{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2e019730-3b3c-4eef-9dfa-7e071bf06db6",
   "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>Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Age in 2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeQuan Jones</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>221</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Darius Miller</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>235</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>210</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>James Jones</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>215</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wesley Johnson</td>\n",
       "      <td>Guard</td>\n",
       "      <td>79</td>\n",
       "      <td>215</td>\n",
       "      <td>26</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>500</th>\n",
       "      <td>Joel Anthony</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>245</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Bismack Biyombo</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>229</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Luis Scola</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>245</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Lavoy Allen</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>225</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Boris Diaw</td>\n",
       "      <td>Center</td>\n",
       "      <td>80</td>\n",
       "      <td>235</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Position  Height  Weight  Age in 2013\n",
       "0       DeQuan Jones    Guard      80     221           23\n",
       "1      Darius Miller    Guard      80     235           23\n",
       "2       Trevor Ariza    Guard      80     210           28\n",
       "3        James Jones    Guard      80     215           32\n",
       "4     Wesley Johnson    Guard      79     215           26\n",
       "..               ...      ...     ...     ...          ...\n",
       "500     Joel Anthony   Center      81     245           31\n",
       "501  Bismack Biyombo   Center      81     229           21\n",
       "502       Luis Scola   Center      81     245           33\n",
       "503      Lavoy Allen   Center      81     225           24\n",
       "504       Boris Diaw   Center      80     235           31\n",
       "\n",
       "[505 rows x 5 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib.pyplot import subplots\n",
    "\n",
    "data = pd.read_csv('nba2013.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1fc64572-5af7-4dab-800b-7a2cbeda7559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float64(220.67326732673268)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = data['Weight'].mean()\n",
    "mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8ecc87bb-e798-4421-a61a-f5a93ba54406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04950495049504951"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = 1.2\n",
    "data[data['Weight'] >= alpha*mean].shape[0]/505"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "193a2a8f-b8ce-4c0c-b006-e7945ec59f00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "abs(1/1.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45b62378-e060-4adf-a52e-181bb978f188",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(79.06534653465347), np.float64(3.454018698179539))"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = data['Height'].mean()\n",
    "sd = data['Height'].std()\n",
    "mean, sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "55ad787d-c914-40ba-9700-a23a4fa0b2e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "95.84158415841584"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "two_sd = (data[abs(data['Height'] - mean) <= 2*sd].shape[0]*100)/data.shape[0]\n",
    "two_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "36127614-12de-44df-b5c8-b19cca4419f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "three_sd = (data[abs(data['Height'] - mean) <= 3*sd].shape[0]*100)/data.shape[0]\n",
    "three_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a05eb738-3c3b-4615-9613-298d89ad92b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.56435643564357"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "one_sd = (data[abs(data['Height'] - mean) <= sd].shape[0]*100)/data.shape[0]\n",
    "one_sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5fe9deff-2efb-4ebe-8734-0781b548b60a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Height'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "891c9486-d8f7-4103-beeb-8da83978b06f",
   "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>Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Age in 2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeQuan Jones</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>221</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Darius Miller</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>235</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>210</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>James Jones</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>215</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wesley Johnson</td>\n",
       "      <td>Guard</td>\n",
       "      <td>79</td>\n",
       "      <td>215</td>\n",
       "      <td>26</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>500</th>\n",
       "      <td>Joel Anthony</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>245</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Bismack Biyombo</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>229</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Luis Scola</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>245</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Lavoy Allen</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>225</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Boris Diaw</td>\n",
       "      <td>Center</td>\n",
       "      <td>80</td>\n",
       "      <td>235</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Position  Height  Weight  Age in 2013\n",
       "0       DeQuan Jones    Guard      80     221           23\n",
       "1      Darius Miller    Guard      80     235           23\n",
       "2       Trevor Ariza    Guard      80     210           28\n",
       "3        James Jones    Guard      80     215           32\n",
       "4     Wesley Johnson    Guard      79     215           26\n",
       "..               ...      ...     ...     ...          ...\n",
       "500     Joel Anthony   Center      81     245           31\n",
       "501  Bismack Biyombo   Center      81     229           21\n",
       "502       Luis Scola   Center      81     245           33\n",
       "503      Lavoy Allen   Center      81     225           24\n",
       "504       Boris Diaw   Center      80     235           31\n",
       "\n",
       "[505 rows x 5 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "de3645ff-02d1-4add-a7ee-6cfb650c5972",
   "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>Name</th>\n",
       "      <th>Position</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Age in 2013</th>\n",
       "      <th>Height_in_std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>DeQuan Jones</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>221</td>\n",
       "      <td>23</td>\n",
       "      <td>0.270599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Darius Miller</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>235</td>\n",
       "      <td>23</td>\n",
       "      <td>0.270599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Trevor Ariza</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>210</td>\n",
       "      <td>28</td>\n",
       "      <td>0.270599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>James Jones</td>\n",
       "      <td>Guard</td>\n",
       "      <td>80</td>\n",
       "      <td>215</td>\n",
       "      <td>32</td>\n",
       "      <td>0.270599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wesley Johnson</td>\n",
       "      <td>Guard</td>\n",
       "      <td>79</td>\n",
       "      <td>215</td>\n",
       "      <td>26</td>\n",
       "      <td>-0.018919</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>500</th>\n",
       "      <td>Joel Anthony</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>245</td>\n",
       "      <td>31</td>\n",
       "      <td>0.560117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>Bismack Biyombo</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>229</td>\n",
       "      <td>21</td>\n",
       "      <td>0.560117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>Luis Scola</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>245</td>\n",
       "      <td>33</td>\n",
       "      <td>0.560117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Lavoy Allen</td>\n",
       "      <td>Center</td>\n",
       "      <td>81</td>\n",
       "      <td>225</td>\n",
       "      <td>24</td>\n",
       "      <td>0.560117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Boris Diaw</td>\n",
       "      <td>Center</td>\n",
       "      <td>80</td>\n",
       "      <td>235</td>\n",
       "      <td>31</td>\n",
       "      <td>0.270599</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>505 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Name Position  Height  Weight  Age in 2013  Height_in_std\n",
       "0       DeQuan Jones    Guard      80     221           23       0.270599\n",
       "1      Darius Miller    Guard      80     235           23       0.270599\n",
       "2       Trevor Ariza    Guard      80     210           28       0.270599\n",
       "3        James Jones    Guard      80     215           32       0.270599\n",
       "4     Wesley Johnson    Guard      79     215           26      -0.018919\n",
       "..               ...      ...     ...     ...          ...            ...\n",
       "500     Joel Anthony   Center      81     245           31       0.560117\n",
       "501  Bismack Biyombo   Center      81     229           21       0.560117\n",
       "502       Luis Scola   Center      81     245           33       0.560117\n",
       "503      Lavoy Allen   Center      81     225           24       0.560117\n",
       "504       Boris Diaw   Center      80     235           31       0.270599\n",
       "\n",
       "[505 rows x 6 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1 = data.assign(Height_in_std = (data['Height'] - mean)/sd)\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f2a5ea4f-24d2-4fc6-bd59-d6be4f43ebb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1['Height_in_std'].hist(density = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6dd61163-80e9-4459-8a50-6d1159e9d721",
   "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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('united_summer2015.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e1c5ca02-d7c9-470c-b949-303f5c1a3d9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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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, 700, 10), density = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "370b1567-823a-4554-9194-d39605fa7d30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(np.float64(16.658155515370705), np.float64(39.48162778440366))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean = data['Delay'].mean()\n",
    "sd = data['Delay'].std()\n",
    "mean, sd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a9577e2-a3d5-4a3d-898e-0162112d961f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data1 = data.assign(Delay_in_std = (data['Delay'] - mean)/sd)\n",
    "data1['Delay_in_std'].hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "d9960694-0527-4a8d-9435-0d4a311f1e4b",
   "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>Birth Weight</th>\n",
       "      <th>Gestational Days</th>\n",
       "      <th>Maternal Age</th>\n",
       "      <th>Maternal Height</th>\n",
       "      <th>Maternal Pregnancy Weight</th>\n",
       "      <th>Maternal Smoker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>120</td>\n",
       "      <td>284</td>\n",
       "      <td>27</td>\n",
       "      <td>62</td>\n",
       "      <td>100</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>113</td>\n",
       "      <td>282</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>135</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>128</td>\n",
       "      <td>279</td>\n",
       "      <td>28</td>\n",
       "      <td>64</td>\n",
       "      <td>115</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>108</td>\n",
       "      <td>282</td>\n",
       "      <td>23</td>\n",
       "      <td>67</td>\n",
       "      <td>125</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>136</td>\n",
       "      <td>286</td>\n",
       "      <td>25</td>\n",
       "      <td>62</td>\n",
       "      <td>93</td>\n",
       "      <td>False</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>1169</th>\n",
       "      <td>113</td>\n",
       "      <td>275</td>\n",
       "      <td>27</td>\n",
       "      <td>60</td>\n",
       "      <td>100</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1170</th>\n",
       "      <td>128</td>\n",
       "      <td>265</td>\n",
       "      <td>24</td>\n",
       "      <td>67</td>\n",
       "      <td>120</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1171</th>\n",
       "      <td>130</td>\n",
       "      <td>291</td>\n",
       "      <td>30</td>\n",
       "      <td>65</td>\n",
       "      <td>150</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1172</th>\n",
       "      <td>125</td>\n",
       "      <td>281</td>\n",
       "      <td>21</td>\n",
       "      <td>65</td>\n",
       "      <td>110</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1173</th>\n",
       "      <td>117</td>\n",
       "      <td>297</td>\n",
       "      <td>38</td>\n",
       "      <td>65</td>\n",
       "      <td>129</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1174 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Birth Weight  Gestational Days  Maternal Age  Maternal Height  \\\n",
       "0              120               284            27               62   \n",
       "1              113               282            33               64   \n",
       "2              128               279            28               64   \n",
       "3              108               282            23               67   \n",
       "4              136               286            25               62   \n",
       "...            ...               ...           ...              ...   \n",
       "1169           113               275            27               60   \n",
       "1170           128               265            24               67   \n",
       "1171           130               291            30               65   \n",
       "1172           125               281            21               65   \n",
       "1173           117               297            38               65   \n",
       "\n",
       "      Maternal Pregnancy Weight  Maternal Smoker  \n",
       "0                           100            False  \n",
       "1                           135            False  \n",
       "2                           115             True  \n",
       "3                           125             True  \n",
       "4                            93            False  \n",
       "...                         ...              ...  \n",
       "1169                        100            False  \n",
       "1170                        120            False  \n",
       "1171                        150             True  \n",
       "1172                        110            False  \n",
       "1173                        129            False  \n",
       "\n",
       "[1174 rows x 6 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('baby.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7b9986c8-620a-4bbf-9461-427a88b2bcc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['Maternal Height'].hist(bins = np.arange(50, 75, 1), density = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "03229315-ea2c-4403-8729-ae9cc5194a89",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x121e21520>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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pTdWIdDFipXFqRWAgpw4L5OvWrcOMGTMwd+5c7Ny5E0OGDMGYMWNQVFTk8viuXbviueeew9atW7Fnzx5MmTJFbp9++qn9mIULF+L111/H8uXLsX37dhnwxTkvXLjQ6hdGZG+Rh4QAV10FT3S8a0/UetkWmWBqhToskC9evBhTp06VwTgxMVEG34CAAKxcudLl8TfffDMmTpyIgQMHok+fPpg+fTqSkpLw1Vdf2VvjS5YswfPPP4/x48fL+1avXo2CggKsX7/e5Tmrq6tRUVHhtBE5EavT5+U58uPikngPVOvdGce62j5k+pzJR+e6Wq2rREYP5DU1NcjKypKpD/sJvLzkvmhxX44I2ps2bUJ2djZuvPFGedvx48dRWFjodM6QkBCZsmnunPPmzZPHKFtMTIw7L4PMQAdplcZ58s71dYg7c1Lr6pDRA3lJSQnq6uoQEWEb+6oQ+yIYN6e8vBxBQUHw8fHBuHHj8MYbb+C2226T9ymPc+ecs2fPludUtjyl5UWkEEuoKQYNgidTXxjUr4R/y+S+DrlCokuXLti9ezcqKytli1zk2OPj42XapTV8fX3lRmSEQH5EHchLczWtC5kgkIeHh8Pb2xunxbSbKmI/MjKy2ceJ9Evfvn1lWYxaOXjwoEyPiECuPE6cQ4xaUZ9THEvUKup1MD1lMYmWBPISBnJq59SKSI2kpKTIVrWivr5e7o8cObLF5xGPER2WQlxcnAzm6nOKzksxesWdcxK5bJF37WpbxMGD5YZGotrb1qZiaoU6JLUi0iKTJ0+WY8OHDx8uR5xUVVXJUSxCeno6evbsKVvcgvgpjhUjVkTw3rBhgxxHvmzZMnm/xWLBU089hZdffhn9+vWTgX3OnDmIjo7GhAkTWvWiyOTEiJWTJx2tcQ8dsaKo8/LGD12vwsDiHMT9eBKd6i7iYkNgJ2oJt/9a0tLSUFxcLC/gEZ2RIv2RmZlp76zMzc2VqRSFCPKPP/448vPz5cU+CQkJeOedd+R5FDNnzpTHPfLIIygrK8P1118vzykuOCJy28GDukmrKI52i5GBXIxc6f3jKRwL50gsajmLVYwJ1DmRihHDEMUIluDgYK2rQ1oT1zQ8/LCtvGQJMH16k9V5PM2TX/8NM756V5YfnTAbmQOuc7qfKwSZT4UbcY1zrZDx6GjEiushiOzwJPcwkJPx6GjEiuJIN0cgF3OTE7mDgZyM2yIXc6yohrR6shNhUfY5V/qWcuQKuYeBnIzl7FnR466bESsKMUrleFhPWY4/kw/v+oYFMYhagIGcjLcqkM7SKoojDSNVfOsuoldZ81NeEDXGQE7G7ejUWSA/qsqTs8OT3MFATsaiwxErrkauME9O7mAgJ2PR4YiVxqkVoX8JVwuilmMgJ2O2yIOCPHZVoObkhPXERYvtX5JzrpA7GMjJOM6dA3JydDdiRVHTqTNOhEXbVwvy4sgVaiEGcjLWiBVlxomBA6FHSnrF72INrip3vQ4uUWMM5GQcOpwsy9XkWQp2eFJLMZCTMceQJyRAj5SFmIU+pfma1oX0g4GcjNki12lqhS1yag3OXk+6p0xL+9nmb9EfkKvtJK44iDqvw9CbY90cLXIGcmoptsjJEMTcJLE/FsiymLNErLqjR+d8/HGyS3dHINf/cgHUARjIyRDEqjo+9RebpCf0SGmVh1RXoXtVmdbVIR1gICdDUKchjuk8kDNPTu5iICdDUAe8o6o8sx4dVV2q34eBnFqAgZwMQR3w1IFQj9giJ3cxkJMh9G0Yc10PC35oWKBBrxjIyV0M5KR/Vqucm0TIC41AdWdf6NmZgBCc8betms6LgqglGMhJ9yLPliKo5nyTKyP1THkdUZWlCKo+p3V1yMMxkJPBOjr1nR931WHLDk+6HAZy0j1jBnJ1npzpFWqHQL506VLExsbCz88Pqamp2LFjR7PHrlixAjfccAPCwsLkNnr06CbHP/TQQ7BYLE7bHXfc0ZqqkQkZMZCrx8Kzw5PaPJCvW7cOM2bMwNy5c7Fz504MGTIEY8aMQVGR67mTN2/ejPvuuw+ff/45tm7dipiYGNx+++04efKk03EicJ86dcq+/e1vf3O3amRSfQ009NDV62AgpzYP5IsXL8bUqVMxZcoUJCYmYvny5QgICMDKlStdHv/uu+/i8ccfR3JyMhISEvDnP/8Z9fX12LRpk9Nxvr6+iIyMtG+i9U7UEsrIjuLAUFT4BcEITgZ3x7mG0TfMkVObBvKamhpkZWXJ9Ij9BF5ecl+0tlvi3LlzqK2tRdeuXZu03Hv06IEBAwbgscceQ2lpabPnqK6uRkVFhdNGJnXmDLqfKzNUWkWwWrzwQ8PIlV5lheKPXusqkVECeUlJCerq6hAREeF0u9gvLCxs0TmeffZZREdHO30YiLTK6tWrZSt9wYIF2LJlC8aOHSufy5V58+YhJCTEvol0DZmUajEJIwVy9ciVTtZ64OhRratDHqxD5yOfP38+1q5dK1vfoqNUMWnSJHt58ODBSEpKQp8+feRxo0aNanKe2bNnyzy9QrTIGcxNShXIjTKG3OXrEa9z0CAtq0NGaZGHh4fD29sbp0+fdrpd7Iu89qUsWrRIBvLPPvtMBupLiY+Pl891tJlWiMinBwcHO21kUqpVgYzXIo9xvfoR0ZUEch8fH6SkpDh1VCodlyNHjmz2cQsXLsRLL72EzMxMDB069LLPk5+fL3PkUVFR7lSPzEgV4NSr6xiB0+thIKe2HLUiUhpibPiqVatw8OBB2TFZVVUlR7EI6enpMvWhEDnvOXPmyFEtYuy5yKWLrbKyUt4vfj7zzDPYtm0bcnJy5IfC+PHj0bdvXzmskaglqZWqzn441SUcRpIjVjqyeDVdWJroSnPkaWlpKC4uRkZGhgzIYlihaGkrHaC5ublyJIti2bJlcrTLPffc43QeMQ79hRdekKmaPXv2yA+GsrIy2REqxpmLFrxIoRA168IF4PhxR+vVYoGR1HTqjNzQCMT9eMoWyOvrxTAxratFHshitep/UUDR2SlGr5SXlzNfbiZ79wIN/S0fDLoFM+78NYxmxT9exG1HG66EPnEC6NVL6yqRB8Y1fryTMfLjBhux4vJ1MU9OzWAgJ/0y8Bhyl+uPMk9OzWAgJ/0y8NBDBYcgUkswkJN+NbRQL1q8kBt26esYDDEEkS1yagYDOemTGMGRnS2LJ8KiUevdGUYkJgErCmyYQI4tcmoGAznpU24ucP68IS8Easz++sRU0WfOaF0d8kAM5GSA/LixA7lTnpzpFXKBgZwMMPTQmB2dCg5BpMthICcDDD1ki5zMjYGc9EnVMv3B4IGck2fR5TCQkz4pAS06Gmd9A2FkcjKwoIYl7BjIyQUGctKf4mJAWQpw4EAYnpgMLCHBVhaThDWM1iFSMJCT/qhbpWYI5OrXKea4O3xY69qQh2EgJ/0xcyAXmF6hRhjISX8YyLWsCXkgBnLSHwZyLWtCHoiBnPRHCWShoUDDylSG16cP0LlhPhkGcrrSpd6INCXWes3Lc7RSDba8W3Nin/8UnwVHoX9pLqoPHkLizH+izsvb5bE588d1eP1IW2yRk76or2w0S1ql0RWsvnUXEVNWqHV1yIMwkJO+mDE/7uJS/b6l+ZrWhTwLAznpi5kDebg6kDekl4gYyEl3TBzI1bMgMpCTGjs7ySPFzvrE5e2bvvgOfQBc6OSDxOX7UO9lnhEcPzCQUzPYIifd6FxXi94/FsjyD117or6ZURtGdd7HD/nBPWS5jwjk4nJ9IgZy0pPeP55CJ2t90zm6TUR53V1qzqNHJZd9IxsGctINdTrBvIGc6RVqo0C+dOlSxMbGws/PD6mpqdixY0ezx65YsQI33HADwsLC5DZ69Ogmx1utVmRkZCAqKgr+/v7ymCNHjrSmamRgDOSNhyAykFMrA/m6deswY8YMzJ07Fzt37sSQIUMwZswYFIkVvl3YvHkz7rvvPnz++efYunUrYmJicPvtt+PkyZP2YxYuXIjXX38dy5cvx/bt2xEYGCjPeeHCBXerR6YJ5MZeFaglQxD7MZBTawP54sWLMXXqVEyZMgWJiYky+AYEBGDlypUuj3/33Xfx+OOPIzk5GQkJCfjzn/+M+vp6bNq0yd4aX7JkCZ5//nmMHz8eSUlJWL16NQoKCrB+/Xp3q0cG1q/EFrguWryQE9YTZnSkWy97mS1yalUgr6mpQVZWlkx92E/g5SX3RWu7Jc6dO4fa2lp07dpV7h8/fhyFhYVO5wwJCZEpm+bOWV1djYqKCqeNjM2rvg59ztiuZswJi0ZNp4YJpEym3L8LigLDZLlfSa7W1SE9BvKSkhLU1dUhotGMc2JfBOOWePbZZxEdHW0P3Mrj3DnnvHnzZLBXNpGuIWOLKT8Nv4s1snwk3NEqNaMjDemV8HPl6HquXOvqkNlGrcyfPx9r167Fhx9+KDtKW2v27NkoLy+3b3nKbHhk+LSKcMSkHZ0KplfoigJ5eHg4vL29cfr0aafbxX5kZOQlH7to0SIZyD/77DOZB1coj3PnnL6+vggODnbayNj6lTrSCEdN3yJ3vP7+TK+Qu4Hcx8cHKSkp9o5KQem4HDlyZLOPE6NSXnrpJWRmZmLo0KFO98XFxcmArT6nyHmL0SuXOieZizoffJiB3F7uy0BOrZlrRQw9nDx5sgzIw4cPlyNOqqqq5CgWIT09HT179pR5bGHBggVyjPiaNWvk2HMl7x0UFCQ3i8WCp556Ci+//DL69esnA/ucOXNkHn3ChAlt/XpJ54G8zuKF413NOWLFVWpJ/U2FzMvtQJ6Wlobi4mIZnEVQFsMKRUtb6azMzc2VI1kUy5Ytk6Nd7rnnHqfziHHoL7zwgizPnDlTfhg88sgjKCsrw/XXXy/PeSV5dDIOi7XePv/2idBIVHfygZn9GBCC4oBQdD9XxtQKtX72w2nTpsmtuQuA1HJyci57PtEqf/HFF+VG1FjP8iL4X6yWZbPnx9UXBnXPLUP3qjKEnq9AmT/7icyMc62Qx1O3Os2eH1dw5AqpMZCTx1Pngc0+9NDVBxrTK8RATroaQ87Uios5VxjITY+BnDxe34YWeT0sOKpaJcfMnFIrqg86MicGcvL4EStKizxXjFjp7Kt1lTxCaWAoShs6OPtzCKLpMZCTR4uuKEFg7QWnOUbIOc0UUXkGwRcqta4OaYiBnDyaOv97VJVOIOeOX6ZXzI2BnDwaL81v6ciVE5rWhbTFQE4eTT20zuzT1zZ2JLy3vcwhiObGQE4ebUBJjn3ECseQO8vu3rvJ+0TmxEBOHr0qkDJiJScsiiNWGjnTMOeK0L+YLXIzYyAnj14VSJlj5bCq9UlNW+ViAq1uVWVaV4c0wkBOHiuh2JEuyA6P1bQunuqwU56cHZ5mxUBOHqt/8QmX+WBqJk+uer/IXBjIyWMNUI3EyFa1PMmBLXISGMjJYw1oSK1Ue3dCTtdoravj8WPJ1akoMhcGcvJIPhdrEXfmpCwf6xaDOi9vravkkap8A5AXEuEYS261al0l0gADOXmk+DP56GStl2WmVS4tu6FVHlRzHj0rirWuDmmAgZw8Oq0iZHfniJVLUb8/6veNzIOBnDzSAFXHHUesuHOFJzs8zYiBnDx+6KF6ZAZdZuQKhyCaEgM5eaSEhoB01scfJ4O7a10dj3as21WobegM5sgVc2IgJ89TUYGrKoocrU2LResaebRa7844HtbT3kmM2lqtq0QdjIGcPM+BA/Yi8+Mto8xF41t3ETh6VOvqUAdjICfPs2ePvcj8eMscUn/gqd4/MgcGcvI8339vLx7sEadpVfTiUPc4l+8fmUOrAvnSpUsRGxsLPz8/pKamYseOHc0eu3//ftx9993yeIvFgiVLljQ55oUXXpD3qbeEhITWVI2MgIHcbU7vEwO56bgdyNetW4cZM2Zg7ty52LlzJ4YMGYIxY8agqMjWOdXYuXPnEB8fj/nz5yMyMrLZ8w4aNAinTp2yb1999ZW7VSMjqK+3B6L84B6o8AvSuka6IEb2lPsG2nZ279a6OuTpgXzx4sWYOnUqpkyZgsTERCxfvhwBAQFYuXKly+OHDRuGV155BZMmTYKvb/MrvHTq1EkGemULDw93t2pkBMePA5WVssjWuBssFsf7VVAAlJRoXSPy1EBeU1ODrKwsjB492nECLy+5v3Xr1iuqyJEjRxAdHS1b7/fffz9yc5tfuqq6uhoVFRVOGxmEKi1wgIHcLUyvmJdbgbykpAR1dXWIiLDNtqYQ+4WFha2uhMizv/XWW8jMzMSyZctw/Phx3HDDDTh79qzL4+fNm4eQkBD7FhPDRXmNGcjjNa2K3jh98DGQm4pHjFoZO3Ys7r33XiQlJcl8+4YNG1BWVob33nvP5fGzZ89GeXm5fcvLsy3QSwbAjs5WO6j+4GMgN5VO7hws8tbe3t44ffq00+1i/1Idme4KDQ1F//79cbSZCxtErv1S+XbSsYaOukoff+SFOn/zo0s7Et4LFy1etul/2eFpKm61yH18fJCSkoJNmzbZb6uvr5f7I0eObLNKVVZW4tixY4iKimqzc5IOlJUBJ2xzrBzqHgurxSO+MOpGdScfOe+KdPCg6NTSukrUQdz+TxFDD1esWIFVq1bh4MGDeOyxx1BVVSVHsQjp6eky9aHuIN29e7fcRPnkyZOyrG5tP/3009iyZQtycnLwzTffYOLEibLlf99997XV6yQ9UF2RyPx469jTUWK+FRHMyRTcSq0IaWlpKC4uRkZGhuzgTE5Olp2USgeoGG0iRrIoCgoKcM0119j3Fy1aJLebbroJmzdvlrfl5+fLoF1aWoru3bvj+uuvx7Zt22SZTIT58Ssm3rcJB7Y43s8hQ7SuEnliIBemTZsmN1eU4KwQV3RaL7OO4Nq1a1tTDTIaVV6Xgbx1Dqov1RfvZ3q6ltWhDsIkJHlei9xi4TqdrXQggiNXzIiBnDzDxYvAvn22cv/+OO/jp3WNdKkkMExc2OEI5Jf5NkzGwEBOnuHwYXHJrq3MvO6VUd6/0lLg5Emta0MdgIGcPENWlqOs6hynVlC/fzt3alkT6iAM5OQZvvvOUU5J0bIm+qd+/9QfkGRYDOTkGdQB59prtayJsQK5+gOSDIuBnLRXVwfs2mUrx8YC3bppXSN9i4sDwsIcH5Ds8DQ8BnLSXna2WIHEVmZa5cpZLI5vNWJeJDE/ORkaAzl5VlqFgbxtME9uKgzkpD0G8rbHQG4qDOSkPQbytjd0qKPMQG54DOSkfUenMtaZHZ3t1+FJhsZATtpiR2f7d3iKZRjZ4WloDOSkLaZV2g/z5KbBQE7aYiBvPwzkpsFATtpiIG8/DOSmwUBOnnFFZ+/e7Ohsa/HxYiVzW5mB3NAYyEk7Bw4AVVVNh8tR23V4Ku/rqVNAXp7WNaJ2wkBO2tm2zVEeOVLLmhjXiBGu328ylFat2UnUGrGzPnHaX7DhfaQ1lO/eVY+sRvdTOwTye+/VsjbUTtgiJ81cU5Atf9Z6eWNfRB+tq2NMqamOMlvkhsVATpoIvlCJ/qW5snygRzyqO/tqXSVjCg8H+vZ1dHjW1GhdI2oHTK2QJpJOHbGXd0UP0LQuRk9hLfaNwc9wVK6JOv6Xf8D3qvc7Z/44DWpIbY0tctLENQWH7GUG8valfn+VdBYZCwM5aUIdUHZFJ2haF6PbqXp/GciNiYGcOp7Vag8opf7ByA2N1LpGhpbdPRbnO/k2+SZEJg/kS5cuRWxsLPz8/JCamoodO3Y0e+z+/ftx9913y+MtFguWLFlyxeckfYv7sQBhF846vvaLC1eo3Vz07oQ9kbYOz17lpxFe9aPWVSKtA/m6deswY8YMzJ07Fzt37sSQIUMwZswYFBUVuTz+3LlziI+Px/z58xEZGdkm5yQj5ceZVunoPHlywWFN60IeEMgXL16MqVOnYsqUKUhMTMTy5csREBCAlStXujx+2LBheOWVVzBp0iT4+vq2yTnJSPlxdnR2BPUH5rUFBzWtC2kcyGtqapCVlYXRo0c7TuDlJfe3bt3aqgq05pzV1dWoqKhw2kh/gbweFuyJ6q91dUxB/YF57UnmyU0dyEtKSlBXV4eIiAin28V+oViFpBVac8558+YhJCTEvsXExLTquanjdamuwsCi47Kc3b03Kn0DtK6SKRR16Yb84B6ynHzqMDrX1WpdJTL7qJXZs2ejvLzcvuVxVjfdSMk/CG9rvSxvj7la6+qYyvaYQfKn38UaDD51VOvqkFaBPDw8HN7e3jh9+rTT7WK/uY7M9jinyLUHBwc7baQPqXn77OUdDOQdSv1+j8jbq2ldSMNA7uPjg5SUFGzatMl+W319vdwf2cppSNvjnOS5hjsFclsLkTqG+hvQ8Lz9mtaFNJ5rRQwTnDx5MoYOHYrhw4fLceFVVVVyxImQnp6Onj17yjy20pl5QCwg0FA+efIkdu/ejaCgIPRtmMzncuckY/CvuYCkQtscK8e6XoWSwDCtq2QqOWHRKAoMQ4+qHzH05AF419dpXSXSKpCnpaWhuLgYGRkZsjMyOTkZmZmZ9s7K3NxcOepEUVBQgGuuuca+v2jRIrnddNNN2Lx5c4vOScYZP965IXgwP64Bi0W+7z899CWCas4j8fQPWteItJz9cNq0aXJzRQnOCnG1ptVqvaJzkvHy40rHG3UsJZALqcyTG4YuR62QPrGjU3vqb0KpzJMbBgM5dYzqavuFQLkhETgV3F3rGpnS0fAYnPG3jfIalr9fjCzQukrUBhjIqWPs2AHfhotQtscM1ro2pmW1eNlHC4VeqAT2Ob4lkX4xkFPH+OILe5HDDrW146qrXf5eSL8YyKljqDrBt/Vii1xL23upAvnnn2tZFWojDOTU/s6dA760jZTID+6OvBAOK9XSwe6x+NGvi21HXIh38aLWVaIrxEBO7U98fa+uthXjruVCEhqr9/LG17HJtp3ycuDbb7WuEl0hBnJqf59+ai/KQE6a2xJ3jcvfD+kTAzm1v88+kz/qLF74pvcQrWtDAL6MvbbJ74f0i4Gc2peYYrhhrp3dUf1R4RekdY0IQGFwOA5362Xb2b4d+JHreOoZAzm1L1Vrj2kVz/KFkl4RFwWpZh8l/WEgpw4L5F+q87KkuS/VH6xMr+gaAzm1n7o6YONGWzk0FN9zfU6PIicuUxZEFx2eLZjcjjwTAzm1n6wsR+511CjUeXlrXSNSudDZD7jhBttObi5w+LDWVaJWYiCn9qMe1jZmjJY1oeaofy+ZmVrWhK4AAzm1n48+cpQZyD3THXc4yv/8p5Y1oSvAQE7tQ3xVF6kV4dprgV4NQ93IswwaBPTpYytv2QKUlmpdI2oFBnJq/9b4hAla1oQuRUyXoPx+ROf0J59oXSNqBQZyah8ffugoT5yoZU3octS/H/XvjXSDgZzanvh6rsxz3bev7es7ea6RIwFloXPRQS1mqyRdYSCntvfxx7av6Uprj7MdejYvL2D8eFv5/HleHKRDDOTU9tRfz5kf1wf174npFd1hIKe2VVXlGD8eGQmMGKF1jaglbr0V6NKw2MS//gXU2tZXJX1gIKe2JYL4hQu2svi6Lr62k+cTl+qPG2cri6txxVBE0g3+l1HbevddR5mjVfTlZz9zlNes0bIm5CYGcmo7Z87YvpYraZVRo7SuEbnjzjuB4GBb+f33OXrF6IF86dKliI2NhZ+fH1JTU7Fjx45LHv/+++8jISFBHj948GBs2LDB6f6HHnoIFovFabtDfekw6cN77zlyq7/4BdCpk9Y1Inf4+wP33msrV1Y6X9RFxgrk69atw4wZMzB37lzs3LkTQ4YMwZgxY1BUVOTy+G+++Qb33XcfHn74YezatQsTJkyQ2759+5yOE4H71KlT9u1vf/tb618VaWP1akc5PV3LmlBrPfig698neTSL1ereJMSiBT5s2DD84Q9/kPv19fWIiYnBr371K8yaNavJ8WlpaaiqqsLHYmxxgxEjRiA5ORnLly+3t8jLysqwfv36FtWhurpaboqKigpZh/LycgQrXw2pYx05AvRvmG988GBgz54mh8TO4uXfniZnfkMHp0KsFhQfD5w4Yeuozs8HoqK0qp6pVVRUICQkpEVxza3vvjU1NcjKysLs2bPtt3l5eWH06NHYunWry8eI20ULXk204BsH7c2bN6NHjx4ICwvDrbfeipdffhndunVzec558+bht7/9rTtVp3agDsz/9+W7mN5Q/l23YVjBoK1PIng/8ADwu9/Zgrr4Ztzo/5d0nlopKSlBXV0dIpTLeRuI/cLCQpePEbdf7niRVlm9ejU2bdqEBQsWYMuWLRg7dqx8LlfEB4n4lFK2PLHAL2nHasXE/f+VxTqLFz5KvEnrGtGVYHpFdzyiN2rSpEn2sugMTUpKQp8+fWQrfZSLkQ++vr5yI88wIm8vepWfluWvew9BURfX36RIJwYMAIYPB8Qghu+/B3btAq7hequGaZGHh4fD29sbp0/b/mkVYj9SDDdzQdzuzvFCfHy8fK6jR4+6Uz3SSHqWo//j74NHa1oXaiNTpjjKb7yhZU2orQO5j48PUlJSZApEITo7xf5IMYOaC+J29fHCxo0bmz1eyM/PR2lpKaLYyeLxoiuKMObINlk+HdQV/x7wE62rRG1B5MlDQhwXB5WUaF0jasvhh6LjcsWKFVi1ahUOHjyIxx57TI5KmdLwCZ6enu7UGTp9+nRkZmbi1VdfxaFDh/DCCy/gu+++w7Rp0+T9lZWVeOaZZ7Bt2zbk5OTIoD9+/Hj07dtXdoqSZ3tw5wZ4W+tl+d3ksaj17qx1lagtBAUBDz9sK4sRYitWaF0jastALoYTLlq0CBkZGXII4e7du2WgVjo0c3Nz5ThwxU9+8hOsWbMGf/rTn+SY87///e9yxMrVV18t7xepmj179uCuu+5C//795Xhz0er/8ssvmQf3cL611Zj0vW2CrBqvTliTzIu4DOWJJxxTEP/xj8DFi1rXiNpqHLnex1tS25k5djoWZr4uy/8YdAt+feevta4StbEV/3gRtx3d4bhs/557tK6SaVS4Edc41wq1jtWKKVmOVddXpfxU0+pQ+3gr5S7Hzu9/r2VV6BIYyKl1NmzAwOIcWdwZPQB7ohqu6iRDEcNJj3SLse189RXw5ZdaV4lcYCAn94lsXEaGfffN1Ls1rQ61I4vF+fer+r2T52AgJ/eJWfF27pTFfRF98Gm/5oeSkv59OOgW/BAWbdvZvBn4/HOtq0SNMJCTe8T8G6pW2eLr7+fiygZX5+WN3193n+OGOXNs38rIYzCQk3v+8Q9g715Z3B3VH//tM0zrGlEH+NfAG4GBA207X38trurTukqkwkBOLVdTw9a4SdV7eQMvvOC44Te/AZqZ1I46HgM5tdxrrwGHDtnK112HL+Ku1bpG1JHEGPKkJFs5K4tXe3oQBnJqGbHQwIsvOuasfv11tsbNRvm9K8RUHI0mxCMTT2NLOvDkk47FeMWl29deC7zHxSPMuJDIq1ffirv3/RcoK8M/Rt3X7BW9TVYfonbDFjld3j//adsEMf3wSy9pXSPS0P+7+X9Q5hcky3fv/xwjcpsu60cdi4GcLk18df7f/3XOkyvTm5IplQaGYuFNk+37r2z4PYIvVGpaJ7NjIKfmiVEJYl5qZVm+sWPF9Jda14o8wN+GjMG3PRNlOab8NBb++/ccW64hBnJq3rx5wH/+YyuLRT7eeosdnCRZLV6YftfT9hTLHYe3YvJOx0pR1LHY2UkuO7RGnvge76ybC++GBZXvv/lX2Lb4W62rRx6kILgHfj3u//CXf9j6TJ7771/wfVR/7I4eoHXVTIctcmoi8fQPePOD39lX/hGXZ2/r1TB+mEhlU99UrBg2QZZ96i/iL3//LeJL87WulukwkJOT3j8WYNV7GQiusQ01/G/8UPxh5M+1rhZ5MNHxuT3GtuJXt/MVeHvdHERWcI3PjsRATg55efKfsPu5Mrn7Xc+BeHzCLNvl2UTNEOu0/vLuOTjQI07u9zxbjLffm8OLhToQAznZ7NoFpKaiV7ntn+9QeG/8zz1zcaGzn9Y1Ix046xuIyfe+iJzQKLnfrzQPGDECOHhQ66qZAgM5Af/+N3DjjUDDotnHw6KQ/vMXUdEwIoGoJYqDwvBA2ks42aW77YacHLH6OrBli9ZVMzwGcrPPZvjcc8CddwKVtgs6sqIT8LMHFqGoSzeta0c6lB8aiYkPLsL+HvG2G8rKgNGjgd/9Drh4UevqGZbFatX/KH53VpumBvv2AQ8+COze7bjt7rsxoPcvUN3ZV8uakQEEVp/D0o8W4ObjWfbbxNquvx43A8e79mxyPOdlubK4xha52Yj0yaOPAsnJjiDeqZOtxfTeewzi1CaqfAPw8D0ZcsSTuA5BuLYgG5/+5Qlk/OdPCDtXrnUVDYWB3CyOHweeeQbo2xd4803HogCJicCOHbaFAsQ0pURtuETcohvTce8vFtg7QcVY8//J+ie2vDkVT3+xGlEVxVpX0xCYWjEykffOzARWrQI++cR5LoygIFtgnzkT8PNrcmUnUVsKqDmPx7e9j4e//Qj+F6vtt1+0eGFjvxEYu+AZ4PbbgYAATeup17jGQG60hZHFeppffGGbI+Wzz4ALF5yP8fGxzWb4/PNAjx5NTsFATu2px9lSPPX1Gty79z/oXN9oqTgRxO+4Axg1yjaKSnxbNPG3xIr2zpEvXboUsbGx8PPzQ2pqKnaIr+aX8P777yMhIUEeP3jwYGzYsMHpfvFZkpGRgaioKPj7+2P06NE4cuRIa6pmntEmx47ZAvWyZcC0acANNwBhYbbct1gEQswfrg7iMTG2PHhenm2VFxdBnKi9idFQv7njV7ju0ZVYct19KAoMc9wpFi754APbwiWDBwPdugE332z7exbpQNE4ESnC2lotX4JHcrtFvm7dOqSnp2P58uUyiC9ZskQG6uzsbPRwERy++eYb3HjjjZg3bx7uvPNOrFmzBgsWLMDOnTtx9dW2y3rFvrh/1apViIuLw5w5c7B3714cOHBABv92a5GL6Vmb+6Nw9bYot4mf6vuVffUmWsfKT7GJnLSyiWFYyiaCcnW1bROBV/wxnz8PnD1r2yoqgDNngNJSoLjY1lkpyi0hFoGYMAGYOBG49VZbp+ZlsEVOHalzXS2uy/keb4XmA+vXAyUtuLRfzMAZHm6bkVP87NYN6NoVEP/7XbrYNn9/xya+hfr62n6K/4HOnQFvb+dNtPzFJs6t/Gy8qZ+/8W2N6+eKeP6ICM9IrYjgPWzYMPzhD3+Q+/X19YiJicGvfvUrzJo1q8nxaWlpqKqqwscfO6a4HDFiBJKTk+WHgXj66Oho/PrXv8bTTz8t7xcVj4iIwFtvvYVJkyY1OWd1dbXcFOL4Xr16IS8vz71AbrArzwqCumF/RDy+uyoRWT0Tkd29t5xulEgPvOvrMKAoByknD2DYyQMYdPoHRFT9CMMYPhzYuNGtQC5ia1lZmQzol2R1Q3V1tdXb29v64YcfOt2enp5uveuuu1w+JiYmxvraa6853ZaRkWFNSkqS5WPHjokPEuuuXbucjrnxxhutTz75pMtzzp07Vz6GGzdu3GDwLS8v77Kx2a35yEtKSlBXVydby2pi/9ChQy4fU1hY6PJ4cbtyv3Jbc8c0Nnv2bMyYMcO+L74VnDlzBt26dYPFwxc+UD5l3f72oBN8ffpn9NdYoZPXJ7IVZ8+elRkLQy4s4evrKze10NBQ6In4A/LkP6Irxdenf0Z/jcE6eH2XTak0cCuBGh4eDm9vb5xuND2l2I8UHWsuiNsvdbzy051zEhFRKwO5j48PUlJSsGnTJqe0htgfOXKky8eI29XHCxs3brQfL0apiICtPkZ89dm+fXuz5yQiIhWrm9auXWv19fW1vvXWW9YDBw5YH3nkEWtoaKi1sLBQ3v/ggw9aZ82aZT/+66+/tnbq1Mm6aNEi68GDB2VHZefOna179+61HzN//nx5jo8++si6Z88e6/jx461xcXHW8+fPW43mwoUL8j0QP42Ir0//jP4aLxjw9bkdyIU33njD2qtXL6uPj491+PDh1m3bttnvu+mmm6yTJ092Ov69996z9u/fXx4/aNAg6yeffOJ0f319vXXOnDnWiIgI+SExatQoa3Z2dmtfExGRqRjiEn0iIjPj1SJERDrHQE5EpHMM5EREOsdATkSkcwzkHkBMACYmERPTC+xWr6GpYzk5OXj44YfldQJiauI+ffpg7ty5qBGzPeqYu1M464WYfVRMhtelSxc5i+mECRPkjKZGNn/+fPk/99RTT0HvGMg9wMyZM1s0n4KeiLl3xMVib775Jvbv34/XXntNznb5G7GknE6JKZzFHD/iA0lMwzxkyBCMGTMGRUVF0LstW7bgiSeewLZt2+QFe7W1tbj99tvlzKVG9O2338q/zaSkJBiC1uMfzW7Dhg3WhIQE6/79+13OAmkkCxculBd66ZW4ZuKJJ56w79fV1Vmjo6Ot8+bNsxpNUVGR/HvcsmWL1WjOnj1r7devn3Xjxo3yupfp06db9Y4tcg2J+WSmTp2Kt99+GwEmWKtQzBvfVSwAoEMiJZSVlSVXr1J4eXnJ/a1bt8KIvytBr7+vSxHfPMaNG+f0u9Q7Xc5+aATiOqyHHnoIjz76KIYOHSpzykZ29OhRvPHGG1i0aBH0qDVTOOuVSImJvPF1111nX8XLKNauXSvTYiK1YiRskbcxsUqS6EC51Cb+8UVQE3MNi7nVjfj61E6ePIk77rgD9957r/wGQp7fYt23b58MekaSl5eH6dOn4913323REpJ6wkv021hxcTFKL7OmZnx8PH7+85/jX//6l9NCGKLFJ6YJvv/+++X6pXp+fWKmTKGgoAA333yzXN5PLN0n0hF6Ta2I9Nff//53OaJDMXnyZLkU10cffQQjmDZtmnwtX3zxhRxxZCTr16/HxIkT5f+Y+n9O/A+Kv0sxekx9n54wkGskNzdXTterEAFPjIAQgUIMa7vqqqugd6Ilfsstt8ipj9955x3d/pMoxO9l+PDh8tuUkoIQa8WK4OdqvVo9EWFArLv74YcfYvPmzejXrx+M5uzZszhx4oTTbVOmTEFCQgKeffZZXaeRmCPXiAgAakFBQfKnGG9tlCAuWuK9e/eWeXHRklfodcEQMfRQtMBFn4YI6EuWLJHD80QwMEI6Zc2aNbI1LsaSK8ssihVqxHUARtClS5cmwTowMFAuEannIC4wkFO7EGORRQen2Bp/MOn1S2BaWpr8QMrIyJCBTlzElZmZ2aQDVI+WLVsmf4oPX7W//vWvslOePBtTK0REOqfPniciIrJjICci0jkGciIinWMgJyLSOQZyIiKdYyAnItI5BnIiIp1jICci0jkGciIinWMgJyLSOQZyIiLo2/8HT9QB7miHE/QAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "\n",
    "mean = data['Maternal Height'].mean()\n",
    "sd = data['Maternal Height'].std()\n",
    "\n",
    "data1 = data.assign(MH_in_std = (data['Maternal Height'] - mean)/sd)\n",
    "\n",
    "fig, ax = subplots(figsize = (4, 4))\n",
    "ax.hist(data1['MH_in_std'], density = True, bins = np.arange(-5, 5, 0.40));\n",
    "x = np.linspace(-5, 5, 100)\n",
    "ax.plot(x, norm.pdf(x), 'r-', lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9c59e32-42ff-4949-9c38-8c410f9a9a38",
   "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",
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