "from scipy.stats import sem\n",
    "from scipy.stats import pearsonr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>salary</th>\n",
       "      <th>exprior</th>\n",
       "      <th>yearsworked</th>\n",
       "      <th>yearsrank</th>\n",
       "      <th>market</th>\n",
       "      <th>degree</th>\n",
       "      <th>otherqual</th>\n",
       "      <th>position</th>\n",
       "      <th>male</th>\n",
       "      <th>Field</th>\n",
       "      <th>yearsabs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>53000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>58000.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.24</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>45500.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35782.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>34731.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0.91</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    salary  exprior  yearsworked  yearsrank  market  degree  otherqual  \\\n",
       "0  53000.0        0            0          0    1.17       1          0   \n",
       "1  58000.0        1            0          0    1.24       1          0   \n",
       "2  45500.0        0            0          0    1.21       1          0   \n",
       "3  35782.0        0            2          1    0.99       1          0   \n",
       "4  34731.0        0            2          2    0.91       1          0   \n",
       "\n",
       "   position  male  Field  yearsabs  \n",
       "0         1     1      3         0  \n",
       "1         1     1      2         0  \n",
       "2         1     1      3         2  \n",
       "3         1     1      4         1  \n",
       "4         1     1      4         1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal_data = pd.read_csv('salary.csv')\n",
    "sal_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Responders and missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary         1\n",
       "exprior        0\n",
       "yearsworked    0\n",
       "yearsrank      0\n",
       "market         0\n",
       "degree         0\n",
       "otherqual      0\n",
       "position       0\n",
       "male           0\n",
       "Field          0\n",
       "yearsabs       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looking at the dataframe we see that we have missing value in the Salary column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sal_data = sal_data.dropna(axis = 0, how ='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "salary         0\n",
       "exprior        0\n",
       "yearsworked    0\n",
       "yearsrank      0\n",
       "market         0\n",
       "degree         0\n",
       "otherqual      0\n",
       "position       0\n",
       "male           0\n",
       "Field          0\n",
       "yearsabs       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sal_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We dropped the Nan value in the salary column to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Responders in the Dataframe:  513\n"
     ]
    }
   ],
   "source": [
    "responders = sal_data.shape[0]\n",
    "print(\"Responders in the Dataframe: \", responders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In this Dataframe we have 513 responders "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lowest & highest salary in the group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lowest salary:  29000.0\n",
      "Highest salary:  96156.0\n"
     ]
    }
   ],
   "source": [
    "ls = np.min(sal_data['salary'])\n",
    "print(\"Lowest salary: \", ls)\n",
    "\n",
    "hs = np.max(sal_data['salary'])\n",
    "print(\"Highest salary: \", hs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean salary & standard error of the mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = sal_data['salary']\n",
    "sal_data= sal_data.fillna(sal_data['salary'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average salary:  50863.22009783626\n",
      "Standard error:  560.0622753925232\n"
     ]
    }
   ],
   "source": [
    "m = np.mean(sal_data['salary'])\n",
    "print(\"Average salary: \", m)\n",
    "\n",
    "sem = (sal_data['salary']).sem()\n",
    "print(\"Standard error: \", sem)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The standard error indicates that the variation of the salary on average is +- 558.97 on the mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Standard deviation for the years worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Standard deviation:  9.437828327328845\n"
     ]
    }
   ],
   "source": [
    "std = np.std(sal_data['yearsworked'])\n",
    "print(\"Standard deviation: \", std)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This standard deviation seems to be high indicating that the data points are spread further away from the mean. \n",
    "- This could be because of numerous outliers in the data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Median salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Median:  50096.0\n"
     ]
    }
   ],
   "source": [
    "med = (sal_data['salary']).median()\n",
    "print(\"Median: \", med)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The median is the mid point of the salary, indicating that half of the popullation of the salary is below the median and another half is above the median"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interquatile range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of the interquartile range for salary in the sample is:  20345.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Boxplot interpretation of Salary')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q1 = sal_data['salary'].quantile(0.25)\n",
    "Q3 = sal_data['salary'].quantile(0.75)\n",
    "\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "print(\"The value of the interquartile range for salary in the sample is: \", IQR)\n",
    "\n",
    "#Plotting a box plot\n",
    "\n",
    "sns.boxplot(y=\"salary\", data=sal_data, palette=\"rocket\")\n",
    "plt.title(\"Boxplot interpretation of Salary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is safe to say that we have a positive skew and the data is more stretched out hence we tend to see more outliers, who earn a salary of 90 000 and more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Total Men & Women in sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Gender  Total\n",
      "0    men    385\n",
      "1  women    128\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Renaming columns and mapping columns\n",
    "\n",
    "sal_data.rename(columns={'male': 'Gender'},inplace=True)\n",
    "assign = {0:'women', 1:'men'}\n",
    "sal_data['Gender'] = sal_data['Gender'].map(assign)\n",
    "\n",
    "a=pd.DataFrame(sal_data.groupby('Gender').size()).reset_index()\n",
    "b=a.rename(columns={0:\"Total\"})\n",
    "\n",
    "print(b)\n",
    "\n",
    "#Plotting a pie chart\n",
    "ratios = [.751, .249]\n",
    "labels=['Males', 'Females']\n",
    "plt.pie(ratios,explode=None, labels=labels, autopct='%1.1f%%', startangle=320)\n",
    "plt.title('Gender', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Our data frame is made up of 75.1% (385) men and 24.9% (128) women"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison between men & women executives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of excutive women:  19\n",
      "Number of excutive men:  191\n"
     ]
    }
   ],
   "source": [
    "ew = len(sal_data[(sal_data.Gender == 'women')&(sal_data.position == 3)])\n",
    "print(\"Number of excutive women: \", ew)\n",
    "\n",
    "em = len(sal_data[(sal_data.Gender == 'men')&(sal_data.position == 3)])\n",
    "print(\"Number of excutive men: \", em)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram for Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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jtLQUgGHDhplHDDZFfTNXhIaGsmLFika/oScnJ/P555/XWxYVFUVQUBBg+X5ee+0184dES903U1ZWxgMPPGDxmkajMY/wVCgUxMfH88UXX5Cdnc3IkSMxmUy37JaNjo7G3d0dvV7PqlWrWLVqFVA7AOZW3aR/+tOfzKMEV65cycqVKy3Kf/e731l0DzbXjX332rVrrFu3zmIoO8D06dPp37//ba//008/5T//+U+9ZSNGjABqu7dvdJ3dOBsJCAgwX/9qzH333ccHH3yAyWTirbfe4q233kKpVBIWFmYxcvd2qdVqFi5cyPPPP49er693xOuNgT3V1dUWf+ufGzlyZLPjaSlyhtUKqVQq/Pz86NmzJ5MnT+bTTz/lpZdesuh2CQoKYt26dfzmN78hPDycdu3aoVar6datG/Hx8Rbf1puic+fO/OIXvyAyMtLcXeTv78/QoUN59913zfc63UpcXBxz584lNDT0ll18Pj4+TJ061fz77Q62UCgUeHh4EBISwuDBg0lMTOTLL7+0apaLcePGMW7cODp16oSXlxdubm6EhoYybdo03n33XfNysbGxvPTSS3Tp0sUmQ4NfeeUVHnnkEQICAvDw8GDEiBH85z//sRjS/qc//YnJkycTGBiIu7s79957r8W1mZuFhISwdOlSevbsadGd2JCePXvyxRdfMHXqVPMFfF9fX4YMGcKKFSt44oknWuS93hAcHMznn3/O7Nmzze2qVqu54447+POf/9ysIe1Q2xMxYMAANBoNbm5ueHl5ERMTw0svvcTs2bPNy8ydO5eQkBA8PT0ZPHgw//73v60eLDNw4EBef/1187HXs2dP/vGPfzBgwIBmxX6z+Ph4PvnkE+Li4ggKCsLNzY3AwED69OnDnDlz+PWvfw3UniH+6le/IjY2loCAAFQqFd7e3vTv35+///3vjBkzpsViai6Fqb4hZEI4ueeee47NmzcTHBzMzp07rf5wFUK4LukSFC5l9uzZXLhwgfz8fACefPJJSVZCtBGSsIRLuXLlCvn5+QQHBzN9+vRbTlkkhGh9pEtQCCGES7DboIu0tDSmT59OXFwc06dPJz09vc4yBoOBxYsXM2bMGMaOHcvatWttWrZu3TomTpxIQkICEydOZPXq1VbVE0II4QAmO5k1a5Zpw4YNJpPJZNqwYYNp1qxZdZZZv3696bHHHjMZDAaTTqczjRw50nTlyhWblZWUlJiMRqP559GjR5vOnDnTaD0hhBD2Z5czLJ1OR0pKCvHx8UDtcMuUlJQ606ckJSUxbdo0lEolgYGBjBkzxjwzgi3KfHx8zEO9Kysr0ev15t8bqieEEML+7DLoIisri5CQEPN9NSqVig4dOpCVlWVxg2ZWVpbFDYZarZbs7GyblUHtDZFvvPEGGRkZPPfcc+abSRurZ43r18swGm1ziVCj8UGnK7XJul2NtIUlaY+fSFtYcvb2UCoVBAR437K8zY8SvO+++7jvvvvIzMxk3rx5jBo1yuIxE83RUMO3BI3GOSakdAbSFpakPX4ibWHJldvDLglLq9WSk5ODwWBApVJhMBjIzc21mGn6xnKZmZn07dsXsDzLsUXZzUJDQ4mNjWX37t2Eh4dbXa8hOl2pzc6wgoN9ycsrscm6XY20hSVpj59IW1hy9vZQKhUNJlS7XMPSaDRERUWxefNmADZv3kxUVFSd+drGjx/P2rVrMRqNFBQUsH37dvND6WxRdvHiRfO2CwoKOHjwIL179260nhBCCPuzW5fgokWLSExMZOXKlfj5+bFkyRKgdk6u+fPnExsbS0JCAsnJyeYp9ufNm2ee1NQWZWvWrGHfvn24ublhMpmYOXOmeXLLhuoJIYSwP7lx2IakS9A+pC0sSXv8RNrCkrO3h1N0CQohhBDNJQlLCCGES5CEJYQQwiW0+fuwhAuqqEFfpjf/WlhuQF9ebXV1d2938JJdXwhXI0etcDn6Mj3nks6bf/f19aSkpNLq+r0f6IW7JCwhXI50CQohhHAJ8jVT2NfPuvNui97YMrEIIVyKJCxhVz/vzrsdPUZ3b6FohBCuRLoEhRBCuARJWEIIIVyCJCwhhBAuQRKWEEIIlyAJSwghhEuQhCWEEMIlyLB24XL0FXp0KXkUXy7CM9ALojtg8nZDoVQ4OjQhhA1JwhIuw2Q0kbb1Inv+uBOj3ggKwATnOIN3R296T4vGr6u/o8NsXDNvnpa5EEVbJXu9cAkGvYGzn5wm/2QuPe7viXcXP/y7+VN5vZLKzFLObTrHsX8eptPwzoTH90Lp5ry93c29eVrmQhRtlez1wukZDUZOvHOM4vRCejzYi1H/724ufZsOgLqDNyE9NPhFakj76gLX9l6hQldBzC9jUbqrHBu4EKJFOe/XUCF+dGVnOsVphUTOiCFsVFcUirrXqtw83eg1OZJeD0VScDafk+8fx1BV44BohRC2IglLOLXSzBIub0+jQ/8QQgZoG10+dFgYkTNiKLx4ndOrT2IyyES5QrQWkrCE0zIajKSuScHNy52ekyKsrhcyQEvvqVFcT9VxYcM5TCaTDaMUQtiLXMMSTiv7YCal10qI/mUs7t7tmlRXO6QTFXnlXNl9Ga9gNWGjutgoSiGEvcgZlnBKJqOJK99exrezH0GxHW5rHd0f6ElQn2AubjrH9QsFLRyhEMLeJGEJp5R/KpdKXQWdR9c/yMIaCqWCiBkxqIPVnPnPKaqKqlo4SiGEPUnCEk7HZDJxZfdlPDVet312dYObpxvRv+yLoaqGlP+cxCiDMIRwWZKwhNMpTi+iJKOYsFFdWmS6Je+OPvSeFk1xWiFpSRdbIEIhhCNIwhJO5+p3l3FTu9NxUGiLrTPkzo6EDgvj6reXSdt+qcXWK4SwH7uNEkxLSyMxMZHCwkLat2/PkiVL6Natm8UyBoOBV199lT179qBQKHjiiSeYNm2azcpWrFhBUlISKpUKNzc3nn32WUaOHAlAYmIi33//PQEBAQCMHz+e3/72t/ZoqjZNX65Hl5JP6PDOqNq17EwVPRJ6U3K1mG//tIsOg0NpHx7QoutvLqPBSElGMWU5pVTklqNQKnBTu+MV5EVghAaVhwzqFW2b3Y6Al19+mUceeYSEhAQ2btzIwoULWb16tcUymzZtIiMjg23btlFYWMikSZMYNmwYYWFhNinr27cvjz32GF5eXpw9e5aZM2eyd+9ePD09AXjiiSeYOXOmvZpIUDvYwmQw0eGOkBZft9JNSfSsWI6vOMLXj23ioa9+gZuXe4tvp6kqdOVk7b9G9pEs9KXVACjdlZhMYKqpveamcFMS2DuQLvd1d2SoQjiUXboEdTodKSkpxMfHAxAfH09KSgoFBZZDjZOSkpg2bRpKpZLAwEDGjBnD1q1bbVY2cuRIvLy8AIiIiMBkMlFYWGiPJhG3kHc8B0+NF76d/Wyyfs9AL+5Zch+6M/l8l7jTJtuwVk1lDRc3nefwkv1c+S4Dv27+RM/uy5A/DWfEa/cw8i/3MOK1e+j32wGEDutE8ZViji0/zM7nvuH6iVz0+RX1/iu8XHjLMn1+BVTIlFXCNdnlDCsrK4uQkBBUqtouHpVKRYcOHcjKyiIwMNBiudDQn65baLVasrOzbVZ2sw0bNtClSxc6duxofu2DDz5gzZo1dO7cmeeee44ePXo0qx1Ew6pLqrl+voAu93a77aHs1ug8qisDnx3CkTcO0nFwKNGPxtpsW7eiO5PPuc9SqC6tpuOgULrFhePh71lnOZWHivY9AmjfI4Du43uQsesy6TvTuPztZaJn9iGgt6ZOHV9fT0pKKm+5bZntXbgq2WuBQ4cO8dZbb7Fq1Srza88++yzBwcEolUo2bNjAb37zG7Zv325OutbQaHxsEa5ZcLCvTddvC4XlBnx9634wA6QfyQYTdBve5ZbLALi7K+uUN7T8z6nV7bh/6TgKTuaxJ3EnPYZ2IWxomNX1m6OmqoYtL+zk1AfH8Q3zZfDTg2nfrb11lX2h/bQY7vrdELY8uYUT7x4j6qEoeoyt+0WqofZQq9vR3gX3ndvliseJLblye9glYWm1WnJycjAYDKhUKgwGA7m5uWi12jrLZWZm0rdvX8Dy7MgWZQDHjh3j+eefZ+XKlYSHh5tfDwn56RrKpEmT+Mtf/kJ2djadOnWy+n3rdKUYjbaZxy442Je8vBKbrNuW9OXVt/z2n3HwKuoQbxR+7Ro8Q9DrjRbljZ1R/Fx5eTX6gjLuXhbHuvGf8MnET3lo6y/w62Lbhz+WZpaw9ddfknssh04jOhM+oSdKd1WTYgfooNXSb94Azv43hTOfn6GirJquN13baqw9ysur0bvgvnM7XPU4sRVnbw+lUtHgF327XMPSaDRERUWxefNmADZv3kxUVJRFdyDUjsRbu3YtRqORgoICtm/fTlxcnM3KTpw4wbPPPsuyZcuIiYmxiCUnJ8f88549e1AqlRZJTLSsqqJKitMK6dDffm3spfFiwieTMeoNbHl0A1VFTUscTZF14Bprx3zM9XMFjFkWR89JEc16XpfKw43oWbF0uLMj6V9d5Op3GS0YrRDOyW5dgosWLSIxMZGVK1fi5+fHkiVLAJgzZw7z588nNjaWhIQEkpOTGTduHADz5s2jc+fOADYpW7x4MZWVlSxcuNAc59KlS4mIiOCFF15Ap9OhUCjw8fHh7bffxs1NelBtpeCsDoCgmObNbNFUAb0CGf/BRDY9/AVbHtlA/JoptPNp2kS7jTn97xPs+dNOfMP8SFg/DV+NullPHL5BoVQQOT0ao97AxS/P4e7jTsidjT+CRQhXpTDJsxdsRroE69LnV9T7YX169QmKLxcx9KURjQ646DG6Oxd3p5l/b2qXYO8HeuEe5GXx2sVN59j2xBZCBmiJ/2/LJC1DtYE9f9xJykcn6XJvN8b+3wN4+Hvesg2s9fP3b6wxcuKdHyi5UsydvxtMx15BjQ+6+Nn7b61c9TixFWdvD6foEhSiISaDkcLzBQRGaGw6OrAhPSb2Ztw7E8g5msWmh9dRnlvWrPUVpRXyRfx/SfnoJP3nD+KBjyfVOwqwJSjdlETNjEXl4UbK6hPUVMqwddE6ScISDld8pZiaipp6h2jbU4+JvYl7Lx7d6TzWjv2YnB+ymrwOk8lE6toUPrvvPxSnFRK3aiLDXhqJUmXbQ83Dz4OoR/tQnlfOyU9P2nRbQjiKJCzhcNdTdaCAgN6BjS9sY+ETejFl8wyU7krWP/gZh5Z+j75Mb1XdglQdXz70OTvmbSUwUsPDO2fRI76XjSP+SUCvQLqO6c61A9coOJtvt+0KYS+SsITDFaTq8O3ij7va8dMkAQTFdmDq148S/kBPjrx+gE/u+oAT7/5AaVbdvv8bD5rc+usvWTN6Nfkncxm15D4mb5pus9k6GtLlvu74dPTh/BepGKoNdt++ELYkw96EQ+nLqim5UkzXMc41R56Xxotx70wg9rE72LfoW/a+uJu9L+4mqE8w6hBv2vl5UHKlmIIz+ejL9HgGetJv7gD6zxuIV5DaYXEr3ZTEPhrL/r/v5/I3lwifYL8zPCFsTRKWcKjr5wvABIGRQY4OpV7aoZ2YuvURCs7puLTlAln7r1KRX0Hhxev4aH2I/EUMHQeF0v3+nrh5OsfhpOmtoePgUK58m0HIAC3eHW0744oQ9uIcR5hos66fL0Dl6eaQ7rOmCOytIbC3Bp4d4uhQrBI+oSd5yTmkb71IzK/6OTocIVqEXMMSDlWcVoh/9/Yt8mRh8RN373aE3d2V/FN5FGcUOTocIVqEnGEJh6kuraY8t5yQgS33ZGFrKKm9gfl2uXu7gwvMdh42qguZ+66Q9tVF+j15p6PDEaLZnP+oE61WcXrts8f8u1s5W3kL0ZfXWMwU0VSu8ngON083utzXnYtfnuP6+QICejn+tgEhmkO6BIXDFKUVonBTOv31K1cWOqwTHv4eXP7mkqNDEaLZJGEJhym6VIhfZz+UbrIb2orSXUWnUV0oulQo17KEy5NPCuEQhioDpddK8LNzd2BbpB3SCZWnSh5BIlyeJCzhEMUZRZiMJrtfv2qL3Dzd0A4JI+9ELpUFtz/YRAhHk4QlHKIorRAU4N/Ntk/5FbU6jeyMAri6R86yhOuShCUcoiitEE3dTOoAACAASURBVG+tD25ezjF/YGvn2d6T4DtCyD6USXVptaPDEeK2SMISdmcymijJKMK/m3QH2lOn4Z0xVBm4mHTB0aEIcVuc/2YS0eqU55ZhqDLg28U1h7M398Zj9MYWi6UpfLv44a314eznKfR9aoBDYhCiOSRhCbsrySgGwK+La16/au6Nxz1GO2ZmeoVCgXZIKBc2nCPvZC7BsR0cEocQt0u6BIXdlVwpRuWpcuhjONqqDndqUXmoSPlInkosXI8kLGF3JVeK8e3kJxPeOoC72p3ucT04v+6M1U9SFsJZSMISdmWoNlCaVeKy169ag8ipUVSXVHNx0zlHhyJEk0jCEnalO5uPyWCS+QMdqOMALX7d/Dn3+RlHhyJEk0jCEnaVdyoPAN/OrjngojVQKBT0nhLJtb1XKMspdXQ4QlhNEpawq7xTubj7tsOjvYejQ2nTek2NwmQ0cWF9qqNDEcJqkrCEXeWdyMU3zA+FQgZcOFJAz0CC+4Vwbt1ZR4cihNUkYQm7qS6tpjDtuly/chK9pkSSl5zD9QsFjg5FCKvYLWGlpaUxffp04uLimD59Ounp6XWWMRgMLF68mDFjxjB27FjWrl1r07IVK1YwYcIEHnzwQaZMmcKePXvMZRUVFfzP//wPY8eOZfz48ezatauFW6TtyUvOARP4yQhBp9BrcgQo4LwMvhAuwm4zXbz88ss88sgjJCQksHHjRhYuXMjq1astltm0aRMZGRls27aNwsJCJk2axLBhwwgLC7NJWd++fXnsscfw8vLi7NmzzJw5k7179+Lp6cn777+Pt7c333zzDenp6Tz66KNs27YNb29vezVZq5OXnAOAT5gkLGfg3dGHsBFdOL8+lUEv3CXdtMLp2eUMS6fTkZKSQnx8PADx8fGkpKRQUGDZFZGUlMS0adNQKpUEBgYyZswYtm7darOykSNH4uXlBUBERAQmk4nCwkIAvvrqK2bMmAFAt27d6NOnD999952NW6p1yz+Vh3eIN+182jk6FPGjHg/2piitEF1KvqNDEaJRdklYWVlZhISEoFKpAFCpVHTo0IGsrKw6y4WGhpp/12q1ZGdn26zsZhs2bKBLly507NgRgMzMTDp16tRoPWG9/FO5aKKCHB2GuEn3+3ugUCq4JDcRCxcgk98Chw4d4q233mLVqlUtul6NxqdF1/dzwcG+Nl1/S9JX6Ll+voCe43rg6+vZrHW5uyvrrKMp66yvfnO372z1Gyr3aKdCVW4AwN/Hk9BBoVzadJ7h84daHYOHnwdeAV5WL+9IrnSc2IMrt4ddEpZWqyUnJweDwYBKpcJgMJCbm4tWq62zXGZmJn379gUsz45sUQZw7Ngxnn/+eVauXEl4eLj59dDQUK5du0ZgYKC53pAhQ5r0vnW6UoxGU5PqWCs42Je8vBKbrNsWco5lYzKY8OsRQElJZbPWpdcbLdbh6+vZpHX+vH5zt+9s9Rtrj/LCKovZ5r20Plw7eI19Kw7hHWLdNdreD/SitKbG+qAdxNWOE1tz9vZQKhUNftG3S5egRqMhKiqKzZs3A7B582aioqLMyeCG8ePHs3btWoxGIwUFBWzfvp24uDiblZ04cYJnn32WZcuWERMTUyeWNWvWAJCens7JkycZOXKk7Rqplcs/mQsgXYJOKCg2GID8kzkOjkSIhtmtS3DRokUkJiaycuVK/Pz8WLJkCQBz5sxh/vz5xMbGkpCQQHJyMuPGjQNg3rx5dO7cGcAmZYsXL6ayspKFCxea41y6dCkRERE8/vjjJCYmMnbsWJRKJa+88go+Prbt4mvN8k/m0s7PA99OvmSfkA9GZ+Lh74lfN3/yTuTSdUx44xWEcBC7JawePXpY3AN1w7vvvmv+WaVSsXjx4nrr26Js3bp1t4xXrVazbNmyW5aLpsk/lUdQn2AZOu2kgmI7cGnTeSryy+U5ZcJpyUwXwuaMBiO6M3kEyRNunVZQn9q/jS4lz8GRCHFrkrCEzRVevE5NeY35Q1E4Hy+NF+oQb/JPy/1YwnlJwhI2d2PARfCPF/eFc9LEBFOUVoi+XJ5ELJyTJCxhc/mn8lB5qGjfK7DxhYXDaKKDwGjieqrO0aEIUS9JWMLm8k/lERihQeWucnQoogF+Xfxx92lH/mm5jiWckyQsYXO6lDw0MdId6OwUSgWaKA0FZ3UYDUZHhyNEHZKwhE2V55VTkVcuNwy7CE10MIbKGorSCh0dihB1WJ2wcnLkZk/RdAVnakedBUrCcgkBvQNRuCll9nbhlKxOWPfccw+zZ89m7dq1FBcX2zIm0YrofkxYmmjpEnQFKg83AnoGoDudh8lkm3kwhbhdVicsLy8vDh48yMKFCxkxYgRPP/00X331FdXV1baMT7g43Zk8vILUqINl9gRXoYkOplJXQXluuaNDEcKC1VMzHTx4kAMHDrBz5052797N9u3b2bFjB2q1mnHjxjFjxgz69etny1iFCyo4ky/Xr1yMJjqI81/UDpaxdvZ2IezB6jMsNzc3RowYwcKFC3n77bcZPnw4JpOJsrIy1q9fz4wZM3jvvfdsGatwMUaDkYKzOgKjJWG5Eo/2nvh08kUnw9uFk2nSoIv333+fhIQEJk2axL59+/Dw8GDixIm88cYbREZGWkxkK0Tx5SJqKmrkDMsFaaKDKL5chL5MuvyF87C6S/Cee+7BZDJhMpmIiopi6tSpPPjgg/j61j69Micnx/zIECEA80gzjZxhuRxNTDCXv0lDdyafjgNDG68ghB1YnbC8vb2ZMGEC06ZNq/OwQ4Bp06aZnzklBPw4pF0BAb01jg5FNJFPJ1/a+XmgOy0JSzgPqxPWggUL6NOnj0WyysnJIT8/n5iYGHx8fOQBh8KCLiUP/+7tcVe7OzoU0UQKhQJNdBC5x7Ix1hhRuskcA8LxrN4LX3nlFbZs2WLx2vvvv8/UqVNbPCjROuhkhKBL00QHY6gyUHjpuqNDEQKw4gzr8OHDAJhMJrKyssy/G41GTp06hVIp37xEXfpyPUVphfSaEunoUMRtat8rAKW7koKUfAKlW1c4gUYT1qxZs1AoFCgUCrZt28a2bdssyrt06WKz4ITrKkjVgUlmuHBlKncVAb0C0aXk0yOhNwqFwtEhiTau0YQVGlp7wTUzMxNvb2/8/f0BUKlUdOzYkWeeeca2EQqXVHBGRgi2BoHRQehS8inPKcO7o1yjFo7VaMLauXMnAPfeey8PPfQQ8+bNs3lQwvXpzuTj5uWGX1d/R4cimkETFcR5am9RkIQlHM3qUYI3EpcQ1ihIyScgQoNSJdc4XZmH/4+zXpzJp8u93RwdjmjjGkxYUVFRzJ49m8TERKKioupdRqFQkJKSYpPghOvSncmj69hwR4chWoAmOojL29PQl1Xj7t3O0eGINqzBr783Zra4+eef/zMa5cmkwlJ5bhkV+RUy4KKVCIwKAhMUnNU5OhTRxjV4hrVjxw7zzcA7duywS0DC9ZmfgSX3YLUKvmF+tPNth+5MPiEDtI4OR7RhDSasTp061fszwNGjR/H19aV37962iUy4LHnKcOuiUCoIjAoi70QuRoNRrksKh7F6z1u4cCEJCQmYTCZ+//vfM3PmTBISEvj4449tGZ9wQbqUfHloYyujiQ7CUFlDUVqho0MRbZjVCWvfvn107NiRiooKvvrqK3r37o23tzcfffSRVfXT0tKYPn06cXFxTJ8+nfT09DrLGAwGFi9ezJgxYxg7dixr1661adnevXuZMmUKffr0qTPT/PLlyxk2bBgJCQkkJCSwePFia5uqzdOdyZf7r1qZgF6BKFQKCn6cgV8IR7B6WHteXh6jRo3iwoULGI1Gli5dyscff8zGjRutqv/yyy/zyCOPkJCQwMaNG1m4cCGrV6+2WGbTpk1kZGSwbds2CgsLmTRpEsOGDSMsLMwmZZ07d+bVV1/l66+/prq67nN/Jk2axAsvvGBtEwl+fGhjaj59ZsvTp1sTlYcb7Xv+OOvFg3IZQDiG1WdYarWaM2fOsGXLFtzc3AgPD0ev1+Ph4dFoXZ1OR0pKCvHx8QDEx8eTkpJCQUGBxXJJSUlMmzYNpVJJYGAgY8aMYevWrTYr69q1K9HR0bi5WZ23RSOK04swVBrkKcOtkCY6iIr8csrzyhwdimijrE5YgwcP5vjx46xevZqhQ4fi7u5Oamoq3bt3b7RuVlYWISEhqFQqoHZapw4dOpCVlVVnuRtTQQFotVqys7NtVtaYLVu2MHHiRB577DGOHTtmVZ22TpdS+1h1GSHY+tz4m+qkW1A4iNWnFq+++irdu3fHYDAwe/Zsqquruffee+t9mGNrMGPGDObOnYu7uzv79u3jqaeeIikpiYCAAKvXodHYdiqb4GBfm67/dpy6XAwK6D28W73PwSosN+Dr69msbbi7K+usoynrrK9+c7fvbPUbKr/d7fv6euIb6ktRagFqdTvaO+H+Vx9nPE4cyZXbw+qE5efnx7PPPmvx2tNPP21VXa1WS05ODgaDAZVKhcFgIDc3F61WW2e5zMxM+vbtC1ieHdmirCHBwT/d9Dp8+HC0Wi3nz59n8ODBVr1nAJ2uFKPRZPXyTREc7EteXolN1t0cV45k0j48gMKySiirrFOuL6+mpKTu602h1xst1uHr69mkdf68fnO372z1G2uP5my/faSGK7svcz27BL1adVvrsCdnPU4cxdnbQ6lUNPhF3+ouwZKSEpYtW8bcuXP55S9/af43e/bsRutqNBqioqLYvHkzAJs3byYqKorAwECL5caPH8/atWsxGo0UFBSwfft24uLibFbWkJycHPPPZ86c4dq1a1Z1f7Z1BWfy5f6rVkwTHQRGE1f3Zjg6FNEGWX2G9dxzz7Fnzx7zVE03WPuMnEWLFpGYmMjKlSvx8/MzDyOfM2cO8+fPJzY2loSEBJKTkxk3bhwA8+bNo3PnzgA2KTty5AgLFiygtLQUk8nEli1beO211xg5ciRvvPEGp0+fRqlU4u7uztKlSy3OukRd+jI9RemF9J5a/7yTwvX5dfHH3dudjN2XifxlX0eHI9oYqxPW4cOH8ff359FHH8XX17fJD3Pr0aOHxT1QN7z77rvmn1Uq1S3vd7JF2cCBA/nuu+/qLfv5fVmicQWp+WCSGS5aM4VSQWBkEFf2ZGCsMaJ0k1kvhP1YnbC6detGZGSkPLCxrauoQV+mr7co93DtqE//jj7o8yvqr6+XyZJdnSY6iJyjWWQfySR0aJijwxFtiNUJ6/777+ftt9+md+/eREZGWty7NGjQIJsEJ5yPvkzPuaTz9ZZd2nYRpbuS7FO55Pw4vP3neoyW64CuLqC3BoWbksvbLknCEnZldcJ64403UCgULF261OJ1eR6WuKEsqxTvjj4olE3rLhauxc3LDe0ALenfpDFs4ShHhyPaEKsTljVDwUXbVpZViiZGBqa0BV3u6cqBv35PUVoh/t3bOzoc0UZYnbB27txpyziEi6suqUJfpse7o21vlhbOoes93Tnw1+9J++oCdzw10NHhiDaiSUN8CgsLWbNmDX/7298oKSnh8OHD5OfLNC2i9uwKwFsrCast8OvshyY6iEtJFxwdimhDrE5Y58+f54EHHmDRokWsWrUKqL2Havny5TYLTriOsuwfE5acYbUZ4RN6kX04k/JcmQxX2IfVCeuvf/0rxcXF5ptufX19GTRoEPv377dZcMJ1lGaW4u7bjna+7RwdirCT7g/0BBOkbb3o6FBEG2F1wjpx4gRjxoxh9OjR5tdCQ0PJzc21RVzCxZRll8rZVRujiQ7Cr6s/adItKOzE6oTl5eVFeXm5xWupqam0by8jhNo6k9FEeXaZXL9qYxQKBeETenJ1TwZVxVWODke0AVYnrAEDBrB37162bdsGwNSpU0lOTmbgQBkh1NZV6Mox1hjxkTOsNqf7A70w6o1c/uaSo0MRbYDVCesPf/gDnTp1Ijs7G5PJxKlTpwgLC2PBggW2jE+4ABkh2HZ1HKhF3cGbS5vrn/1EiJZk9X1YgYGBJCYmUlJSgk6nQ6PREBcXh5eXly3jEy6gLKsUFKAO8XZ0KMLOFEoF4fE9OfPJKfSl1bj7yKAbYTtWJayvv/6al19+maKiIvNr/v7+qNVq82M7RNtVll2KV5AaVTvnf6CfaHk9EyI4tSqZ9O1p9JoU4ehwRCvWaJfgiRMneO655ygsLMRkMpn/FRYWsmDBAk6ePGmPOIUTK8sqkxGCbVjHwaGoO3hzcWOqo0MRrVyjCWvVqlXU1NQwYcIEkpKSSE5OJikpiQceeICamho+/PBDO4QpnJWh2kCFrlyuX7VhSpWS8PieXN6Rhr602tHhiFbMqjOs7t2787e//Y3w8HA8PDwIDw/nb3/7G926deP48eP2iFM4qbLsUjDJgIu2rmdCBIZKA+kyWlDYUKMJ6/r169x5550olZaLqlQq7rzzTgoKCmwWnHB+N6Zk8pGE1aaZuwW/POfoUEQr1uigi8rKSgoKCtixY0edsoKCAqqq5IbBtqwsqxSluxLPQBkt2pYpVUp6TOxFyscnqS6tpp2MFhQ20GjCMplM7N69m927d9shHOFqyrLL5KGNAoCekyI4+f5x0pIuEPFwtKPDEa1QowlLHtwoGlKWVYomOsjRYQgn0HFwKL5d/Dj/xVlJWMImGk1Y8uBGcSvVJdXoS6tlSLsAaucW7DU5kmP/PEx5XjnqYLWjQxKtTJMe4CjEzczPwJIBF+JHvaZEYjKYuPil3JMlWp4kLHHbSq+VAOATKglL1NJEBaGJDuLcurOODkW0QpKwxG0rzSzBw98Dd28ZESZ+0mtKJDlHsihKL3R0KKKVkYQlbltZZgneob6ODkM4mV5TIgE4L2dZooVJwhK3xaA3UJZbjk8nSVjCkm+YH51GdObsmtOYTCZHhyNaEbslrLS0NKZPn05cXBzTp08nPT29zjIGg4HFixczZswYxo4dy9q1a21atnfvXqZMmUKfPn1YsmSJ1bEIKM8uA6NJrl+JekU8HE1xehHZBzMdHYpoRax+HlZzvfzyyzzyyCMkJCSwceNGFi5cyOrVqy2W2bRpExkZGWzbto3CwkImTZrEsGHDCAsLs0lZ586defXVV/n666+prq62OhZRe/0KkDMsUa8e8b34LnEnZz87jXZoJ0eHI1oJu5xh6XQ6UlJSiI+PByA+Pp6UlJQ68xAmJSUxbdo0lEolgYGBjBkzhq1bt9qsrGvXrkRHR+PmVjdvN1RP1I4QVHmq8AyQKZnaIiWgz6+45T8qDXQfF86FDeeouFJct7yixtFvQbggu5xhZWVlERISgkpV+4A/lUpFhw4dyMrKIjAw0GK5m2fW0Gq1ZGdn26yssZhvp15bUZpZgo/WV6ZkaqP05TVc3J3W4DLqjt7oS6vZ//cDhNzZ0aKs9wO9cPeyWwePaCVkj7Ehjca213eCg+3fHVdYbsDH24OyrFI6D++Mr69nk+q7uyubXMeadTRlnc2NwRXqN1Ruq7/Bz/n003JO40X+sWx63t3Nokytbkd7O+2/jjhOnJkrt4ddEpZWqyUnJweDwYBKpcJgMJCbm4tWq62zXGZmJn379gUsz3JsUdZYzLdT72Y6XSlGo21GSQUH+5KXV2KTdTdEX15NbnoBhioDHkFqSkoqm1Zfb2xyncbW4evr2aR1NjcGZ6/fWHvY4m9wKx0GaLn8zSXyLl+3mNG/vLwavR32X0cdJ87K2dtDqVQ0+EXfLtewNBoNUVFRbN68GYDNmzcTFRVl0R0IMH78eNauXYvRaKSgoIDt27cTFxdns7KG3G69tqD02o9TMnWSEYKiYR0H137JyzokowVF89mtS3DRokUkJiaycuVK/Pz8zMPI58yZw/z584mNjSUhIYHk5GTGjRsHwLx58+jcuTOATcqOHDnCggULKC0txWQysWXLFl577TVGjhzZYL22rjSzBIVSgXeIJCzRMM/2ngRGaMg+lEm3sd1RqOTWT3H77JawevToUe+9TO+++675Z5VKxeLFi+utb4uygQMH8t133zW5XltXeq0EdYg3Sjf58BGN0w7txOkPT6A7qyMoJtjR4QgXJp84oklMJhOlV4vx7ezn6FCEiwiMCqKdbzuyD15zdCjCxUnCEk1SmlmKvkyPb5gkLGEdpUpJyKBQdGfyqSps3mAP0bZJwhJNkncqFwCfMNcdGivsTzvkx8EXcpYlmkESlmiS/NN5KFQKfGSWdtEEXho1gREasg5cw2gwOjoc4aIkYYkmyTuVi7fWRwZciCYLvasz1SXV6E7lOToU4aLkU0dYzWQykX86T65fidsSGKnBM9CLa/uuODoU4aIkYQmrFacVUl1SLSMExW1RKBVoh3Wi6FIhBecLGq8gxM9IwhJWyz2eAyAJS9w27eBQFG5KUj495ehQhAuShCWslns8B5WHCnWIt6NDES7K3bsdIf1DOL8xlUoZ4i6aSBKWsFpecjaayCCUMr2OaIZOI7tQU1HDmY9OOjoU4WLkk0dYxWgwkncil+A+HRwdinBxPqG+aIeEcuL9Yxj0BkeHI1yIJCxhlcLzBejL9AT1kbngRPPFzupLWWYpaUkXHB2KcCGSsIRVbjweIuSOjo0sKUTjuozuhl83f5L/9YOjQxEuRBKWsEr24Uy8grzw6yIjBEXzKZQK+j15JzlHs2S6JmE1SVjCKtmHM+k4MBSFQuHoUEQrETmjD56BnhxbccTRoQgXIQlLNKoiv5yiS4Xmp8cK0RLcvd3p89gdpG+9SME5naPDES5AEpZoVPaRLAA6DpKEJVpW7OP9cfNy47icZQkrSMISjco+nInSXUlwvxBHhyJaGS+NF1GP9OHc52cozSpxdDjCyUnCEo3KPpRJcN8Q3DzdHB2KaIX6/XYAJqOJ4yuPOjoU4eQkYYkGGaoN5B7Plu5AYTN+XfyJmBbN6X8nU55T5uhwhBOThCUalH8yF0OVgY6DtI4ORbRiA/5nMMZqI8dWyrUscWuSsESDbtwwLCMEhS35hwfQ66HI2rOsvHJHhyOclCQs0aDM76/g180f7xAfR4ciWrkBzw6hpqKG5LflLEvUTxKWuCVjjZHM768SNrKLo0MRbUBAz0B6TY7k5PvHKcspdXQ4wglJwhK3lHcih+qSasJGScIS9jH4D8Mw6o0cfeOgo0MRTkgSlrilq3uuABB6V2cHRyLaCv/wAKJmxpLy0UmK0godHY5wMpKwxC1d25NBYFQQ6mC1o0MRbcjA54agdFdyaMn3jg5FOBm7Jay0tDSmT59OXFwc06dPJz09vc4yBoOBxYsXM2bMGMaOHcvatWsdVrZ8+XKGDRtGQkICCQkJLF68uIVbxLkZqmrIOnSNsJFydiXsyzvEh75P3Mn5L86Sm5zj6HCEE7Hb1AUvv/wyjzzyCAkJCWzcuJGFCxeyevVqi2U2bdpERkYG27Zto7CwkEmTJjFs2DDCwsLsXgYwadIkXnjhBXs1kVPJPpKFodIgAy6EQ/R/ZhBnPj7J3hd3MXnTdHlKgADsdIal0+lISUkhPj4egPj4eFJSUigoKLBYLikpiWnTpqFUKgkMDGTMmDFs3brVIWVt3bU9GSiUCrTDwhwdimiDPPw8GPKnEWQfyuTC+lRHhyOchF3OsLKysggJCUGlUgGgUqno0KEDWVlZBAYGWiwXGvrTDaparZbs7GyHlAFs2bKFvXv3EhwczDPPPEP//v2b9L41GtveuxQc7GuzdecczCR0YChhPYIsXi8sN+Dr63nb63V3Vzar/q3W0ZR1NjcGV6jfULmt/gZNoVa3o30j++/I+UNJ/c8pDr66l4Ez+9HOu91tbcuWx4krcuX2kNlMb2HGjBnMnTsXd3d39u3bx1NPPUVSUhIBAQFWr0OnK8VoNNkkvuBgX/LybDO7dWVhJVcPXKX/M4PqbENfXk1JSeVtr1uvNzarfn3r8PX1bNI6mxuDs9dvrD1s8TdoqvLyavRW7L9DF41i/YNr+PqlHQz904gmb8eWx4krcvb2UCoVDX7Rt0uXoFarJScnB4PBANQOdsjNzUWr1dZZLjMz0/x7VlYWHTt2dEhZcHAw7u7uAAwfPhytVsv58+eb2RKuIWNHGiaDiW7jwh0dimjjtEM7EfFwNMf/eYT803mODkc4mF0SlkajISoqis2bNwOwefNmoqKiLLoDAcaPH8/atWsxGo0UFBSwfft24uLiHFKWk/PT6KQzZ85w7do1unfvbtuGchLpX1/CK0hNyJ0y4a1wvOGv3I1Hew92L9iG0WB0dDjCgezWJbho0SISExNZuXIlfn5+LFmyBIA5c+Ywf/58YmNjSUhIIDk5mXHjxgEwb948OneuHVZt77I33niD06dPo1QqcXd3Z+nSpQQHB9u8nRzNoDeQsTOd8PieKJQyMkvYhhLQ51dYtawKGJY4nJ2/387xfxwidnY/3L3dwUuuaLQ1dvuL9+jRw+I+pxveffdd888qleqW9zvZu+xGQm1rsg5co7q4iu5xPRwdimjF9OU1XNydZvXyJpOJwKggDr1xgBqjif6/vgN3SVhtjsx0ISykf30RlYeKsFFdHR2KEGYKhYLe06JQtVNx5uOT1FTVODok4QCSsISZyWQifeslwkZ1qe1yEcKJePh5EDE9mrLMUg6/ccDR4QgHkIQlzK6n6ijOKKLbOOkOFM5JEx1MpxGdOfXRSS4lXXB0OMLOJGEJs/MbUlEoFXQbL8PZhfMKn9CT4NgObH/qK3QpMtS9LZGEJQAwGU2c+/wMYaO6yNOFhVNTuqsYu3w8Hn7tSPrlRiryyx0dkrATSVgCgKxD1yjJKKb3tChHhyJEo7w7eDP+3wmU55bx1ewv0ZfpHR2SsANJWAKAc2vP4KZ2J/yBXo4ORQirhPTvyJgV95NzNIuvZm+kplJGDrZ2krAENZU1XNh4jvAJPWV0oHApPSb25p5/jOPqdxlse2ILhmqDo0MSNiQJS5C+7RLVfobyQwAAEKFJREFUxVVEPBzt6FCEaLLIGTGM+uu9pG+9yJZHN1BdWu3okISNSMISnP30FN4dvek0Qp4uLFxTn8fu4J63xnFtbwYbp6yVgRitlCSsNq7gnI6MHelE/7IvSpXsDsJ1Rf2iD/d/+CAFZ/P5PO4T8k7kNF5JuBT5hGrjTvzfD6g8VfT5VT9HhyJEs3WL68GkjdMxGox8Ef9fklcnOzok0YIkYbVh5XnlpH6WQsS0aLyC1I4OR4gWEdK/I9O+mUnInVo2zN7AN79NoqqoeQ+sFM5BElYbduqD4xiqDPSbO8DRoQjRotTBah78fCqjF4/mwoZU1tzzERk7rZ8dXjgnSVhtlL5cz+kPk+k6LpyAXoGNVxDCxSjdlNy98G6mbJ6Bm6cbm2es5+vHN1Ga6byPiBcNk4TVRh1fcYSK/ArufGaQo0MRwqZCBmiZvmsWg/84nPRvLvHJXR9yfOURDHq5Z8vVyBPQ2qDSrBKOrThMjwd7ox3SydHhCNFk1j6xuLDcgL689r6sfrP60v2ebnz/2l6+X/QdZ/97mqEvjqDruHAUCnm6tiuQhNUGHXxtH8YaE8P+30hHhyLEbbH2icW+vp6UlFgOuOge35PIqVEc/Pt+kmZtJGRARwYnDidsVBdJXE5OugTbmJxj2aR+lkK/J+/Er6u/o8MRwu4UCgXd7uvOL/b9itFvjKUsu4xN09axccpaMg9cdXR4ogGSsNoQfZmeHU9vRR3izYD/GezocIRwKJW7iuiZsTx64NeM/Ms9FJ6/zoYHP2Pd/Z9ycdM5jDVGR4cofka6BNuQPX/aSeGFAh5cO5V2vh6ODkcIp6DycCP28f5E/qIPqf89zfF/HeXrxzfj3dGbyEf6EDkjBv9u7R0dpkASVptx7vMznP30NAMWDCFsVBdHhyOE03FXu9PnsTuInt2Xy9sucfqjkxx98yBH3zhIh/4h9Hgwgq5juxPQK1CudTmIJKw24OqeDHYt2IZ2SCcG/X6Yo8MRwuEaG2UYNqgTYYM6UZpZwsWvLnDpqwvsX/wd+xd/h4/Wh9BhYYQMCiW4Xwfa9wjAM8DLfsG3YZKwWrlr318haeYG/Lu3Z/wHE1G6yWVLIawdZQjgpfUh5rE7qCyooOCcjuupBVz5LoNzX5w1L+MR4Il/9/b4d2+PXxd/1B3UqDt4ow7xQd1BjXeIN25e8qy55pKE1Ypd2nKe7fO24tvFnwc/nybzBQrRDJ6BXoQODSN0aBgR9/ekJKsU3dl8ijKKKb5cSNHlIrK+v8qFDamYDKY69dv5tsMrSI06WI1PiDcegV6og9Sog71RB6nx7uiNb5gfqnYqq+Jx93YHr7b1Ed623m0bUVNZw/eLvuXUqmSC7whhwkeTUAdLshKipdRUGMg8ng2AZwc1nh3UdBgUCoDJaEJfVk11STXVxVVUF1dTVVKFvqSaquIqynLLKL1WQlluGUb9z0YiKhV4BXriFextPkvzCq79v51PO4tFez/QC3dJWMJVmYwmzq8/y6El31OcXkS/uQMY+tIIq7+xCSGaT6FU0M7Xo3Ykbqhvvcv0GN2dC7suYagy/JjUqqgqqqI8t4zyvHL+f3v3HtPkvcYB/NsLlxap3LRAlOENDkwyzphzc7ooCgxtgRkqJ04yMxET2JzE/cGyTMg6spFsQTdlc8sys5zEnWNiQIkhYoAwNWIxG94QJiggV2mp2CKX0uf8wTk9MkSdru1b+3wSEtrn/b383oeWh/fS5713exiDvxtA911aL5V7QK70gY/SZ7KQKbwwZ1koZoXOcpuLQBxWsG7cuIH8/HwYjUb4+fmhuLgY4eHhU5aZmJjAp59+il9++QUikQjZ2dnQaDSCiwmNudeE349eQ9PhKxhs1iMwOgiqf21E2JpwZ0+NMTYDkUgEqbcUUm8p5HN9psXJShgZHMG922YM9w9juN8Mc58Zty/2wzI8jtZjLQAmDw36LQlAQEQg/Bb7QxE2G75hCijCZkM2R/5MFTOHFayCggJs3rwZqampKC8vx549e/DTTz9NWeb48ePo6OjAyZMnYTQakZaWhldffRXz5s0TVMyZLPfGYWw1oq+/Hb/X3EDPua7JO6sSMPfvSiQcXI/FqZEQiZ+dFylj7kgkFkEWKIMsUIaAv02NjZnGELjAH0N9Jgy26DHYYsCtunY0//vqlOWkcil858+GIkwB33kKBIX7g+RSyIJkk4cdg2SQzZHDY5anSxQ2hxQsvV6Pq1ev4scffwQAqFQqaLVaGAwGBAT8/9YWJ06cgEajgVgsRkBAANatW4fKykpkZWUJKva4xE9QNIb7zWj65yWMDo3Bcm8cluFxjN+zYGxoFPcGhjF6Z9S2rMRLgqCYuYj6RzTCkxZBEWb/VksSDzG8FU/+oWOJp+Spxj9oHZ4+nvAWTT/Jba85CH38o/Jhj9+BUMfPlAtXmf9MvBVemLdiHqT+3lOet9yzwNR91/Zl7rqLu113Yeo2ob+hB53VNx88Hy8JpHIPeMg8IJVJ4SGXQirzhEQmhYdMCrGHGBCLIJaIIRKLIZYAIqkYIpEIIokIIokYIrEIZCVIvSSISI+Cl5/3A3/Wwzzqb6ZDClZPTw+USiUkkslzKRKJBHPnzkVPT8+UgtXT04PQ0FDb45CQEPT29gou9rj8/afv5j9KYOAszC9S/ulxDhM4C8rFQU+1itCYp9++p10Hj+ffgauPn4ly3rPblYM/lMMYY8wlOKRghYSEoK+vDxMTkzdMm5iYQH9/P0JCQqYt193dbXvc09OD4OBgwcUYY4w5nkMKVmBgIKKiolBRUQEAqKioQFRU1JTDgQDwxhtv4MiRI7BarTAYDDh16hSSkpIEF2OMMeZ4DrtKsLCwEPn5+SgtLYVCoUBxcTEAYPv27di5cydiYmKQmpqKxsZGJCYmAgByc3Mxf/58ABBUjDHGmOOJiOjxL69ijDHGnIQvumCMMeYSuGAxxhhzCVywGGOMuQQuWIwxxlwCFywnGBwcxPbt25GUlAS1Wo13330XBoMBAPDbb78hJSUFSUlJeOedd6DX623j7BETkv379yMyMhItLZNNPd01F6OjoygoKEBiYiLUajU+/vhjAJMNpDMyMpCUlISMjAzcvHnTNsYeMSGoqalBWloaUlNToVarcfLkSQDuk4vi4mLEx8dPeV8Ajt9+weSGmMMNDg7SuXPnbI8///xz+vDDD8lqtdK6detIp9MREdGBAwcoPz+fiMguMSG5fPkybdu2jVavXk3Nzc1unQutVktFRUVktVqJiOj27dtERJSZmUllZWVERFRWVkaZmZm2MfaIOZvVaqWXXnqJmpubiYioqamJYmNjaWJiwm1yodPpqLu7m9asWWPLA5HjXwtCyQ0XLAGorKykt99+mxobG2nDhg225/V6PcXGxhIR2SUmFKOjo7Rp0ybq6OiwvTHdNRcmk4ni4uLIZDJNeX5gYIDi4uLIYrEQEZHFYqG4uDjS6/V2iQmB1Wqll19+mRoaGoiI6Pz585SYmOiWubi/YDl6+4WUG76Bo5NZrVYcPnwY8fHx0xruBgQEwGq1wmg02iXm5yeMJpn79u1DSkrKlA9mu2suOjs74efnh/3796O+vh4+Pj54//334e3tPWMDaSL6y2N/7ELjDCKRCHv37kVOTg7kcjnMZjMOHjz40Gbaz2ou7ufo7RdSbvgclpNptVrI5XJs2bLF2VNxil9//RWXLl3C5s2bnT0VQbBYLOjs7ER0dDSOHj2KDz74AO+99x6Gh4edPTWHs1gsOHjwIEpLS1FTU4NvvvkGeXl5bpkLNon3sJyouLgY7e3t+PbbbyEWi6c13DUYDBCJRPDz87NLTAh0Oh3a2tqwdu1aAEBvby+2bduGzMxMt8sFAISGhkIqlUKlUgEAXnjhBfj7+8Pb29vWQFoikUxpIE1Ef3lMCJqamtDf34+4uDgAQFxcHGQyGby8vNwuF/e7v5m4I7ZfSLnhPSwnKSkpweXLl3HgwAF4enoCAJYuXYqRkRE0NDQAAH7++WckJyfbLSYE2dnZOH36NKqrq1FdXY3g4GD88MMPyMrKcrtcAJOHKZcvX44zZ84AmLw6S6/XIzw8fMYG0g9rLv2kMSEIDg5Gb28v2traAACtra0YGBjAc88953a5uJ89ttFlcuPYU2aMiKilpYUiIiIoMTGRUlJSKCUlhXJycoiI6MKFC6RSqSghIYG2bt1qu0LMXjGhuf/ksrvmoqOjg7Zs2UIqlYrS0tKotraWiIiuX79O6enplJiYSOnp6dTa2mobY4+YEJSXl5NKpSK1Wk1qtZqqqqqIyH1yodVqadWqVRQVFUUrVqyg9evXE5Hjt18oueHmt4wxxlwCHxJkjDHmErhgMcYYcwlcsBhjjLkELliMMcZcAhcsxhhjLoELFmPPuFu3biEyMhKRkZHOngpjT4U7XTDmAjo7O1FcXIwLFy7AZDLB398fS5YsQUFBAcLCwpw9PcYcggsWYy4gNzcXzc3NeOWVVxAeHo6+vj7odDr09/c7pGBZLBZIpfzngjkXvwIZEzij0Yjm5mYoFAocOnQIIpEIADA2Nmbr65aXl4fr16/DZDLB19cXK1euxJ49e6BQKB64zt27d0On08FgMMDDwwMxMTH46KOPbIcN4+Pj0dXVhV27dqGiogJtbW3YuXMn9u7di4yMDHzyyScAgO+++w5ffvklNm3aBK1W65iEMLfF57AYEzgfHx/I5XIMDQ0hLS0Nn332GU6dOgWLxQKZTAaz2YyRkRHEx8dDo9Fg9uzZOH78OL744osZ19nV1YVly5ZBo9Hg+eefR319PXbt2jVtua+//hoRERFISEjAxo0bIZFIUFlZibGxMQBAdXU1ANia9TJmT1ywGBM4Dw8PFBUVwdfXF9euXcOhQ4eQm5uLhIQEXLx4EQsWLIBWq8XChQshk8mwePFiAEB9ff2M69y3bx9iY2Ph4+Nj26tqa2tDX1/flOV27NiBkpISfPXVV1AqlXj99ddx584d1NbWQq/Xo7GxEUqlEsuWLbNfAhj7Lz4kyJgLWL9+PdauXYvz58+joaEBR44cwcDAAEpLS6FSqbB79+5pYwwGwwPXdfPmTbz55psPvK/U4OAglEql7fGLL744JZ6eno6amhocO3YMQ0NDsFqt2LBhA8Ri/t+X2R+/yhgTuPHxcTQ0NMDLywurVq1CXl4esrOzAQBmsxknTpwAAGg0Gly6dAklJSUAgJn6WtfW1mJ4eBgRERFoaGjA2bNnbbE/jvnfrW/+Z/Xq1ZgzZw5qa2tRVlYGAFCr1X/NhjL2CLyHxZjAjY2N4a233sKiRYsQFRUFmUyGqqoqAMBrr71muyllXV0dCgsLUVdX99D1BQUFAQDa29tRVFSEpqamx56LVCpFWloavv/+e+h0OixcuBDR0dFPuGWM/Tm8h8WYwHl5eWHr1q3w9PREXV0dysvLoVAokJOTg6ysLOTm5mL58uUwGo24cuUKduzY8dD1JScnIz09HVKpFGfPnn3k8n+k0Whs3/PFFsyR+H5YjLE/LTk5GW1tbaiqquIPLjOH4UOCjLHHdvr0aZw5cwY3btzAypUruVgxh+KCxRh7bBUVFTh27BiWLl2KwsJCZ0+HuRk+JMgYY8wl8EUXjDHGXAIXLMYYYy6BCxZjjDGXwAWLMcaYS+CCxRhjzCVwwWKMMeYS/gNWIcl02t2Q1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set()\n",
    "plt.hist(sal_data['salary'])\n",
    "plt.title('Salary Variance', weight = 'bold', fontsize= 16)\n",
    "plt.xlabel('Salaries', weight = 'bold')\n",
    "plt.ylabel('Frequency', weight = 'bold')\n",
    "plt.show()\n",
    "\n",
    "sns.distplot(sal_data['salary'], hist=True,kde = True, color = 'purple')\n",
    "plt.xlabel('Salary', weight= 'bold')\n",
    "plt.ylabel('Density', weight= 'bold')\n",
    "plt.title('Density Distribution of salaries', weight = 'bold', fontsize= 16)\n",
    "plt.subplots_adjust(wspace=0.7, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- According to the histogram above, we notice that the data is skewed to the right showing that the distribution of salary is between 40 000 and 60 000\n",
    "\n",
    "- The density distribution is a variation of a histogram with its peaks showing that more people are getting salary of 40 000 , 45 000 and 60 000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average salaries of men and women."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "men = sal_data[sal_data.Gender == 'men'].salary\n",
    "women = sal_data[sal_data.Gender == 'women'].salary\n",
    "\n",
    "# plotting histogram\n",
    "plt.hist(men, label='men', color=\"blue\")\n",
    "plt.hist(women, label='women', color=\"green\")\n",
    "plt.xlabel(\"Salaries\")\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Average salaries of men and women', weight= 'bold', fontsize= 16)\n",
    "\n",
    "#plotting axline to illustrate the average of men and women\n",
    "plt.axvline(men.mean(), color='red', linestyle=':')\n",
    "plt.axvline(women.mean(), color='black', linestyle=':')\n",
    "plt.legend()\n",
    "\n",
    "f, axes= plt.subplots(1, 2, figsize=(10,5))\n",
    "sns.barplot(x=\"Gender\", y=\"salary\", data=sal_data, ax= axes[1])\n",
    "sns.boxplot(x='Gender', y='salary', data=sal_data, palette=\"deep\", ax=axes[0])\n",
    "plt.subplots_adjust(wspace=0.9, hspace=0.3)\n",
    "plt.title('Err bar of the average salary of each gender', weight = 'bold', fontsize= 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The graph tells us that more men are working than women and most of these men are earning more than females.\n",
    "- It is safe to say that we have a positive skew because in our graph we see that most men are earning an average\n",
    "of R60 000 meaning that our mean is greater than our mode.\n",
    "- Also looking at our box plot we see that we have a positive skew for men and negative skew for females. We see\n",
    "that by the streched out data from the bottom up in men that it is positive and this is where you tend to see most\n",
    "outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationship between Years Worked and Salary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, ' Relationship between Years Worked and Salary')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.regplot(x='yearsworked', y='salary', marker='p', x_jitter=.2, color ='g', data= sal_data, ci= None)\n",
    "plt.title(\" Relationship between Years Worked and Salary\", weight=\"bold\", fontsize= 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The are lot of points close to our trend line and there is a positive linear relationship between the salary and \n",
    "years worked.\n",
    "- This means that as the years worked increased the more they salaries became more.\n",
    "- With our observation of the scatter plot we see that we clearly have a few outliers of people who worked less but\n",
    "got higher salaries."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Nature of the relationship between Years Worked and Salary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "corr is:  0.6235888657598052 with p_value of:  1.2873351342912745e-56\n"
     ]
    }
   ],
   "source": [
    "corr, p = pearsonr(sal_data.yearsworked, sal_data.salary)\n",
    "print(\"corr is: \", corr, \"with p_value of: \", p)        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- A correlation of 0.623 indicates a positive relationship between years worked and salary being earned.\n",
    "    This means that they move together. \n",
    "    \n",
    "- The small p-value of 1.274779518969558e-56 indicates that there is strong evidence to conclude that an increase in salary earned is due to an increase in years worked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}