Statistics_Assignment_1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b4aa3535",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statistics as stat\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcbeb268",
   "metadata": {},
   "source": [
    "# question 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3df8dc3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "q7 = pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science Assignments/Basic Statistics 1/Q7.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "508e7e10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Valiant</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.460</td>\n",
       "      <td>20.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duster 360</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Merc 240D</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.190</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Merc 230</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Merc 280</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Merc 280C</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Merc 450SE</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Merc 450SL</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Merc 450SLC</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.780</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cadillac Fleetwood</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lincoln Continental</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.424</td>\n",
       "      <td>17.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chrysler Imperial</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.345</td>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fiat 128</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
       "      <td>19.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Honda Civic</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
       "      <td>18.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Toyota Corolla</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Toyota Corona</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dodge Challenger</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AMC Javelin</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Camaro Z28</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pontiac Firebird</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.845</td>\n",
       "      <td>17.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fiat X1-9</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Porsche 914-2</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lotus Europa</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ford Pantera L</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ferrari Dino</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Maserati Bora</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Unnamed: 0  Points  Score  Weigh\n",
       "0             Mazda RX4    3.90  2.620  16.46\n",
       "1         Mazda RX4 Wag    3.90  2.875  17.02\n",
       "2            Datsun 710    3.85  2.320  18.61\n",
       "3        Hornet 4 Drive    3.08  3.215  19.44\n",
       "4     Hornet Sportabout    3.15  3.440  17.02\n",
       "5               Valiant    2.76  3.460  20.22\n",
       "6            Duster 360    3.21  3.570  15.84\n",
       "7             Merc 240D    3.69  3.190  20.00\n",
       "8              Merc 230    3.92  3.150  22.90\n",
       "9              Merc 280    3.92  3.440  18.30\n",
       "10            Merc 280C    3.92  3.440  18.90\n",
       "11           Merc 450SE    3.07  4.070  17.40\n",
       "12           Merc 450SL    3.07  3.730  17.60\n",
       "13          Merc 450SLC    3.07  3.780  18.00\n",
       "14   Cadillac Fleetwood    2.93  5.250  17.98\n",
       "15  Lincoln Continental    3.00  5.424  17.82\n",
       "16    Chrysler Imperial    3.23  5.345  17.42\n",
       "17             Fiat 128    4.08  2.200  19.47\n",
       "18          Honda Civic    4.93  1.615  18.52\n",
       "19       Toyota Corolla    4.22  1.835  19.90\n",
       "20        Toyota Corona    3.70  2.465  20.01\n",
       "21     Dodge Challenger    2.76  3.520  16.87\n",
       "22          AMC Javelin    3.15  3.435  17.30\n",
       "23           Camaro Z28    3.73  3.840  15.41\n",
       "24     Pontiac Firebird    3.08  3.845  17.05\n",
       "25            Fiat X1-9    4.08  1.935  18.90\n",
       "26        Porsche 914-2    4.43  2.140  16.70\n",
       "27         Lotus Europa    3.77  1.513  16.90\n",
       "28       Ford Pantera L    4.22  3.170  14.50\n",
       "29         Ferrari Dino    3.62  2.770  15.50\n",
       "30        Maserati Bora    3.54  3.570  14.60\n",
       "31           Volvo 142E    4.11  2.780  18.60"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30bc919b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.596563</td>\n",
       "      <td>3.217250</td>\n",
       "      <td>17.848750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.534679</td>\n",
       "      <td>0.978457</td>\n",
       "      <td>1.786943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.760000</td>\n",
       "      <td>1.513000</td>\n",
       "      <td>14.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.080000</td>\n",
       "      <td>2.581250</td>\n",
       "      <td>16.892500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.695000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>17.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.920000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>18.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.930000</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>22.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Points      Score      Weigh\n",
       "count  32.000000  32.000000  32.000000\n",
       "mean    3.596563   3.217250  17.848750\n",
       "std     0.534679   0.978457   1.786943\n",
       "min     2.760000   1.513000  14.500000\n",
       "25%     3.080000   2.581250  16.892500\n",
       "50%     3.695000   3.325000  17.710000\n",
       "75%     3.920000   3.610000  18.900000\n",
       "max     4.930000   5.424000  22.900000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
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   "source": [
    "q7.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6075c782",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.6950000000000003"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.median(q7[\"Points\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "195a087a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Points     3.695\n",
       "Score      3.325\n",
       "Weigh     17.710\n",
       "dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bdec3c8c",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.07</td>\n",
       "      <td>3.44</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.92</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "   Points  Score  Weigh\n",
       "0    3.07   3.44  17.02\n",
       "1    3.92    NaN  18.90"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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    }
   ],
   "source": [
    "q7.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ccdc2ae8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.92"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(q7['Points'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "75f67bcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.44"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(q7['Score'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "46abd1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17.02"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(q7['Weigh'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "9b64b35e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Points    0.285881\n",
       "Score     0.957379\n",
       "Weigh     3.193166\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "35b949b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "q7.rename(columns={'Unnamed: 0':'Cars'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c405e513",
   "metadata": {},
   "outputs": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cars</th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Valiant</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.460</td>\n",
       "      <td>20.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duster 360</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Merc 240D</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.190</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Merc 230</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Merc 280</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Merc 280C</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Merc 450SE</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Merc 450SL</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Merc 450SLC</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.780</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cadillac Fleetwood</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lincoln Continental</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.424</td>\n",
       "      <td>17.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chrysler Imperial</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.345</td>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fiat 128</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
       "      <td>19.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Honda Civic</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
       "      <td>18.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Toyota Corolla</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Toyota Corona</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dodge Challenger</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AMC Javelin</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Camaro Z28</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pontiac Firebird</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.845</td>\n",
       "      <td>17.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fiat X1-9</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Porsche 914-2</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lotus Europa</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ford Pantera L</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ferrari Dino</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Maserati Bora</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Cars  Points  Score  Weigh\n",
       "0             Mazda RX4    3.90  2.620  16.46\n",
       "1         Mazda RX4 Wag    3.90  2.875  17.02\n",
       "2            Datsun 710    3.85  2.320  18.61\n",
       "3        Hornet 4 Drive    3.08  3.215  19.44\n",
       "4     Hornet Sportabout    3.15  3.440  17.02\n",
       "5               Valiant    2.76  3.460  20.22\n",
       "6            Duster 360    3.21  3.570  15.84\n",
       "7             Merc 240D    3.69  3.190  20.00\n",
       "8              Merc 230    3.92  3.150  22.90\n",
       "9              Merc 280    3.92  3.440  18.30\n",
       "10            Merc 280C    3.92  3.440  18.90\n",
       "11           Merc 450SE    3.07  4.070  17.40\n",
       "12           Merc 450SL    3.07  3.730  17.60\n",
       "13          Merc 450SLC    3.07  3.780  18.00\n",
       "14   Cadillac Fleetwood    2.93  5.250  17.98\n",
       "15  Lincoln Continental    3.00  5.424  17.82\n",
       "16    Chrysler Imperial    3.23  5.345  17.42\n",
       "17             Fiat 128    4.08  2.200  19.47\n",
       "18          Honda Civic    4.93  1.615  18.52\n",
       "19       Toyota Corolla    4.22  1.835  19.90\n",
       "20        Toyota Corona    3.70  2.465  20.01\n",
       "21     Dodge Challenger    2.76  3.520  16.87\n",
       "22          AMC Javelin    3.15  3.435  17.30\n",
       "23           Camaro Z28    3.73  3.840  15.41\n",
       "24     Pontiac Firebird    3.08  3.845  17.05\n",
       "25            Fiat X1-9    4.08  1.935  18.90\n",
       "26        Porsche 914-2    4.43  2.140  16.70\n",
       "27         Lotus Europa    3.77  1.513  16.90\n",
       "28       Ford Pantera L    4.22  3.170  14.50\n",
       "29         Ferrari Dino    3.62  2.770  15.50\n",
       "30        Maserati Bora    3.54  3.570  14.60\n",
       "31           Volvo 142E    4.11  2.780  18.60"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d7d9186e",
   "metadata": {},
   "outputs": [],
   "source": [
    "q7.set_index(('Cars'), inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83d54733",
   "metadata": {
    "collapsed": true
   },
   "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>Cars</th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Valiant</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.460</td>\n",
       "      <td>20.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Duster 360</td>\n",
       "      <td>3.21</td>\n",
       "      <td>3.570</td>\n",
       "      <td>15.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Merc 240D</td>\n",
       "      <td>3.69</td>\n",
       "      <td>3.190</td>\n",
       "      <td>20.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Merc 230</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.150</td>\n",
       "      <td>22.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Merc 280</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Merc 280C</td>\n",
       "      <td>3.92</td>\n",
       "      <td>3.440</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Merc 450SE</td>\n",
       "      <td>3.07</td>\n",
       "      <td>4.070</td>\n",
       "      <td>17.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Merc 450SL</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.730</td>\n",
       "      <td>17.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Merc 450SLC</td>\n",
       "      <td>3.07</td>\n",
       "      <td>3.780</td>\n",
       "      <td>18.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Cadillac Fleetwood</td>\n",
       "      <td>2.93</td>\n",
       "      <td>5.250</td>\n",
       "      <td>17.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Lincoln Continental</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.424</td>\n",
       "      <td>17.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Chrysler Imperial</td>\n",
       "      <td>3.23</td>\n",
       "      <td>5.345</td>\n",
       "      <td>17.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Fiat 128</td>\n",
       "      <td>4.08</td>\n",
       "      <td>2.200</td>\n",
       "      <td>19.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Honda Civic</td>\n",
       "      <td>4.93</td>\n",
       "      <td>1.615</td>\n",
       "      <td>18.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Toyota Corolla</td>\n",
       "      <td>4.22</td>\n",
       "      <td>1.835</td>\n",
       "      <td>19.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Toyota Corona</td>\n",
       "      <td>3.70</td>\n",
       "      <td>2.465</td>\n",
       "      <td>20.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Dodge Challenger</td>\n",
       "      <td>2.76</td>\n",
       "      <td>3.520</td>\n",
       "      <td>16.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>AMC Javelin</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.435</td>\n",
       "      <td>17.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Camaro Z28</td>\n",
       "      <td>3.73</td>\n",
       "      <td>3.840</td>\n",
       "      <td>15.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Pontiac Firebird</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.845</td>\n",
       "      <td>17.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Fiat X1-9</td>\n",
       "      <td>4.08</td>\n",
       "      <td>1.935</td>\n",
       "      <td>18.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Porsche 914-2</td>\n",
       "      <td>4.43</td>\n",
       "      <td>2.140</td>\n",
       "      <td>16.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Lotus Europa</td>\n",
       "      <td>3.77</td>\n",
       "      <td>1.513</td>\n",
       "      <td>16.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Ford Pantera L</td>\n",
       "      <td>4.22</td>\n",
       "      <td>3.170</td>\n",
       "      <td>14.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Ferrari Dino</td>\n",
       "      <td>3.62</td>\n",
       "      <td>2.770</td>\n",
       "      <td>15.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Maserati Bora</td>\n",
       "      <td>3.54</td>\n",
       "      <td>3.570</td>\n",
       "      <td>14.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Volvo 142E</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.780</td>\n",
       "      <td>18.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Cars  Points  Score  Weigh\n",
       "0             Mazda RX4    3.90  2.620  16.46\n",
       "1         Mazda RX4 Wag    3.90  2.875  17.02\n",
       "2            Datsun 710    3.85  2.320  18.61\n",
       "3        Hornet 4 Drive    3.08  3.215  19.44\n",
       "4     Hornet Sportabout    3.15  3.440  17.02\n",
       "5               Valiant    2.76  3.460  20.22\n",
       "6            Duster 360    3.21  3.570  15.84\n",
       "7             Merc 240D    3.69  3.190  20.00\n",
       "8              Merc 230    3.92  3.150  22.90\n",
       "9              Merc 280    3.92  3.440  18.30\n",
       "10            Merc 280C    3.92  3.440  18.90\n",
       "11           Merc 450SE    3.07  4.070  17.40\n",
       "12           Merc 450SL    3.07  3.730  17.60\n",
       "13          Merc 450SLC    3.07  3.780  18.00\n",
       "14   Cadillac Fleetwood    2.93  5.250  17.98\n",
       "15  Lincoln Continental    3.00  5.424  17.82\n",
       "16    Chrysler Imperial    3.23  5.345  17.42\n",
       "17             Fiat 128    4.08  2.200  19.47\n",
       "18          Honda Civic    4.93  1.615  18.52\n",
       "19       Toyota Corolla    4.22  1.835  19.90\n",
       "20        Toyota Corona    3.70  2.465  20.01\n",
       "21     Dodge Challenger    2.76  3.520  16.87\n",
       "22          AMC Javelin    3.15  3.435  17.30\n",
       "23           Camaro Z28    3.73  3.840  15.41\n",
       "24     Pontiac Firebird    3.08  3.845  17.05\n",
       "25            Fiat X1-9    4.08  1.935  18.90\n",
       "26        Porsche 914-2    4.43  2.140  16.70\n",
       "27         Lotus Europa    3.77  1.513  16.90\n",
       "28       Ford Pantera L    4.22  3.170  14.50\n",
       "29         Ferrari Dino    3.62  2.770  15.50\n",
       "30        Maserati Bora    3.54  3.570  14.60\n",
       "31           Volvo 142E    4.11  2.780  18.60"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "644bb671",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q7[\"Points\"], bins = 10, edgecolor= 'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "3ac57dda",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x = 'Points', data =q7)\n",
    "plt.xlabel('Points')\n",
    "plt.ylabel('Density')\n",
    "plt.savefig(\"PointsInferences.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bd59abd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q7[\"Score\"], bins = 20, edgecolor = 'y')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "731e1f2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x = 'Score', data= q7)\n",
    "plt.xlabel('Scores')\n",
    "plt.ylabel('Density')\n",
    "plt.savefig(\"ScoresInferences.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "275f232b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q7[\"Weigh\"], bins=20, edgecolor = 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ea41c711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x= \"Weigh\", data = q7)\n",
    "plt.xlabel('Weigh')\n",
    "plt.ylabel('Density')\n",
    "plt.savefig(\"WeighInferences.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "27c9b952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.barh(q7[\"Cars\"], q7[\"Points\"])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0877b3c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.barh(q7[\"Cars\"], q7[\"Score\"])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5937fa82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,9))\n",
    "plt.barh(q7[\"Cars\"], q7[\"Weigh\"])\n",
    "plt.yticks(fontsize=14)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb902dd8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86b887c3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c5599d41",
   "metadata": {},
   "source": [
    "# question 6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "76fde6f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def expected_value(values, weights):\n",
    "    values = np.asarray(values)\n",
    "    weights = np.asarray(weights)\n",
    "    return (values * weights).sum() / weights.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "6e532cd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.09"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_count = [1,4,3,5,6,2]\n",
    "ch_prob = [0.015,0.20,0.65,0.005,0.01,0.120]\n",
    "expected_value(c_count, ch_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4a77f23",
   "metadata": {},
   "source": [
    "# question 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1989d91a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "145.33333333333331"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weigh = [108,110,123,134,135,145,167,187,199]\n",
    "probs = [1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9]\n",
    "expected_value(weigh, probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6fed3063",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1111111111111111"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ch = 1/9\n",
    "ch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3a76fe3",
   "metadata": {},
   "source": [
    "# question 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59e487f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c32f8157",
   "metadata": {},
   "outputs": [],
   "source": [
    "conf_94 =stats.t.interval(alpha = 0.97, df=1999, loc=200, scale=30/np.sqrt(2000))\n",
    "print(np.round(conf_94,0))\n",
    "print(conf_94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "701e4899",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[199. 201.]\n",
      "(198.7376089443071, 201.2623910556929)\n"
     ]
    }
   ],
   "source": [
    "conf_94 =stats.t.interval(alpha = 0.94, df=1999, loc=200, scale=30/np.sqrt(2000))\n",
    "print(np.round(conf_94,0))\n",
    "print(conf_94)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "48800c95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[198. 202.]\n",
      "(198.4381860483216, 201.5618139516784)\n"
     ]
    }
   ],
   "source": [
    "conf_98= stats.t.interval(alpha = 0.98, df = 1999, loc = 200, scale=30/np.sqrt(2000))\n",
    "print(np.round(conf_98,0))\n",
    "print(conf_98)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f19b7b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[199. 201.]\n",
      "(198.6214037429732, 201.3785962570268)\n"
     ]
    }
   ],
   "source": [
    "conf_96 = stats.t.interval( alpha = 0.96, df = 1999 , loc = 200 , scale = 30/np.sqrt(2000))\n",
    "print(np.round(conf_96,0))\n",
    "print(conf_96)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "099ed16a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([199., 201.])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_z_94 = stats.norm.interval(0.94, loc = 200, scale = 30/np.sqrt(2000))\n",
    "np.round(conf_z_94,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "863783fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([199., 201.])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_z_96 = stats.norm.interval(0.96, loc = 200, scale = 30/np.sqrt(2000))\n",
    "np.round(conf_z_94,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f14c5e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([198., 202.])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conf_z_98 =  stats.norm.interval(0.98, loc=200,scale=30/np.sqrt(2000))\n",
    "np.round(conf_z_98,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "11a3806d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d253647f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.8818614764780115"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.t.ppf(0.03,df=1999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b649ffff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.3282147761069725"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.t.ppf(0.01,df=1999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "369dc2df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.055089962825778"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.t.ppf(0.02,df=1999)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f267f36",
   "metadata": {},
   "source": [
    "# question 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ef8ac352",
   "metadata": {},
   "outputs": [],
   "source": [
    "q12 = [34,36,36,38,38,39,39,40,40,41,41,41,41,42,42,45,49,56]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cdcf1997",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat.mean(q12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a5dad287",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.5"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat.median(q12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc93b818",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.529411764705884"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat.variance(q12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bee13fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.05266382858645"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stat.stdev(q12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "466f8cb4",
   "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>students</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>9.500000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.338539</td>\n",
       "      <td>5.052664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.250000</td>\n",
       "      <td>38.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.500000</td>\n",
       "      <td>40.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>13.750000</td>\n",
       "      <td>41.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        students      marks\n",
       "count  18.000000  18.000000\n",
       "mean    9.500000  41.000000\n",
       "std     5.338539   5.052664\n",
       "min     1.000000  34.000000\n",
       "25%     5.250000  38.250000\n",
       "50%     9.500000  40.500000\n",
       "75%    13.750000  41.750000\n",
       "max    18.000000  56.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q12_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "70c7ac53",
   "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></th>\n",
       "      <th>students</th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    students  marks\n",
       "0          1     34\n",
       "1          2     36\n",
       "2          3     36\n",
       "3          4     38\n",
       "4          5     38\n",
       "5          6     39\n",
       "6          7     39\n",
       "7          8     40\n",
       "8          9     40\n",
       "9         10     41\n",
       "10        11     41\n",
       "11        12     41\n",
       "12        13     41\n",
       "13        14     42\n",
       "14        15     42\n",
       "15        16     45\n",
       "16        17     49\n",
       "17        18     56"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q12_df = pd.DataFrame({'students':[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18],\n",
    "                    'marks':(q12)})\n",
    "q12_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "065537fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .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></th>\n",
       "      <th>marks</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>students</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          marks\n",
       "students       \n",
       "1            34\n",
       "2            36\n",
       "3            36\n",
       "4            38\n",
       "5            38\n",
       "6            39\n",
       "7            39\n",
       "8            40\n",
       "9            40\n",
       "10           41\n",
       "11           41\n",
       "12           41\n",
       "13           41\n",
       "14           42\n",
       "15           42\n",
       "16           45\n",
       "17           49\n",
       "18           56"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q12_df.set_index('students')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b257062",
   "metadata": {},
   "source": [
    "# Question 24"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2285e97c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_bar = 260\n",
    "pop_mean = 270"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3eb1ea5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.4714045207910317"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_value = (260-270)/(90/np.sqrt(18))\n",
    "t_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bebc5961",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.32167253567098353"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1-stats.t.cdf(abs(t_value),df = 17)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc26bc44",
   "metadata": {},
   "source": [
    "# question 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e9c8a332",
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>53.700681</td>\n",
       "      <td>89</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>45.696322</td>\n",
       "      <td>92</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>50.504232</td>\n",
       "      <td>92</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>322</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>50</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>238</td>\n",
       "      <td>19.197888</td>\n",
       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>263</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>295</td>\n",
       "      <td>19.833733</td>\n",
       "      <td>119</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "0    49  53.700681   89  104.185353  28.762059\n",
       "1    55  50.013401   92  105.461264  30.466833\n",
       "2    55  50.013401   92  105.461264  30.193597\n",
       "3    70  45.696322   92  113.461264  30.632114\n",
       "4    53  50.504232   92  104.461264  29.889149\n",
       "..  ...        ...  ...         ...        ...\n",
       "76  322  36.900000   50  169.598513  16.132947\n",
       "77  238  19.197888  115  150.576579  37.923113\n",
       "78  263  34.000000   50  151.598513  15.769625\n",
       "79  295  19.833733  119  167.944460  39.423099\n",
       "80  236  12.101263  107  139.840817  34.948615\n",
       "\n",
       "[81 rows x 5 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q20 = pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science Assignments/Basic Statistics 1/Cars.csv\")\n",
    "q20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "815c7722",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "641a615e",
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>81.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>117.469136</td>\n",
       "      <td>34.422076</td>\n",
       "      <td>98.765432</td>\n",
       "      <td>121.540272</td>\n",
       "      <td>32.412577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>57.113502</td>\n",
       "      <td>9.131445</td>\n",
       "      <td>22.301497</td>\n",
       "      <td>14.181432</td>\n",
       "      <td>7.492813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>49.000000</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>99.564907</td>\n",
       "      <td>15.712859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>84.000000</td>\n",
       "      <td>27.856252</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>113.829145</td>\n",
       "      <td>29.591768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>35.152727</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>118.208698</td>\n",
       "      <td>32.734518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>140.000000</td>\n",
       "      <td>39.531633</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>126.404312</td>\n",
       "      <td>37.392524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>322.000000</td>\n",
       "      <td>53.700681</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>52.997752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               HP        MPG         VOL          SP         WT\n",
       "count   81.000000  81.000000   81.000000   81.000000  81.000000\n",
       "mean   117.469136  34.422076   98.765432  121.540272  32.412577\n",
       "std     57.113502   9.131445   22.301497   14.181432   7.492813\n",
       "min     49.000000  12.101263   50.000000   99.564907  15.712859\n",
       "25%     84.000000  27.856252   89.000000  113.829145  29.591768\n",
       "50%    100.000000  35.152727  101.000000  118.208698  32.734518\n",
       "75%    140.000000  39.531633  113.000000  126.404312  37.392524\n",
       "max    322.000000  53.700681  160.000000  169.598513  52.997752"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q20.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "41e14513",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(MPG>38)= 0.348\n"
     ]
    }
   ],
   "source": [
    "Prob_MPG_greater_than_38 = np.round(1 - stats.norm.cdf(38, loc= q20.MPG.mean(), scale= q20.MPG.std()),3)\n",
    "print('P(MPG>38)=',Prob_MPG_greater_than_38)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "4e4f4bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(MPG<40)= 0.729\n"
     ]
    }
   ],
   "source": [
    "prob_MPG_less_than_40 = np.round(stats.norm.cdf(40, loc = q20.MPG.mean(), scale = q20.MPG.std()),3)\n",
    "print('P(MPG<40)=',prob_MPG_less_than_40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "20fa2c0a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p(MPG>20)= 0.943\n"
     ]
    }
   ],
   "source": [
    "prob_MPG_greater_than_20 = np.round(1-stats.norm.cdf(20, loc = q20.MPG.mean(), scale = q20.MPG.std()),3)\n",
    "print('p(MPG>20)=',(prob_MPG_greater_than_20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4fbe688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(MPG<50)= 0.956\n"
     ]
    }
   ],
   "source": [
    "prob_MPG_less_than_50 = np.round(stats.norm.cdf(50, loc = q20.MPG.mean(), scale = q20.MPG.std()),3)\n",
    "print('P(MPG<50)=',(prob_MPG_less_than_50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c16c4809",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(20<MPG<50)= 0.013000000000000012\n"
     ]
    }
   ],
   "source": [
    "prob_MPG_greaterthan20_and_lessthan50= (prob_MPG_less_than_50) - (prob_MPG_greater_than_20)\n",
    "print('P(20<MPG<50)=',(prob_MPG_greaterthan20_and_lessthan50))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb44faa",
   "metadata": {},
   "source": [
    "# Question 22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76dcc1e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z score for 60% Conifidence Intervla = -1.6449\n"
     ]
    }
   ],
   "source": [
    "# z value for 90% confidence interval\n",
    "print('Z score for 60% Conifidence Intervla =',np.round(stats.norm.ppf(.05),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ec36233b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z score for 60% Conifidence Intervla = -1.8808\n"
     ]
    }
   ],
   "source": [
    "# z value for 94% confidence interval\n",
    "print('Z score for 60% Conifidence Intervla =',np.round(stats.norm.ppf(.03),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5993d4fd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Z score for 60% Conifidence Intervla = -0.8416\n"
     ]
    }
   ],
   "source": [
    "# z value for 60% confidence interval\n",
    "print('Z score for 60% Conifidence Intervla =',np.round(stats.norm.ppf(.2),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2320d9b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T score for 95% Confidence Interval = -2.0639\n"
     ]
    }
   ],
   "source": [
    "# t score for 95% confidence interval\n",
    "print('T score for 95% Confidence Interval =',np.round(stats.t.ppf(0.025,df=24),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4e7bba1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T score for 94% Confidence Inteval = -1.974\n"
     ]
    }
   ],
   "source": [
    "# t value for 94% confidence interval\n",
    "print('T score for 94% Confidence Inteval =',np.round(stats.t.ppf(0.03,df=24),4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6eb4d242",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "T score for 95% Confidence Interval = -2.7969\n"
     ]
    }
   ],
   "source": [
    "# t value for 99% Confidence Interval\n",
    "print('T score for 95% Confidence Interval =',np.round(stats.t.ppf(0.005,df=24),4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1519bc33",
   "metadata": {},
   "source": [
    "# Question 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9ac8d5df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>13</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>14</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>15</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>15</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>16</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>17</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>17</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>17</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>18</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>18</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>18</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>18</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>19</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>19</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>19</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>20</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>20</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>20</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>20</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>20</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>22</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>23</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>24</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>24</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>24</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>24</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>25</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       speed  dist\n",
       "Index             \n",
       "1          4     2\n",
       "2          4    10\n",
       "3          7     4\n",
       "4          7    22\n",
       "5          8    16\n",
       "6          9    10\n",
       "7         10    18\n",
       "8         10    26\n",
       "9         10    34\n",
       "10        11    17\n",
       "11        11    28\n",
       "12        12    14\n",
       "13        12    20\n",
       "14        12    24\n",
       "15        12    28\n",
       "16        13    26\n",
       "17        13    34\n",
       "18        13    34\n",
       "19        13    46\n",
       "20        14    26\n",
       "21        14    36\n",
       "22        14    60\n",
       "23        14    80\n",
       "24        15    20\n",
       "25        15    26\n",
       "26        15    54\n",
       "27        16    32\n",
       "28        16    40\n",
       "29        17    32\n",
       "30        17    40\n",
       "31        17    50\n",
       "32        18    42\n",
       "33        18    56\n",
       "34        18    76\n",
       "35        18    84\n",
       "36        19    36\n",
       "37        19    46\n",
       "38        19    68\n",
       "39        20    32\n",
       "40        20    48\n",
       "41        20    52\n",
       "42        20    56\n",
       "43        20    64\n",
       "44        22    66\n",
       "45        23    54\n",
       "46        24    70\n",
       "47        24    92\n",
       "48        24    93\n",
       "49        24   120\n",
       "50        25    85"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q9a = pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science Assignments/Basic Statistics 1/Q9_a.csv\", index_col = 'Index')\n",
    "q9a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "851d0956",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For Cars Speed Skewness value= -0.12 and Kurtosis value= -0.51\n"
     ]
    }
   ],
   "source": [
    "print('For Cars Speed', \"Skewness value=\", np.round(q9a.speed.skew(),2), 'and' , 'Kurtosis value=', np.round(q9a.speed.kurt(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "dfffdbab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness value = 0.81 and Kurtosis value = 0.41 for Cars Distance\n"
     ]
    }
   ],
   "source": [
    "print('Skewness value =', np.round(q9a.dist.skew(),2),'and', 'Kurtosis value =', np.round(q9a.dist.kurt(),2), 'for Cars Distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e4839a1e",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>28.762059</td>\n",
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       "    <tr>\n",
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       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0          SP         WT\n",
       "0            1  104.185353  28.762059\n",
       "1            2  105.461264  30.466833\n",
       "2            3  105.461264  30.193597\n",
       "3            4  113.461264  30.632114\n",
       "4            5  104.461264  29.889149\n",
       "..         ...         ...        ...\n",
       "76          77  169.598513  16.132947\n",
       "77          78  150.576579  37.923113\n",
       "78          79  151.598513  15.769625\n",
       "79          80  167.944460  39.423099\n",
       "80          81  139.840817  34.948615\n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q9b =pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science Assignments/Basic Statistics 1/Q9_b.csv\")\n",
    "q9b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c33a7ed2",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
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       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
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       "      <th>3</th>\n",
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       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
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       "      <td>104.461264</td>\n",
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       "      <th>79</th>\n",
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       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Index          SP         WT\n",
       "0       1  104.185353  28.762059\n",
       "1       2  105.461264  30.466833\n",
       "2       3  105.461264  30.193597\n",
       "3       4  113.461264  30.632114\n",
       "4       5  104.461264  29.889149\n",
       "..    ...         ...        ...\n",
       "76     77  169.598513  16.132947\n",
       "77     78  150.576579  37.923113\n",
       "78     79  151.598513  15.769625\n",
       "79     80  167.944460  39.423099\n",
       "80     81  139.840817  34.948615\n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q9b.rename(columns = {'Unnamed: 0':'Index'}, inplace = True)\n",
    "q9b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bc770a42",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>104.185353</td>\n",
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       "      <td>2</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
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       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Index          SP         WT\n",
       "0       1  104.185353  28.762059\n",
       "1       2  105.461264  30.466833\n",
       "2       3  105.461264  30.193597\n",
       "3       4  113.461264  30.632114\n",
       "4       5  104.461264  29.889149\n",
       "..    ...         ...        ...\n",
       "76     77  169.598513  16.132947\n",
       "77     78  150.576579  37.923113\n",
       "78     79  151.598513  15.769625\n",
       "79     80  167.944460  39.423099\n",
       "80     81  139.840817  34.948615\n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q9b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73721767",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For SP Skewness = 1.61 kurtosis = 2.98\n"
     ]
    }
   ],
   "source": [
    "print('For SP Skewness =', np.round(q9b.SP.skew(),2), 'kurtosis =', np.round(q9b.SP.kurt(),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6abb82b9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For WT Skewness = -0.61 Kurtosis = 0.95\n"
     ]
    }
   ],
   "source": [
    "print('For WT Skewness =', np.round(q9b.WT.skew(),2), 'Kurtosis =', np.round(q9b.WT.kurt(),2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce176233",
   "metadata": {},
   "source": [
    "# Question 21"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "50f1375b",
   "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>HP</th>\n",
       "      <th>MPG</th>\n",
       "      <th>VOL</th>\n",
       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>49</td>\n",
       "      <td>53.700681</td>\n",
       "      <td>89</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>55</td>\n",
       "      <td>50.013401</td>\n",
       "      <td>92</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>45.696322</td>\n",
       "      <td>92</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>53</td>\n",
       "      <td>50.504232</td>\n",
       "      <td>92</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>322</td>\n",
       "      <td>36.900000</td>\n",
       "      <td>50</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>238</td>\n",
       "      <td>19.197888</td>\n",
       "      <td>115</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>263</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>50</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>295</td>\n",
       "      <td>19.833733</td>\n",
       "      <td>119</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>236</td>\n",
       "      <td>12.101263</td>\n",
       "      <td>107</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     HP        MPG  VOL          SP         WT\n",
       "0    49  53.700681   89  104.185353  28.762059\n",
       "1    55  50.013401   92  105.461264  30.466833\n",
       "2    55  50.013401   92  105.461264  30.193597\n",
       "3    70  45.696322   92  113.461264  30.632114\n",
       "4    53  50.504232   92  104.461264  29.889149\n",
       "..  ...        ...  ...         ...        ...\n",
       "76  322  36.900000   50  169.598513  16.132947\n",
       "77  238  19.197888  115  150.576579  37.923113\n",
       "78  263  34.000000   50  151.598513  15.769625\n",
       "79  295  19.833733  119  167.944460  39.423099\n",
       "80  236  12.101263  107  139.840817  34.948615\n",
       "\n",
       "[81 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q21a = pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science Assignments/Basic Statistics 1/Cars.csv\")\n",
    "q21a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "69a228b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "mean, cov = [0, 0], [(1, .6), (.6, 1)]\n",
    "x, y = np.random.multivariate_normal(mean, cov, 100).T\n",
    "y += x + 1\n",
    "\n",
    "f, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "ax.scatter(x, y, c=\".3\")\n",
    "ax.set(xlim=(-3, 3), ylim=(-3, 3))\n",
    "\n",
    "# Plot your initial diagonal line based on the starting\n",
    "# xlims and ylims.\n",
    "diag_line, = ax.plot(ax.get_xlim(), ax.get_ylim(), ls=\"--\", c=\".3\")\n",
    "\n",
    "def on_change(axes):\n",
    "    # When this function is called it checks the current\n",
    "    # values of xlim and ylim and modifies diag_line\n",
    "    # accordingly.\n",
    "    x_lims = ax.get_xlim()\n",
    "    y_lims = ax.get_ylim()\n",
    "    diag_line.set_data(x_lims, y_lims)\n",
    "\n",
    "# Connect two callbacks to your axis instance.\n",
    "# These will call the function \"on_change\" whenever\n",
    "# xlim or ylim is changed.\n",
    "ax.callbacks.connect('xlim_changed', on_change)\n",
    "ax.callbacks.connect('ylim_changed', on_change)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "acd62208",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q21a[\"MPG\"], bins = 20, edgecolor=  'black')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "bff653fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x= 'MPG', data =q21a)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "09929130",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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GtFoxX07Rj79vws7Mu4EXd7l+ALhoZWFqmvW7k3bQqh6AgweLyVw3SZeqqZL4zyrX4rd8OiK+WFdAap7Z2e61/bk52Lt36OFIU6/KDVx/W954BUBE/BTwufpCUlO0Wi3s21esu29n2UaqT5UR/08BV0ZE6w/yWWBPRNxDcZ/Wi2qLTlOrc0I3s0j+mcVI37KNVJ8qif/i2qNQoywvw7ZtR27Eamklfcs7Ur2q3Lm7r2ykdnr766vcwCV1ao30O5N+iy2TpfpVWc75GxS9ef6eslEbK7yBS2oZtHTTVgtS/aqUen6OojXz43UHo+lV5c5cJ3Sl4aiS+O8FTsbWClqlKnfmbtxoqwVpWKok/ndSLOm8l7Y+/Jl5aW1RaaoMKu/MzJj0pWGqkvh3UfTYuQc4XG84mkb9JmxduikNX5XE/0hm/m7tkWhqeWeuNF6q3Ll7R0S8MyLOj4hzWz+1R6apsbRUlHPaOZErjU6VEX+r0dp5bddczqnKWmWcHTuKso8N1aTRimJr3PG2sLCQu3fvHnUYkjRRIuKOzFzovF5lxE9EvAo4Gzi+dS0z/8v6hSdJGpaBNf6I+ADwOuCXKDZbvwyYqzkuSVJNqkzu/ovMvBL4Vma+HTifom+P1Fer7fKGDcVxeXnUEUmCaon/++XxUET8M+AJ4Iz6QtIkayX7CHjDG4plnJnFcft2k780Dqok/psj4mTgt4A7gb3AdTXGpAnVas3QWrPfuW7g0KFiZY+k0VrRqp6IeDpwfGY+Wl9Ix3JVz2Ro7abVTwQc9v5vaSh6rerpOeKPiJ+MiB9pO78SuB74jYg4pZ4wNcmq9NK37bI0ev1KPf8deBwgIn4GeBfwIeBRYGf9oWlStOr6g/549G5daTz0S/wbM/Ng+fh1wM7MvCEzfw14fv2haZx0rtC5+upjJ3G7aW2iPjdnB05pXPRN/BHRusHrIuAv2p6rdOOXJkuV5N5aofP7v997Erdlbg4+/OHi+b17TfrSuOiXwK8DPhMRj1As6fxLgIh4PkW5R2OutetVqz/OJZfALbd0Pz/lFHjsMXi83GetldxbVtrZI8LOm9K46ruqJyLOA7YCn8zM75XXfhQ4YZibrbuqZ+Wq7HpVJ1suS6O3ql49mfn5Lte+sp6BqR6Ddr2qk5O40nircgOXJlCVpZXryUlcaXKY+KdUHevl25P7VVcVxwgncaVJ4+qcKbW0tPIa/6ZNcNJJcPBg98lgN0+RpoOJf0p12/Wq36oeE7vUHCb+Kba4aCKXdCxr/FPC3veSqnLEPwU61+y3et+DI35Jx3LEPwW6rdm3972kXkz8U6DXmv1hr+WXNBlM/FOg15p9e99L6sbEPwWWloo2Ce1smyCpl9oSf0ScHhGfjog9EXFfRFxTXj8lIm6NiK+Wx2fWFUNTLC4WbRLa76S1bYKkXuoc8T8JvDUzfww4D3hTRLwAuBa4LTPPBG4rz1VRr2Wbi4tFu4TDh22bIKm/2hJ/Zj7Qat2cmY8Be4DnAK8BdpUv2wX8bF0xTLJuCb61bLN9Q5Tt212zL2ll+vbjX7cPiZgHPgu8ENifmSe3PfetzDym3BMR24HtALOzs/98X6+9/aZQt176MzPwjGfAgQPHvt7e95K66dWPv/bJ3Yg4AbgBeEtmfqfq72XmzsxcyMyFLVu21BfgiHUb2fdal98t6YPLNiWtTK2JPyI2UST95cz8k/LygxGxtXx+K/BQnTHUpWqLhH6v61W6WekfNy7blLQSda7qCeCDwJ7MfE/bUzcB28rH24Ab64qhqpX2ualaax/0ul4j+40bu3/u5s0u25S0drXV+CPipyk2aL8HOFxe/o/AF4DrgVlgP3BZZh7s91517rnbq57ebznk/Hz3UXlnrX3Q6zZs6L2J+cxM95jg6FbLtlKW1EuvGv9QJnfXqs7EXzWJt+uVsCOK5ZRVX9fvs5eWTPCS1mZkk7vjbjV9bqq2SBj0un533LouX1JdGp/4V9PnpmqLhEGv845bSaPQ+MS/mj43VRN2ldc5spc0bI2v8cORtfPW0yVNk141fnfgwr1pJTVL40s9ktQ0Jn5JahgTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYUz8ktQwJn5JahgTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYUz8ktQwJn5JahgTvyQ1jIlfkhrGxC9JDWPil6SGMfFLUsOY+CWpYSYy8S8vw/w8bNhQHJeXRx2RJE2O40YdwEotL8P27XDoUHG+b19xDrC4OLq4JGlSTNyIf8eOI0m/5dCh4rokabCJS/z796/suiTpaBOX+GdnV3ZdknS0iUv8S0swM3P0tZmZ4rokabCJS/yLi7BzJ8zNQURx3LnTiV1JqmriVvVAkeRN9JK0OhM34pckrY2JX5IaxsQvSQ1j4pekhjHxS1LDRGaOOoaBIuJhYF+NH3Eq8EiN7z/p/H5687vpz++nt2F8N3OZuaXz4kQk/rpFxO7MXBh1HOPK76c3v5v+/H56G+V3Y6lHkhrGxC9JDWPiL+wcdQBjzu+nN7+b/vx+ehvZd2ONX5IaxhG/JDWMiV+SGsbEX4qI34qIL0XE3RHxsYg4edQxjZOIuCwi7ouIwxHh8jwgIi6OiC9HxNci4tpRxzNOIuIPIuKhiLh31LGMm4g4PSI+HRF7yv+mrhl2DCb+I24FXpiZLwK+AvzqiOMZN/cC/xr47KgDGQcRsRH4PeCVwAuAyyPiBaONaqz8L+DiUQcxpp4E3pqZPwacB7xp2P/umPhLmfnJzHyyPP08cNoo4xk3mbknM7886jjGyEuAr2Xm1zPzceCjwGtGHNPYyMzPAgdHHcc4yswHMvPO8vFjwB7gOcOMwcTf3S8AHx91EBprzwH+oe38fob8H68mX0TMAy8GvjDMz53IHbhWKyI+BfxIl6d2ZOaN5Wt2UPwptjzM2MZBle9HPxRdrrk2WpVFxAnADcBbMvM7w/zsRiX+zHx5v+cjYhvwauCibOANDoO+Hx3lfuD0tvPTgG+OKBZNmIjYRJH0lzPzT4b9+ZZ6ShFxMfA24NLMPDTqeDT2/gY4MyLOiIinAa8HbhpxTJoAERHAB4E9mfmeUcRg4j/i/cCJwK0RcVdEfGDUAY2TiHhtRNwPnA/8WUR8YtQxjVK5EODNwCcoJueuz8z7RhvV+IiI64C/As6KiPsj4o2jjmmMvBR4A3BhmWvuiohLhhmALRskqWEc8UtSw5j4JalhTPyS1DAmfklqGBO/JDWMiV/qJiKJ+HDb+XFEPEzEzeX5z5fndxHxd0T8YttrLybir4n4Uvn8HxMxO/R/BqmHRt25K63A94AXEvEMMr8PvAL4vx2v+WMy30zEs4D7iLgJ2AK8D7iUzD0ARFwKzAP7hxW81I8jfqm3jwOvKh9fDlzX9VWZDwF/D8xR3P39X3+Y9Ivnb6LoVimNBRO/1NtHgdcTcTzwInp1UIx4LvBc4GvA2cCdwwpQWg0Tv9RL5t0UJZrLgVu6vOJ1RNxF8ZfAvyPz6P7zEZvLGv9XiPiVmqOVKrPGL/V3E/Bu4AJgc8dzRY3/aPcB5wJfJPMAcE6Z9E+oO1CpKhO/1N8fAI+SeQ8RF1R4/W8CHyPi8211/pm6gpNWw8Qv9ZN5P/DeFbz+HorNsz9ExInAAYrVPL9eT4DSytmdU5IaxsldSWoYE78kNYyJX5IaxsQvSQ1j4pekhjHxS1LDmPglqWH+P6fhX10cCdufAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.qqplot(q21a['MPG'])\n",
    "plt.xlabel('MPG', color ='red')\n",
    "plt.savefig('MPG of cars.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "264aad3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YT9Lm8QmTOYDe/JMRnE4rZjKUC4lO+ureyXyJ9PG3irTwzwDeBJoDnZIZlIgkX/STtPVYxX1czUe0oxHL6NTgNS5tNJzF9kcaNYJGjTQnvyZJpKunjpnVIST+R919rZlV/2W7RCSu3r1DH3+7orEMpBu7MYfBW3Rnm37/5pmu26Y7PEmiRFr8A4C5wNbABDPLAdTHL5LhOrT/kc/zLmIsx7Oe2py74zjqPvU45yjp13iVWnPXzLZw93VJiCcmrbkrUsVefhl69oQlS+C66+CWW6Bu3XRHJVWs0mvumtmOZjbEzEZH9lsBnZMQo4gk24IFcNZZ8Le/MW35TrRZX0Dus3eT/4qSfjZJpKvnKeBtYKfI/lfAFUmKR0SSobgYBg2CVq1Y9/pobq7ThzZrJjGV1hQWhr7+/Px0Bympkkjib+zuw4FigEgXz/pEP8DMapvZp2b2emS/oZmNMbPZke32lYpcRBLz9ddw3HHQrRsTf2tDy7Wfc/faa1kfNbejqCjM65fskEjiX2VmjQAHMLN2wMoKfEYvYFbU/g3AWHffAxgb2ReRqrZuHfTpA/vuy5qPP+WyPwzk0NXv8TV7xDxdK2Rlj0QS/1XASGA3M/sf8DTwj0Rubma7ACcDg6MOnw4MjbweSpgmKiJVaepUOPhguP56OOkkDt9+Jv3WdCX6QazStEJW9ig38bv7FOAo4FCgO7C3u3+e4P0fBK4j0k0UsaO7L4zceyGwQ6wLzaybmRWYWcGSJUsS/DiRmq+ksJoZbLHFxttdGv3Kg3VvZF2bPBZN+Z6zGU7jCa/wyfc7xb2nnsbNLuU+wGVmF5Q61NbMcPeny7nuFGCxu082s6MrGpi7DwQGQpjOWdHrRWqi0oXV1q//fXsk4xm0vCt/YjZP8H9cw338SENYHv+eKraWfRJ5cvfAqNdbAccBUwhdPvEcBpxmZu0j121jZsOAH8ysqbsvNLOmwOJKxC2SdfLzoXPn35N9iW1Yyb+5nksYwDe04Dje5T2OK/d+9eqp/EK2Kjfxu/tG/flmti3wTALX3QjcGLnmaOAad+9oZn0JzwHcG9mOqHDUIlmmpKVfOumfykj604M/soj7uYpbuYMitk7onkr62asyC7EUQRnTAhJzL3CCmc0GTojsi0gcpUso78APPM+5jOR0ltGIdnzENdyfcNLPyVHSz2aJ9PGPIjKVk/CLohUwvCIf4u7jgHGR18sggb9DRWqw/PyQzAsLoXbt0JIva9uoUfRSiM4FPM0DXMnWrOJm7qIv17KWPyT82RrIlUT6+O+Ler0OKHT375IUj0iNF2+ANta2JOnn8i0D6M6fGcMHHE5XBvElewEb/5Iouab0seXLw5RNDeRKIn3841MRiEhNFt3Cr6harOcfPEJvbqaYWlzKYzzOJdStV4th6qeXSigz8ZvZz/zexbPRW4C7+zZJi0qkBilvbdt49mEag+nCwXzM65xMD/rzHc0ADc5K5ZWZ+N29QSoDEampylrbNp4/sJqbuJsbuYeVbMv5PMvznEfJk7canJXNkfBi62a2A2E+PgDursoeIgmoaA2cdkxkCBfTilk8Q0eu5AGW0XjD+xqclc2VSD3+0yJTL78FxhNW4xqd5LhEaoT8fKiV4KTpbWr9wkNczv84jK1ZxSm13uQCnoFGjbXmrVSpRFr8dwLtgHfdvY2ZHQOcn9ywRDJfWQ9dRdvw9GzD0XDJJTB/PvTsSU7v3rzeQL2tkhyJtEXWRube1zKzWu7+PtA6uWGJZL7y+vZzcmDo/Uvp8FYnaN8ett4a/vtfePhhUNKXJEqkxb/CzOoDE4B8M1tMmM8vIqUkMm3TDIrXOzz3HPTqBStXwq23wk03wZZbpi5YyVpltvjN7G9mthWhfn4RcCXwFvANcGpqwhPJHCVdO+XN1T94p/lwyimho75FC5gyBW6/XUlfUiZeV08HYB7QHziRMHd/qLs/HOn6EZEo5XXtGMVcWedRJixrBePGwQMPwIcfwj77pCxGEYiT+N39TGB3wvKIlwPzzay/mR2ZquBEMkV+fvyW/p58wQSO5D9r/0GdIw6B6dPhiitCXQWRFIs7uOvuP0Va+X8B9gWmAo+Y2fxUBCdS3eXnQ+PG0LFj7PfrsIabuYvP2J+9a82Cp56Ct9+GXXdNaZwi0RJ6gMvMtgfOAs4FGgIvJzMokeoiXhVNM/A4a8Pl8QlDuJj9mMZLtc/BHn6Yv3beMXXBi5Qh3uBuAzPrZGZvArMIK3HdBTR39ytSFJ9I2pQerC1dNbOspF+XIu7jaj6iHY1YRpcmI1g99AX+eqmSvlQP8Vr83wJvEwZ333L3takJSaR6qEyNneN4l4F0owXfMqz+JXT87l4Gb7ttcgIUqaR4ffzN3b2Du49S0pdskZ8PubmhG6ciJZS340eGcBHvcgLr2II/bzkee7w/KOlLNRRvVk8lisiKZK5E5+FvzPkrLzGLllzA09zDDRzb8DM6DzlS9XSk2qrMmrsiGSu6Rb/FFhtvO3asWNdOUxbwCmfxEmfzPTtzwraf0HzYPXy3rK6SvlRrCZdlFsl05S15mKjatZz/Kx5MX65lS1Zz93Z9yH3oSt6/QP85SWaItwJX9CLrm3D305ISkUiSVGawNlpODswdMzv89hg3Do4+GgYN4qbdd6+qEEVSIl4TpWSR9bOAPwLDIvvnE2ryi2SUii6IEq1B3XW8esh/YL/bQk2dgQOhS5fQRySSYeItvTgewMzudPfoMg2jzGxC0iMTqWLNm1dusfP2TT/lmT9cTMPnP4Uzz4RHH4Wddqr6AEVSJJHB3SZm1qJkx8x2BZokLySR5OjdOyx8koh69eC5J37Fr7+BNxYfSMPfFsBLL8ErryjpS8ZLZDTqSmCcmc2J7OcC3ZMWkUiSlMy0KasEQ8k2JwcGdRzPCfd0hdmz4aKL4L77YPvt0/sFRKpIuYnf3d8ysz2AvSKHvnD31ckNSyQ5OnQoZ73alSvhuuug98BQK//dd+G441IWn0gqJLLYej3gWqCnu38GNDezU5IemUiqjRwJrVrB4MFw9dUwbZqSvtRIifTxPwmsAQ6J7H9HKNYmUjP88AOccw6cfjo0agQffRS6dhIdEBDJMIkk/t3cvQ+wFsDdfwU0h00yn3uoj9+yJYwYAXfdBZMnw4EHpjsykaRKZHB3jZnVJfIwl5ntBqiPXzLbnDnQvXvowz/ssNC9s9de5V8nUgMk0uK/jbDIejMzyycsxXhdUqMSSZb168Nat/vuC5MmwWOPwYQJSvqSVeImfjOrBZSsvnUh8ByQ5+7jkh6ZSBXKz4cTd5rGpC0Ohauu4vs/HQMzZsCll0It1SqU7FLemrvFhNk8y9z9DXd/3d2Xpig2kUqLrsK5la3mm4638vrCtuzKt5zHc/zpy1HkT2iW7jBF0iKRps4YM7vGzJqZWcOSn6RHJlJJ0XX1D+FDptCGW7mT5ziflsziBc6j6Ffj5pvTHalIeiQyuHtRZHtZ1DEHWsQ4VyTtbr4ZahX9zMPcxGU8xnyacRKjeZuTNjpvc4q2iWSyRJ7c3TUVgYhUhfx8aFk4mse5hGbM51F6cjO9+YUGm5zbvHkaAhSpBhJ6ctfM/mlmAyP7eyTy5K6ZbWVmH5vZZ2Y2w8xujxxvaGZjzGx2ZKsCKFIlXnp8KbU7d2Q07VnF1hzOf+nFwzGTfr16oWibSDaqyJO7h0b2E31ydzVwrLvvD7QGTjKzdsANwFh334MwNfSGigYtshF3yM/nmMtactb64dzOrbThUyZu+L/sxho1CuX0tTyiZKukPbnrwS+R3TqRHwdOB4ZGjg8FzqhgzJJlYq2T27hx+Glu8xhd62To2JHZxbvRlin8i9tZw5ab3CcnB4YNg6VLlfQluyX1yV0zqw1MBnYHHnP3SWa2o7svBHD3hWa2QxnXdgO6ATRXZ2zWKmud3OXLirmUftzDjdSimF48yKP0pJjam9wjJwfmzk1dzCLVXVKf3HX39e7eGtgFOMjM9kk0MHcf6O557p7XpInWfclWsdbJ3YtZfMARPMo/+JBD2ZsZPEyvmElfffkim0pkVs8YM5sCtCN08fSq6ENc7r7CzMYBJwE/mFnTSGu/KbC4EnFLFsjP33ipxDqs4Tr6cAt38gv1uYChPEMn4vU8qi9fZFNltvjNrG3JD5ADLAQWEOrxty3vxmbWxMy2i7yuCxwPfAGMBDpHTusMjNisbyA1UkkXT4kD+ZgC8riLW3iNM2jFTJ7hAuIl/ZwcJX2RWOK1+O+PbLcC8oDPCP+V7QdMAg4v595NgaGRfv5awHB3f93MJgLDzexiYB5w9mbELzVUSRdPPVZxJ7fQi4dYSFNOYwSjOK3c69XFI1K2MhO/ux8DYGbPA93cfVpkfx/gmvJu7O6fA21iHF8GaFkjiWvePDiOdxlIN1rwLf25hBu4l5/YFghTMgGWLYu9Zm7v3mrti5QlkVk9e5UkfQB3n25mrZMXkmS95ct5od7VnL3qKb5iD45kPB9wJKAZOiJVIZFZPV+Y2WAzO9rMjjKzQcCsZAcmWcidDy5/kSVNWnHmqme4hxvZj883JH1134hUjURa/BcCPYBekf0JQP9kBSRZasEC5p92KUdMHkEBB3ACb/EZrTEDXN03IlUpbuKPDMy+7u7HAw+kJiTJKsXFMHgwa664lka/ruUa+vIgV7A+8n9Nd3XviFS1uInf3debWZGZbevuK1MVlGSJ2bOha1cYP56JtY7hIgYxh902OU3lk0WqViJdPb8B08xsDLCq5KC7X560qKRmW7cO7r+f9bfcxqp1W3Elg3mi+CLKmpOvih0iVSuRxP9G5Edk802ZAl26wKef8nrtM+nhj7KQnco8XQO6IlUvkcT/AqHImgPfuPtvyQ1JaqRff4Xbb4f77oMmTbik8UsMWPrXuJfUrq2SCyLJEK9kwxZm1odQf38oMAyYb2Z9zKxOqgKUGmD8eNh/f/j3v+HCC2HmTAYui5/069WDoUOV9EWSId48/r5AQ2BXdz/A3dsAuwHbAfelIDbJdCtWhII7Rx8dHqkdOxYGD4btt4/bb5+To5a+SDLFS/ynAF3d/eeSA+7+E2FOf/tkByYZ7rXXoFUrGDIErrkGpk2DY4/d8Hbv3qFVH61evbBQyty5SvoiyRQv8bu7e4yD64ksyiKyiUWL4Oyz4cwzoUkTmDQJ+vbdJMt36BBa9Tk5YUUttfJFUide4p9pZheUPmhmHQnllUV+5w5PPhla+aNGwV13QUEB5OWVeUmHDqF1X1ysVr5IKsWb1XMZ8IqZXURYPtGBA4G6wJkpiE0yxZw50L07vPsuHH546Mffc890RyUiZYhXlvl74GAzOxbYm/B0zWh3H5uq4KSaW78eHnoIbrklzL3s1y/8AqiVSO0/EUmXcv8Ldff33P0Rd39YSV82mDYNDjkErr46DNrOmAE9eiSU9PPzITc3nJqbG/ZFJHXUNJOKWb06tPDbtg0d8889ByNHQrNmcS8rSfZm0KlTWEvXPWy7dVPyF0klJX5J3P/+B61bh4Hb886DmTPD1spe9xZ+Xz+3ZOH00nPFiorCUosikhpK/FK+n3+Gnj3hiCNClh49Gp55Bho3TujykvVz41EFTpHUUeKX+N58E/beOwzc/uMfoS//pJMqdItEkroqcIqkjhK/xLZkSZhYf/LJ0KBB6OZ56CGoX3+TU6MHaxs3Dj/Rrzd9DHBjqsApklpK/LIx95DJW7aEF1+E226DKVPIn3PIRjNxLr009mDtsmXhJ/p1LCXDAnpiVyT1lPizWOlplTd3msf79U6Gjh359OfdufOsT8l96l/YVltuMhOnf/+yB2vLk5MThgjc9cSuSDokUo9faqCSmTZFRWAUc0phP24ovBHDuZyHeGzNZRS/UHvD+RVN7mUx0/q5IummxJ+lSmba7MUsBtOFw/iQtziRS3icQnKT9rkaxBVJP3X1ZKmFhWv4J3cyldbsxRd04mn+wuikJn0N4opUD2rxZ6OPP+azOhez19rpPMd59OIhlrDDZt3SLHQHNWoU9pcvh4YNf3/dvHlI+urPF0k/Jf5ssmpVKLfw0EM037Ypfy0axSurT0n48pLknpMD7duHKf7z5impi2QadfVkizFjYJ994IEHoFs36s2dyVlDTtloIZQePYi7Hz0Tp18/1dIXyVRq8dd0y5fDVVeFlcv/9CeYMCGUXiAkayVskeyjFn9N5Q7Dh4cHsYYNg5tu4rkbPiO30xEqhyyS5dTir4m+/z48WjtyZCif/Pbb5M9ovWHePvxeDhnU6hfJNmrx1yTFxTBgQFj3dsyYsMj5pEnQunXMCpkqhyySndTirym++gq6dg19+MccA4MGwW67bXi7rAqZKocskn3U4s90a9fCvffCfvvBZ5+FhD927EZJH8p+YlZP0opkHyX+TDZlChx0ENx4YyifPGsWdOkSc0Ws3r3Dk7PR9CStSHZKWuI3s2Zm9r6ZzTKzGWbWK3K8oZmNMbPZke32yYqhxvr1V7j++pD0Fy2Cl18OP02bbjildOVNCOWPo+flqxyySHZKZot/HXC1u7cE2gGXmVkr4AZgrLvvAYyN7Euixo0L3Tp9+sCFF/Li7TPJveqsjaZoRq9xG72gOeihKxEB86qqt1veB5mNAB6N/Bzt7gvNrCkwzt33jHdtXl6eFxQUpCLM6mvFCrjuutCH36IFDBxI/qLjNpqiCaH7pm7d2Aug5OSoJLJINjGzye6eV/p4Svr4zSwXaANMAnZ094UAkW3M6mBm1s3MCsysYMmSJakIs/oaMYKiXVuxftAQ7uMa9lo7jfxFx5U5RbOsVa80g0dEIAWJ38zqAy8DV7j7T4le5+4D3T3P3fOaNGmSvABToHR/e6wnZmOes2gRnH02nHEG36xswsFM4lr68uX8ehu6cipCM3hEBJKc+M2sDiHp57v7K5HDP0S6eIhsFyczhqqWSBIvfX6s/vbo6zY9x/ngoidZvXsrGDWKPtvdTVsvYDK//8VWVAS1a8f4QEJpZM3gEZEyuXtSfgADngYeLHW8L3BD5PUNQJ/y7nXAAQd4dTBsmHu9eu4hPYefevXC8bLk5Gx8fslPTk7sc3blG3+H493BP9ryCPcvvnCz2Pco+fxY8QwbFu5rFrbxYhSRmgko8Fj5OdbBqvgBDgcc+ByYGvlpDzQizOaZHdk2LO9e1SXxJ5LESysraZttfE4t1vmV3O+rqOsraeDd6e+1WF/u5yrBi0hZykr8SSvZ4O7/jbT6YzkuWZ+bTJUpe9C8eey++Oj+9j//8XPuWNiFg/iEkZzKpfTje3YhJye837s3MWfvlCx+ommZIlIRenK3AipT9iDuE7OrV8Mtt/Dm4gPIpZBzeIHTGcH37LJRn3yHDnr4SkSqjhJ/BVSm7EGZSTv3f9C6Ndx1F7U6/J3x/Wfycc45mFnMxN6hgx6+EpGqkbIHuDZHdXqAKz8/lDKu9FqzP/0Uauv06xd+CwwYACeemLR4RSR7lfUAl8oyV9Bm9am/8QZccklYKOWKK+DOO6F+/aoMT0SkXOrqSYUlS8Jvi1NOgW23hYkTw6LnSvoikgZK/MnkHta7bdkSXnwR/vWvUEr54IPTHZmIZDF19SRLYSH06AGjR8Mhh8DgwWFJRBGRNFOLv6oVF8Mjj8Dee4dlEB9+GD74QElfRKoNtfir0syZYQWsiRPhpJPg8cfZ8BSWiEg1oRZ/VVizBu64A9q0CYueP/MMvPmmkr6IVEtq8W+uSZNCK3/6dDj/fHjwQdgh5hIDIiLVglr8lbVqFVx5ZRi4XbECRo2CZ59V0heRak8t/sp45x3o3j3UTrj0UrjnHthmm3RHJSKSELX4K2L5crjwwlBiYcstw2ydxx5T0heRjKLEnwh3GD48PIhVUqxn6lQ4/PB0RyYiUmHq6inP99+H7pyRIyEvL3Tz7L9/uqMSEak0tfjLUlwcKme2agVjxsB994X5+Ur6IpLh1OKP5auvwpJX48fDsceG4vi77ZbuqEREqoRa/NHWroV774X99oPPPoMhQ+Ddd5X0RaRGUYu/xJQpcPHFYdD2r38N9XaaNk13VCIiVU4t/qIiuP56OOggWLQIXnkFXnpJSV9EaqzsbvG//z507QrffBPKLvTtC9ttl+6oRESSKjtb/CtWhMHbY48N+2PHwqBBSvoikhWyL/G/+mqYojlkCFx7LXz++e+/AEREskD2dPUsWgQ9e8LLL4e5+KNGwQEHpDsqEZGUq/ktfnd44olQbuH11+Huu+GTT5T0RSRr1djEn58PR+w8h3drnQAXX8ziHfcNc/NvvBHq1El3eCIiaVMjE39+fhi7vWjBnRzEx1xCf1rMG0d+wZ7pDk1EJO3M3dMdQ7ny8vK8oKAg4fNzc6GwEBqxlK34je/ZBQgrIc6dm5wYRUSqGzOb7O55pY/XyMHdefPCdhmNYx4XEclmNbKrp3nzih0XEckmNTLx9+4N9eptfKxevXBcRCTb1cjE36FDqKSckwNmYTtwYDguIpLtamQfP4Qkr0QvIrKpGtniFxGRsiUt8ZvZE2a22MymRx1raGZjzGx2ZLt9sj5fRERiS2aL/yngpFLHbgDGuvsewNjIvoiIpFDSEr+7TwCWlzp8OjA08noocEayPl9ERGJLdR//ju6+ECCy3aGsE82sm5kVmFnBkiVLUhagiEhNV21n9bj7QGAggJktMbPCNIcUT2NgabqDSKNs/v7Z/N1B37+6f/+cWAdTnfh/MLOm7r7QzJoCixO5yN2bJDmuzWJmBbHqYWSLbP7+2fzdQd8/U79/qrt6RgKdI687AyNS/PkiIlkvmdM5nwMmAnua2XdmdjFwL3CCmc0GTojsi4hICiWtq8fdzy/jreOS9ZlpNDDdAaRZNn//bP7uoO+fkd8/I+rxi4hI1VHJBhGRLKPELyKSZZT4q4iZ9TWzL8zsczN71cy2S3dMqWJmZ5vZDDMrNrOMm9pWWWZ2kpl9aWZfm1lWlR+JVYsrW5hZMzN738xmRf5/3yvdMVWUEn/VGQPs4+77AV8BN6Y5nlSaDpwFTEh3IKliZrWBx4C/AK2A882sVXqjSqmn2LQWV7ZYB1zt7i2BdsBlmfa/vRJ/FXH3d9x9XWT3I4is8J4F3H2Wu3+Z7jhS7CDga3ef4+5rgOcJtaiyQhm1uLKCuy909ymR1z8Ds4Cd0xtVxSjxJ8dFwOh0ByFJtTMwP2r/OzLsP37ZfGaWC7QBJqU5lAqptrV6qiMzexf4Y4y3bnb3EZFzbib8KZifytiSLZHvnmUsxjHNjc4iZlYfeBm4wt1/Snc8FaHEXwHufny8982sM3AKcJzXsAckyvvuWeg7oFnU/i7AgjTFIilmZnUIST/f3V9JdzwVpa6eKmJmJwHXA6e5e1G645Gk+wTYw8x2NbM/AOcRalFJDWdmBgwBZrn7f9IdT2Uo8VedR4EGwBgzm2pmj6c7oFQxszPN7DvgEOANM3s73TElW2QgvyfwNmFwb7i7z0hvVKlTRi2ubHEY0Ak4NvLf+lQza5/uoCpCJRtERLKMWvwiIllGiV9EJMso8YuIZBklfhGRLKPELyKSZZT4RWIxc8yeidrfArMlmL0e2b8wsj8Vs5mYdY069yTMPsbsi8j7L2DWPOXfQaQMenJXJLZVwD6Y1cX9V8Ia0d+XOucF3HtitgMwA7ORQBPgEeA03GcBYHYakAvMS1XwIvGoxS9SttHAyZHX5wPPxTzLfTHwDZBDeHr77g1JP7w/klDNUqRaUOIXKdvzwHmYbQXsR1kVGM1aAC2Ar4G9gSmpClCkMpT4Rcri/jmhi+Z84M0YZ5yL2VTCXwLdcd+4Pr1Zo0gf/1eYXZPkaEUSpj5+kfhGAvcBRwONSr0X+vg3NgNoC3yG+zKgdSTp1092oCKJUuIXie8JYCXu0zA7OoHz+wCvYvZRVD9/vWQFJ1IZSvwi8bh/BzxUgfOnERbffhqzBsAywmye25IToEjFqTqniEiW0eCuiEiWUeIXEckySvwiIllGiV9EJMso8YuIZBklfhGRLKPELyKSZf4fv+tBiIN+osAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.probplot(q21a['MPG'], dist=\"norm\", plot=plt)\n",
    "plt.xlabel('MPG', color ='red')\n",
    "plt.savefig('MPG of cars.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a1634101",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.distplot(q21a['MPG'],kde=True, bins =10)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f12cc9eb",
   "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>Waist</th>\n",
       "      <th>AT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.75</td>\n",
       "      <td>25.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>72.60</td>\n",
       "      <td>25.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>81.80</td>\n",
       "      <td>42.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>83.95</td>\n",
       "      <td>42.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>74.65</td>\n",
       "      <td>29.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>100.10</td>\n",
       "      <td>124.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>93.30</td>\n",
       "      <td>62.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>101.80</td>\n",
       "      <td>133.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>107.90</td>\n",
       "      <td>208.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>108.50</td>\n",
       "      <td>208.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>109 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Waist      AT\n",
       "0     74.75   25.72\n",
       "1     72.60   25.89\n",
       "2     81.80   42.60\n",
       "3     83.95   42.80\n",
       "4     74.65   29.84\n",
       "..      ...     ...\n",
       "104  100.10  124.00\n",
       "105   93.30   62.20\n",
       "106  101.80  133.00\n",
       "107  107.90  208.00\n",
       "108  108.50  208.00\n",
       "\n",
       "[109 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q21b = pd.read_csv(\"C:/Users/Moin Dalvi/Documents/EXcelR Study and Assignment Material/Data Science Assignments/Basic Statistics 1/wc-at.csv\")\n",
    "q21b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2ae401cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(q21b['Waist'], edgecolor= 'red')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f4220787",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(x = 'Waist', data= q21b)\n",
    "plt.title(\"Waist\")\n",
    "plt.savefig('Waist.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4f842887",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.distplot(q21b['Waist'], \n",
    "             bins=10,\n",
    "            kde = True\n",
    "            )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "7fa1a00a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.qqplot(q21b['Waist'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44a040ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(q21b['Waist'], dist = 'norm', plot = plt)\n",
    "plt.xlabel('Waist', color= 'red')\n",
    "plt.savefig('Waist.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5ce1b283",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.distplot(q21b['AT'], bins =10, kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "bba6fa01",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Moin Dalvi\\anaconda3\\lib\\site-packages\\statsmodels\\graphics\\gofplots.py:993: UserWarning: marker is redundantly defined by the 'marker' keyword argument and the fmt string \"bo\" (-> marker='o'). The keyword argument will take precedence.\n",
      "  ax.plot(x, y, fmt, **plot_style)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "sm.qqplot(q21b['AT'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "31608dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "stats.probplot(q21b['AT'], dist = 'norm', plot = plt)\n",
    "plt.xlabel('AT', color= 'red')\n",
    "plt.savefig('AT.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "587b7980",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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