Honey_Sam_SMDM_Project Calculations (2).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5c033be6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import norm \n",
    "from scipy.stats import ttest_1samp, ttest_ind, ttest_rel\n",
    "from statsmodels.stats.power import ttest_power\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2004b8a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\honey\\\\Documents\\\\Data Science\\\\Great Learning\\\\Python Scripts'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c660030",
   "metadata": {},
   "source": [
    "# Wholesale+Customers+Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11cc1328",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data1 = pd.read_csv('Wholesale+Customers+Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "efa062f4",
   "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>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>12669</td>\n",
       "      <td>9656</td>\n",
       "      <td>7561</td>\n",
       "      <td>214</td>\n",
       "      <td>2674</td>\n",
       "      <td>1338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>7057</td>\n",
       "      <td>9810</td>\n",
       "      <td>9568</td>\n",
       "      <td>1762</td>\n",
       "      <td>3293</td>\n",
       "      <td>1776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>6353</td>\n",
       "      <td>8808</td>\n",
       "      <td>7684</td>\n",
       "      <td>2405</td>\n",
       "      <td>3516</td>\n",
       "      <td>7844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>13265</td>\n",
       "      <td>1196</td>\n",
       "      <td>4221</td>\n",
       "      <td>6404</td>\n",
       "      <td>507</td>\n",
       "      <td>1788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Retail</td>\n",
       "      <td>Other</td>\n",
       "      <td>22615</td>\n",
       "      <td>5410</td>\n",
       "      <td>7198</td>\n",
       "      <td>3915</td>\n",
       "      <td>1777</td>\n",
       "      <td>5185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Buyer/Spender Channel Region  Fresh  Milk  Grocery  Frozen  \\\n",
       "0              1  Retail  Other  12669  9656     7561     214   \n",
       "1              2  Retail  Other   7057  9810     9568    1762   \n",
       "2              3  Retail  Other   6353  8808     7684    2405   \n",
       "3              4   Hotel  Other  13265  1196     4221    6404   \n",
       "4              5  Retail  Other  22615  5410     7198    3915   \n",
       "\n",
       "   Detergents_Paper  Delicatessen  \n",
       "0              2674          1338  \n",
       "1              3293          1776  \n",
       "2              3516          7844  \n",
       "3               507          1788  \n",
       "4              1777          5185  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data1.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3014d1c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 440 entries, 0 to 439\n",
      "Data columns (total 9 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   Buyer/Spender     440 non-null    int64 \n",
      " 1   Channel           440 non-null    object\n",
      " 2   Region            440 non-null    object\n",
      " 3   Fresh             440 non-null    int64 \n",
      " 4   Milk              440 non-null    int64 \n",
      " 5   Grocery           440 non-null    int64 \n",
      " 6   Frozen            440 non-null    int64 \n",
      " 7   Detergents_Paper  440 non-null    int64 \n",
      " 8   Delicatessen      440 non-null    int64 \n",
      "dtypes: int64(7), object(2)\n",
      "memory usage: 31.1+ KB\n"
     ]
    }
   ],
   "source": [
    "Data1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc0cee39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(440, 9)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "379eed01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Buyer/Spender       0\n",
       "Channel             0\n",
       "Region              0\n",
       "Fresh               0\n",
       "Milk                0\n",
       "Grocery             0\n",
       "Frozen              0\n",
       "Detergents_Paper    0\n",
       "Delicatessen        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "698043e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Buyer/Spender</th>\n",
       "      <th>Channel</th>\n",
       "      <th>Region</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440</td>\n",
       "      <td>440</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Hotel</td>\n",
       "      <td>Other</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>298</td>\n",
       "      <td>316</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.161315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>110.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>330.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Buyer/Spender Channel Region          Fresh          Milk  \\\n",
       "count      440.000000     440    440     440.000000    440.000000   \n",
       "unique            NaN       2      3            NaN           NaN   \n",
       "top               NaN   Hotel  Other            NaN           NaN   \n",
       "freq              NaN     298    316            NaN           NaN   \n",
       "mean       220.500000     NaN    NaN   12000.297727   5796.265909   \n",
       "std        127.161315     NaN    NaN   12647.328865   7380.377175   \n",
       "min          1.000000     NaN    NaN       3.000000     55.000000   \n",
       "25%        110.750000     NaN    NaN    3127.750000   1533.000000   \n",
       "50%        220.500000     NaN    NaN    8504.000000   3627.000000   \n",
       "75%        330.250000     NaN    NaN   16933.750000   7190.250000   \n",
       "max        440.000000     NaN    NaN  112151.000000  73498.000000   \n",
       "\n",
       "             Grocery        Frozen  Detergents_Paper  Delicatessen  \n",
       "count     440.000000    440.000000        440.000000    440.000000  \n",
       "unique           NaN           NaN               NaN           NaN  \n",
       "top              NaN           NaN               NaN           NaN  \n",
       "freq             NaN           NaN               NaN           NaN  \n",
       "mean     7951.277273   3071.931818       2881.493182   1524.870455  \n",
       "std      9503.162829   4854.673333       4767.854448   2820.105937  \n",
       "min         3.000000     25.000000          3.000000      3.000000  \n",
       "25%      2153.000000    742.250000        256.750000    408.250000  \n",
       "50%      4755.500000   1526.000000        816.500000    965.500000  \n",
       "75%     10655.750000   3554.250000       3922.000000   1820.250000  \n",
       "max     92780.000000  60869.000000      40827.000000  47943.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data1.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "47cd4fa2",
   "metadata": {},
   "outputs": [
    {
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mqqKiQjt37pTX6zWpQwAAAABArCHowKDx+/2SpPz8/D7HQ9dDdQAAAAAAnCyCDgya7OxsSZLP5+tzPHQ9VAcAAAAAsSQQCKixsVG1tbVqbGxUIBAwu6W4QNCBQWO325WTk6PKykoFg8GIsWAwqKqqKuXm5sput5vUIQAAAAAMDk6fNA9BBwaNxWJRTU2NGhoa5HA4Ik5dcTgcamhoUHV1NRuRAgAAAIgpnD5pLk5dOQFOXTl5brdbZWVlamlpCV/Lzc1VdXW1nE6neY0BAAAAwADj9MnBw6kriCpH52lHL2UBAAAAgFjA6ZPmI+jAoApN2Tr//PMjpmydf/75TNkCAAAAEHM4fdJ8BB0YNIFAQGVlZSouLpbH41FBQYFGjhypgoICeTweFRcXq7y8nJ2HAQAAAMQMTp80H0EHBg1TtgAAAADEG06fNB9BBwYNU7YAAAAAxBtOnzQfQQcGDVO2AAAAAMQjp9Opuro6vf3227LZbEpLS5PNZpPP51NdXR2nTw4yjpc9AY6X/eY4VgmngkAgIK/XK7/fr+zsbNntdn4fAQAAMCB4rzmw+nt/njSEPSHOhKZsuVwuORwOVVRUKD8/Xz6fT1VVVWpoaFBdXR3/R4dp3G63ysrK1NLSEr6Wk5OjmpoaUnYAAACcNIvFosLCQrPbiDssXcGgYsoWolXo6GOr1RqxbtJqtXL0MaJCIBBQY2Ojamtr1djYyAlVAAAA/cTSlRNg6crAYMoWognLqhDtmG0EAADQW3/vz5nRASDucPQxohmzjQAAAE4OQQcGndvtVl5enoqKilRaWqqioiLl5eXxZh2m4ehjRKtAIKCysjIVFxfL4/GooKBAI0eOVEFBgTwej4qLi1VeXs4yFgAAgOMg6MCg4pNJRCOOPka0YrYRAADAyWOPjhNgj45vjn0QEK343US0qq2tVWlpqTo7OzVy5Mhe452dnUpLS9OqVat09dVXm9AhAACAedijA6bjk0lEq9DRxw0NDXI4HBGzjRwOhxoaGlRdXU3IgSHHbCMAAICTR9CBQcM+CIhmHH2MaGS325WTk6PKykoFg8GIsWAwqKqqKuXm5sput5vUIQAAQPRLMrsBxK4jP5n8/ve/3+t4WT6ZhNmcTqdKSko4+hhRIzTbyOVyyeFwqKKiQvn5+fL5fKqqqlJDQ4Pq6ur4HQUAADgO9ug4Afbo+OZC+yCMHTtWe/bs0YcffhgeO+usszRu3Dh9/vnn7IMAAEdxu90qKytTS0tL+Fpubq6qq6uZbQQAAOIWe3TAdBaLRT/60Y/05ptvqru7W48++qg+/fRTPfroo+ru7tabb74pl8tFyAEAR3E6nfrggw+0YcMGrVq1Shs2bFBzczMhBwAAQD8wo+MEmNHxzR05o+Ozzz7r9cnkmDFjmNEB0wUCAZauAAAAAKeA/t6fs0cHBk3o1JXa2to+9+h4/fXXZbPZ5PV6VVhYaHa7iEN9LQ/IyclRTU0Nn5wDAAAApyiWrmDQHHnqisViUWFhoa6++moVFhbKYrFw6gpM5Xa75XK5ZLVaI46XtVqtcrlccrvdZrcIAAAA4Bsg6MCgOfLUlb5w6grMEggEVFZWpuLiYnk8HhUUFGjkyJEqKCiQx+NRcXGxysvLFQgEzG4VAAAAwN+JoAODxm63KycnR5WVlTp06JAaGxtVW1urxsZGHTp0SFVVVcrNzZXdbje7VcSZ0LKqRYsWKTEx8q/BxMREVVRUaOfOnfJ6vSZ1CAAAAOCbYo8ODBqLxaKamhrNnj1b6enp6urqCo+lpqaqq6tL9fX1bPyIIXfksqq+sKwKAAAAOHUxowNDoru7+7iPgaHEsioAAAAgdnG87AlwvOw3FwgENH78eLW1temf/umfNHHiRHV1dSk1NVXNzc169tlnlZGRoU8//ZRZHRhSoaOPrVarPB5PxPKVYDAoh8Mhn8/H0ccAAABAFOF4WZiusbFRbW1tmjRpkt599109++yz4bGcnBxNmjRJ27dvV2Njo6ZPn25ip4g3oWVVLpdLDodDFRUVys/Pl8/nU1VVlRoaGlRXV0fIAQAAAJyCCDowaBobGyVJ7733nmbOnKnbb789vDfHunXrtHbt2nAdQQeGmtPpVF1dncrKymSz2cLXc3NzVVdXJ6fTaWJ3AAAAAL4pgg4MmmAwKEmaOHGi3n77bTU0NITHzjrrLE2cOFHvv/9+uA4Yak6nUyUlJfJ6vfL7/crOzpbdbmcmBwAAAHAKYzNSDJrTTz9dkvT+++/LarWqqalJnZ2dampqktVq1fvvvx9RB5jBYrGosLBQV199tQoLCwk5AAAAgFMcQQcGTUZGRvh7wzB6ffVVBwAAAADAyWDpCgbN559/Hv7+pZdeCu/JIUnDhw/vsw4AAAAAgJPBjA4MmnHjxkmSLrjggvD3R45dcMEFEXUAAAAAAJwsZnRg0EyYMEGStHXrVs2cOVN33HFH+NSV9evXh2d4hOoAAAAAADhZCcaRmyWgl46ODqWnp6u9vV1paWlmt3NKCQQCysvL09ixY/XZZ5+ppaUlPJabm6sxY8bo888/V3NzMxtAAgAAAACOq7/358zowKCxWCyqqamRy+XSzJkzVV5e3mtGR11dHSEHAAAAAGDAEHRgUDmdTtXV1amsrEwNDQ3h67m5uaqrq5PT6TSxOwAAAMSCQCAgr9crv9+v7Oxs2e12PkwD4hibkWJIHL1CKhgMmtQJAAAAYonb7VZeXp6KiopUWlqqoqIi5eXlye12m90aAJMQdGBQud1uuVwunX/++WpqalJnZ6eampp0/vnny+Vy8Q8QAAAAvrHQe02r1RrxXtNqtfJeE4hjbEZ6AmxG+s2FNiO1Wq2qr6/Xq6++Gp5OOHXqVM2ePVs+n4/NSAEAAPB3O/K9psfjUWLi15/hBoNBORwO3msCMaa/9+fM6MCg8Xq9amlpkc1m0znnnBMxnfCcc87RJZdcop07d8rr9ZrdKgAAAE4xofeaixYtigg5JCkxMVEVFRW81wTiFEEHBo3f75ckLVq0qM/phL/85S8j6gAAAID+Cr2HzM/P73M8dJ33mkD8GfSgo6qqSgkJCVqwYEH4mmEYWrx4scaPH6/U1FQVFhbqnXfeifhzBw8e1C233KKxY8dqxIgRuuqqq/TJJ59E1Ozdu1fz5s1Tenq60tPTNW/ePO3bty+i5qOPPtKsWbM0YsQIjR07Vrfeeqt6enoG6+XiCBkZGZKkqVOnqr6+Xt3d3XrmmWfU3d2t+vp6TZ06NaIOAPC1QCCgxsZG1dbWqrGxUYFAwOyWACCqZGdnS5J8Pl+f46HroToA8WNQg4433nhDjz76qM4///yI60uWLNHSpUu1fPlyvfHGG8rKytLll1+uzs7OcM2CBQv09NNPa/Xq1dq4caO+/PJLFRcXR7zRKy0t1datW7V+/XqtX79eW7du1bx588LjgUBAM2fO1P79+7Vx40atXr1a9fX1KisrG8yXjaN89tlnfS5d+eyzz8xuDQCiEicIAMCJ2e125eTkqLKysteJfsFgUFVVVcrNzZXdbjepQwCmMQZJZ2enMXHiROP55583Lr30UuMXv/iFYRiGEQwGjaysLOM3v/lNuLa7u9tIT083Hn74YcMwDGPfvn3GsGHDjNWrV4drdu3aZSQmJhrr1683DMMw3n33XUOSsXnz5nBNU1OTIcnYvn27YRiG8eyzzxqJiYnGrl27wjW1tbVGSkqK0d7e3q/X0d7ebkjqdz2+tmrVKkOSIcnIzMw0Hn30UePTTz81Hn30USMzMzM8tmrVKrNbBYCoUV9fbyQkJBizZs0ympqajM7OTqOpqcmYNWuWkZCQYNTX15vdIgBEjSP/zty0aZPR0dFhbNq0ib8zgRjV3/vzQZvRcdNNN2nmzJm67LLLIq7v3LlTra2tmjFjRvhaSkqKLr30Um3atEmStGXLFh06dCiiZvz48crPzw/XNDU1KT09XRdffHG4pqCgQOnp6RE1+fn5Gj9+fLjmiiuu0MGDB7Vly5Y++z548KA6OjoivvDNhJakTJ48WSkpKbruuus0fvx4XXfddTrttNM0adKkiDoAiHeBQEBlZWUqLi6Wx+NRQUGBRo4cqYKCAnk8HhUXF6u8vJxlLADw/zidTtXV1entt9+WzWZTWlqabDabfD6f6urq5HQ6zW4RgAmSBuNJV69erbfeektvvPFGr7HW1lZJUmZmZsT1zMxMffjhh+Ga5ORkjR49uldN6M+3trb2eYOckZERUXP0zxk9erSSk5PDNUerqqrSPffc05+XiX4yDKPXTtgJCQkmdQMA0St0gkBtbe0xTxCw2Wzyer0qLCw0p0kAiDJOp1MlJSXyer3y+/3Kzs6W3W7nSFkgjg140PHxxx/rF7/4hZ577jmddtppx6w7+kbXMIwT3vweXdNX/TepOVJFRYUWLlwYftzR0aEzzzzzuH2hb21tbZKk7du3KzMzU2VlZfr2t7+tv/3tb3ryySfV0tISUQcA8Y4TBADgm7FYLATAAMIGPOjYsmWL2traNGXKlPC1QCCgV155RcuXL9d7770n6avZFkfugNzW1haefZGVlaWenh7t3bs3YlZHW1ubbDZbuGb37t29fv6ePXsinue1116LGN+7d68OHTrUa6ZHSEpKilJSUr7JS8dRQjNuJkyYoNbWVtXU1ITHLBaLJkyYoF27drF0BQD+nyNPECgoKOg1zgkCAAAAJzbge3RMnz5db7/9trZu3Rr+uuiii3TNNddo69at+va3v62srCw9//zz4T/T09Ojl19+ORxiTJkyRcOGDYuo8fv98vl84ZpLLrlE7e3tev3118M1r732mtrb2yNqfD5fxCdfzz33nFJSUiKCGAyuXbt26corr9SKFSv02GOPacWKFbryyiu1a9cus1sDgKjCCQIAAAAnb8BndIwaNarXlNsRI0ZozJgx4esLFixQZWWlJk6cqIkTJ6qyslLDhw9XaWmpJCk9PV0/+9nPVFZWpjFjxuj0009XeXm5rFZreHPTyZMn68orr9S1116rRx55RJJ03XXXqbi4WOeee64kacaMGTrvvPM0b9483Xffffriiy9UXl6ua6+9VmlpaQP90nGUI/dBSUhI0AUXXKD8/Hz5fD6tW7euzzpgqAUCAdb0ImpYLBbV1NTI5XLJ4XCooqIi/PdmVVWVGhoaVFdXx+8oAADAcQzKZqQncscdd6irq0vz58/X3r17dfHFF+u5557TqFGjwjXLli1TUlKS5syZo66uLk2fPl1/+MMfIt7cPfXUU7r11lvDp7NcddVVWr58eXjcYrFo7dq1mj9/vqZOnarU1FSVlpaqurp66F5sHNuzZ48k6cYbb9S6devCM20kKTc3VzfccIMefvjhcB0w1Nxut8rKysL7xUhSTk6Oampq2KUdpgmdIFBWVtbr701OEACAvvHBBYAjJRiGYZjdRDTr6OhQenq62tvbmQXyd3rqqac0d+5c/eAHP9CaNWv06quvhv/xmTp1qkpKSrRu3To9+eSTuuaaa8xuF3HG7XbL5XKpuLhYixYtCn9qXllZGf7UnBtKmIk37QDQP3xwAcSP/t6fD/geHUDIhAkTJEnr16/X7NmzlZKSouLiYqWkpGj27Nlav359RB0wVAKBgMrKylRcXCyPx6OCggKNHDlSBQUF8ng8Ki4uVnl5uQKBgNmtIo6FThC4+uqrVVhYSMgBAH0IfXBhtVrV1NSkzs5ONTU1yWq1yuVyye12m90iABMwo+MEmNHxzQUCAeXl5Wns2LH67LPPIlL23NxcjRkzRp9//rmam5t5A48h1djYqKKiIjU1NfV5skVTU5NsNps2bNjAUXUAAESp0HtNq9Uqj8ejxMSvP8MNBoNyOBzy+Xy81wRiSH/vz03ZowPx4chN9WbOnKny8nKlpqaqq6tL69ev19q1a9lUD6YIncR09MbJIaHrR57YBAAAoovX61VLS4tqa2sjQg5JSkxMVEVFhWw2m7xeLx9cAHGGoAOD6shN9RoaGsLX2VQPZsrOzpYk+Xy+Pmd0+Hy+iDoAABB9+OACwLGwRwcGndPp1AcffKANGzZo1apV2rBhg5qbmwk5YBq73a6cnBxVVlYqGAxGjAWDQVVVVSk3N1d2u92kDgEAwIkc+cFFX/jgAohf7NFxAuzRAcSmI09dqaioCJ+6UlVVxakrAACcAtijA4g/nLoCAMcRWlb19ttvy2azKS0tTTabTT6fj5ADAIBTQGg/uIaGBjkcjohTVxwOhxoaGlRdXU3IAcQhZnScADM6gNgWCATk9Xrl9/uVnZ0tu93OGyJEBX43EY34vUQ0crvdKisr63XCX3V1NR9cADGGU1dwUg4cOKDt27cP6HN2dXWppaVFOTk5Sk1NHdDnnjRpkoYPHz6gzwkAZunrTXtOTo5qamp40w7T8HuJaOV0OlVSUkIIByCMoAN92r59u6ZMmWJ2G/22ZcsWXXjhhWa3gVMMb9oRjY7cP6a2tja8f0xlZaVcLhdLq2AKfi8R7SwWC0fIAghj6coJxOvSlcGY0bFt2zbNnTtXTz75pCZPnjygz82MDvy9jnzTvmjRoog37WxGCrOwsR6iEb+XAIBo0d/7c4KOE4jXoGMwvPXWW5oyZQqzL2A63rQjWjU2NqqoqEhNTU0qKCjoNd7U1CSbzaYNGzbwySWGDL+XAIBowakrAHAMXq9XLS0tWrRoUUTIIUmJiYmqqKjQzp075fV6TeoQ8crv90uS8vPz+xwPXQ/VAUOB30sAwKmGoANA3OFNO6JVdna2JMnn8/U5HroeqgOGAr+XAIBTDUEHgLjDm3ZEK7vdrpycHFVWVioYDEaMBYNBVVVVKTc3V3a73aQOEY/4vQQAnGoIOgDEHd60I1pZLBbV1NSooaFBDodDTU1N6uzsVFNTkxwOhxoaGlRdXc3eMRhS/F4CAE41BB0A4g5v2hHNnE6n6urq9Pbbb8tmsyktLU02m00+n4/TgGAafi8BAKeSJLMbAAAzhN60l5WVyWazha/n5ubyph2mczqdKi4u1kMPPaQdO3bo7LPP1vz585WcnGx2a4hzRx/Wd/SsOAAAogEzOgDELafTqQ8++EAbNmzQqlWrtGHDBjU3NxNywHRut1vnnnuubrvtNi1fvly33Xabzj33XLndbrNbQ5xyu91yuVw6//zzI2bBnX/++XK5XPxuAgCiCkEHgLhmsVhUWFioq6++WoWFhSxXgelCN5RWqzXihtJqtXJDCVMEAgGVlZWpuLhYHo9HBQUFGjlypAoKCuTxeFRcXKzy8nIFAgGzWwUAQBJBBwAAUYMbSkQjr9erlpYWLVq0SImJkW8dExMTVVFRoZ07d8rr9ZrUIQAAkQg6AACIEtxQIhr5/X5JUn5+fp/joeuhOgAAzEbQAQBAlOCGEtEoOztbkuTz+focD10P1QEAYDaCDgAAogQ3lIhGdrtdOTk5qqys7HXKSjAYVFVVlXJzc2W3203qEACASAQdAABEiSNvKA8dOqTGxkbV1taqsbFRhw4d4oYSprBYLKqpqVFDQ4McDkfEJrkOh0MNDQ2qrq5mM2cAQNRIMrsBAADwldAN5ezZs5Wenq6urq7wWGpqqrq6ulRfX88NJYac0+lUXV2dysrKZLPZwtdzc3NVV1fHsdwAgKhC0AEAQJRJSEjo81pf14Gh4nQ6VVJSIq/XK7/fr+zsbNntdoI3AEDUIegAACBKHHm8bH19vV599dXwDeXUqVM1e/ZslZeXq6SkhJtLmMJisaiwsNDsNgAAOC6CDgAAokToeNna2loNGzas1w1lRUWFbDabvF4vN5sAAADHwGakAABECY6XBQAAOHkEHQAARAmOlwUAADh5BB0AAESJI4+XDQaDEWPBYJDjZQEAAPqBPToAAIgSoeNlXS6XSkpKdOWVV4aPlV2/fr3Wrl2ruro6NiIFAAA4DoIOAACiiNPpVHl5uZYuXaqGhobw9aSkJJWXl8vpdJrYHQAAQPRj6QoAAFHE7Xbrvvvu07BhwyKuJyUl6b777pPb7TapMwAAgFMDQQcAAFEiEAjohhtukCRddtllampqUmdnp5qamnTZZZdJkm688UYFAgEz2wQAAIhqBB0AAESJxsZG7dmzR9OmTdOaNWtUUFCgkSNHqqCgQGvWrNG0adPU1tamxsZGs1sFAACIWgQdAABEiVCAcc899ygxMfKf6MTERN19990RdQAAAOiNoAMAAAAAAMQMgg4AAKJEYWGhJOnuu+9WMBiMGAsGg1q8eHFEHQAAAHoj6AAAIEoUFhYqIyNDGzduVElJScRmpCUlJXr11VeVkZFB0AEAAHAcSWY3AAAAvmKxWLRy5Uq5XC69+OKLamhoCI8NHz5cCQkJWrlypSwWi4ldAgAARDdmdAAAEEWcTqfq6uqUmZkZcT0zM1N1dXVyOp0mdQYAAHBqYEYHAABRxul0qri4WA899JB27Nihs88+W/Pnz1dycrLZrQEAAEQ9gg4AAKKM2+1WWVmZWlpawtceeOAB1dTUMKMDAADgBFi6AiCuBQIBNTY2qra2Vo2NjQoEAma3hDjndrvlcrlktVojNiO1Wq1yuVxyu91mtwgAABDVEgzDMMxuIpp1dHQoPT1d7e3tSktLM7udU9pbb72lKVOmaMuWLbrwwgvNbgeQ2+3WwoUL9eGHH4avnXXWWVq6dCmfmsMUgUBAeXl5slqt8ng8Skz8+vOIYDAoh8Mhn8+n5uZmNiQFAABxp7/358zoABCX3G63Zs+erba2tojrbW1tmj17Np+awxRer1ctLS1atGhRRMghSYmJiaqoqNDOnTvl9XpN6hAAACD6EXQAiDuBQEA33HCDJGn69OkRywOmT58uSbrxxhtZxoIh5/f7JUn5+fl9joeuh+oAAADQG0EHgLjT2NioPXv2aNq0aVqzZo0KCgo0cuRIFRQUaM2aNZo2bZra2trU2NhodquIM9nZ2ZIkn8/X53joeqgOAAAAvRF0AIg7oQDjnnvu6XN5wN133x1RBwwVu92unJwcVVZWqru7W/fff79uueUW3X///eru7lZVVZVyc3Nlt9vNbhUAACBqcbwsAABRwmKxqKamRrNnz9bw4cN15H7hCxculGEYqq+vZyNSAACA42BGB4C4U1hYKEm6++67FQwGI8aCwaAWL14cUQcMpc2bN0uSEhISIq6HZh+FxgEAANA3gg4AcaewsFAZGRnauHGjSkpKIjYjLSkp0auvvqqMjAyCDgy5np4eLVu2TJmZmers7NSyZct08803a9myZero6FBmZqaWLVumnp4es1sFAACIWixdARB3LBaLVq5cKZfLpRdeeEENDQ3hsdTUVCUkJGjlypUsD8CQe+ihh3T48GE5nU595zvfUUtLS3jsgQce0A9/+EM9/PDDeuihh7RgwQLT+gQAAIhmzOgAEJecTqfKy8t1+PDhiOuHDh1SeXm5nE6nSZ0hnu3YsUOStHLlSlmt1ojZRlarVQ8//HBEHQAAAHpjRgeAuOR2u1VdXa2ZM2fqBz/4gVJTU9XV1aV169apurpaBQUFhB0Ycjk5OZKk888/Xx6PJ7wvR0FBgTwejy644AL99a9/DdcBAACgN4IOAHEnEAiorKxMxcXFETeTknTDDTfI4XCovLxcJSUlLF/BkLJarZKkTz75RMFgMOJ3MxgM6uOPP46oAwAAQG8sXQEQd7xer1paWrRo0aKIG0npq5MtKioqtHPnTnm9XpM6RLz6/PPPJUlffPGFzjjjDD366KP69NNP9eijj+qMM87Q3r17I+oAAADQGzM6AMQdv98vScrPz1cgEJDX65Xf71d2drbsdrvy8/Mj6oChkp2dLUm65ppr9Kc//UnXX399eCwpKUmlpaVatWpVuA4AAAC9EXQAiDuhm8Tly5frkUceiTjZIicnR9ddd11EHTBU7Ha7cnJy1NHRoc7OTj388MPasWOHzj77bN1www2aM2eOcnNzZbfbzW4VAAAgarF0BUDcsdvtysjIUEVFhfLz8yNOtsjPz9eiRYuUkZHBzSSGnMViUU1NjRoaGjRnzhxdfPHFqqys1MUXX6w5c+aooaFB1dXV7B0DAABwHMzoABCXDMOI+D70BZjN6XSqrq5Ot912m2w2W/j6WWedpbq6Ok4DAgAAOAFmdACIO16vV3v27FFVVZV8Pp9sNpvS0tJks9n0zjvvqLKyUm1tbWxGCtP88Y9/1EcffRRx7cMPP9Qf//hHkzoCgOgWCATU2Nio2tpaNTY2KhAImN0SABMRdACIO6FNRm+++WZ98MEH2rBhg1atWqUNGzaoublZN998c0QdMJQcDofWrFmj5ORk3XXXXfrggw901113KTk5WWvWrJHD4TC7RQCIKm63W3l5eSoqKlJpaamKioqUl5cnt9ttdmsATELQASDuhDYZ9fl8slgsKiws1NVXX63CwkJZLBb5fL6IOmCodHV1hUOOffv26YorrtDrr7+uK664Qvv27QuHHV1dXWa3CgBRwe12y+VyyWq1Ruy5ZbVa5XK5CDuAOJVgsCj9uDo6OpSenq729nalpaWZ3c4p7a233tKUKVO0ZcsWXXjhhWa3gzgWCASUl5cnq9Uqj8ejxMSvM99gMCiHwyGfz6fm5mY2fcSQuvnmm7VixQr98Ic/1F/+8pdeJwJ973vfk8fj0U033aTly5eb1ygARAH+PQfiT3/vz5nRASDuHHmyhcPhiPgEyOFwcLIFTNPc3CxJ8ng8ys/P14oVK/TYY49pxYoVys/P15o1ayLqACCeeb1etbS0aNGiRREhhyQlJiaqoqJCO3fuZM8tIA5x6gqAuBQ62WLhwoURJ1vk5ORwsgVMc/bZZ0uSMjMz9fbbb6uhoSE8dtZZZykzM1Otra3hOgCIZ6G9tPLz8/scD11nzy0g/jCjA0BcS0hIMLsFIKykpESS1Nraqu985zsRs42+853vqLW1NaIOAOLZkXtu9YU9t4D4RdABIC6xeRmi0WeffRb+/oUXXtDTTz+tTz/9VE8//bReeOGFPusAIF7Z7Xbl5OSosrJSwWAwYiwYDKqqqkq5ubmy2+0mdQjALAQdAOJOIBBQWVmZiouL5fF4VFBQoJEjR6qgoEAej0fFxcUqLy9XIBAwu1XEmT179kiSvvvd76qnp0dLlizRueeeqyVLlqinp0ff/e53I+oAIJ6x5xaAYyHoABB3jty8zDAMNTY2qra2Vo2NjTIMg83LYJpx48ZJksaPH68vv/xSN910k2bMmKGbbrpJX375pcaPHx9RBwDxLrTn1ttvvy2bzaa0tDTZbDb5fD723ALiGJuRAog7oU3JduzYoauvvrrXEZ733ntvRB0wVCZMmCBJWr9+va6++mpVVFQoPz9fPp9PV199tdavXx9RBwD4KuwoKSmR1+uV3+9Xdna27HY7MzmAOEbQASDuhDYlmzdvnoqLi1VbWxu+maysrNS8efMi6oChElpvPnbsWP31r3/tdSLQlClT9Pnnn7PeHACOYrFYVFhYaHYbAKIEQQeAuGOz2ZSUlKQxY8bI7XYrKemrvwoLCgrkdrt1xhln6PPPP4+4yQSGQmi9+ezZs3uNtbS0qKWlRfX19XxKCQAAcBzs0QEg7mzatEmHDx/W7t275XQ6IzYvczqd2r17tw4fPqxNmzaZ3Sri0ObNmyVJiYmR/0SHwo3QOAAAAPpG0AEg7oT23njyySf73LzsySefjKgDhkpPT4+WLVumzMxMHThwQBs2bNCqVau0YcMG7d+/X5mZmVq2bJl6enrMbhUAokpPT4/uv/9+3XLLLbr//vv5exKIcwQdAOJOaO+Ns88+Wx988EHEzWRzc7O+/e1vR9QBQ+Whhx7S4cOHde+994aXVIUkJSXpV7/6lQ4fPqyHHnrIpA4BIPrccccdGjFihG677TYtX75ct912m0aMGKE77rjD7NYAmISgA0DcCW34WFlZqYSEBBUWFurqq69WYWGhEhISVFVVpdzcXDZ8xJDbsWOHJCkhIUF5eXkqKipSaWmpioqKlJeXF17OEqoDgHh3xx136L777tOYMWP0u9/9Tn6/X7/73e80ZswY3XfffYQdQJwi6AAQd0IbPjY0NMjhcETs0eFwONTQ0KDq6mo2fMSQO/vssyVJP//5z2W1WiN+N61Wq6699tqIOgCIZ0cu9/vkk0/085//XFlZWfr5z3+uTz75hOV+QBwj6AAQl5xOp+rq6vrco6Ourk5Op9PsFhGHrr/+eklScnKy/vSnP6m7u1vPPPOMuru79ac//UnJyckRdQAQz1juB+BYOF4WQFwzDCPicTAYNKkTQHrttdckffUp5ciRIyN+HxMTE8OPX3vtNRUWFprRIgBEjdAyvuLi4j7HQ9dZ7gfEH2Z0AIhLbrdbLpdLVqtVK1as0GOPPaYVK1bIarXK5XLJ7Xab3SLi0JEn/Rwdwh35mBOBAODrZXwNDQ19joeus9wPiD8JxtHvpBCho6ND6enpam9vV1pamtntnNLeeustTZkyRVu2bNGFF15odjuIY4FAQHl5eRo7dqz27NmjDz/8MDx21llnady4cfr888/V3NzMPh0YUi+++KIuu+wyTZs2Tc8//7wefvhh7dixQ2effbZuuOEGXX755dq4caNeeOEFTZ8+3ex2AcBUPT09GjFihMaMGaNPPvkkYvnK4cOHdcYZZ+jzzz/X/v37w0v/AJza+nt/zowOAHHH6/WqpaVFb775ps4///yIDR/PP/98vfnmm9q5c6e8Xq/ZrSKOJScna8GCBXrwwQe1YMECJScn95rlAQDxLDk5Wbfddpt2796tM844Q48++qg+/fRTPfroozrjjDO0e/du3XbbbYQcQBxijw4AcWfXrl2SpB/84AfyeDzhIzsLCgrk8XhUXFysdevWheuAodLW1iZJ2rhxoxwOhyoqKpSfny+fz6eqqiq9+uqrEXUAEO+WLFkiSVq2bFnERs1JSUm6/fbbw+MA4gtBB4C4s2fPHklfnbwSCjlCEhMT5XA4tG7dunAd0B8HDhzQ9u3bT+o5Ojs7JUk333yz3G63bDZbeGzChAm66aabtGLFCnV2duqtt976xj9n0qRJGj58+En1CgDRYsmSJbr33nv10EMPhZf7zZ8/n5kcQBwj6AAQd8aNGyfpqw1Jf/rTn0aEHcFgUB6PJ6IO6I/t27drypQpA/Jcy5cv73Vt165dWrFihaSTP16WvZIAxJrQcj8AkAg6AMShCRMmSJLWr1+vkpISXXnllUpNTVVXV5fWr1+v9evXR9QB/TFp0iRt2bLlpJ/npZde0h133CG73a6ioiLdc889uvvuu7VhwwZ5vV4tWbJE//iP/3jSvQIAAMQqTl05AU5dGTicuoJoETp1xWKxaOfOnQoGg+Exi8WinJwcBYNBTl2Badxut8rKytTS0hK+lpubq+rqajmdTvMaAwAAMBGnrgDAMVgsFv3oRz/Sjh07eo0ZhqEdO3bI5XIRcsA0TqdTH3zwgR555BFJ0iOPPKLm5mZCDgAAgH4g6AAQdwKBgP7whz9IklJSUiLGTjvtNEnS448/rkAgMNStAWEWi0UXXXSRJOmiiy4ieAMAAOgngg4AcaexsVF79uzRtGnT9MUXX2jZsmW6+eabtWzZMn3++eeaNm2a2tra1NjYaHarAAAAAP5ObEYKIO6EAozLLrtMkydPjtgH4YEHHtCPf/xjbdy4UY2NjZo+fbo5TQIAAAD4RpjRASBuLV68WFarVU1NTers7FRTU5OsVqvuueces1sDAAAA8A0RdACIO//wD/8gSRo9erT+/Oc/q7u7W88884y6u7v15z//WaNHj46oAwAAAHDqYOkKgLiTmPhVxrt3716lp6fr4MGD4bGUlJTw41AdAAAAgFMH7+IBxJ22trbw90eGHEc/PrIOAAAAwKmBoANA3MnIyAh/n5CQEDF25OMj6wAAAACcGli6AiDuBAKB8Pc/+MEPNHPmTKWmpqqrq0tr167Vs88+26sOAAAAwKmBGR0A4s7LL78c/j4xMVEXXHCBXC6XLrjggoh9OY6sAwAA0SsQCKixsVG1tbVqbGzkwwogzg140FFVVaXvf//7GjVqlDIyMuRwOPTee+9F1BiGocWLF2v8+PFKTU1VYWGh3nnnnYiagwcP6pZbbtHYsWM1YsQIXXXVVfrkk08iavbu3at58+YpPT1d6enpmjdvnvbt2xdR89FHH2nWrFkaMWKExo4dq1tvvVU9PT0D/bIBnEI+/vhjSdLPf/5z+Xw+2Ww2paWlyWaz6Z133tFPf/rTiDoAABC93G638vLyVFRUpNLSUhUVFSkvL09ut9vs1gCYZMCDjpdfflk33XSTNm/erOeff16HDx/WjBkztH///nDNkiVLtHTpUi1fvlxvvPGGsrKydPnll6uzszNcs2DBAj399NNavXq1Nm7cqC+//FLFxcUR6Wxpaam2bt2q9evXa/369dq6davmzZsXHg8EApo5c6b279+vjRs3avXq1aqvr1dZWdlAv2wAp5AzzzxTkuTz+fT+++9rw4YNWrVqlTZs2KD33ntP7777bkQdAACITm63Wy6XS1arVU1NTers7FRTU5OsVqtcLhdhBxCvjEHW1tZmSDJefvllwzAMIxgMGllZWcZvfvObcE13d7eRnp5uPPzww4ZhGMa+ffuMYcOGGatXrw7X7Nq1y0hMTDTWr19vGIZhvPvuu4YkY/PmzeGapqYmQ5Kxfft2wzAM49lnnzUSExONXbt2hWtqa2uNlJQUo729vV/9t7e3G5L6XY9j27JliyHJ2LJli9mtIM698MILhiRDklFcXGxs2rTJ6OjoMDZt2mQUFxeHx1544QWzW0Wc4+9NADi2w4cPGzk5OcasWbOMnp4eY8OGDcaqVauMDRs2GD09PcasWbOM3Nxc4/Dhw2a3CmCA9Pf+fND36Ghvb5cknX766ZKknTt3qrW1VTNmzAjXpKSk6NJLL9WmTZskSVu2bNGhQ4ciasaPH6/8/PxwTVNTk9LT03XxxReHawoKCpSenh5Rk5+fr/Hjx4drrrjiCh08eFBbtmzps9+DBw+qo6Mj4gtAbCksLNS4ceMkSS+++GLE0pWXXnpJ0lcnrhQWFprYJQAAOB6v16uWlhbZbDadc845EUtXzjnnHF1yySXauXOnvF6v2a0CGGKDGnQYhqGFCxdq2rRpys/PlyS1trZKkjIzMyNqMzMzw2Otra1KTk7W6NGjj1vT19GPGRkZETVH/5zRo0crOTk5XHO0qqqq8J4f6enpTF0HYpDFYtHDDz983JqVK1fKYrEMUUcAAODv5ff7JUmLFi3qc+nKL3/5y4g6APFjUIOOm2++WX/9619VW1vbaywhISHisWEYva4d7eiavuq/Sc2RKioq1N7eHv5iM0IgNjmdTtXX1/cKTDMyMlRfXy+n02lSZwAAoD9C/4ZPnTpV9fX16u7u1jPPPKPu7m7V19dr6tSpEXUA4kfSYD3xLbfcov/6r//SK6+8ojPOOCN8PSsrS9JXsy2ys7PD19va2sKzL7KystTT06O9e/dGzOpoa2uTzWYL1+zevbvXz92zZ0/E87z22msR43v37tWhQ4d6zfQISUlJUUpKyjd5yQBOMU6nUyUlJfJ6vfL7/crOzpbdbmcmBwAcQyAQ4O9MRJ3PPvtM55xzjlpaWsLXcnJydNppp5nXFABTDfiMDsMwdPPNN8vtduull15Sbm5uxHhubq6ysrL0/PPPh6/19PTo5ZdfDocYU6ZM0bBhwyJq/H5/+BhISbrkkkvU3t6u119/PVzz2muvqb29PaLG5/NFTFd77rnnlJKSoilTpgz0SwcAAIhZHOGJaNPW1iZJ2r59uw4cOKCysjKtWLFCZWVlOnDggLZv3x5RByB+DPiMjptuukmrVq3SmjVrNGrUqPBeGOnp6UpNTVVCQoIWLFigyspKTZw4URMnTlRlZaWGDx+u0tLScO3PfvYzlZWVacyYMTr99NNVXl4uq9Wqyy67TJI0efJkXXnllbr22mv1yCOPSJKuu+46FRcX69xzz5UkzZgxQ+edd57mzZun++67T1988YXKy8t17bXXKi0tbaBfOoBTjNvtVllZWa9PgGpqali6AgBHCB3hWVxcrNraWuXn58vn86myslIul0t1dXX8vYkhF1qSMmHCBLW2tqqmpiY8lpSUpAkTJmjXrl0sXQHi0IDP6Fi5cqXa29tVWFio7Ozs8Nef/vSncM0dd9yhBQsWaP78+brooou0a9cuPffccxo1alS4ZtmyZXI4HJozZ46mTp2q4cOH65lnnomYHvnUU0/JarVqxowZmjFjhs4//3w98cQT4XGLxaK1a9fqtNNO09SpUzVnzhw5HA5VV1cP9MsGcIoJvWnva/Myl8vFJ5QA8P8EAgGVlZWpuLhYHo9HBQUFGjlypAoKCuTxeFRcXKzy8nIFAgGzW0Wc2rVrl6688kqtWLFCjz32mFasWKErrrhCu3btMrs1ACYZ8BkdhmGcsCYhIUGLFy/W4sWLj1lz2mmn6cEHH9SDDz54zJrTTz9dTz755HF/1re+9S01NDScsCcA8ePIN+319fV69dVX9cwzzyg7O1v19fWaPXu2ysvLVVJSwtpzAHEvdIRnbW2tDMNQY2NjxB4dFRUVstls8nq9HMuNIXXkKYoJCQm64IILwrON1q1b12cdgPgwqKeuAEA0Cr1pt9lsOueccyLWm59zzjm65JJLtHPnTnm9XrNbBQDThfY627Fjh84+++yIvzPPPvts/e1vf4uoA4bKnj17JEk33nhjeC+/tLQ02Ww2vfPOO7rhhhsi6gDED4IOAHEn9Ga8oqKiz6UrixYtiqgDgHgWOiVv7ty5vTZ1bGtr09y5cyPqgKEybtw4SVJLS4u2bdumZcuW6eabb9ayZcv07rvv6sMPP4yoAxA/Bu14WQCIVqFNyaZNmyaPx6PExK8y39B683/4h3/Qq6++yuZlACDJZrMpMTFRwWBQ06dP1y9/+cvw8oBf//rXamhoUGJiYvjUO2CoTJgwQZK0bt06nX766erq6gqPLVq0KPw4VAcgfjCjAwCOkpCQYHYLABA1vF6vgsFg+LFhGOGvkGAwyHI/DDm73R7+UOJY+wRmZGTIbrcPZVsAogBBB4C4E5p6/eqrr8rhcEQsXXE4HHr11Vcj6gAgnjU2NkqSFi9e3Oc+CHfffXdEHTCUQgHHP/7jP+rWW2/Vddddp1tvvVVFRUUmdwbATCxdARB3QuvIKysr9cgjj0RMt87NzdWvf/1rLVq0iPXmAHCE0AkrDz30UHhj0vnz5+uVV14xuzXEKa/Xqz179uiaa67Rn/70Jz377LPhsaSkJJWWlmrVqlWcCATEIYIOAHHHbrcrJydHmzZt0rZt2/Twww+H37TfcMMNmjNnjnJzc5nqCgCSCgsLde+99+qmm25Sd3e3WlpawmMPPPCATjvttHAdMJRCm4avWrVKM2fO1A9+8AOlpqaqq6tL69atU21tbUQdgPhB0AEg7lgsFtXU1Gj27NkaPXq0uru7w2MVFRXq7u5WfX29LBaLiV0CQHQoLCxUenq6tm/f3msPow8//FCGYSg9PZ2gA0MutD/H1KlTtWbNmvDm4pJ0ww036NJLL9XGjRvZXByIQwQdAOLakSFHX48BAF/vg5CQkBCx6WPo8bE2ggTMxO8lokEgEJDX65Xf71d2drbsdjsfpg0Bgo4Y0tzcrM7OTrPbOKZt27ZF/DeajRo1ShMnTjS7DQySQCCgefPmSZKSk5Plcrl00UUX6c0331RdXZ16eno0b948lZSU8A8RgLjX2Niojo4OTZgwQa2trRFjiYmJys7O1q5du9TY2Kjp06eb1CXiUWjT8I0bN8rhcKiioiJ89HFVVRWbi8N0brdbZWVlEUv+cnJyVFNTI6fTaV5jcYCgI0Y0NzfrnHPOMbuNfpk7d67ZLfTL+++/T9gRo55//nkdOHBAw4YNU2dnp5KTk8Nj//Ef/6GRI0fqwIEDev7553XllVea2CkAmC90msquXbtUXFzcax+EhoaGcB1BB4ZSaNPwqqqqPjcXr6ysZHNxmMbtdsvlcqm4uFi1tbXhEK6yslIul0t1dXWEHYOIoCNGhGZyPPnkk5o8ebLJ3fStq6tLLS0tysnJUWpqqtntHNO2bds0d+7cqJ4dg5OzdOlSSdItt9wSEXJIX83wuOmmm3T//fdr6dKlBB0A4l4wGJQkFRQU9LkPwtSpU7V58+ZwHTBUjtxc/P3339err74aXh4wdepUzZ49m83FYYpAIKCysjIVFxfL4/GE/94sKCiQx+ORw+FQeXk5s4cHEUFHjJk8ebIuvPBCs9s4pqlTp5rdAqC9e/dKki655BL19PT0Oirx4osvjqgDgHg2ZswYSV99YNGXAwcORNQBQyW0ubjL5dLs2bNVUVGh4uJi+Xw+zZ49Ww0NDaqrq+NGEkPO6/WqpaVFtbW1EeGw9NWSv4qKCtlsNo4+HkQEHQDizve//329+eabuvbaa/Uv//IvCgQC4bHy8nKNHDkyXAcA8S4zM1OS9D//8z+66qqrei1d+etf/xpRBwwlp9Opuro6lZWV9Vq6wtIAmCV0pHF+fn6f46HrHH08eAg6AMSdmpoarVy5Uvv27dOwYcN0++2362c/+5l+//vfq6amRu3t7eE6AIh3EyZMCH+/du1arV279oR1wFByOp0qLi7uNUPz6OWpwFAJ7Qvj8/lUUFDQa9zn80XUYeAlnrgEAGLLkVNYDx06pN/85jeaOHGifvOb3+jQoUN91gFAvLLb7Ro3bpykr46TPVLocUZGBvsgwDRut1vnnnuubrvtNi1fvly33Xabzj33XLndbrNbQ5wK7R9TWVnZa/+iYDCoqqoq9o8ZZAQdAOLOQw89JEn63ve+1+f4d7/73Yg6AIh3Bw8elCSNHTtWc+bM0f/+3/9bc+bM0dixYyVJ3d3dZraHOBY62cJqtaqpqUmdnZ1qamqS1WqVy+Ui7IApQvvHNDQ0yOFwRPxuOhwONTQ0qLq6mg/VBhFBB4C4s2PHDknSunXrdODAAd10002aMWOGbrrpJh04cEDPPvtsRB0AxLPGxkZ1dHRowoQJ+uKLL/Sf//mf+o//+A/953/+p/bu3asJEyaoo6MjfAwtMFSOPNmivr5e3d3deuaZZ9Td3a36+noVFxervLw8Yi8uYKiE9o95++23ZbPZlJaWJpvNJp/Px/4xQ4A9OgCcUg4cOKDt27ef1HOE1uyuWLFCP/zhD3X11VeHjz7etm1b+NOf5ORkvfXWWyf1syZNmqThw4ef1HMAgJlCAcann36qmTNn9tqMNLRnR2Njo6ZPn25ip4g3oZMtrr/+ep1zzjlqaWkJj+Xk5Oi6667TM888w8kWMI3T6VRJSYm8Xm/46GO73c5MjiFA0AHglLJ9+3ZNmTJlQJ7r3nvv1b333nvM8aVLl2rp0qUn9TO2bNkS1Uc+A8CJhNaXFxQUaM2aNRFHJd5www2aOnWqNm/e3GsdOjDYQidWLFq0SMXFxaqtrVV+fr58Pp8qKyv1y1/+MqIOQPwg6ABwSpk0aZK2bNly0s/zwAMP6I9//KNOP/10ORwOPfbYY/rpT38qj8ejL774Qv/6r/+qX/ziFwPSLwCcyk4//XRJ0v79+/scD10P1QFDJSMjQ5I0depUeTyecAhXUFAgj8ejSy+9VBs3bgzXAUPN7XZr4cKF+vDDD8PXzjrrLC1dupSlK4OMoAPAKWX48OEDMkPi8ccfV2ZmppYtW6bHHntMkvTYY48pKSlJt99+u5YsWXLSPwMAYkFWVpYk6a9//atKSkq0aNGiiE/N33777Yg6wAyBQECvvPJKeHnA1KlTZRiG2W0hjrndbs2ePVupqakR19va2jR79mzV19cTdgwiNiMFELeWLFmi/fv3a+HChZKkhQsXav/+/YQcAHCECRMmhL9/8cUXIzbVe+mll/qsA4ZCW1ubJGnjxo1KT09XUVGRSktLVVRUpPT0dL366qsRdcBQCQQCuuGGGyRJ06dPjzh1JbSX0Y033shGuYOIoANAXEtOTtY111wjSbrmmmvCG5UCAL5it9uVk5Ojiy66qNcSgIyMDF100UXKzc2V3W43qUPEq+zs7GOOJSQk9KsOGAyNjY3as2ePpk2bpjVr1qigoEAjR44M73U0bdo0tbW1cVrVICLoAAAAwDFZLBbV1NRoy5YtslqtWr58uX7/+99r+fLlys/P15YtW1RdXc0pAhhyNptNSUlJyszM1O7duyOOi29tbVVmZqaSkpJks9nMbhVxJhRg3HPPPREbOEtSYmKi7r777og6DDz26AAAAMBxOZ1O1dXVaeHChWpoaAhfz8nJUV1dHevMYYpNmzbp8OHD2r17t9LT08N7cjz33HN66KGHwo83bdrE8bJAnGFGBwAAAL4RNnuEmY48NvbIpSpHP+Z4WQy1ULB299139zp6OxgMavHixRF1GHgEHQAAADgut9stl8ul3bt3R1zfvXu3XC6X3G63SZ0hno0dO1aSNGLECJ155pkRY2eeeaZGjBgRUQcMlcLCQmVkZGjjxo0qKSmJ2Iy0pKREr776qjIyMgg6BhFBBwAAAI4pEAjoxhtvlGEYfX5qbhgGpwfAFKGjjffv3y+r1RpxM2m1WrV///6IOmCoWCwWrVy5UgkJCX2eVpWQkKCVK1eyt9EgIugAAADAMTU2NoaP5zzWMYmcHgAz7NixI+KxYRjhr+PVAUMhtLdRZmZmxPXMzEz2NhoCBB0AAAA4ppdeekmSdMkll8jtdqu7u1vPPPOMuru75Xa7VVBQEFEHDJXQDKMf/vCHevvttyM+Nff5fCopKYmoA4aa0+nUBx98oA0bNmjVqlXasGGDmpubCTmGAKeuAAAA4Jg++ugjSdJ5552nc845Ry0tLeGxnJwc/eM//qM2b94crgOGysUXX6wVK1Zo/fr16unpiRj7+OOPw3vKXHzxxWa0B0j6ahkLe3EMPWZ0AAAA4Ji+9a1vSZJ+//vfKz8/P2LpSn5+vh577LGIOmCohDYg7erq6rVHTCAQUFdXV0QdgPhB0AEAAIBjOvqTyGPtg8Anlhhq/Z2pwYwOIP4QdAAAAOCYEhO/frv4wgsvROyD8OKLL/ZZBwyF5cuXh78/+vfvyMdH1gGID/yLBAAAgGMKnbiSkJDQ56aOoWuhOmCoeDye8PfDhg2LGEtOTu6zDkB8IOgAAADAMWVnZ0uSKisrlZGRETGWmZmpX//61xF1wFDp6OiQJI0bN05ZWVkRY5mZmRo7dmxEHYD4wakrAAAAOCa73a6cnBzV19f3GjMMQ263W7m5ubLb7SZ0h3gWCt727Nmj0047LWJs9+7d6u7ujqgDED+Y0QEAAIBjslgs+tGPfqQ333xTH3/8ccTYxx9/rDfffFMul0sWi8WkDhGvvv/974e/DwaDuvPOO/X+++/rzjvvVDAY7LMOQHwg6AAAAMAxBQIBPf7445Ii9z2QpJSUFEnS448/3ut4T2CwjRkzJvx9T0+Pfvvb3+qcc87Rb3/7W/X09PRZByA+EHQAAADgmBobG9XW1qZp06apo6NDGzZs0KpVq7Rhwwa1t7dr6tSpamtrU2Njo9mtIs7s27dvQOsAxA6CDgAAABxTKMC45557+jzCc/HixRF1wFA58vcxNTU1YuzIxxx9DMQf/l8PAACAE/J6vcrLy1NRUZFKS0tVVFSkvLw8bdy40ezWEKcKCwslSZMnT9a4ceMixjIyMjRp0qSIOgDxg1NXAAAAcEyFhYW69957tXjxYs2cOVO33367UlNT1dXVpWeffVb33HNPuA4YSoWFhRo3bpy2bdummTNn6o477gj/bq5bt05r165VRkYGv5tAHCLoAAAAwDHZ7XYlJiYqGAzqpZde0tq1a8NjoeUBiYmJHC+LIWexWPTwww9r9uzZvX43hw8fLklauXIlJwIBcYigAwAQl5qbm9XZ2Wl2G8e1bdu2iP9Gq1GjRmnixIlmt4FBsmnTpvBRnV1dXRFjocfBYFCbNm3ik3MMOafTqdtvv101NTUR1w8ePKjbb79dTqfTpM4AmImgAwAQd5qbm3XOOeeY3Ua/zZ071+wWTuj9998n7IhRu3btGtA6YCC53W5VV1frtNNOiwjikpOTVV1drYKCAsIOIA4RdAAA4k5oJseTTz6pyZMnm9zNsXV1damlpUU5OTm9ThSIFtu2bdPcuXOjfnYMvrnW1tYBrQMGSiAQ0I033ijDMMKzjkKCwaAMw9CNN96okpISlq8AcYagAwAQtyZPnqwLL7zQ7DaOa+rUqWa3gDi3Z8+e8PehvTr6enxkHTAUGhsb1dbWJknq6emJGAs9bmtrU2Njo6ZPnz7k/QEwD8fLAgAA4JjefPPN8Pd9fWreVx0wFF566aXw9xkZGfrd734nv9+v3/3ud8rIyOizDkB8YEYHAAAAjsnv9x9zLCEhQYZhnLAOGAw7d+6U9NWGyJ988omSkr66tfn5z3+un/zkJxo9erS+/PLLcB2A+EHQAQAAgH658sorNWLECO3du1ejR4/W/v37tX79erPbQpwKLVsZN26cEhMjJ6onJiYqIyNDX375ZbgOQPwg6AAAAMAxpaSkhL8/XqhxZB0wFEaOHClJ+tvf/qaSkhItWrRI+fn58vl8qqys1N/+9reIOgDxg6ADAAAAx3TmmWfqL3/5S7/qgP46cOCAtm/fflLP8e1vfzv8/fPPP6+Ghobw49NOOy2i7q233jqpnzVp0iQNHz78pJ4D8SkQCMjr9crv9ys7O1t2u51TgIYAQQcAAACOaerUqfqv//qvftUB/bV9+3ZNmTJlwJ7v4MGDEY+7u7vD3y9btkzLli07qeffsmVL1J/ShejjdrtVVlamlpaW8LWcnBzV1NTI6XSa11gcIOgAAADAMR190srJ1gHSVzMktmzZctLP88ADD+iPf/xjxMa40tdHH//rv/6rfvGLX5z0z5k0adJJPwfii9vtlsvlUnFxsWprayOWVblcLtXV1RF2DCKCDgAAABzTM8880++6u+66a5C7QawYPnz4gMyQePzxx5WZmamlS5cqEAiErycmJqqsrExLliw56Z8B/L0CgYDKyspUXFwsj8cT3iy3oKBAHo9HDodD5eXlKikpYRnLICHoiBEJh7t1QVaiUve9L32aeOI/gGNK3fe+LshKVMLh7hMXAwAQ4/bu3TugdcBAW7Jkie69915VVFRo6dKlWrhwoaqqqpScnGx2a4hTXq9XLS0tqq2tlWEYamxsjNijo6KiQjabTV6vV4WFhWa3G5MIOmLEaV9+pLeuHym9cr30itndnNomS3rr+pHa9uVHkmxmtwMAgKmO3vvgZOuAwZCcnKxrrrlGS5cu1TXXXEPIAVP5/X5J0o4dO3T11Vf32qPj3nvvjajDwCPoiBHdI7+lCx/5Uk899ZQms4bwpGzbvl3XXHONfv9P3zK7FQAATPfZZ59FPL7iiiv07//+7/rVr36l//7v/z5mHQDEq+zsbEnS3LlzNWvWrF57dMydOzeiDgOPoCNGGEmn6S+tQXX9r3Ok8d8zu51TWldrUH9pDcpIOu3ExQAAxLjQ2vKQ//7v/44IOI5VBwDxymazKSkpSWPGjJHb7VZS0le33QUFBXK73TrjjDP0+eefy2Zj9vhg4V8kAAAAHFNaWtqA1gFArNu0aZMOHz6strY2OZ1ONTU1qbOzU01NTXI6nWpra9Phw4e1adMms1uNWczoAAAAiFEHDhzQ9u3bT+o5MjIy9NFHH/Wr7q233jqpnzVp0iQNHz78pJ4DAMwW2nvjiSee0L/9279FzNzIzc3VE088oblz57JHxyAi6AAAAIhR27dv15QpU4bkZ7355psn/bO2bNkyIEeOAoCZQntvnH322Xrvvff00EMPaceOHTr77LM1f/58bdmyJaIOA4+gAwAAIEZNmjQp/Ib6m+rq6tK0adNOWLdx40alpqae1M+axIbqAGKA3W5XTk6ObrnlFn322WcRp6488MADGjt2rHJzc2W3281rMsYRdAAYVM3Nzers7DS7jePatm1bxH+j2ahRozRx4kSz2wBwihg+fPiAzJAoKSnRmjVrlJSUpOzsbH388cc688wz5ff7dfjwYZWUlGjq1KkD0DEAnPosFot+9KMf6b777lNGRobmzJmjESNGaP/+/WpsbNSbb76p22+/XRaLxexWYxZBB4BB09zcrHPOOcfsNvotdNRXtHv//fcJOwAMKY/HI4fDoTVr1ujjjz+WpPB/S0pK5PF4TOwOAKJLIBDQn//8Z2VmZmr37t36z//8z4jxzMxM1dXVqaqqirBjkBB0ABg0oZkcTz75pCZPnmxyN8fW1dWllpYW5eTknPS068G0bds2zZ07N+pnyACITR6PR11dXfrxj3+sP//5z/rRj36kxx9/PKr/3gQAM3i93vBylcTERAWDwfBYYmKidu/eHa4rLCw0ocPYR9ABYNBNnjw56jeXY8o1AJxYamqq7rrrLv35z3/WXXfdRcgBAH0IzXiTpCuvvFIzZ85Uamqqurq6tHbtWj377LO96jCwCDoAAAAAABggmzZtkiRlZWXpnXfeCQcbknTWWWcpKytLra2t2rRpk+bNm2dWmzGNoAMAAAAAgAHi9/slSa2trUpJSYkYa21t1cGDByPqMPASzW4AAAAAAIBYMXLkyPD3hw4dihg78vGRdRhYBB0AAAAAAAwQq9Ua/j4pKXIRxbBhw/qsw8Ai6AAAAAAAYIDs27cv/H1PT0/EWGjZytF1GFgEHQAAAAAADJBPPvlkQOvw9yPoAAAAAABggJx++ukDWoe/H0EHAAAAAAADZM2aNeHvExISIsaOfHxkHQYWQQcAAAAAAAPkiy++CH9vGEbE2JGPj6zDwEo6cQkAALEl4XC3LshKVOq+96VPyfxPRuq+93VBVqISDneb3QoAAFEhJSVFnZ2d/arD4CDoAADEndO+/EhvXT9SeuV66RWzuzm1TZb01vUjte3LjyTZzG4HAADTXXrppaqvr+9XHQYHQQcAIO50j/yWLnzkSz311FOaPGmS2e2c0rZt365rrrlGv/+nb5ndCgAAUeHoJSlnnXWWfvjDH+rpp5/Whx9+eMw6DByCDgBA3DGSTtNfWoPq+l/nSOO/Z3Y7p7Su1qD+0hqUkXSa2a0AABAVdu/eHfH4ww8/1P3333/COgwcFiYDAAAAADBAEhO/vs1OTk6OGDtyX44j6zCwmNEBAAAA4Liam5v7tbmimbZt2xbx32g2atQoTZw40ew2MEisVqt8Pp8kyWKxRIwdGW5YrdYh7SueEHQAAP7/9u4+psr6/+P46wiJcutdpAQoqwxCMeVYAdnKbja+jWV/dDcwbNZkI1NZW6utZaZQazZqzTNhjbQ7WmuYf6TEVuKqseLwZTlFoGnTEmK6lFtpyvn98f1BP0Ymv6/n+LnO53o+Nv84F3J8rX04cb2uz/W+AAC4pM7OTi1cuNB0jEkrKioyHWFSOjo6KDsstWbNGn3yySeSpKGhoXFfO3/+/Li/h9Cg6AAQMjzCM7h4jCfgDk6/cs5Vc/cZXY8ffvihMjIyDKe5tKGhIf3yyy9asGCBpk+fbjrOJbW1tamoqMjRP+duNjg4qKNHj17Re8ycOVMxMTEaGBiY8LVAICBJio2N1cyZM9XS0vJf/zvp6emKjo7+r7/fZhQdAEKGR3gGF4/xBOwXTlfOuWruPhkZGVq2bJnpGP8oLy/PdASEuaNHjyo7Ozvk/05/f79uu+22K3oPv9/v+J9JUyg6AIQMj/AMLh7jCdgvHK6cc9UcgM3S09Pl9/uD8l5ff/21tm/fru7u7rFj8+bNU1lZmVauXHnF75/O79eXRNFhicHBQUm6oq1PoRZOvxghOHiEZ3DxGE/APZx+5Zyr5gBsFR0dHbTP32XLlmnTpk167733tG7dOu3cuVNr166dMKAUwUfRYYnR+8ieeeYZw0nsERcXZzoCAAAAgDAWEREhr9crSfJ6vZQcVwlFhyVWrVolydkDaUa3kDp5O+4ohpcBAAAAQHii6LDEnDlz9PTTT5uOMSlO344LAAAAAAhfPO8RAAAAAABYgx0dAADXCYcBzlJ4DHFmgDMAAHAaig4AgOswwDn4GOAMAACcgqIDAOA64TDAWQqfIc4McAYAmNLZ2am+vj7TMf7R6O5Hp++CtOn/5xQdAADXCacBzhJDnAEA+DudnZ1auHCh6RiTVlRUZDrCZXV0dFhRdlB0AAgZ5iAEl9OvAgC4cp4L57V07hRNP9shnWJm/JWYfrZDS+dOkefCedNRAITI6E4Op+98DIffNUd3kTp9d8xkUXQACBnmIIQGsxAAe03rP6GWdbHSwXXSQdNpwluGpJZ1sWrrPyEp13QcACEUDjsf8/LyTEdwFYoOACHDHITgs+neSQATnY9N1bKd/froo4+UkZ5uOk5Yazt6VIWFhXrvX6mmowAArjKKDgAhwxwEAPj/CURO07+7RzQ0Y6GUdKvpOGFtqHtE/+4eUSBymukoAEKE2/2Cx7bb/Sg6AAAAHCIcZhuFw73mEnONgomTyeCy7YTSJG73Cx7bbvej6AAAAHAIZhsFH3ONrhwnk8Fl2wmlSdzuFzy23e7niqJjx44devPNN9XV1aXMzExVVlZqxYoVpmMBAACMEw6zjZhr5D6cTAaXbSeUJg38+Z9b1L471q+hGSOm41xSOOyEa+u6aNXtftYXHZ9++qk2btyoHTt2KC8vTzt37lR+fr6OHDmi1FQ+XAAAgHOE02wj5hq5ByeTwWXbCaVJ7IILPlt2wVlfdLz11ltau3bt2C8NlZWVqq+vl8/nU0VFxYS/Pzw8rOHh4bHXvb29Vy2rkwwODo59cATL6L2yobhn1slXvhBcrE04VTitTdale4TTupRYm07FyWRo2HJCaVIodsGNFmbBdPz4cb388st67bXXlJaWFrT3DXapZ9MuOE8gEAiYDhEqf/75p6Kjo/XZZ5/p4YcfHju+YcMGtba2qrGxccL3bN68Wa+++uqE4+fOnVN8fHxI8zpJS0uLsrOzTceYNL/fz1Ull2BtwqnCaW2yLt0jnNalxNp0qtOnT2vPnj1BPZkcvQUqXAT7Vi2bTihtE06fm278zOzt7VVCQsJlz8+tLjpOnTql66+/Xt99951yc/8a9FNeXq5du3apvb19wvf83Y6OlJQU1xUdobgCFMrthFwBcg/WJpwqnNYm69I9wmldSqxNN2FtwqnCaW26cV1SdOivouP7779XTk7O2PFt27bpgw8+mNQCnux/SAAAAAAAEDqTPT+3+kHYc+bMUUREhLq7u8cd7+np0XXXXWcoFQAAAAAACBWri46pU6cqOztbDQ0N4443NDSMu5UFAAAAAADYwfqnrpSVlWn16tXyer3KyclRVVWVTpw4oZKSEtPRAAAAAABAkFlfdDz22GM6c+aMtmzZoq6uLi1atEhffvml5s+fbzoaAAAAAAAIMquHkQYDw0gBAAAAADCPYaQAAAAAAMB1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1KDoAAAAAAIA1Ik0HcLpAICBJ6u3tNZwEAAAAAAD3Gj0vHz1PvxSKjsvo6+uTJKWkpBhOAgAAAAAA+vr6lJCQcMmvewKXq0JcbmRkRKdOnVJcXJw8Ho/pOGGtt7dXKSkpOnnypOLj403HAcawNuFUrE04EesSTsXahFOxNoMnEAior69PSUlJmjLl0pM42NFxGVOmTFFycrLpGFaJj4/nBxyOxNqEU7E24USsSzgVaxNOxdoMjn/ayTGKYaQAAAAAAMAaFB0AAAAAAMAaFB24aqKiovTKK68oKirKdBRgHNYmnIq1CSdiXcKpWJtwKtbm1ccwUgAAAAAAYA12dAAAAAAAAGtQdAAAAAAAAGtQdAAAAAAAAGtQdAAAAAAAAGtQdAAAAAAAAGtQdCDkDh48qIKCAiUlJcnj8WjPnj2mIwGSpIqKCi1fvlxxcXFKTEzUqlWr1N7ebjoWXM7n8ykrK0vx8fGKj49XTk6O9u3bZzoWMEFFRYU8Ho82btxoOgpcbvPmzfJ4POP+zJ0713QsQL/99puKioo0e/ZsRUdH69Zbb5Xf7zcdyxUoOhByAwMDWrJkid59913TUYBxGhsbVVpaqqamJjU0NOjChQt64IEHNDAwYDoaXCw5OVmvv/66mpub1dzcrJUrV+qhhx7S4cOHTUcDxvz444+qqqpSVlaW6SiAJCkzM1NdXV1jfw4dOmQ6Elzujz/+UF5enq655hrt27dPR44c0fbt2zVjxgzT0Vwh0nQA2C8/P1/5+fmmYwAT7N+/f9zrmpoaJSYmyu/366677jKUCm5XUFAw7vW2bdvk8/nU1NSkzMxMQ6mAv/T396uwsFDV1dXaunWr6TiAJCkyMpJdHHCUN954QykpKaqpqRk7tmDBAnOBXIYdHQDwv86dOydJmjVrluEkwH9cvHhRtbW1GhgYUE5Ojuk4gCSptLRUDz74oO677z7TUYAxnZ2dSkpKUlpamh5//HEdO3bMdCS43N69e+X1evXII48oMTFRS5cuVXV1telYrkHRAQCSAoGAysrKdOedd2rRokWm48DlDh06pNjYWEVFRamkpER1dXW65ZZbTMcCVFtbq5aWFlVUVJiOAoy5/fbbtXv3btXX16u6ulrd3d3Kzc3VmTNnTEeDix07dkw+n0833XST6uvrVVJSoueee067d+82Hc0VuHUFACQ9++yz+umnn/Ttt9+ajgLo5ptvVmtrq86ePavPP/9cxcXFamxspOyAUSdPntSGDRv01Vdfadq0aabjAGP+7y3SixcvVk5Ojm644Qbt2rVLZWVlBpPBzUZGRuT1elVeXi5JWrp0qQ4fPiyfz6cnn3zScDr7saMDgOutX79ee/fu1TfffKPk5GTTcQBNnTpVN954o7xeryoqKrRkyRK9/fbbpmPB5fx+v3p6epSdna3IyEhFRkaqsbFR77zzjiIjI3Xx4kXTEQFJUkxMjBYvXqzOzk7TUeBi8+bNm3CBIiMjQydOnDCUyF3Y0QHAtQKBgNavX6+6ujodOHBAaWlppiMBfysQCGh4eNh0DLjcvffeO+FJFk899ZTS09P1wgsvKCIiwlAyYLzh4WG1tbVpxYoVpqPAxfLy8tTe3j7uWEdHh+bPn28okbtQdCDk+vv79fPPP4+9Pn78uFpbWzVr1iylpqYaTAa3Ky0t1ccff6wvvvhCcXFx6u7uliQlJCRo+vTphtPBrV566SXl5+crJSVFfX19qq2t1YEDByY8JQi42uLi4ibMMIqJidHs2bOZbQSjnn/+eRUUFCg1NVU9PT3aunWrent7VVxcbDoaXGzTpk3Kzc1VeXm5Hn30Uf3www+qqqpSVVWV6WiuQNGBkGtubtY999wz9nr0Xsni4mK9//77hlIBks/nkyTdfffd447X1NRozZo1Vz8QIOn333/X6tWr1dXVpYSEBGVlZWn//v26//77TUcDAEf69ddf9cQTT+j06dO69tprdccdd6ipqYkr5zBq+fLlqqur04svvqgtW7YoLS1NlZWVKiwsNB3NFTyBQCBgOgQAAAAAAEAwMIwUAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABYg6IDAAAAAABY438A4kv9V1hgvjoAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_1 =  Data1.Fresh\n",
    "data_2 =  Data1.Milk\n",
    "data_3 =  Data1.Grocery\n",
    "data_4 =  Data1.Frozen\n",
    "data_5 =  Data1.Detergents_Paper\n",
    "data_6 =  Data1.Delicatessen\n",
    "data = [data_1, data_2, data_3, data_4, data_5, data_6]\n",
    " \n",
    "fig = plt.figure(figsize =(10, 7))\n",
    " \n",
    "ax = fig.add_axes([0, 0, 1, 1])\n",
    " \n",
    "bp = ax.boxplot(data)\n",
    " \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cd48e5db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Fresh', 'Milk', 'Grocery', 'Frozen', 'Detergents_Paper', 'Delicatessen']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns = ['Fresh', 'Milk', 'Grocery','Frozen', 'Detergents_Paper', 'Delicatessen']\n",
    "columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "47aca25f",
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Region</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Lisbon</th>\n",
       "      <td>854833</td>\n",
       "      <td>422454</td>\n",
       "      <td>570037</td>\n",
       "      <td>231026</td>\n",
       "      <td>204136</td>\n",
       "      <td>104327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oporto</th>\n",
       "      <td>464721</td>\n",
       "      <td>239144</td>\n",
       "      <td>433274</td>\n",
       "      <td>190132</td>\n",
       "      <td>173311</td>\n",
       "      <td>54506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Other</th>\n",
       "      <td>3960577</td>\n",
       "      <td>1888759</td>\n",
       "      <td>2495251</td>\n",
       "      <td>930492</td>\n",
       "      <td>890410</td>\n",
       "      <td>512110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Fresh     Milk  Grocery  Frozen  Detergents_Paper  Delicatessen\n",
       "Region                                                                   \n",
       "Lisbon   854833   422454   570037  231026            204136        104327\n",
       "Oporto   464721   239144   433274  190132            173311         54506\n",
       "Other   3960577  1888759  2495251  930492            890410        512110"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Region= Data1.groupby('Region')[columns].sum()\n",
    "Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "04b85080",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Region\n",
      "Lisbon     2386813\n",
      "Oporto     1555088\n",
      "Other     10677599\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df1 = Region.sum(axis=1)\n",
    "print(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "33e9096e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot(kind='bar')\n",
    "plt.xticks(rotation=0, horizontalalignment=\"center\")\n",
    "plt.title(\"Amount spent by consumers\")\n",
    "plt.xlabel(\"Region\")\n",
    "plt.ylabel(\"Total Amount spent in millions\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a81916ae",
   "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>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Channel</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Hotel</th>\n",
       "      <td>4015717</td>\n",
       "      <td>1028614</td>\n",
       "      <td>1180717</td>\n",
       "      <td>1116979</td>\n",
       "      <td>235587</td>\n",
       "      <td>421955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Retail</th>\n",
       "      <td>1264414</td>\n",
       "      <td>1521743</td>\n",
       "      <td>2317845</td>\n",
       "      <td>234671</td>\n",
       "      <td>1032270</td>\n",
       "      <td>248988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Fresh     Milk  Grocery   Frozen  Detergents_Paper  Delicatessen\n",
       "Channel                                                                    \n",
       "Hotel    4015717  1028614  1180717  1116979            235587        421955\n",
       "Retail   1264414  1521743  2317845   234671           1032270        248988"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2= Data1.groupby('Channel')[columns].sum()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a77b640a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Channel\n",
      "Hotel     7999569\n",
      "Retail    6619931\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "df3= df2.sum(axis=1)\n",
    "print(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1b7c4d7b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3.plot(kind='bar')\n",
    "plt.xticks(rotation=0, horizontalalignment=\"center\")\n",
    "plt.title(\"Amount spent by consumers\")\n",
    "plt.xlabel(\"Channel\")\n",
    "plt.ylabel(\"Total Amount spent in millions\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50b55f4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Dataset= Data1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6e72fa77",
   "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>Buyer/Spender</th>\n",
       "      <th>Fresh</th>\n",
       "      <th>Milk</th>\n",
       "      <th>Grocery</th>\n",
       "      <th>Frozen</th>\n",
       "      <th>Detergents_Paper</th>\n",
       "      <th>Delicatessen</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>440.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>12000.297727</td>\n",
       "      <td>5796.265909</td>\n",
       "      <td>7951.277273</td>\n",
       "      <td>3071.931818</td>\n",
       "      <td>2881.493182</td>\n",
       "      <td>1524.870455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>127.161315</td>\n",
       "      <td>12647.328865</td>\n",
       "      <td>7380.377175</td>\n",
       "      <td>9503.162829</td>\n",
       "      <td>4854.673333</td>\n",
       "      <td>4767.854448</td>\n",
       "      <td>2820.105937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>110.750000</td>\n",
       "      <td>3127.750000</td>\n",
       "      <td>1533.000000</td>\n",
       "      <td>2153.000000</td>\n",
       "      <td>742.250000</td>\n",
       "      <td>256.750000</td>\n",
       "      <td>408.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>220.500000</td>\n",
       "      <td>8504.000000</td>\n",
       "      <td>3627.000000</td>\n",
       "      <td>4755.500000</td>\n",
       "      <td>1526.000000</td>\n",
       "      <td>816.500000</td>\n",
       "      <td>965.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>330.250000</td>\n",
       "      <td>16933.750000</td>\n",
       "      <td>7190.250000</td>\n",
       "      <td>10655.750000</td>\n",
       "      <td>3554.250000</td>\n",
       "      <td>3922.000000</td>\n",
       "      <td>1820.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>440.000000</td>\n",
       "      <td>112151.000000</td>\n",
       "      <td>73498.000000</td>\n",
       "      <td>92780.000000</td>\n",
       "      <td>60869.000000</td>\n",
       "      <td>40827.000000</td>\n",
       "      <td>47943.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CV</th>\n",
       "      <td>0.576695</td>\n",
       "      <td>1.053918</td>\n",
       "      <td>1.273299</td>\n",
       "      <td>1.195174</td>\n",
       "      <td>1.580332</td>\n",
       "      <td>1.654647</td>\n",
       "      <td>1.849407</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Buyer/Spender          Fresh          Milk       Grocery        Frozen  \\\n",
       "count     440.000000     440.000000    440.000000    440.000000    440.000000   \n",
       "mean      220.500000   12000.297727   5796.265909   7951.277273   3071.931818   \n",
       "std       127.161315   12647.328865   7380.377175   9503.162829   4854.673333   \n",
       "min         1.000000       3.000000     55.000000      3.000000     25.000000   \n",
       "25%       110.750000    3127.750000   1533.000000   2153.000000    742.250000   \n",
       "50%       220.500000    8504.000000   3627.000000   4755.500000   1526.000000   \n",
       "75%       330.250000   16933.750000   7190.250000  10655.750000   3554.250000   \n",
       "max       440.000000  112151.000000  73498.000000  92780.000000  60869.000000   \n",
       "CV          0.576695       1.053918      1.273299      1.195174      1.580332   \n",
       "\n",
       "       Detergents_Paper  Delicatessen  \n",
       "count        440.000000    440.000000  \n",
       "mean        2881.493182   1524.870455  \n",
       "std         4767.854448   2820.105937  \n",
       "min            3.000000      3.000000  \n",
       "25%          256.750000    408.250000  \n",
       "50%          816.500000    965.500000  \n",
       "75%         3922.000000   1820.250000  \n",
       "max        40827.000000  47943.000000  \n",
       "CV             1.654647      1.849407  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Dataset.loc[('CV'), :] = Dataset.loc[('std'), :]/Dataset.loc[('mean'), :]\n",
    "Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9230d6b",
   "metadata": {},
   "source": [
    "# Survey-1 Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ba9ebf1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data2 = pd.read_csv('Survey-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "336f947c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(62, 14)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5dfcf796",
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>360</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   ID  Gender  Age   Class       Major Grad Intention  GPA  Employment  \\\n",
       "0   1  Female   20  Junior       Other            Yes  2.9   Full-Time   \n",
       "1   2    Male   23  Senior  Management            Yes  3.6   Part-Time   \n",
       "2   3    Male   21  Junior       Other            Yes  2.5   Part-Time   \n",
       "3   4    Male   21  Junior         CIS            Yes  2.5   Full-Time   \n",
       "4   5    Male   23  Senior       Other      Undecided  2.8  Unemployed   \n",
       "\n",
       "   Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "0    50.0                  1             3       350   Laptop            200  \n",
       "1    25.0                  1             4       360   Laptop             50  \n",
       "2    45.0                  2             4       600   Laptop            200  \n",
       "3    40.0                  4             6       600   Laptop            250  \n",
       "4    40.0                  2             4       500   Laptop            100  "
      ]
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       "      <td>NaN</td>\n",
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       "      <td>1.516129</td>\n",
       "      <td>3.741935</td>\n",
       "      <td>482.016129</td>\n",
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       "      <th>std</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>12.080912</td>\n",
       "      <td>0.844305</td>\n",
       "      <td>1.213793</td>\n",
       "      <td>221.953805</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "               ID  Gender        Age   Class                Major  \\\n",
       "count   62.000000      62  62.000000      62                   62   \n",
       "unique        NaN       2        NaN       3                    8   \n",
       "top           NaN  Female        NaN  Senior  Retailing/Marketing   \n",
       "freq          NaN      33        NaN      31                   14   \n",
       "mean    31.500000     NaN  21.129032     NaN                  NaN   \n",
       "std     18.041619     NaN   1.431311     NaN                  NaN   \n",
       "min      1.000000     NaN  18.000000     NaN                  NaN   \n",
       "25%     16.250000     NaN  20.000000     NaN                  NaN   \n",
       "50%     31.500000     NaN  21.000000     NaN                  NaN   \n",
       "75%     46.750000     NaN  22.000000     NaN                  NaN   \n",
       "max     62.000000     NaN  26.000000     NaN                  NaN   \n",
       "\n",
       "       Grad Intention        GPA Employment     Salary  Social Networking  \\\n",
       "count              62  62.000000         62  62.000000          62.000000   \n",
       "unique              3        NaN          3        NaN                NaN   \n",
       "top               Yes        NaN  Part-Time        NaN                NaN   \n",
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       "75%               NaN   3.400000        NaN  55.000000           2.000000   \n",
       "max               NaN   3.900000        NaN  80.000000           4.000000   \n",
       "\n",
       "        Satisfaction     Spending Computer  Text Messages  \n",
       "count      62.000000    62.000000       62      62.000000  \n",
       "unique           NaN          NaN        3            NaN  \n",
       "top              NaN          NaN   Laptop            NaN  \n",
       "freq             NaN          NaN       55            NaN  \n",
       "mean        3.741935   482.016129      NaN     246.209677  \n",
       "std         1.213793   221.953805      NaN     214.465950  \n",
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       "25%         3.000000   312.500000      NaN     100.000000  \n",
       "50%         4.000000   500.000000      NaN     200.000000  \n",
       "75%         4.000000   600.000000      NaN     300.000000  \n",
       "max         6.000000  1400.000000      NaN     900.000000  "
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     "execution_count": 22,
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   "source": [
    "Data2.describe(include= \"all\")"
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  {
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   "execution_count": 23,
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   ID  Gender   Age   Class                Major Grad Intention  GPA  \\\n",
       "0   1  Female  21.0  Senior  Retailing/Marketing            Yes  3.0   \n",
       "1   2     NaN   NaN     NaN                  NaN            NaN  3.1   \n",
       "2   3     NaN   NaN     NaN                  NaN            NaN  3.4   \n",
       "3   4     NaN   NaN     NaN                  NaN            NaN  NaN   \n",
       "4   5     NaN   NaN     NaN                  NaN            NaN  NaN   \n",
       "\n",
       "  Employment  Salary  Social Networking  Satisfaction  Spending Computer  \\\n",
       "0  Part-Time    40.0                1.0           4.0     500.0   Laptop   \n",
       "1        NaN     NaN                NaN           NaN       NaN      NaN   \n",
       "2        NaN     NaN                NaN           NaN       NaN      NaN   \n",
       "3        NaN     NaN                NaN           NaN       NaN      NaN   \n",
       "4        NaN     NaN                NaN           NaN       NaN      NaN   \n",
       "\n",
       "   Text Messages  \n",
       "0          300.0  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  "
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     "execution_count": 23,
     "metadata": {},
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   "source": [
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  {
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   "execution_count": 24,
   "id": "c0aee23d",
   "metadata": {},
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62 entries, 0 to 61\n",
      "Data columns (total 14 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   ID                 62 non-null     int64  \n",
      " 1   Gender             62 non-null     object \n",
      " 2   Age                62 non-null     int64  \n",
      " 3   Class              62 non-null     object \n",
      " 4   Major              62 non-null     object \n",
      " 5   Grad Intention     62 non-null     object \n",
      " 6   GPA                62 non-null     float64\n",
      " 7   Employment         62 non-null     object \n",
      " 8   Salary             62 non-null     float64\n",
      " 9   Social Networking  62 non-null     int64  \n",
      " 10  Satisfaction       62 non-null     int64  \n",
      " 11  Spending           62 non-null     int64  \n",
      " 12  Computer           62 non-null     object \n",
      " 13  Text Messages      62 non-null     int64  \n",
      "dtypes: float64(2), int64(6), object(6)\n",
      "memory usage: 6.9+ KB\n"
     ]
    }
   ],
   "source": [
    "Data2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "98f2dccc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    33\n",
       "Male      29\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "64768ac5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Major</th>\n",
       "      <th>Accounting</th>\n",
       "      <th>CIS</th>\n",
       "      <th>Economics/Finance</th>\n",
       "      <th>International Business</th>\n",
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       "      <th>Other</th>\n",
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       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Major   Accounting  CIS  Economics/Finance  International Business  \\\n",
       "Gender                                                               \n",
       "Female           3    3                  7                       4   \n",
       "Male             4    1                  4                       2   \n",
       "\n",
       "Major   Management  Other  Retailing/Marketing  Undecided  \n",
       "Gender                                                     \n",
       "Female           4      3                    9          0  \n",
       "Male             6      4                    5          3  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Major'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2c74ef56",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Class   Junior  Senior  Sophomore\n",
       "Gender                           \n",
       "Female      14      17          2\n",
       "Male        11      14          4"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Class'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10f72253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GPA\n",
       "2.3    1\n",
       "2.4    1\n",
       "2.5    6\n",
       "2.6    2\n",
       "2.8    3\n",
       "2.9    4\n",
       "3.0    7\n",
       "3.1    7\n",
       "3.2    6\n",
       "3.3    5\n",
       "3.4    7\n",
       "3.5    6\n",
       "3.6    3\n",
       "3.7    2\n",
       "3.8    1\n",
       "3.9    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['GPA']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "fe908a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Grad Intention  No  Undecided  Yes\n",
       "Gender                            \n",
       "Female           9         13   11\n",
       "Male             3          9   17"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Grad Intention'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ee2188ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Employment</th>\n",
       "      <th>Full-Time</th>\n",
       "      <th>Part-Time</th>\n",
       "      <th>Unemployed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Employment  Full-Time  Part-Time  Unemployed\n",
       "Gender                                      \n",
       "Female              3         24           6\n",
       "Male                7         19           3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Employment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0bbf5ead",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Computer</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Tablet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "Computer  Desktop  Laptop  Tablet\n",
       "Gender                           \n",
       "Female          2      29       2\n",
       "Male            3      26       0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Computer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "5d14a15b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability that a randomly selected CMSU student will be Male: 0.46774193548387094\n"
     ]
    }
   ],
   "source": [
    "print('probability that a randomly selected CMSU student will be Male:',29/len(Data2['Gender']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "df79afab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "probability that a randomly selected CMSU student will be Female: 0.532258064516129\n"
     ]
    }
   ],
   "source": [
    "print('probability that a randomly selected CMSU student will be Female:',33/len(Data2['Gender']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77f1b6df",
   "metadata": {},
   "source": [
    " Find the conditional probability of different majors among the male students in CMSU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0e220fec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Major</th>\n",
       "      <th>Accounting</th>\n",
       "      <th>CIS</th>\n",
       "      <th>Economics/Finance</th>\n",
       "      <th>International Business</th>\n",
       "      <th>Management</th>\n",
       "      <th>Other</th>\n",
       "      <th>Retailing/Marketing</th>\n",
       "      <th>Undecided</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Major   Accounting  CIS  Economics/Finance  International Business  \\\n",
       "Gender                                                               \n",
       "Female           3    3                  7                       4   \n",
       "Male             4    1                  4                       2   \n",
       "\n",
       "Major   Management  Other  Retailing/Marketing  Undecided  \n",
       "Gender                                                     \n",
       "Female           4      3                    9          0  \n",
       "Male             6      4                    5          3  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Major'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85564789",
   "metadata": {},
   "source": [
    "P (majors | male) = P (majors ∩ male)/ P(male)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3e027d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among Male students:\n",
      "Probability of students in Accounting: 0.13793103448275862\n",
      "Probability of students in  CIS: 0.034482758620689655\n",
      "Probability of students in Economics/Finance: 0.13793103448275862\n",
      "Probability of students in International Business: 0.06896551724137931\n",
      "Probability of students in Management: 0.20689655172413793\n",
      "Probability of students in Other 0.1724137931034483\n",
      "Probability of students in Retailing/Marketing 0.1724137931034483\n",
      "Probability of students who have Undecided Major 0.10344827586206896\n"
     ]
    }
   ],
   "source": [
    "print('Among Male students:')\n",
    "print('Probability of students in Accounting:',4/29)\n",
    "print('Probability of students in  CIS:',1/29)\n",
    "print('Probability of students in Economics/Finance:',4/29)\n",
    "print('Probability of students in International Business:',2/29)\n",
    "print('Probability of students in Management:',6/29)\n",
    "print('Probability of students in Other', 5/29)\n",
    "print('Probability of students in Retailing/Marketing',5/29)\n",
    "print('Probability of students who have Undecided Major',3/29)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48cf157e",
   "metadata": {},
   "source": [
    "Find the conditional probability of different majors among the female students of CMSU."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "776b4c37",
   "metadata": {},
   "source": [
    "P (majors | female) = P (majors ∩ female)/ P(female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ce08dd3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Among Female students:\n",
      "Probability of students in Accounting: 0.09090909090909091\n",
      "Probability of students in  CIS: 0.09090909090909091\n",
      "Probability of students in Economics/Finance: 0.21212121212121213\n",
      "Probability of students in International Business: 0.12121212121212122\n",
      "Probability of students in Management: 0.12121212121212122\n",
      "Probability of students in Other 0.09090909090909091\n",
      "Probability of students in Retailing/Marketing 0.2727272727272727\n",
      "Probability of students who have Undecided Major 0.0\n"
     ]
    }
   ],
   "source": [
    "print('Among Female students:')\n",
    "print('Probability of students in Accounting:',3/33)\n",
    "print('Probability of students in  CIS:',3/33)\n",
    "print('Probability of students in Economics/Finance:',7/33)\n",
    "print('Probability of students in International Business:',4/33)\n",
    "print('Probability of students in Management:',4/33)\n",
    "print('Probability of students in Other', 3/33)\n",
    "print('Probability of students in Retailing/Marketing',9/33)\n",
    "print('Probability of students who have Undecided Major',0/33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "47f31e1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Undecided  Yes\n",
       "Gender                            \n",
       "Female           9         13   11\n",
       "Male             3          9   17"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Grad Intention'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71b31cda",
   "metadata": {},
   "source": [
    "Find the probability That a randomly chosen student is a male and intends to graduate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bef60caa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Grad Intention ∩ Male) = P (Grad Intention| Male) x P (male) =  0.27419354838709675\n"
     ]
    }
   ],
   "source": [
    "print(\"P(Grad Intention ∩ Male) = P (Grad Intention| Male) x P (male) = \",(17/29) * (29/62))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0811066",
   "metadata": {},
   "source": [
    "Find the probability that a randomly selected student is a female and does NOT have a laptop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a6feebab",
   "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>Computer</th>\n",
       "      <th>Desktop</th>\n",
       "      <th>Laptop</th>\n",
       "      <th>Tablet</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>2</td>\n",
       "      <td>29</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Computer  Desktop  Laptop  Tablet\n",
       "Gender                           \n",
       "Female          2      29       2\n",
       "Male            3      26       0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Computer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6b0a5f92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P( Not Laptop ∩ Female) = P (Not Laptop | Female) x P (Female) =  0.06451612903225806\n"
     ]
    }
   ],
   "source": [
    "print(\"P( Not Laptop ∩ Female) = P (Not Laptop | Female) x P (Female) = \",(4/33) * (33/62))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3f56fb",
   "metadata": {},
   "source": [
    "Find the probability that a randomly chosen student is a male or has a full-time employment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "eb7abd94",
   "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>Employment</th>\n",
       "      <th>Full-Time</th>\n",
       "      <th>Part-Time</th>\n",
       "      <th>Unemployed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Employment  Full-Time  Part-Time  Unemployed\n",
       "Gender                                      \n",
       "Female              3         24           6\n",
       "Male                7         19           3"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Employment'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8ec38d8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P( full-time employment OR Male)= P( full-time employment ∪ Male) =  1.4285714285714286\n"
     ]
    }
   ],
   "source": [
    "print(\"P( full-time employment OR Male)= P( full-time employment ∪ Male) = \",10/7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "ecb52a2a",
   "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>Major</th>\n",
       "      <th>Accounting</th>\n",
       "      <th>CIS</th>\n",
       "      <th>Economics/Finance</th>\n",
       "      <th>International Business</th>\n",
       "      <th>Management</th>\n",
       "      <th>Other</th>\n",
       "      <th>Retailing/Marketing</th>\n",
       "      <th>Undecided</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Major   Accounting  CIS  Economics/Finance  International Business  \\\n",
       "Gender                                                               \n",
       "Female           3    3                  7                       4   \n",
       "Male             4    1                  4                       2   \n",
       "\n",
       "Major   Management  Other  Retailing/Marketing  Undecided  \n",
       "Gender                                                     \n",
       "Female           4      3                    9          0  \n",
       "Male             6      4                    5          3  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Major'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a924b39c",
   "metadata": {},
   "source": [
    " Find the conditional probability that given a female student is randomly chosen, she is majoring in international business or management."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5fc8953c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Female| International Business or Management)= P(F | IB ∪ M) =  0.5\n"
     ]
    }
   ],
   "source": [
    "print(\"P(Female| International Business or Management)= P(F | IB ∪ M) = \", 8/16 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "94ba3e87",
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Male</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>360</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Gender  Age   Class       Major Grad Intention  GPA  Employment  \\\n",
       "0   1  Female   20  Junior       Other            Yes  2.9   Full-Time   \n",
       "1   2    Male   23  Senior  Management            Yes  3.6   Part-Time   \n",
       "2   3    Male   21  Junior       Other            Yes  2.5   Part-Time   \n",
       "3   4    Male   21  Junior         CIS            Yes  2.5   Full-Time   \n",
       "8   9  Female   20  Junior  Management            Yes  3.6  Unemployed   \n",
       "\n",
       "   Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "0    50.0                  1             3       350   Laptop            200  \n",
       "1    25.0                  1             4       360   Laptop             50  \n",
       "2    45.0                  2             4       600   Laptop            200  \n",
       "3    40.0                  4             6       600   Laptop            250  \n",
       "8    30.0                  0             4       500   Laptop            400  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2.drop(Data2.index[Data2['Grad Intention'] == 'Undecided'], inplace = True)\n",
    "Data2.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "3690f19e",
   "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>Grad Intention</th>\n",
       "      <th>No</th>\n",
       "      <th>Yes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Grad Intention  No  Yes\n",
       "Gender                 \n",
       "Female           9   11\n",
       "Male             3   17"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2['Gender'],Data2['Grad Intention'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "012e4420",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender\n",
       "Female    20\n",
       "Male      20\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2['Gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f6418e95",
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Other</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Junior</td>\n",
       "      <td>CIS</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age   Class              Major Grad Intention  GPA Employment  \\\n",
       "0    1  Female   20  Junior              Other            Yes  2.9  Full-Time   \n",
       "2    3    Male   21  Junior              Other            Yes  2.5  Part-Time   \n",
       "3    4    Male   21  Junior                CIS            Yes  2.5  Full-Time   \n",
       "10  11  Female   23  Senior  Economics/Finance            Yes  2.8  Full-Time   \n",
       "23  24    Male   22  Senior          Undecided            Yes  2.6  Full-Time   \n",
       "\n",
       "    Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "0     50.0                  1             3       350   Laptop            200  \n",
       "2     45.0                  2             4       600   Laptop            200  \n",
       "3     40.0                  4             6       600   Laptop            250  \n",
       "10    50.0                  2             5       400   Laptop            200  \n",
       "23    45.0                  1             5       400   Laptop            600  "
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2_GPA = Data2[Data2.GPA < 3.0]\n",
    "Data2_GPA.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4141ed09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>GPA</th>\n",
       "      <th>2.4</th>\n",
       "      <th>2.5</th>\n",
       "      <th>2.6</th>\n",
       "      <th>2.8</th>\n",
       "      <th>2.9</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Male</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "GPA     2.4  2.5  2.6  2.8  2.9\n",
       "Gender                         \n",
       "Female    1    2    0    1    3\n",
       "Male      0    3    2    1    1"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2_GPA['Gender'],Data2_GPA['GPA'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9ff727f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>Yes</td>\n",
       "      <td>2.9</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>350</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>Female</td>\n",
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       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>55.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age   Class              Major Grad Intention  GPA Employment  \\\n",
       "0    1  Female   20  Junior              Other            Yes  2.9  Full-Time   \n",
       "10  11  Female   23  Senior  Economics/Finance            Yes  2.8  Full-Time   \n",
       "14  15    Male   21  Senior         Management            Yes  3.2  Part-Time   \n",
       "18  19    Male   19  Junior  Economics/Finance            Yes  3.5  Part-Time   \n",
       "24  25  Female   20  Junior  Economics/Finance            Yes  3.0  Part-Time   \n",
       "\n",
       "    Salary  Social Networking  Satisfaction  Spending Computer  Text Messages  \n",
       "0     50.0                  1             3       350   Laptop            200  \n",
       "10    50.0                  2             5       400   Laptop            200  \n",
       "14    54.0                  3             4       600   Laptop            400  \n",
       "18    52.0                  2             5       500   Laptop            300  \n",
       "24    55.0                  1             3       600   Laptop            300  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2_Salary= Data2[Data2.Salary >= 50 ]\n",
    "Data2_Salary.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "37262ad3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Salary</th>\n",
       "      <th>50.0</th>\n",
       "      <th>52.0</th>\n",
       "      <th>54.0</th>\n",
       "      <th>55.0</th>\n",
       "      <th>60.0</th>\n",
       "      <th>65.0</th>\n",
       "      <th>70.0</th>\n",
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       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Female</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <th>Male</th>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Salary  50.0  52.0  54.0  55.0  60.0  65.0  70.0\n",
       "Gender                                          \n",
       "Female     4     0     0     2     3     0     1\n",
       "Male       4     1     1     1     2     1     0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Data2_Salary['Gender'],Data2_Salary['Salary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "d2a4dc74",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>25.0</td>\n",
       "      <td>1</td>\n",
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       "      <td>360</td>\n",
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       "      <th>2</th>\n",
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       "      <td>Junior</td>\n",
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       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
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       "      <td>200</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Yes</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>40.0</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>250</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
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       "      <td>Junior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.6</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>Female</td>\n",
       "      <td>23</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.8</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>No</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>37.0</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Management</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.2</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>54.0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>600</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Male</td>\n",
       "      <td>19</td>\n",
       "      <td>Junior</td>\n",
       "      <td>Economics/Finance</td>\n",
       "      <td>Yes</td>\n",
       "      <td>3.5</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>52.0</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>500</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Undecided</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2.6</td>\n",
       "      <td>Full-Time</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>400</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ID  Gender  Age   Class              Major Grad Intention  GPA  \\\n",
       "0    1  Female   20  Junior              Other            Yes  2.9   \n",
       "1    2    Male   23  Senior         Management            Yes  3.6   \n",
       "2    3    Male   21  Junior              Other            Yes  2.5   \n",
       "3    4    Male   21  Junior                CIS            Yes  2.5   \n",
       "8    9  Female   20  Junior         Management            Yes  3.6   \n",
       "10  11  Female   23  Senior  Economics/Finance            Yes  2.8   \n",
       "11  12    Male   21  Senior          Undecided             No  3.5   \n",
       "14  15    Male   21  Senior         Management            Yes  3.2   \n",
       "18  19    Male   19  Junior  Economics/Finance            Yes  3.5   \n",
       "23  24    Male   22  Senior          Undecided            Yes  2.6   \n",
       "\n",
       "    Employment  Salary  Social Networking  Satisfaction  Spending Computer  \\\n",
       "0    Full-Time    50.0                  1             3       350   Laptop   \n",
       "1    Part-Time    25.0                  1             4       360   Laptop   \n",
       "2    Part-Time    45.0                  2             4       600   Laptop   \n",
       "3    Full-Time    40.0                  4             6       600   Laptop   \n",
       "8   Unemployed    30.0                  0             4       500   Laptop   \n",
       "10   Full-Time    50.0                  2             5       400   Laptop   \n",
       "11   Full-Time    37.0                  2             3       500   Laptop   \n",
       "14   Part-Time    54.0                  3             4       600   Laptop   \n",
       "18   Part-Time    52.0                  2             5       500   Laptop   \n",
       "23   Full-Time    45.0                  1             5       400   Laptop   \n",
       "\n",
       "    Text Messages  \n",
       "0             200  \n",
       "1              50  \n",
       "2             200  \n",
       "3             250  \n",
       "8             400  \n",
       "10            200  \n",
       "11            100  \n",
       "14            400  \n",
       "18            300  \n",
       "23            600  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "40f32309",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1Sd38R1O++93vJoMHD26w7Oyzz0723nvvzgitTdaWT1N22GGHZMqUKR0YUfu0JJ+ucj2os7acutL1oM7a8ulq14OW6NLv9Pj888/j2WefjTFjxjRYPmbMmHjiiSdyFFVuLF68OJYuXdqgL0pLS+OAAw7IbF989tlnERGx8cYbR0Rh9cHq1avjjjvuiJUrV8awYcMKKvcJEybEIYccEqNGjWqwvFD64PXXX4+KiorYaqut4oQTToi33norIgoj/wceeCCGDh0axx57bPTt2zd22223+OlPf1r/fCH0AYWjuro6IqLBX+1269YtevToEY8//niuwmqXrL9ur5lf1rQkv88++yyKiooavAOvq1hXfp9//nncfPPN0bt379hll106M7R2ayq32traOOWUU+I73/lO7LjjjrkKLRXNHbt58+ZF3759Y9ttt41/+7d/i48++igX4bXbmvnV1tbGgw8+GNtuu22MHTs2+vbtG3vttVejj0iEjtTS17xbbrkl3nzzzbj88ss7I6x2aUlOdfcj06dPj8033zy23Xbb+Pd///f429/+1llhtlhL8hk+fHi8//77MWvWrEiSJD788MP4zW9+E4ccckhnhdkia85/NOXJJ59sNDc4duzYWLhwYdTU1HRGmC3WknzWVFtbG8uXL8/LcWZL8+lK14OW5NSVrgctyaerXA9ao3uuA2iPjz/+OFavXh2bbbZZg+WbbbZZLF26NEdR5UZdvk31xTvvvJOLkDpUkiQxceLE2HfffWPIkCERURh98OKLL8awYcPi73//e2ywwQZx7733xg477FA/OZTl3CMi7rjjjnjuuediwYIFjZ4rhOO/1157xX//93/HtttuGx9++GFceeWVMXz48Hj55ZcLIv+33norbrzxxpg4cWJccskl8cwzz8T5558fpaWlceqppxZEH1A4Bg8eHIMGDYpJkybFT37yk1h//fXjmmuuiaVLl8YHH3yQ6/BaLeuv203llyUtye/vf/97XHzxxXHiiSdGeXl5J0fYPmvL73e/+12ccMIJsWrVqujfv39UVlbGJptskqNIW6+53K6++uro3r17nH/++TmMrv2ay2/cuHFx7LHHxqBBg2Lx4sVx2WWXxYEHHhjPPvtslJaW5jDi1mkqv48++ihWrFgR06ZNiyuvvDKuvvrqmD17dhx99NExd+7cOOCAA3IcNVnX0te8119/PS6++OJ47LHHonv3/J56amlOb731Vjz++OOx3nrrxb333hsff/xxnHvuufHXv/41bz7HP6Ll+QwfPjxuvfXWOP744+Pvf/97fPHFF3H44YfHj370o06MtnnNzX80ZenSpU2OKb/44ov4+OOPo3///p0R8lq1Jp81/eAHP4iVK1fGcccd18FRtlxr8ukq14PW5NQVrgetySffrwdtkb9nWisUFRU1eJwkSaNlhaJQ+uK8886LP/7xj03+tWuW+2C77baLRYsWxbJly+Luu++O8ePHx/z58+ufz3Lu7733XlxwwQUxZ86ctX5ecZb7YNy4cfX/32mnnWLYsGGxzTbbxC9/+cv6L+fMcv61tbUxdOjQuOqqqyIiYrfddouXX345brzxxjj11FPr18tyH1A4SkpK4u67744zzzwzNt544+jWrVuMGjWqwXWgK8n66/ba8suCdeVXU1MTJ5xwQtTW1sYNN9zQydG139ryGzlyZCxatCg+/vjj+OlPfxrHHXdcPP3009G3b98cRNp6TeX27LPPxvXXXx/PPfdcl/tdW1Nzx+7444+v//+QIUNi6NChMWjQoHjwwQfj6KOP7uww26yp/GprayMi4ogjjohvfetbERGx6667xhNPPBE33XSTogcdriWveatXr44TTzwxpkyZEttuu20nRtc2LX0dr62tjaKiorj11lujd+/eERFxzTXXxDHHHBM//vGPo2fPnp0R7jq1NJ8//elPcf7558d//Md/xNixY+ODDz6I73znO3HOOefEzJkzOyna5jU3/9HcpG1TY8qmludKa/Opc/vtt8fkyZPj/vvvz6vxR0vz6UrXg9Yco65wPWhNPvl+PWiTzvwsrbRVV1cn3bp1S+65554Gy88///xk//33z1FUnSPW+OzvN998M4mI5Lnnnmuw3uGHH56ceuqpnRxdxzrvvPOSAQMGJG+99VaD5YXUB3UOOuig5Bvf+EZB5H7vvfcmEZF069at/icikqKioqRbt27JG2+8kfk+aMqoUaOSc845pyDOgS222CI588wzGyy74YYbkoqKiiRJCvMaQHas+br+ZcuWLUs++uijJEn+8V1m5557bidG1n5Zf91uLr8v68rf6bGu/D7//PPkyCOPTHbeeefk448/7uTo2q8lx+/LvvKVryRXXXVVB0eVjuZyu/baa+vHT18eUxUXFyeDBg3KTbBt0JZj9+XvEMp3zeVXXV2ddO/ePbniiisaLP/ud7+bDB8+vDNDpAC19Pfu008/bXTvVlRUVL/s97//fSdFvG6tuZaceuqpyTbbbNNg2Z/+9KckIpLXXnuto0Jsldbkc/LJJyfHHHNMg2WPPfZYEhHJkiVLOirENqub/2jKfvvtl5x//vkNlt1zzz1J9+7dk88//7wzwmu1teVT54477kh69uyZ/O53v+ukqNquuXy60vVgTWs7Rl3herCmteXT1a4HLdGlv9OjR48esfvuu0dlZWWD5ZWVlTF8+PAcRZUbW221VfTr169BX3z++ecxf/78zPRFkiRx3nnnxT333BN/+MMfYquttmrwfCH0wZqSJInq6uqCyP2ggw6KF198MRYtWlT/M3To0DjppJNi0aJFsfXWW2e+D9ZUXV0dr7zySvTv378gzoF99tknXn311QbLXnvttRg0aFBEFOY1gMLQu3fv2HTTTeP111+PhQsXxhFHHJHrkFok66/b68qvq2tJfjU1NXHcccfF66+/Ho888kj06dMnB5G2TVuPX93YK5+tK7dTTjkl/vjHPzYYU1VUVMR3vvOdePjhh3MUdcu15dh98skn8d577+XFx5usy7ry69GjR+yxxx5rHRNB2lr7e1deXt7o3u2cc86p/6vjvfbaq5Mib15briX77LNPLFmyJFasWFG/7LXXXovi4uIYMGBAR4a7Tm3JZ9WqVVFc3HBasFu3bvXt5Zu1vQYPGzas0dzgnDlzYujQoVFSUtIZ4bXausYUt99+e5x22mlx2223dYnvVWgun65wPWjO2o5RPl8PmrO2fLra9aBFOrfGkr477rgjKSkpSWbOnJn86U9/Si688MJk/fXXT95+++1ch5a65cuXJ88//3zy/PPPJxGRXHPNNcnzzz+fvPPOO0mSJMm0adOS3r17J/fcc0/y4osvJl//+teT/v37J1VVVTmOPB3/5//8n6R3797JvHnzkg8++KD+Z9WqVfXrZLkPJk2alDz66KPJ4sWLkz/+8Y/JJZdckhQXFydz5sxJkiTbuTfngAMOSC644IL6x1nvg29/+9vJvHnzkrfeeit56qmnkkMPPTTp1atX/fUu6/k/88wzSffu3ZPvf//7yeuvv57ceuutSVlZWfKrX/2qfp2s9wHZsq7X9V//+tfJ3LlzkzfffDO57777kkGDBiVHH310jqNuuay/brckv08++SR5/vnnkwcffDCJiOSOO+5Inn/++eSDDz7IYeQts678ampqksMPPzwZMGBAsmjRogbrVFdX5zj6dVtXfitWrEgmTZqUPPnkk8nbb7+dPPvss8mZZ56ZlJaWJi+99FKOo1+7lpybaxo0aFBy7bXXdl6Q7bCu/JYvX558+9vfTp544olk8eLFydy5c5Nhw4Ylm2++eWauLffcc09SUlKS3Hzzzcnrr7+e/OhHP0q6deuWPPbYYzmMnCxryXl58cUXJ6ecckqzbVx++eXJLrvs0gnRtkxbclq+fHkyYMCA5JhjjklefvnlZP78+clXv/rV5KyzzspFCg20JZ9bbrkl6d69e3LDDTckb775ZvL4448nQ4cOTfbcc89cpNDAuuY/1szlrbfeSsrKypJvfetbyZ/+9Kdk5syZSUlJSfKb3/wmVyk00Np8brvttqR79+7Jj3/84wbHc9myZblKoYHW5rOmfLseJEnrc8rn60GStD6ffL4etFWXL3okSZL8+Mc/TgYNGpT06NEj+Zd/+Zdk/vz5uQ6pQ8ydOzeJiEY/48ePT5IkSWpra5PLL7886devX1JaWprsv//+yYsvvpjboFPUVO4Rkdxyyy3162S5D84444z683zTTTdNDjrooPqLVZJkO/fmrFn0yHofHH/88Un//v2TkpKSpKKiIjn66KOTl19+uf75rOefJEny29/+NhkyZEhSWlqaDB48OLn55psbPF8IfUB2rOt1/frrr08GDBiQlJSUJFtssUVy6aWXdonJ5DpZf91uSX633HJLk+tcfvnlOYu7pdaVX91HdjX1M3fu3JzG3hLryu9vf/tbctRRRyUVFRVJjx49kv79+yeHH3548swzz+Q28BZoybm5pq5U9FhXfqtWrUrGjBmTbLrppvXXz/HjxyfvvvtubgNvoZYev5kzZyZf+cpXkvXWWy/ZZZddkvvuuy83AVMQWnJejh8/PjnggAOabSPfJjnbmtMrr7ySjBo1KunZs2cyYMCAZOLEiWstKneWtubzwx/+MNlhhx2Snj17Jv37909OOumk5P333+/c4JuwrvmPpnKZN29esttuuyU9evRIttxyy+TGG2/s5Kib19p8DjjggLXeJ+RaW47Pl+Xb9SBJ2pZTvl4PkqRt+eTr9aCtipKkq75HBQAAAAAA4J+69Hd6AAAAAAAA1FH0AAAAAAAAMkHRAwAAAAAAyARFDwAAAAAAIBMUPQAAAAAAgExQ9AAAAAAAADJB0QMAAAAAAMgERQ8AAAAAACATFD0AAAAAAIBMUPQAAAAAAAAyQdEDAAAAAADIBEUPAAAAAAAgExQ9AAAAAACATFD0AAAAAAAAMkHRAwAAAAAAyARFDwAAAAAAIBM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",
      "text/plain": [
       "<Figure size 2000x2000 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Data2.hist(figsize=(20,20));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c0e23268",
   "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>ID</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Class</th>\n",
       "      <th>Major</th>\n",
       "      <th>Grad Intention</th>\n",
       "      <th>GPA</th>\n",
       "      <th>Employment</th>\n",
       "      <th>Salary</th>\n",
       "      <th>Social Networking</th>\n",
       "      <th>Satisfaction</th>\n",
       "      <th>Spending</th>\n",
       "      <th>Computer</th>\n",
       "      <th>Text Messages</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>40.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Senior</td>\n",
       "      <td>Retailing/Marketing</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Part-Time</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Laptop</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>35.425000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.362500</td>\n",
       "      <td>1.475000</td>\n",
       "      <td>3.775000</td>\n",
       "      <td>496.875000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>264.62500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.363687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.435806</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.386304</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.935702</td>\n",
       "      <td>0.816104</td>\n",
       "      <td>1.349026</td>\n",
       "      <td>254.862174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>219.85831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>24.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.875000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>337.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>37.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.150000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>51.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>54.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>600.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>312.50000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>62.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.900000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1400.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>900.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               ID  Gender        Age   Class                Major  \\\n",
       "count   40.000000      40  40.000000      40                   40   \n",
       "unique        NaN       2        NaN       3                    8   \n",
       "top           NaN  Female        NaN  Senior  Retailing/Marketing   \n",
       "freq          NaN      20        NaN      19                    8   \n",
       "mean    35.425000     NaN  21.200000     NaN                  NaN   \n",
       "std     18.363687     NaN   1.435806     NaN                  NaN   \n",
       "min      1.000000     NaN  19.000000     NaN                  NaN   \n",
       "25%     24.750000     NaN  20.000000     NaN                  NaN   \n",
       "50%     37.500000     NaN  21.000000     NaN                  NaN   \n",
       "75%     51.250000     NaN  22.000000     NaN                  NaN   \n",
       "max     62.000000     NaN  26.000000     NaN                  NaN   \n",
       "\n",
       "       Grad Intention        GPA Employment     Salary  Social Networking  \\\n",
       "count              40  40.000000         40  40.000000          40.000000   \n",
       "unique              2        NaN          3        NaN                NaN   \n",
       "top               Yes        NaN  Part-Time        NaN                NaN   \n",
       "freq               28        NaN         27        NaN                NaN   \n",
       "mean              NaN   3.100000        NaN  46.362500           1.475000   \n",
       "std               NaN   0.386304        NaN  10.935702           0.816104   \n",
       "min               NaN   2.400000        NaN  25.000000           0.000000   \n",
       "25%               NaN   2.875000        NaN  40.000000           1.000000   \n",
       "50%               NaN   3.150000        NaN  48.750000           1.000000   \n",
       "75%               NaN   3.400000        NaN  54.250000           2.000000   \n",
       "max               NaN   3.900000        NaN  70.000000           4.000000   \n",
       "\n",
       "        Satisfaction     Spending Computer  Text Messages  \n",
       "count      40.000000    40.000000       40       40.00000  \n",
       "unique           NaN          NaN        3            NaN  \n",
       "top              NaN          NaN   Laptop            NaN  \n",
       "freq             NaN          NaN       35            NaN  \n",
       "mean        3.775000   496.875000      NaN      264.62500  \n",
       "std         1.349026   254.862174      NaN      219.85831  \n",
       "min         1.000000   100.000000      NaN        0.00000  \n",
       "25%         3.000000   337.500000      NaN      100.00000  \n",
       "50%         4.000000   500.000000      NaN      200.00000  \n",
       "75%         5.000000   600.000000      NaN      312.50000  \n",
       "max         6.000000  1400.000000      NaN      900.00000  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data2.describe(include= \"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "9eb093f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking empirical rule to check Normal distribution for GPA\n",
    "mean1 = 3.100000\n",
    "SD1 = 3.150000\n",
    "one_sd = norm.cdf(SD1, mean1, SD1) - norm.cdf(-SD1, mean1, SD1)\n",
    "two_sd = norm.cdf(2 * SD1, mean1, SD1) - norm.cdf(-2 * SD1, mean1, SD1)\n",
    "three_sd = norm.cdf(3 * SD1, mean1, SD1) - norm.cdf(-3 * SD1, mean1, SD1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "626e23ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of values within one SD = 0.4827113084199802\n",
      "Fraction of values within two SD = 0.8437331263151738\n",
      "Fraction of values within three SD = 0.9780595077023675\n"
     ]
    }
   ],
   "source": [
    "print(\"Fraction of values within one SD =\", one_sd)\n",
    "print(\"Fraction of values within two SD =\", two_sd)\n",
    "print(\"Fraction of values within three SD =\", three_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "2cc1a719",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking empirical rule to check Normal distribution for Salary\n",
    "mean2 = 46.36\n",
    "SD2 = 10.93\n",
    "first_sd = norm.cdf(SD2, mean2, SD2) - norm.cdf(-SD2, mean2, SD2)\n",
    "second_sd = norm.cdf(2 * SD2, mean2, SD2) - norm.cdf(-2 * SD1, mean2, SD2)\n",
    "third_sd = norm.cdf(3 * SD2, mean2, SD2) - norm.cdf(-3 * SD2, mean2, SD2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "4a62929c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of values within first_sd = 0.0005943552656031757\n",
      "Fraction of values within second_sd = 0.01249492879984014\n",
      "Fraction of values within third_sd = 0.10720370932127128\n"
     ]
    }
   ],
   "source": [
    "print(\"Fraction of values within first_sd =\", first_sd)\n",
    "print(\"Fraction of values within second_sd =\", second_sd)\n",
    "print(\"Fraction of values within third_sd =\", third_sd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "49409a2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking empirical rule to check Normal distribution for Spending\n",
    "mean3 = 496.875\n",
    "SD3 = 254.86\n",
    "first_sd3 = norm.cdf(SD3, mean3, SD3) - norm.cdf(-SD3, mean3, SD3)\n",
    "second_sd3 = norm.cdf(2 * SD3, mean3, SD3) - norm.cdf(-2 * SD3, mean3, SD3)\n",
    "third_sd3 = norm.cdf(3 * SD3, mean3, SD3) - norm.cdf(-3 * SD3, mean3, SD3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "411a25f6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of values within first_sd3 = 0.16956689603538241\n",
      "Fraction of values within second_sd3 = 0.5200591283670073\n",
      "Fraction of values within third_sd3 = 0.8532325558297389\n"
     ]
    }
   ],
   "source": [
    "print(\"Fraction of values within first_sd3 =\", first_sd3)\n",
    "print(\"Fraction of values within second_sd3 =\", second_sd3)\n",
    "print(\"Fraction of values within third_sd3 =\", third_sd3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "bc811dc7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Checking empirical rule to check Normal distribution for Text Messages\n",
    "mean4 = 264.625\n",
    "SD4 = 219.85\n",
    "first_sd4 = norm.cdf(SD4, mean4, SD4) - norm.cdf(-SD4, mean4, SD4)\n",
    "second_sd4 = norm.cdf(2 * SD4, mean4, SD4) - norm.cdf(-2 * SD4, mean4, SD4)\n",
    "third_sd4 = norm.cdf(3 * SD4, mean4, SD4) - norm.cdf(-3 * SD4, mean4, SD4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7eeffe65",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fraction of values within first_sd4 = 0.4055349046376787\n",
      "Fraction of values within second_sd4 = 0.7864038583844697\n",
      "Fraction of values within third_sd4 = 0.9637665123038764\n"
     ]
    }
   ],
   "source": [
    "print(\"Fraction of values within first_sd4 =\", first_sd4)\n",
    "print(\"Fraction of values within second_sd4 =\", second_sd4)\n",
    "print(\"Fraction of values within third_sd4 =\", third_sd4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b2d910f",
   "metadata": {},
   "source": [
    "# A & B shingles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "930cdde2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import f\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_1samp, ttest_ind\n",
    "from statsmodels.stats.power import ttest_power"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "caaa2a57",
   "metadata": {},
   "outputs": [],
   "source": [
    "Data3 = pd.read_csv('A+%26+B+shingles-1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "8c2fd6b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.44</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.47</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "      A     B\n",
       "0  0.44  0.14\n",
       "1  0.61  0.15\n",
       "2  0.47  0.31\n",
       "3  0.30  0.16\n",
       "4  0.15  0.37"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data3.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ca39283c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 2 columns):\n",
      " #   Column  Non-Null Count  Dtype  \n",
      "---  ------  --------------  -----  \n",
      " 0   A       36 non-null     float64\n",
      " 1   B       31 non-null     float64\n",
      "dtypes: float64(2)\n",
      "memory usage: 708.0 bytes\n"
     ]
    }
   ],
   "source": [
    "Data3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3587a450",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.316667</td>\n",
       "      <td>0.273548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.135731</td>\n",
       "      <td>0.137296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.207500</td>\n",
       "      <td>0.160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.290000</td>\n",
       "      <td>0.230000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.392500</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.720000</td>\n",
       "      <td>0.580000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B\n",
       "count  36.000000  31.000000\n",
       "mean    0.316667   0.273548\n",
       "std     0.135731   0.137296\n",
       "min     0.130000   0.100000\n",
       "25%     0.207500   0.160000\n",
       "50%     0.290000   0.230000\n",
       "75%     0.392500   0.400000\n",
       "max     0.720000   0.580000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data3.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "5f83cf4b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0.44</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.61</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.47</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.30</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.15</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.24</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.16</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.20</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.20</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.20</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.26</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.14</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.72</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       A\n",
       "0   0.44\n",
       "1   0.61\n",
       "2   0.47\n",
       "3   0.30\n",
       "4   0.15\n",
       "5   0.24\n",
       "6   0.16\n",
       "7   0.20\n",
       "8   0.20\n",
       "9   0.20\n",
       "10  0.26\n",
       "11  0.14\n",
       "12  0.33\n",
       "13  0.13\n",
       "14  0.72\n",
       "15  0.51\n",
       "16  0.28\n",
       "17  0.39\n",
       "18  0.39\n",
       "19  0.25\n",
       "20  0.16\n",
       "21  0.20\n",
       "22  0.22\n",
       "23  0.42\n",
       "24  0.24\n",
       "25  0.21\n",
       "26  0.49\n",
       "27  0.34\n",
       "28  0.36\n",
       "29  0.29\n",
       "30  0.27\n",
       "31  0.40\n",
       "32  0.29\n",
       "33  0.43\n",
       "34  0.34\n",
       "35  0.37"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data3_A= Data3.loc[0:36,['A']]\n",
    "Data3_A"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f919032",
   "metadata": {},
   "source": [
    "Here we select α = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "40fab568",
   "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>A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>36.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.135731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.130000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.207500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.290000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.392500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.720000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A\n",
       "count  36.000000\n",
       "mean    0.316667\n",
       "std     0.135731\n",
       "min     0.130000\n",
       "25%     0.207500\n",
       "50%     0.290000\n",
       "75%     0.392500\n",
       "max     0.720000"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data3_A.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "89efcd0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sample size for this problem is 36\n"
     ]
    }
   ],
   "source": [
    "print(\"The sample size for this problem is\",len(Data3_A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "9f406906",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One sample t test \n",
      "t statistic: [-1.47350463] p value: [0.14955266] \n"
     ]
    }
   ],
   "source": [
    "#one sample t-test\n",
    "# null hypothesis: expected value <= 0.35 \n",
    "t_statistic, p_value = ttest_1samp(Data3_A,0.35 )\n",
    "print('One sample t test \\nt statistic: {0} p value: {1} '.format(t_statistic, p_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "b833a130",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level of significance: 0.05\n",
      "We have no evidence to reject the null hypothesis since p value > Level of significance\n",
      "Our one-sample t-test p-value= [0.14955266]\n"
     ]
    }
   ],
   "source": [
    "# p_value < 0.05 => alternative hypothesis:\n",
    "\n",
    "alpha_value = 0.05 # Level of significance\n",
    "print('Level of significance: %.2f' %alpha_value)\n",
    "if p_value < alpha_value: \n",
    "    print('We have evidence to reject the null hypothesis since p value < Level of significance')\n",
    "else:\n",
    "    print('We have no evidence to reject the null hypothesis since p value > Level of significance') \n",
    "\n",
    "print (\"Our one-sample t-test p-value=\", p_value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "54453efd",
   "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>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       B\n",
       "0   0.14\n",
       "1   0.15\n",
       "2   0.31\n",
       "3   0.16\n",
       "4   0.37\n",
       "5   0.18\n",
       "6   0.42\n",
       "7   0.58\n",
       "8   0.25\n",
       "9   0.41\n",
       "10  0.17\n",
       "11  0.13\n",
       "12  0.23\n",
       "13  0.11\n",
       "14  0.10\n",
       "15  0.19\n",
       "16  0.22\n",
       "17  0.44\n",
       "18  0.11\n",
       "19  0.11\n",
       "20  0.31\n",
       "21  0.43\n",
       "22  0.26\n",
       "23  0.18\n",
       "24  0.44\n",
       "25  0.43\n",
       "26  0.16\n",
       "27  0.52\n",
       "28  0.36\n",
       "29  0.22\n",
       "30  0.39"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data3_B= Data3.loc[0:30,['B']]\n",
    "Data3_B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "19c5fb1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "One sample t test \n",
      "t statistic:[-3.10033131] p value:[0.00418095] \n"
     ]
    }
   ],
   "source": [
    "#one sample t-test\n",
    "# null hypothesis: expected value <= 0.35 \n",
    "t_statistic, p_value = ttest_1samp(Data3_B,0.35 )\n",
    "print('One sample t test \\nt statistic:{0} p value:{1} '.format(t_statistic, p_value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c46bcbd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Level of significance: 0.05\n",
      "We have evidence to reject the null hypothesis since p value < Level of significance\n",
      "Our one-sample t-test p-value= [0.00418095]\n"
     ]
    }
   ],
   "source": [
    "# p_value < 0.05 => alternative hypothesis:\n",
    "\n",
    "alpha_value = 0.05 # Level of significance\n",
    "print('Level of significance: %.2f' %alpha_value)\n",
    "if p_value < alpha_value: \n",
    "    print('We have evidence to reject the null hypothesis since p value < Level of significance')\n",
    "else:\n",
    "    print('We have no evidence to reject the null hypothesis since p value > Level of significance') \n",
    "\n",
    "print (\"Our one-sample t-test p-value=\", p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a18530fa",
   "metadata": {},
   "source": [
    "3.2 Do you think that the population mean for shingles A and B are equal? Form the hypothesis and conduct the test of the hypothesis. What assumption do you need to check before the test for equality of means is performed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "07705069",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tstat 1.289628271966112\n",
      "P Value 0.2017496571835328\n"
     ]
    }
   ],
   "source": [
    "# 2 sample t test for independent samples\n",
    "t_statistic, p_value  = ttest_ind(Data3['A'],Data3['B'],nan_policy = 'omit')\n",
    "print('tstat',t_statistic)    \n",
    "print('P Value',p_value)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "ec7c4d7c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "two-sample t-test p-value= 0.2017496571835328\n",
      "We do not have enough evidence to reject the null hypothesis in favour of alternative hypothesis\n",
      "We conclude that the mean of Shingles A and Shingles B is equal.\n"
     ]
    }
   ],
   "source": [
    "# p_value < 0.05 => alternative hypothesis:\n",
    "# they don't have the same mean at the 5% significance level\n",
    "print (\"two-sample t-test p-value=\", p_value)\n",
    "\n",
    "alpha_level = 0.05\n",
    "\n",
    "if p_value < alpha_level:\n",
    "    print('We have enough evidence to reject the null hypothesis in favour of alternative hypothesis')\n",
    "    print('We conclude that the mean of Shingles A and Shingles B is not equal.')\n",
    "else:\n",
    "    print('We do not have enough evidence to reject the null hypothesis in favour of alternative hypothesis')\n",
    "    print('We conclude that the mean of Shingles A and Shingles B is equal.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6fdb9b8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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