{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7df2b2f-1a07-484c-b4a5-3eade02422c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0532506a-b317-4b29-9aa0-fe980c85177d",
   "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>Date</th>\n",
       "      <th>Flight Number</th>\n",
       "      <th>Destination</th>\n",
       "      <th>Delay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6/1/15</td>\n",
       "      <td>73</td>\n",
       "      <td>HNL</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6/1/15</td>\n",
       "      <td>217</td>\n",
       "      <td>EWR</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6/1/15</td>\n",
       "      <td>237</td>\n",
       "      <td>STL</td>\n",
       "      <td>-3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6/1/15</td>\n",
       "      <td>250</td>\n",
       "      <td>SAN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6/1/15</td>\n",
       "      <td>267</td>\n",
       "      <td>PHL</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13820</th>\n",
       "      <td>8/31/15</td>\n",
       "      <td>1978</td>\n",
       "      <td>LAS</td>\n",
       "      <td>-4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13821</th>\n",
       "      <td>8/31/15</td>\n",
       "      <td>1993</td>\n",
       "      <td>IAD</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13822</th>\n",
       "      <td>8/31/15</td>\n",
       "      <td>1994</td>\n",
       "      <td>ORD</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13823</th>\n",
       "      <td>8/31/15</td>\n",
       "      <td>2000</td>\n",
       "      <td>PHX</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13824</th>\n",
       "      <td>8/31/15</td>\n",
       "      <td>2013</td>\n",
       "      <td>EWR</td>\n",
       "      <td>-2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>13825 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Flight Number Destination  Delay\n",
       "0       6/1/15             73         HNL    257\n",
       "1       6/1/15            217         EWR     28\n",
       "2       6/1/15            237         STL     -3\n",
       "3       6/1/15            250         SAN      0\n",
       "4       6/1/15            267         PHL     64\n",
       "...        ...            ...         ...    ...\n",
       "13820  8/31/15           1978         LAS     -4\n",
       "13821  8/31/15           1993         IAD      8\n",
       "13822  8/31/15           1994         ORD      3\n",
       "13823  8/31/15           2000         PHX     -1\n",
       "13824  8/31/15           2013         EWR     -2\n",
       "\n",
       "[13825 rows x 4 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('united_summer2015.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "011cfda9-3344-4857-9c7e-69484b8a9c97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Delay'].hist(bins = np.arange(-15, 600, 20), density = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9e3011ef-d6d5-4714-b9ed-6c4ce5ac9722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(16.658155515370705), np.float64(39.48162778440366))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_delay = data['Delay'].mean()\n",
    "sd_delay = data['Delay'].std()\n",
    "mean_delay, sd_delay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d286c6bc-6fb5-4bb1-8753-fe366bc7bab5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x10dee9970>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_repetitions = 10000\n",
    "\n",
    "L_100 = []\n",
    "for i in range(num_repetitions):\n",
    "    sample = data.sample(100, replace = True)\n",
    "    L_100.append(sample['Delay'].mean())\n",
    "\n",
    "L_400 = []\n",
    "for i in range(num_repetitions):\n",
    "    sample = data.sample(400, replace = True)\n",
    "    L_400.append(sample['Delay'].mean())\n",
    "\n",
    "L_625 = []\n",
    "for i in range(num_repetitions):\n",
    "    sample = data.sample(625, replace = True)\n",
    "    L_625.append(sample['Delay'].mean())\n",
    "\n",
    "fig, ax = subplots(figsize = (8, 4))\n",
    "ax.hist(L_100, bins = np.arange(0, 35, 0.5), density = True, alpha = 0.5, label='sample size 100');\n",
    "ax.hist(L_400, bins = np.arange(0, 35, 0.5), density = True, alpha = 0.5, label='sample size 400');\n",
    "ax.hist(L_625, bins = np.arange(0, 35, 0.5), density = True, alpha = 0.5, label='sample size 625');\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e6cf187-cd7a-477f-9656-85c13a891847",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "population mean:  16.658155515370705\n",
      "population sd:  39.48162778440366\n",
      "SD of 100 size sample:  3.9409088987581278 population sd/sqrt(n):  3.9481627784403663\n",
      "SD of 400 size sample:  1.96880221956649 population sd/sqrt(n):  1.9740813892201832\n",
      "SD of 625 size sample:  1.5809695898057818 population sd/sqrt(n):  1.5792651113761464\n"
     ]
    }
   ],
   "source": [
    "print('population mean: ', mean_delay)\n",
    "print('population sd: ', sd_delay)\n",
    "print('SD of 100 size sample: ', np.std(L_100), 'population sd/sqrt(n): ', sd_delay/np.sqrt(100))\n",
    "print('SD of 400 size sample: ', np.std(L_400), 'population sd/sqrt(n): ', sd_delay/np.sqrt(400))\n",
    "print('SD of 625 size sample: ', np.std(L_625), 'population sd/sqrt(n): ', sd_delay/np.sqrt(625))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a85c6170-837b-4129-8ce3-596b4fe76184",
   "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>vehicle</th>\n",
       "      <th>year</th>\n",
       "      <th>msrp</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>mpg</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Prius (1st Gen)</td>\n",
       "      <td>1997</td>\n",
       "      <td>24509.74</td>\n",
       "      <td>7.46</td>\n",
       "      <td>41.26</td>\n",
       "      <td>Compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tino</td>\n",
       "      <td>2000</td>\n",
       "      <td>35354.97</td>\n",
       "      <td>8.20</td>\n",
       "      <td>54.10</td>\n",
       "      <td>Compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Prius (2nd Gen)</td>\n",
       "      <td>2000</td>\n",
       "      <td>26832.25</td>\n",
       "      <td>7.97</td>\n",
       "      <td>45.23</td>\n",
       "      <td>Compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Insight</td>\n",
       "      <td>2000</td>\n",
       "      <td>18936.41</td>\n",
       "      <td>9.52</td>\n",
       "      <td>53.00</td>\n",
       "      <td>Two Seater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Civic (1st Gen)</td>\n",
       "      <td>2001</td>\n",
       "      <td>25833.38</td>\n",
       "      <td>7.04</td>\n",
       "      <td>47.04</td>\n",
       "      <td>Compact</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>S400</td>\n",
       "      <td>2013</td>\n",
       "      <td>92350.00</td>\n",
       "      <td>13.89</td>\n",
       "      <td>21.00</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>Prius Plug-in</td>\n",
       "      <td>2013</td>\n",
       "      <td>32000.00</td>\n",
       "      <td>9.17</td>\n",
       "      <td>50.00</td>\n",
       "      <td>Midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>C-Max Energi Plug-in</td>\n",
       "      <td>2013</td>\n",
       "      <td>32950.00</td>\n",
       "      <td>11.76</td>\n",
       "      <td>43.00</td>\n",
       "      <td>Midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>Fusion Energi Plug-in</td>\n",
       "      <td>2013</td>\n",
       "      <td>38700.00</td>\n",
       "      <td>11.76</td>\n",
       "      <td>43.00</td>\n",
       "      <td>Midsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Chevrolet Volt</td>\n",
       "      <td>2013</td>\n",
       "      <td>39145.00</td>\n",
       "      <td>11.11</td>\n",
       "      <td>37.00</td>\n",
       "      <td>Compact</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>153 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   vehicle  year      msrp  acceleration    mpg       class\n",
       "0          Prius (1st Gen)  1997  24509.74          7.46  41.26     Compact\n",
       "1                     Tino  2000  35354.97          8.20  54.10     Compact\n",
       "2          Prius (2nd Gen)  2000  26832.25          7.97  45.23     Compact\n",
       "3                  Insight  2000  18936.41          9.52  53.00  Two Seater\n",
       "4          Civic (1st Gen)  2001  25833.38          7.04  47.04     Compact\n",
       "..                     ...   ...       ...           ...    ...         ...\n",
       "148                   S400  2013  92350.00         13.89  21.00       Large\n",
       "149          Prius Plug-in  2013  32000.00          9.17  50.00     Midsize\n",
       "150   C-Max Energi Plug-in  2013  32950.00         11.76  43.00     Midsize\n",
       "151  Fusion Energi Plug-in  2013  38700.00         11.76  43.00     Midsize\n",
       "152         Chevrolet Volt  2013  39145.00         11.11  37.00     Compact\n",
       "\n",
       "[153 rows x 6 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('hybrid.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b769dd49-9eea-4fcf-826c-fdda0564e2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A7INjRN3oCShO+OH7Or9q4uNWnCRk9bEAYC8cE1M3egKKU3748vlllwsPimqJj1ttkpCVxwKA/XCcqDMsDh0b1wnrezrph8/n9/HOqcLlwuMF3gJKEb/3vn3Fx434jLQijwVobSsAeuKo8IuRWHUQUCtWjY9rwUltBeYHoh4mnPjDt2J8XCtOaiswN47K/WIG7OpTd9pkK184qa3AnLoGUTcA/PABcBYFYdQ1Rw6UGo2VBgEBANYCom5DcCcAgHOBqNtMxA+fKaIFW49SfkGxY2L2AIA/gKjbcODVEySVAsA5qLY05uTkUO/evSkxMZEiIiJo0aJFPss+/fTTosybb74ZbD2BigRhdswtAwAIUU+9qKiI0tLSaOjQoTRgwACf5bKzs2nLli1C/IH+cW5/CcL85ZbZ8vOvFBkZ4bUeiMUDGaXfBXxnbCDqPXv2FJs/Tpw4Qc888wwtX76cevXq5bdscXGx2FytP3YjFN70QAnCfDFy/k46f7m0Qj0Yp/nnQXDfVyfOuXDkjNKysjIaMmQIjR8/nlq1ahWwfFZWlvBvyltSUhLZiVDlUNea+MtV0OV6PD13p9jsmOcdhOb7aue1AayO7qI+bdo0qly5Mj377LOKymdmZgpDvrwdO3aM7LLGJL/2pcWhyaGuV+Ivf++MWLyzUJrzv+Rqma3XBrA6urpfduzYQW+99Rbt3LlTDJAqISYmRmxmQO/byXdXH6D8AmU51NVORgqUGVAvgqkjsBZKc/7/e/NhxWsD4Dtj8Z76+vXr6fTp05ScnCx667wdOXKExo0bRykpKWRm9L6d5PJvrDwQslCKmpWGasVGUbBYPc870O8zPnL2kq7HAybuqXMsPT093e257t27i+efeOIJsuptJ4sm7+eFG5SM7MvHC3UoRc4M6O3uYuBtyZRSt6o4dpkk0eD//VbTewRbR2AdlH7GjeKr6no8YLCoFxYW0sGDB8sf5+XlUW5uLsXHx4seep067rdbUVFRlJCQQM2aNSOzomapOSW3k2qcKcHmUFeyShBfZLSGauQFHsrKJDHOoJcdEzY46y72MaRjSlALoACTifr27dupW7du5Y/Hjh0r/mZkZNCcOXPIiui91Jya2049cqgHShAmh2o4jMTv5PpDdH3sa9/l0ms0+MM/evrxcdHUr22iuJioFWTY4MxLoO8Jw/ujK0cqKocLtTEg9S6RcLkM+mBLwHILhnVQ1FN/a+UBemPl/oDlxqQ3pefSm1C48CeojOe+2lWj6NwldwukJ2oEWR638PzCyT99pDEwB/Cp6w/yqYdZ1Dkc0GXa6oC3kxsm3B2w98HH6jx1tV/XC1OvejRtykwPe2/GX+jDdV/dajE07tNct8RgvohQIMjyOfYVllJzjkHowYxSfUE+dZPedir5svIXPJCgM3c0uc6QH4W/UI3rPr57USLoMoEGkvUetwgExCY8Of+xNoD5gKgHcJIkqLydVBpP/7+dJ2jlT6fF/+ddQhxmuX1VMy6gRJBDMR7hC4QFgJOBqKt0kuhp43IVc7OlydViR/MnyEqPF6wNzlfc3iznFQDLpQmwOvLtZN+2DcRftbfssi1M642+WaZZa2mHP0EOdLwIHeydSqe5Y/o6sDMQ9RDF54ORDddwhlGombGqRJD9HU8vG5yauD0AdgWibmKMnmYtjzPwuIIv1Aiyr+PxYz3CIuGM2wNgVhBT1xm1KQLMPs3adZxh5Z58ys49QWeL/hgLqB0XRa/0ba1YkAONWwTjWglX3N6qwBHkDCDqOqN18QozT7OWxxl4a9conv7+xS46W1Qi9rHAT1myV6ympFTYfdnggnWtnCsqIdYoXyFzs53XcAJHkHNA+EVngr21N/M0axYGXjlJFnQ9F0YINkumXLdAY6BmPK+hBgtaOAuIus6oubXnafi1qkaFJL6sN6F0lgR7bCXrtbKOz/zzzaY7r6EGjiDngfCLzihZvILzm88cfAt1uOG3EIQV4pyhnBEa7LGVhLxYs2rHmWMxlnAS7pm8wHjQU9eZQNY93qY+2IY631jXMoIeameJ2mN7LjmoJC2D1rpZHTiCnAd66galHLDawFUonSVqju3tvMXHKVvZyYmuFziCnAdEPUS2seKrZTTjT2ni/vZMUbFbT9yKU9nPFQVO7qV1RqjSxRm4DiPnf1ehjKvF0htOdr0oPbdOPDd2BaKuI/5633K8Uu+l88IB15lti4GY1Eubs0RJlkw+9pQl/gdDreYmsloGUmANEFMPs23MilPZlXrva8dFa36PQLNN+dha/P9mdROFk1DP5AXmAj31MNnGJn7+I1WPibLkwFW46uxvtikPimphUq8WEC2dMpACawBRD1NPltPs8jqfvL6n1QauwjnY5mu2qZZjs1xx2Kh76/oQLyxo4RgQftEBNT1UnsruDz1S0OpNONLmBlsHq4SyAAg1EHUdUNOLlBTsv7/1b7fJes7y8/R282NvzxmVNlfPVMBmDmUBEGoQfgnTLFIlyMmoPtx4WGx6+da9uXLk9ARKl9LTa7m/YG2io9Ob0IKtR1Wtn2qmUBYAoSZCkiTJqatu6wkL59Nzd+p6TLlHGoxDwZcnXuv7hTt9q7cLUkKNKjTo9mRKjo8VMXMOafnzYG+YcDdi6sAxuobwiw+UhiZk2FngmZwrWEKZKEvr+wW73J8eNtFTBVfozZX7KTa6Er3Wv7WhYSEAzAbCL17QMoWfe6/eFpIOllAmytL7/fRE6SQt7oUbFRYCwIxA1D3QOoU/1INxoUyUpfdr9UDNJC14sAH4A0eLumd8uF2j2pqn8Id6MC6UibL0fq0RE57gwQbA4aLuK9ufv+RQ/kITejlg9Ey4pKVOZknwhOyCAIRpoDQnJ4d69+5NiYmJFBERQYsWLSrfV1paShMmTKA2bdpQXFycKPPYY4/RyZMnyUz4GoALlO3PXy9SiZdbLcEO9qn1dptpcNEME54AcISoFxUVUVpaGs2cObPCvkuXLtHOnTtp0qRJ4u/nn39O+/btoz59+pBZUOsIUdM79JU4qWZsFFWLqaT6ffRIuOSrTrVMspQefx4bD5yhGct/ohnL99HGg2fEc2aY8ASA43zq3FPPzs6mfv36+Syzbds2uv322+nIkSOUnJxsuJ+T7YmDPtii6bVKfc8sSu+uPkAfbzxM5y+rd8Sw2M4cdAt10NEy6M1fzhg5uMh3TJzozNM1xO2fOqCNJRcTAcBon3rIY+rcCBb/WrVqed1fXFwsNtfGhxKtrg41vcMVe/LpzZUHNN0NiOXuBrShzk1+W+5OL3wNJBo1uOhvshaLPO97//e7BjhbADCJqF+5ckXE2AcNGuTz6pSVlUUvv/wyhQulA2vVq1Smi1euqvY9BxPe4QyOGR1TxHR4vqOQxSuYXnbJ1TL69+bDdOTsJWoUX5WGdEyh6MrGzjnj9ry0eHfAcq5OIzhbADBY1HnQ9OGHHyaO7syaNctnuczMTBo7dqxbTz0pKSlU1VLsCGFBZ5Ht1zZRCIvS3qGWCT9MXEwlioqMoDdW7i9/juvZJ60+Lf7+F015W7KW7qEP1ueJfDIyry7dS8PuSKXM+3+LVxsBnyMluVvMMAkKAKsRGUpB5zj6ihUr/MaQYmJixH7XLZSocYRwThGOi1+4XKL4dp9DL2qQj1pUfI1OXSyuIGr/zMmrcJFgMfeMQ3uusMSCzq/1nO3Pj/l53m8UakJgRk+CCmeqCQBMKeqyoB84cIBWrlxJdeqYr5flyxESbO4VLrMoV519s0ZsZYqLVu+M8VfXyyXXRA/dH7yfQzNGoMZbblUfOl9cu0xbLQbln1uYK/7yY/miC4BpRL2wsJByc3PFxuTl5Yn/jx49KgT9oYceou3bt9O8efPo2rVrlJ+fL7aSEv+LQxgh7Oxi4eXO9FpogcucDbAIhrcLQVHJNdIDua6vLd1ToYfuCe/nWLsRcCgroUZMwHJW9aErXa8WAFOIOgv2zTffLDaG4+H8/+TJk+nEiRO0ePFiOn78OLVt25bq169fvm3atInMBodU6lYPLC5KwwBaQgWFxfoIuiuHf72kqNz6A/8lo877S31aBSxnRR+6kvVqtWbdBCAkA6V33XWXGPz0hcnSs4d1OvrhM8rENNSk1KlK6w8ELrd2/xnRazTC783vyZZFbz712lWjKOt3n7rVUJOIDAPAIBQ4NveLUjeM0lwoLI6c49toYipH0oQeLejfW44qKu8rQVk4kD3oW37+lTYfOiPONgtdhxtCm6fdTInIANAbx4u67IbhWCfLiKRhwpEeqQf0gj3uOw4rX2hZSa8xlKsd8XF4opXek62MAonIgNE4XtT1WH9Tqzc9VMxef0hVeX+9RkzTN+bODwCtQNR/J5jp6Ga7lS64oi7fjK9eo9YFQ5yMHnd+AAQD1ijVYf1Ns91KpzWsJXqLgfCXvhYuDv3nQRiRBRM4D/TUdYBXTGL9N4u+3duinhgw/XBjYB+6r14jXBzBgURkwCgg6jqw48g5xYLueUuuN5ysK2POtvLHvi429apH08t9W/vsNcLFETxIRAaMAOEXHVAqbEM7pwRMTRAsnlP/5WkDnv3D/xaW0HdHz/k8DlwcAFgT9NR1QKmw8e34C71ailtyXuHn3TUHNb/nmw/dRDuOnxezRzml7je78+l0YcUUBXIn3bOzLif2YrxlbISLAwBrgp56mNfTlG/Jx9zbVNFgpi+mrdhPL/VpTf9+sj31uinRq6ArwVdiLywnB4A1gajrkFKVUSuAsmhqlUTXJGPBxLU9E3u5tq1mbDTN/DNcHABYCYRfVOJvMo7aCUy+Jj0pRRbzYOPavCqSv7ZxJsvacTFwcQBg94Wnrb5Aq1p8TcaR5Y0FWouNTZ6Gv/Hgf+ndNT8rrs+CYR1EKIdfz7m6A63m5AsW7Qa1Y722jeHao2cOgDV0DeEXhQSajMObvO6m2glMf8TZm/mNzbviOmlIzWpOnnD1/ty+kd/cNfw8JhoBYA0g6gpRkt+F1918d7V2R4urOPsjwkuM3tcsxqoBVlXi9Upzj50P2DalC4UAAIwFMXWFKB2M5IWjmyVU0xyqCBRn95dMy9csxunL9lZYgJqvB/IC1Nk7jyuqW/6Fy5raBAAIHxB1hagZjAw2R7mrOOcXXKGzhcUUHxdNCTVjA8bovc1iZOEed19z4XLhQVH2tQ/pmCJmnzJKl+BTu1QfACD8QNRVetGVuFRcc6JozUWu9xRzFvAn77jB6774asqW9FNaDgBgHBB1lfHup+fuVFSeRdwqucgTalTRtRwAwDgwUKoCFuIx6U0UleX1Sq2yorx8F+IPXyl6AQDmAqKuklF3N/HbYxU5UWrE0IKtRy2Ti9x1dqu3GbHe3DYAAHMCUVcJC9tLfXwLIDPo9mQxwKkkF3ko0xeouWhgYQcA7AFi6iFY05QXf9bDJqlmkFWP+D0WdgDA+kDUNeJPAOUkX8HYJNWItJ5riWJhBwCsDURdJb56z/z8lkO/CkGXSKLaVSvTuUtXvR4jUC5yNSIdKH1BhA6+eQCAdYCoq8BX77lPWn36z/bjdP5SacBjBMpFrlaksZYoAMAVDJQqRO49ewooP+YVhJQIupKBRzUizWAtUQCAK+ipK8Bf71kJ8VWjaNIDLRVN81cr0lhLFADgCkRdpwyN/jh7qVQIeqDwB188zlwsViXSwa4lqjWNAQDAJuGXnJwc6t27NyUmJlJERAQtWrTIbT+vuTF58mSqX78+xcbGUnp6Oh04cICsjB6hi0DH4PAOL3QxZclev+Vc1zsNdi1R+T0HfbCFnluYK/7yYzPNdgUAhFjUi4qKKC0tjWbOnOl1//Tp0+ntt9+m999/n7799luKi4uj7t2705Ur1ozpquk9B2Nf9BavJ4UirWXikK/3NGMaAwBAmJaz4556dnY29evXTzzmQ3EPfty4cfTXv/5VPMfLN9WrV4/mzJlDAwcOtNRydt7cLlrgtAEbJ97j0+3CvWMl7xFoMpHSUEqg95RDNhsm3I1QDAA6EE5d0zWmnpeXR/n5+SLkIsMNad++PW3evNmrqBcXF4vNtfFmwJdXXAsv9WnlUxyVxut5HdHHO6eqzqWu5T1hgwTAuuhqaWRBZ7hn7go/lvd5kpWVJYRf3pKSkshoLpdco/H/94MiQefe81+6plKtqlEV9vFz7weYzak0Xl+3eoxuvWbYIAGwL4a7XzIzM2ns2LFuPXUjhT1r6R6avT6PlASlXHvPz/doUT6jlPu6HW+oSx0ULDxthCURNkgA7Iuuop6QkCD+njp1SrhfZPhx27Ztvb4mJiZGbGaABZ0nEinFtffMfzvfWFdsajhXFHgQVu9c5sHaIAEADgm/pKamCmFftWqVW8+bXTAdO3YkM1NytUwszqwGz54sD0BuPHCGZiz/iWYs30cbD57xm/6W9wWyMDKTeumbyzwYGyQAwGY99cLCQjp48KDb4Ghubi7Fx8dTcnIyjR49ml555RVq0qSJEPlJkyYJR4zskDErn2zKI6Xpxz17sizO764+SP/M+ZkulVwrL/fumoMirj51QBuvGR2VDpLWjoumcKcPRv50ABwi6tu3b6du3bqVP5bj4RkZGcK2+Pzzzwsv+1NPPUXnz5+nLl260LJly6hKFfPGZ9np8sZKdROk5J4sv3bi5z/6zP3Cz/O6pizurmU4/NGz9W/hKqMGLJE/HQD7EZRPPRSE26eu1rpYo0plmv7QTUIQ+bVKF6L2hGVT6XsuGNYB1kIALEyBVX3qTkjUtfav3Si+WnT5a7Uip9GNiCCfYR8MWAIA1OLo1LtaEnW9sXKf5td6wlouCzoGLAEAeuBoUdcSqz786yXxd8Ue75OptHBX07pUOy5K84LPwSw4DQCwF44Ov2iZXFOlciT9vy9300cbD+tWj7X7z4i/8XHR1K9tohi8VDpgqceC0wAA++DonjoLZ63YitP7/bFi72ldBd2Vc0Ul9PHGw3Thckn5uqf+euDItAgA8MTRPXUWzic6p6i2M6ohLroSFZVcU+R2cV2DtKyMaMoS3z1wLDgNAPCGo3vqzKi7mwjh1Zuk+Fh64f4W9N3k+0RSL89c54EyJI6Y778HrnYtU2B+MDYC9MDRPXWGe7FPdW1Mb6zcr+txj529TK8u3UsfbcwTvWvOTc4C+/WuX+hfm49oOqZrD/z57s0UvQaZFq0BxkaAXji+p86MuvtG1bF1pci9a3bL8ASinkH+QOUe+JnCEkXlkWnR/GBsBOgJRP333vrUB9uE5NjyDTT3wvh2Ws6QGGyU+901B1StZQrMSaCxEdfvDgBKgKj/Dt/icuy7Woz+ESnX+LZrhsRguHD5qs99mLhkHTA2AvQGou4h7Dsn3Uu1vaxipAdyfFvOkBiqkE+9GjGKJy4BY8EqVEBvIOoeRFeOpKwBbX7LyxKgrNpOsGt8mwV35p9voVBQcg236lYBq1ABvXG8+8Ub7O0end5ETAQ6f9k9Xe7A25IppW5V8SNr16g27ThyjvILrtCUr3bT2aJSVYm5eLk7fysQaeVsUYkYYHN6b53j0GZPK4xVqIDeQNQVWMs4TPJE51ThkvEUBTklbmxUpBBSRlIY35bj6/w6Nal4leLkyUdWsQj6+w5gbARowdbhF7WTOXxZyy5cLqU3V+73m8RLjpN7TjIKlJjL1+uCxckDbFazCGr97gDgqEUy1PbUWPC7TFvt04kg3wbzJCJ/vSatt/zy64KZnOSNtwa2pb5tG5BT0OtzNAIrhIuANrBIRohWM5J7at56P2qsZf5WIeIfoZZVilxfp0TUq1epRBev/LEeqi+cNsCm1+doBFq/OwDYOvyidTKHWaxlgSYnyZOKtv7tXpGq1xdOnXxkls8RAKOwnahrncxhFmuZ6+Qkf6shxUZXotf6t/ZqvXTyAJtZPkcAjMJ2oq61p6a0hxyOnq/SgTMMsFXETJ8jAEZgu5i61p6a2axlLMhsRww0cOavXKCBNzsOzJntcwQg3NhO1NVM5nAVtbrVYqh6DPvRU2hR7kkxgUcmwSB/s9KBM2/lArl/rOLj1oJ8B+PZPqM+RwDCiS0tjbL7hXz01PgHz3j+6F2Jj4ui/m0bULqK9UKNwFtvm/303tw/cgue6ppKs3PyfO63S+jGjnciwJoUhNHSaEtRZ/z1RBlvomc1gfPWxoQaMXTlahmdv+Q9ZQHDuuZrHpaZfdwAWJUC+NSDx1esmeHJKWrWCzXjVHufXvyC4oCv9Tex1sw+bgCAg0XdV6yZ0wX4szxaQeD8efH1Aj5uAKyJ7SyNoRArswlcIC++HsDHDYA1sXVPXS+xClbg9B6wC/Yiw2/NIylI9QqA/dC9p37t2jWaNGkSpaamUmxsLDVu3JimTJlCZhmPVbNGqB4TVTj2zTH8QR9soecW5oq//DiYTIFaLzLy7NNhd6SWP/bcz8DHDYB10V3Up02bRrNmzaJ3332X9u7dKx5Pnz6d3nnnHTJDml1/0/D1FrhQpYBVMmuyVtUoSqjhfaZp5v0tMRMVAJuiu6XxgQceoHr16tGHH35Y/tyDDz4oeu1z586tUL64uFhsrtafpKQkVdYfLRNpvL3GlWAn4oQ6BawSL36gGanwcQMQHixtaezUqRPNnj2b9u/fT02bNqXvv/+eNmzYQK+//rrX8llZWfTyyy+HNc2uN8sjzyjlg5wpKtZF4EKdAlbprMlQpAkGAJgX3UV94sSJ4qrUvHlzqlSpkoixv/rqqzR48GCv5TMzM2ns2LEVeup6pNkN5DMPpaiFIwWs0vwwAADnoLuof/rppzRv3jyaP38+tWrVinJzc2n06NGUmJhIGRkZFcrHxMSIzW4LIoQrBSx62wCAkIr6+PHjRW994MCB4nGbNm3oyJEjIsziTdTtuiCCPJgZKKYO6yAAwNTul0uXLlFkpPthOQxTVlbmqAURuAfdJ83/ICusgwAA0/fUe/fuLWLoycnJIvzy3XffiUHSoUOHGppm15NQOz94AJczIfqCMyXCOggAML2l8eLFi2LyUXZ2Np0+fVrE0gcNGkSTJ0+m6Gjfa2pqtf4osfZ5imeoconLF4r8C5dpypK9bjnZXUEmRACcRQFS76prvBqR9mWBDDbVbiDfuzcWDOuAQU4AHECBlX3qRqDU2hesBdIXvi4UVksUBgCwPrYQdaXWvlBYIINJg4tMiAAAvbGNqBtlgdSSBhd2RgBAqHBUPvVQWCDVhlCQCREAEEocIepyBkd2pcTHRfvNbqg21a7aEAoyIQIAQontwy9KXSlae9CBvPJMfFwUTXqglUiFi9wsAIBQYuueuq985nr2oJXkZ8/omEJ90hLF4CsEHQAQSmzhU9eSz1zvHnSo87MDAKxLQRh96rbtqStxpZwtKhWCrkcPmsWaZ4iOSW/qdX+wqx0BAICjRd2oDI4Ltx31+rx8O8S9eV9L7QEAQLDYVtSNyOCoZnITAACEAtuKupLFmdXaF62c3x0A4AxsK+r+XCmhmgBk5vzuAABnYFtRd12cme2K4ZgAZMTdAQAAOGryUTgXZ5bvDtjlEuEjvzvSAwAAQoltfepGEqpFOAAA1qQA+dStTTjvDgAAwBWIuoH53QEAQG9sPVAKAABOA6IOAAA2AqIOAAA2AjH1MMN5XzCACgAIFRD1MAKrIwAg1CD8YvCCHUjJCwDQE4h6mEIu3EP3NssLKXkBAHoCUQ8DSMkLAAgXEPUwgJS8AIBwAVEPA0jJCwCwtKifOHGCHn30UapTpw7FxsZSmzZtaPv27eRUkJIXAGBZUT937hx17tyZoqKi6Ouvv6Y9e/bQ//zP/1Dt2rXJqRixYAcAwJnonnp34sSJtHHjRlq/fr2m19sh9a4v4FMHwJkUhFHXdBf1li1bUvfu3en48eO0bt06atCgAY0YMYKGDRvmtXxxcbHYXBuflJRkS1FnMKMUAOdREEZR1z38cujQIZo1axY1adKEli9fTsOHD6dnn32WPvnkE6/ls7KyRGPljQXdCSl5+7ZtIP5C0AEApu6pR0dH06233kqbNm0qf45Ffdu2bbR582Zyek8dAOA8CqzcU69fv74IwbjSokULOnr0qNfyMTExopGuGwAAAJOIOjtf9u3b5/bc/v37qVGjRnq/FQAAgFCL+pgxY2jLli302muv0cGDB2n+/Pk0e/ZsGjlypN5vBQAAINSiftttt1F2djYtWLCAWrduTVOmTKE333yTBg8erPdbAQAACPVAabDY2acOAHAmBWHUNdMtkiFfY/gkAACAHSj4Xc/C0Yc2nahfvHhR/LW7Xx0A4DwuXrwoeuyOCr+UlZXRyZMnqXr16hQRYfzEHNk3f+zYMduEg9Ama2C3NtmtPWraxDLLgp6YmEiRkZHO6qlzgxs2bEhmw44eerTJGtitTXZrj9I2hbqHLoN86gAAYCMg6gAAYCMg6gHgNAYvvvii+GsX0CZrYLc22a09Zm2T6QZKAQAAaAc9dQAAsBEQdQAAsBEQdQAAsBEQdQAAsBEQdQAAsBG2E3Ve85TT/3Kageuvv5769etXYdGOK1euiPzuderUoWrVqtGDDz5Ip06dcivDKzX16tWLqlatKo4zfvx4unr1qluZtWvX0i233CLsTDfeeCPNmTOnQn1mzpxJKSkpVKVKFWrfvj1t3bo16DZOnTpVpFAYPXq0pdt04sQJevTRR0WdY2NjqU2bNrR9+/by/WzMmjx5slhNi/enp6fTgQMH3I5x9uxZkdaZZ/PVqlWLnnzySSosLHQr88MPP9Add9wh6stTuqdPn16hLp999hk1b95clOF6LF26VHV7rl27RpMmTaLU1FRR38aNG4vU064GM7O3KScnh3r37i2ms/N3bNGiRW77zVR/JXXJ8dOe0tJSmjBhgjh2XFycKPPYY4+JNCVmbY8iJJvRvXt36eOPP5Z27dol5ebmSvfff7+UnJwsFRYWlpd5+umnpaSkJGnVqlXS9u3bpQ4dOkidOnUq33/16lWpdevWUnp6uvTdd99JS5culerWrStlZmaWlzl06JBUtWpVaezYsdKePXukd955R6pUqZK0bNmy8jILFy6UoqOjpY8++kjavXu3NGzYMKlWrVrSqVOnNLdv69atUkpKinTTTTdJzz33nGXbdPbsWalRo0bS448/Ln377bfivZcvXy4dPHiwvMzUqVOlmjVrSosWLZK+//57qU+fPlJqaqp0+fLl8jI9evSQ0tLSpC1btkjr16+XbrzxRmnQoEHl+y9cuCDVq1dPGjx4sPhOLFiwQIqNjZX++c9/lpfZuHGjaOf06dNFu//+979LUVFR0o8//qiqTa+++qpUp04d6auvvpLy8vKkzz77TKpWrZr01ltvWaZN/L144YUXpM8//5yvRFJ2drbbfjPVX0ldlvppz/nz58Xv4T//+Y/0008/SZs3b5Zuv/12qV27dm5tNlN7lGA7Uffk9OnT4sNct25d+QfJJ5N/cDJ79+4VZfhDlb8IkZGRUn5+fnmZWbNmSTVq1JCKi4vF4+eff15q1aqV23s98sgj4qIiw1+QkSNHlj++du2alJiYKGVlZWlqy8WLF6UmTZpIK1askO68885yUbdimyZMmCB16dLF5/6ysjIpISFB+sc//lH+HLczJiZG/GgY/nFwG7dt21Ze5uuvv5YiIiKkEydOiMfvvfeeVLt27fI2yu/drFmz8scPP/yw1KtXL7f3b9++vfSXv/xFVZv4GEOHDnV7bsCAAeLHbsU2eYqgmeqvpC6eeLtIees0cbkjR46Yvj2+sF34xRNOSs/Ex8eLvzt27BC3XXxrI8O3RMnJybR582bxmP/y7VG9evXKy3Tv3l1kZNu9e3d5GddjyGXkY5SUlIj3ci3Dycr4sVxGLRxe4fCJ5/tasU2LFy+mW2+9lf70pz+JUNDNN99MH3zwQfn+vLw8ys/Pd3svTojE4R7XNvHtMB9Hhstznb799tvyMl27dqXo6Gi3NnFI7ty5c4rarZROnTrRqlWrxJq8zPfff08bNmygnj17WrZNrpip/krqolUvOEzDbbBqe2wt6pzGl+POvBg2L63H8Injky9/aDIsdrxPLuMqfvJ+eZ+/MiySly9fpjNnzogYq7cy8jHUsHDhQtq5c6cYM/DEim06dOgQzZo1i5o0aULLly+n4cOH07PPPkuffPKJW538vRf/5QuCK5UrVxYXcD3arbZNEydOpIEDB4oLalRUlLhQ8fdPXsrRim1yxUz1V1IXtfC4FMfYBw0aVJ5x0YrtMV3qXT3hnu2uXbtEb8nKcK7m5557jlasWCEGWexyweXeDy9QzrAA8mf1/vvvU0ZGBlmRTz/9lObNmycWW2/VqhXl5uYKUecBOKu2ySmUlpbSww8/LAYrubNhZWzbUx81ahR99dVXtGbNGrf87AkJCSKMcP78ebfy7BThfXIZT+eI/DhQGb7C88h13bp1qVKlSl7LyMdQCoc8Tp8+LVwp3Evgbd26dfT222+L//lqbrU28Qh/y5Yt3Z5r0aKFcOi41snfe/FfPi+usJuH3Qp6tFttm9hNJPfWOdQ1ZMgQGjNmTPndlRXb5IqZ6q+kLmoF/ciRI6Lj5JoX3YrtsZ2o85WWBT07O5tWr14t7GWutGvXTtwac+xThmNfLCYdO3YUj/nvjz/+6PZhyh+2LERcxvUYchn5GBwO4fdyLcO9U34sl1HKPffcI+rDPT95414u39bL/1utTRwS87Saciy6UaNG4n/+3PjL7PpeHAbiOKZrm/hCxhc9Gf7MuU4ci5TLsK2Nf7iubWrWrBnVrl1bUbuVcunSpQqr2vBFkOtj1Ta5Yqb6K6mLGkFn6+DKlSuFvdYVq7VHINmM4cOHC1vQ2rVrpV9++aV8u3Tpkpv9j22Oq1evFva/jh07is3T/nffffcJWyRb+q677jqv9r/x48cLp8nMmTO92v949HrOnDliFP2pp54S9j9XB4pWXN0vVmwTuwwqV64sbIAHDhyQ5s2bJ9577ty5bhYvPvYXX3wh/fDDD1Lfvn292uduvvlmYYvcsGGDcAe52s3YQcB2syFDhgi7Gdef38fTbsZ1mTFjhmj3iy++qMnSmJGRITVo0KDc0sg2OraNsqvIKm1ihxVbXnljeXj99dfF/7IbxEz1V1KXi37aU1JSImyDDRs2FL8JV71wdbKYqT1KsJ2o8wfnbWPvugyfpBEjRggbEp/8/v37iw/SlcOHD0s9e/YUflP+YY4bN04qLS11K7NmzRqpbdu2wrd9ww03uL2HDHu9WWy5DNsB2euqB56ibsU2ffnll+JCwxeJ5s2bS7Nnz3bbzzavSZMmiR8Ml7nnnnukffv2uZX59ddfxQ+M/eBsz3ziiSfED9kV9vyyfZKPwaLLPx5PPv30U6lp06aiTWzrXLJkier2FBQUiM+Ez02VKlXE+WOPtKtAmL1N/Pl7+/3wBcts9VdSlzV+2sMXXl96wa8zY3uUgHzqAABgI2wXUwcAACcDUQcAABsBUQcAABsBUQcAABsBUQcAABsBUQcAABsBUQcAABsBUQcAABsBUQcAABsBUQcAABsBUQcAALIP/x+UTIBF8LjCrAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (4,4))\n",
    "ax.scatter(data['msrp'], data['acceleration']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "495921a2-8b14-4697-9b94-63e9840bfda9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10e167280>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = subplots(figsize = (4,4))\n",
    "ax.scatter(data['mpg'], data['acceleration'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52f66b43-540b-431b-9fba-45130461114d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}