{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../../data/LLCP2018.XPT\n"
     ]
    }
   ],
   "source": [
    "!ls ../../data/LLCP2018.XPT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>_STATE</th>\n",
       "      <th>FMONTH</th>\n",
       "      <th>IDATE</th>\n",
       "      <th>IMONTH</th>\n",
       "      <th>IDAY</th>\n",
       "      <th>IYEAR</th>\n",
       "      <th>DISPCODE</th>\n",
       "      <th>SEQNO</th>\n",
       "      <th>_PSU</th>\n",
       "      <th>CTELENM1</th>\n",
       "      <th>...</th>\n",
       "      <th>_MAM5022</th>\n",
       "      <th>_RFPAP34</th>\n",
       "      <th>_RFPSA22</th>\n",
       "      <th>_RFBLDS3</th>\n",
       "      <th>_COL10YR</th>\n",
       "      <th>_HFOB3YR</th>\n",
       "      <th>_FS5YR</th>\n",
       "      <th>_FOBTFS</th>\n",
       "      <th>_CRCREC</th>\n",
       "      <th>_AIDTST3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b'01052018'</td>\n",
       "      <td>b'01'</td>\n",
       "      <td>b'05'</td>\n",
       "      <td>b'2018'</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>b'2018000001'</td>\n",
       "      <td>2.018000e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b'01122018'</td>\n",
       "      <td>b'01'</td>\n",
       "      <td>b'12'</td>\n",
       "      <td>b'2018'</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>b'2018000002'</td>\n",
       "      <td>2.018000e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b'01082018'</td>\n",
       "      <td>b'01'</td>\n",
       "      <td>b'08'</td>\n",
       "      <td>b'2018'</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>b'2018000003'</td>\n",
       "      <td>2.018000e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b'01032018'</td>\n",
       "      <td>b'01'</td>\n",
       "      <td>b'03'</td>\n",
       "      <td>b'2018'</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>b'2018000004'</td>\n",
       "      <td>2.018000e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>b'01122018'</td>\n",
       "      <td>b'01'</td>\n",
       "      <td>b'12'</td>\n",
       "      <td>b'2018'</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>b'2018000005'</td>\n",
       "      <td>2.018000e+09</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 275 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   _STATE  FMONTH        IDATE IMONTH   IDAY    IYEAR  DISPCODE  \\\n",
       "0     1.0     1.0  b'01052018'  b'01'  b'05'  b'2018'    1100.0   \n",
       "1     1.0     1.0  b'01122018'  b'01'  b'12'  b'2018'    1100.0   \n",
       "2     1.0     1.0  b'01082018'  b'01'  b'08'  b'2018'    1100.0   \n",
       "3     1.0     1.0  b'01032018'  b'01'  b'03'  b'2018'    1100.0   \n",
       "4     1.0     1.0  b'01122018'  b'01'  b'12'  b'2018'    1100.0   \n",
       "\n",
       "           SEQNO          _PSU  CTELENM1  ...  _MAM5022  _RFPAP34  _RFPSA22  \\\n",
       "0  b'2018000001'  2.018000e+09       1.0  ...       NaN       NaN       NaN   \n",
       "1  b'2018000002'  2.018000e+09       1.0  ...       NaN       1.0       NaN   \n",
       "2  b'2018000003'  2.018000e+09       1.0  ...       NaN       NaN       NaN   \n",
       "3  b'2018000004'  2.018000e+09       1.0  ...       NaN       NaN       2.0   \n",
       "4  b'2018000005'  2.018000e+09       1.0  ...       NaN       NaN       NaN   \n",
       "\n",
       "   _RFBLDS3  _COL10YR  _HFOB3YR  _FS5YR  _FOBTFS  _CRCREC  _AIDTST3  \n",
       "0       NaN       NaN       NaN     NaN      NaN      NaN       2.0  \n",
       "1       NaN       NaN       NaN     NaN      NaN      NaN       2.0  \n",
       "2       NaN       NaN       NaN     NaN      NaN      NaN       2.0  \n",
       "3       2.0       2.0       2.0     2.0      2.0      2.0       2.0  \n",
       "4       NaN       NaN       NaN     NaN      NaN      NaN       2.0  \n",
       "\n",
       "[5 rows x 275 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = '../../data/LLCP2018.XPT'\n",
    "df = pd.read_sas(filename)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0    238911\n",
       "1.0    197412\n",
       "9.0       682\n",
       "7.0       431\n",
       "Name: SEX1, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['SEX1'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "197412"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male = df['SEX1'] == 1\n",
    "male.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "238911"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female = df['SEX1'] == 2\n",
    "female.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    420974.000000\n",
       "mean        169.895887\n",
       "std          10.721788\n",
       "min          91.000000\n",
       "25%         163.000000\n",
       "50%         170.000000\n",
       "75%         178.000000\n",
       "max         241.000000\n",
       "Name: HTM4, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['HTM4'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178.015133313527, 8.272961809579046)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_male = df.loc[male, 'HTM4']\n",
    "height_male += np.random.normal(0, 2, male.sum())\n",
    "\n",
    "height_male.mean(), height_male.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04647336243603734"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_male.std() / height_male.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(163.11558651312797, 7.753027176098179)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_female = df.loc[female, 'HTM4']\n",
    "height_female += np.random.normal(0, 2, female.sum())\n",
    "\n",
    "height_female.mean(), height_female.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.047530878819322366"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "height_female.std() / height_female.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def estimate_std(series, num_sigmas):\n",
    "    ps = norm.cdf([-num_sigmas/2, num_sigmas/2])\n",
    "    ipr = series.quantile(ps)\n",
    "    std = np.diff(ipr) / num_sigmas\n",
    "    return std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f51e555bf70>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.linspace(1, 6, 51)\n",
    "std_series_male = pd.Series(1.0, index=index)\n",
    "\n",
    "for num_sigmas in index:\n",
    "    std_series_male[num_sigmas] = estimate_std(height_male, num_sigmas)\n",
    "    \n",
    "std_series_male.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f51e56adfd0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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idBxjTAOrdx3m+IkaLuvfsjcAPxMrej/0o4t60CEylP/5eKvTUYwxDSzZcpA2ocFc0DPO6SjfsqL3Q9ERodw9vhcrdhxi5c5DTscxxrioKku3FDK2TxwRocFOx/mWFb2fumlUd7q0b8OfFm+1+8oa4yM255dysLSSCf1842ybU85a9CKSKiKZ9T5KReReN9uNc63fLCLL6y2fLCLbRGSniDzg7RfQWkWEBnPvhN5szC3hw00HnI5jjAGWbClABC7p6zvj89CIolfVbaqapqppwDCgHJhbfxsRaQ88B0xV1QHA913Lg4FngSlAf2CGiPT37ktova4d2pW+SdH84aOtNl+9MT5gaXYBw87rQKxDtww8HU+Hbi4FclR1b4PlNwDvq+o+AFUtdC0fAexU1V2qegJ4C5jWlMDmX4KDhIenDiDvaIVNjWCMw/KOVrA5v9RnLpKqz9Oinw686WZ5H6CDiHwuIutE5BbX8i7A/nrb5bqWfYeIzBSRDBHJKCoq8jBW6zUyJZarBndm9vIc9h8udzqOMa3Wp66bA03w56IXkTBgKvCOm9Uh1A3rXAFMAn4rIn0Ad1cLuH3nUFVfVNV0VU2Pj49vbCwD/MflfQkW4XcfbnE6ijGt1pItBaTERfnEJGYNeXJEPwVYr6ru7mmXCyxW1eOqegj4AhjsWt6t3nZdAbtVkpd1imnDPZf04uPNBazYYX8NGdPSyipPsnpXsU8ezYNnRT8D98M2APOBi0QkREQigZFANvA10FtEerj+IpgOLGhKYOPenRf1oHtsJA8t2MyJ6lqn4xjTqnyx/RAna9TnTqs8pVFF7yrvy4D36y2bJSKzAFQ1G1gMbATWAi+rapaqVgP3AB9TV/z/VNXN3n0JBiA8JJgHr+xPTtFxXlu1x+k4xrQqS7ML6BAZyrDuHZyO4lZIYzZS1XIgtsGy2Q2+fgx4zM1jPwI+akJG00iX9ktkfGo8T366g2lDOpMQHeF0JGMC3smaWj7bWsiEfokE+8gkZg3ZlbEB5sGrBnCiupY/LdrmdBRjWoWMPUcoqTjpU5OYNWRFH2B6xEVxx0U9eG99Luv3HXE6jjEBb2l2AWEhQVzU23fPFrSiD0D3jO9FXNswnlhqd6Iyprl9trWQC3rGEhXeqJFwR1jRB6Co8BBuuyCZL7YXkX2g1Ok4xgSsAyUV7D50nAt7+c6UxO5Y0Qeom0Z1JzIsmJdW7HI6ijEBa82uwwCMSok9y5bOsqIPUO0jw/hBejcWZOZzoKTC6TjGBKTVu4ppFxFCv07tnI5yRlb0AeyOC3tQq8qclXucjmJMQPpqVzEjU2J99rTKU6zoA1i3jpFcfn4n3lizj7LKk07HMSag5B+tYG9xuc8P24AVfcCbOTaFsqpq3lq7/+wbG2Mabc3uYgBGpXR0OMnZWdEHuEFd2zMqpSOvrtzNyRqbA8cYb1mdc5iYNqH0S/Lt8Xmwom8Vfjy2JwdKKlm40SYONcZbVu8uZkSPjgT5+Pg8WNG3Chf3iad3QlteWL7LbiRujBf40/g8WNG3CkFBwo/GprD1YBkrdhxyOo4xfu/U+PxoK3rjS6aldSYhOtwuoDLGC77KKSamTSh9k6KdjtIoVvStRHhIMLeNSWbFjkNszi9xOo4xfm31rsOM9JPxebCib1VuHNmdqLBgm+zMmCbIO1rBvsP+Mz4PVvStSkybUO65pDdLthSwbFuh03GM8Utrdp06fz6Ail5EUkUks95HqYjc22CbcSJSUm+bB+ut2yMim1zLM5rjRZjGu+PCHqTER/HQgs1UnqxxOo4xfmf1rmLaR/rP+Dw0ouhVdZuqpqlqGjAMKAfmutl0xantVPWRBuvGu5aneyGzaYKwkCAemTqQvcXlvPiFvTFrjKf8bXwePB+6uRTIUdW9zRHGtIwLe8dxxaBOPLtsJ/sPlzsdxxi/4Y/j8+B50U8H3jzNutEi8o2ILBKRAfWWK/CJiKwTkZnnlNJ43X9e0Y/gIOHhD7Y4HcUYv7E6x//G58GDoheRMGAq8I6b1euB7qo6GHgamFdv3RhVHQpMAe4WkbGn+f4zRSRDRDKKiooa/QLMuekU04afXdqbpdkFfJpd4HQcY/zCqfH51ET/GZ8Hz47opwDrVfU7raCqpap6zPX5R0CoiMS5vs53/VtI3dj+CHffXFVfVNV0VU2Pj/fdm+wGkh+O6UGvhLY89IG9MWtMY6zeXex34/PgWdHP4DTDNiKSJCLi+nyE6/sWi0iUiES7lkcBE4GspkU23lL3xuwA9h+u4PnPc5yOY4xPyz1Szv7DFX4z7UF9jSp6EYkELgPer7dslojMcn15HZAlIt8ATwHTtW72rETgS9fytcCHqrrYmy/ANM0FveK4anBnnl+ew97i407HMcZnfXt/2J7+V/QhjdlIVcuB2AbLZtf7/BngGTeP2wUMbmJG08x+c3k/Pssu4P73NvKPO0YSEmzX0RnT0Fe7iukQGUqfBP8anwe7MtYASTERPDxtIKt3HeZ/Pt7mdBxjfI6q8lVOMSN7xPrd+DxY0RuX64Z15eZR3Xnxi112gxJjGtiYW0Le0QrG9/XPE0Ws6M23fntlf4Z178Cv3t3ItoNlTscxxmfMz8wnLDiIyQM7OR3lnFjRm2+FhQTx3I1DiQoP4cd/z6Ck4qTTkYxxXE2t8sHGfMb3jSemTajTcc6JFb35fxLbRfDcjUPJPVLBL97OpLbWbj1oWrevcoopKqtiWloXp6OcMyt68x3Dkzvy4FX9+XRrIU99ZnPXm9ZtfmYe0eEhXNI3weko58yK3rh186juXDu0C08s3cGSLTZFgmmdKk/WsDjrIJMGJhERGux0nHNmRW/cEhH++5rzGdilHXf9Yx1/X72XumvgjGk9lm0tpKyqmmlpnZ2O0iRW9Oa0IkKDef3OUVzUO47fzsvigfc2UVVtc+KY1mN+Zj5xbcO5oGec01GaxIrenFFMm1BevnU494zvxdsZ+7n+hdUcLKl0OpYxza6k4iSfbSvkqsGdCPbDi6Tqs6I3ZxUcJPz7pFRm3zSU7QVlXPXMl2TsOex0LGOa1cdZBzlRXcvVfny2zSlW9KbRJg/sxLy7xxAVFsyMl1bzz4z9TkcyptnM/yaP5NhIBnWNcTpKk1nRG4/0SYxm/t0XMqJHR34zdxM7C485HckYryssrWRVTjFT07rgmoHdr1nRG4/FRIby5PQhRIQG8/AHm+1sHBNwPth4AFX8/mybU6zozTmJaxvOLy/rw4odh/h4s51nbwLL/Mw8zu8SQ8/4tk5H8QorenPObhrVnb5J0Ty6cAsVJ+y0SxMYdhUdY2NuScAczYMVvWmCkOAgHp46gLyjFTz/+U6n4xjjFQu+yUcErhpsRW8MACNTYrk6rTOzv9hltyI0fq+2Vpmfmc/olFgS20U4Hcdrzlr0IpIqIpn1PkpF5N4G24wTkZJ62zxYb91kEdkmIjtF5IHmeBHGWb++vB+hQcIjH2xxOooxTTJ3Qx67Dx3n+uHdnI7iVWctelXdpqppqpoGDAPKgbluNl1xajtVfQRARIKBZ4EpQH9ghoj091584wsS20Xwswm9+XRrIZ9m2xuzxj+VVZ7kD4u2ktatPVcNCpxhG/B86OZSIEdV9zZy+xHATlXdpaongLeAaR4+p/EDPxzTg14JbXlk4RYqT9obs8b/PLl0B8XHq3hk2gC/vC/smXha9NOBN0+zbrSIfCMii0RkgGtZF6D+5ZO5rmXfISIzRSRDRDKKioo8jGWcFhocxENXDWBvcTkvLN/ldBxjPLKjoIw5q/ZwfXo3BnVt73Qcr2t00YtIGDAVeMfN6vVAd1UdDDwNzDv1MDfbur26RlVfVNV0VU2Pj/fPG/C2dhf2juOK8zvxv0u3c+drX7Mlv7TFnrus8iRvf73PrtQ1HlNVHvpgM5Fhwdw3KdXpOM0ixINtpwDrVfU7g7CqWlrv849E5DkRiaPuCL7+uxpdgfxzDWt835+/P5j+ndvxwvIcLn9qBVcO6sTPL+tz2gtPKk/WcOhYFfHR4YSHeH5jB9W6syR+/1E2RWVVBAlcN6wrP5vQhy7t2zT15ZhWYHHWQVbuLObhqQOIbRvudJxmIY29fF1E3gI+VtW/ulmXBBSoqorICOBdoDsQDGynbmw/D/gauEFVN5/pudLT0zUjI8OjF2J8S0n5SV5asYtXV+6m8mQN1w7tyowR3cg/Wsn2gjK2F5Sxo+AYe4qPU6sgAgnR4XTtEEnXDm3o0r4N3WMjGda9Iz3jo9zON7LtYBm/nZ/F2t2HGdQ1hvsmpbJsaxH/WF33FtJNo7pz9/ieAfvDa5qu4kQNE/6ynOiIEBb+9EJCgv33jHMRWaeq6W7XNaboRSSSurH2FFUtcS2bBaCqs0XkHuAuoBqoAH6hqqtc210OPEFd6b+qqr8/2/NZ0QeOQ8eqeP7zHP6+ei8nqmsBCBJIjo2iT2I0fRLb0ql9GwpKK8k9UkHekQpyj5aTf7SSGteNyROiwxmVEsvonrGMSoklrm0YTyzdwZxVe4iOCOFXk/py/fBu384Znne0gieXbufddbm0CQ3mjotSGJcaT2K7COLbhhMW8v9/mKuqa8gpPM7Wg6VkHyhle8ExRveM5cdjUwJiQitzen/5ZBtPfbaTt2eOYmRKrNNxmqTJRd/SrOgDz4GSCjbsO0pybBQp8VFnvf9mdU0t+w6Xs2b3Yb7KKearXcUUlVUBEBosVNcq04efx68mpdIhKszt99hZeIy/LNnGR5sO/r/lHaPCSIgOJz46nMLSKnKKjlHt+qUSFhJE55gI9hSXc9Xgzjx23SC/vleoOb19xeVM+N/lTBmYxJPThzgdp8ms6I3fU1Vyio6zelcx2wvK+N7Qrgzu1rizI/YcOs7uQ8cpKK2ksKyKgtJKCkqrKCqrJLZtOH2TounbqR39O0WTHBtFcJDwwhe7+NPirQzu2p4XbxlGQnTgXCVp6tz5Wgarcg7x2S/HkRTj//99z1T0nrwZa4xjRIReCW3pleD5bILJcVEkx0V59JhZF/ckOTaKn7+dydXPrOTlW4fTv3M7j5/b+KasvBKWZhdw36TUgCj5s/Hfdx6MaWaTBybxzqzR1CpcN3sVS7bYVb+BYt6GPEKDhRtGnOd0lBZhRW/MGQzsEsP8e8bQK6EtM/+ewZyVu52OZJqoplZZ8E0+F/dJOO37O4HGit6Ys0hsF8HbM0dzWb9EHvpgC++uy3U6kmmC1buKKSyr4uohgTWfzZlY0RvTCG3Cgnn6hiGM6RXL/e9ttMnb/NjcDXm0DQ9hQr9Ep6O0GCt6YxopPCSYF25Op3+ndtz9xnrW7T3sdCTjocqTNSzOOsjkgUmt6rRZK3pjPNA2PIS//nA4nWLacPucDLYXlDkdyXjg0+xCjlVVc3Wa27kVA5YVvTEeimsbzt9uH0F4SBC3vLKWvKMVTkcyjTQvM4+E6HBG9/Tvq2A9ZUVvzDno1jGS124fwfET1dz8yhoOHz/hdCRzFkfLT/D5tkKmDu787XQZrYUVvTHnqF+ndrxy63DyjlQw7dkvmbNyN2WVJ52OZU7jw00HOFmjXD2kdQ3bgBW9MU0yokdH5vxwBPFtw3nogy2M/sNnPLRgM7sP2Y3Sfc38Dfn0jI9iQCu8wtmmQDCmiUb3jOX9n4zhm/1HmbNqD6+v2cucVXsYnxrPHRemMKZXrM2C6bDcI+Ws3XOYf5/Yp1X+t7AjemO8ZHC39vzv9WmsfOAS7p3Qm015pdz0yhquf3E1X++xUzGdND+z7n5H01rZ2TanWNEb42UJ0RHcO6EPKx8YzyPTBrD70HG+P/srbvvrWrLySpyO1+rU3YUsj2HdO9CtY6TTcRxhRW9MMwkPCeaW0cl8cd94HpjSlw37jnLl01/yk9fX2b1tW1D2gTK2FxxrlW/CnmJFb0wzaxMWzKyLe7Li/vH82yW9WL6tiCueWsFXOcVOR2sV5mXmERIkXHF+J6ejOMaK3pgW0i4ilF9MTGXZfeM4r2Mkd7z2Nev2HnE6VkCrqVUWZOZzcZ94OraSmSrdOWvRi0iqiGTW+ygVkXtPs+1wEakRkevqLaup99gF3gxvjD9KiI7g9TtHkhAdbuP2zezjzQc5WFrZqgcKMdsAAA41SURBVIdtoBFFr6rbVDVNVdOAYUA5MLfhdiISDPwJ+LjBqopTj1fVqd4IbYy/S2gXwes/GkW7iFBufmWNzZnTDCpP1vDfH2XTNymaKQOTnI7jKE+Hbi4FclR1r5t1PwXeAwqbnMqYVqBL+za8fudIQoODuPHlNXaRlZe9unI3uUcqePDK/oQEt+5Rak9f/XTgzYYLRaQLcA0w281jIkQkQ0RWi8jVp/vGIjLTtV1GUVGRh7GM8U/JcVG8fudIamqVG19aTe6RcqcjBYTC0kqe/Wwnl/VP5IJecU7HcVyji15EwoCpwDtuVj8B3K+qNW7Wnee6M/kNwBMi0tPd91fVF1U1XVXT4+PjGxvLGL/XOzGav98xgmNV1dz48hpKKmy+nKZ67ONtnKip5TeX93M6ik/w5Ih+CrBeVd3dWicdeEtE9gDXAc+dOnpX1XzXv7uAz4EhTQlsTCAa0DmGV28bTu6RCh5esNnpOH5tU24J767P5YdjepAcF+V0HJ/gSdHPwM2wDYCq9lDVZFVNBt4FfqKq80Skg4iEA4hIHDAG2NLEzMYEpPTkjtwzvhfvb8hj0aYDTsfxS6rKIws30zEyjHsu6eV0HJ/RqKIXkUjgMuD9estmicisszy0H5AhIt8Ay4A/qqoVvTGncc8lvRjUNYb/mLuJwtJKp+P4nQ83HeDrPUf45cRU2kWEOh3HZ4iqOp3hO9LT0zUjI8PpGMY4YmfhMa54agUX9Izl1duGt8rZFs9F5ckaLn18Oe3ahLLwpxe2upuLiMg61/uh39G6zzkyxgf1SmjLA1P6smxbEW+u3e90HL/x8opd5B2t4LdX9mt1JX82VvTG+KBbRyczplcsv/twC3uL7fz6s8k7WsFzn+cwaUAiF/S00ykbsqI3xgcFBQmPXTeY4CDhl//8hppa3xti9RWllSe5/a9fEyTCby7v73Qcn2RFb4yP6ty+DY9OG0jG3iO88EWO03F80onqWu76xzpyio7x/E1DOS+2dc43fzZ2K0FjfNi0tM58suUgj3+yna9yipnQL5FL+yXQtYMVmqrywHsbWbmzmMe/P5iLetuFlqdjRW+MDxMR/vi9QXTrsJMlWwr4rwWb+a8Fm+mbFM2l/RKY2D+Jwd3aOx3TEY9/sp33N+Txy8v68L1hXZ2O49Ps9Epj/MiuomN8ml3I0uwCMvYeoaZW+fHYFO6f3JegVnSmyRtr9vEfczcxY0Q3/vua8+0UVM58eqUd0RvjR1Li25IS35YfjU3haPkJ/vzJNl74YhdFZVX86bpBhLaCWRo/21rAf87bxPjUeB6dNtBKvhGs6I3xU+0jw3h02kASoyN4fMl2io+f4LkbhxIVHpg/1qrKwo0H+NW7GxnQOYZnbhja6qcfbizbS8b4MRHhp5f25o/Xns+KHUXc8PIaDh8/4XQsr1u39zDXPr+Kn765gR5xUbxyW3rA/kJrDranjAkA00ecR8eoMH765gaue34Vr90+gm4d/f/MnL3Fx/nT4q18tOkgCdHh/M91g/je0K525auH7M1YYwLI13sOc8ecrwkPDebJ6Wl+e5VoWeVJnli6g799tYeQoCB+fHEKM8emEBlmx6anY3PdGNNKDE/uyDuzLqBNaDA3vLSGn7+dSVFZldOxPJJ/tILrnv+KV1fu5tohXVl+3zjundDHSr4JbM8ZE2BSk6L55OdjeXbZTmYvz+HT7ALun9KXGcPP8/lTMDfnl3D7nK8pr6rhH3eMZIzdBtAr7IjemAAUERrMLyemsuhnYxnQOYbfzM3i2udXsTm/xOlop/X5tkJ+MPsrgkR4964LrOS9yMbojQlwqsr8zHx+9+EWDh8/wfjUBK4Y1IkJ/RN95uYcb63dx2/mZdEnMZq/3jacpJgIpyP5HbtgyphWTES4ekgXxqcm8PzyHBZk5vHp1kLCgoMY2yeurvT7JRLtQOmrKo9/sp1nlu1kbJ94nrtxKG3ttEmvsyN6Y1qZ2lolM/coH248wEebDnCgpJKwkCCmDEzi9jE9WmzuHFXlN/OyeGPNPqYP78ajVw9sFVf2NpczHdGftehFJBV4u96iFOBBVX3CzbbDgdXA9ar6rmvZrcB/ujb5naq+drbAVvTGtIzaWmXD/qN88E0+767L5VhVNcO6d+D2MT2YNCCx2a48VVUeXZjNqyt3c9e4nvxqUqpNZdBETSr6Bt8oGMgDRqrqXjfrlgCVwKuq+q6IdAQygHRAgXXAMFU9cqbnsaI3puWVVZ7knYxc5qzaw77D5XRp34ZbRnfnxlHdvT6c8uePt/HMsp38cEwyD17Z30reC7x5Hv2lQE7Dknf5KfAeUFhv2SRgiaoedpX7EmCyh89pjGkB0RGh3H5hD5b9+zhevHkY3Tq24Q+LtnLDS6spP1Htted5dtlOnlm2k+nDu1nJtxBPi3468GbDhSLSBbgGmN1gVReg/t2Nc13LvkNEZopIhohkFBUVeRjLGOMtwUHCxAFJvDVzNLNvGkZWXgk/fWMD1TW1Tf7er365m8c+3sa0tM783qYXbjGNLnoRCQOmAu+4Wf0EcL+q1jR8mJtt3Y4VqeqLqpququnx8XanGGN8weSBSTw8dQCfbi3koQ8205STN95au49HFm5h0oBEHv/+YJuvpgV5MvA2BVivqgVu1qUDb7l+O8cBl4tINXVH8OPqbdcV+PyckhpjHHHz6GRyj1bwwvJddGkfyV3jenr8PeZuyOXXczdxcZ94npoxxKYXbmGeFP0M3AzbAKhqj1Ofi8gcYKGqznO9GfvfItLBtXoi8OtzzGqMccj9k/qSf7SSPy3eSuf2EUxLczsC69aba+vuBjWqRywv3DyM8JDgZkxq3GlU0YtIJHAZ8ON6y2YBqGrDcflvqephEXkU+Nq16BFVPXzucY0xTggKEv78/UEUlFZy3zsbSWwXwaiU2LM+7pUvd/Powi2MS41n9k3DiAi1kneCXTBljGm0kvKTfG/2KgpLK3njR6MY2CXG7XaqyjOf7eTxJduZMjCJJ6cPISzEhmuak01TbIzxipjIUP5623DCQ4O58ukvufmVNSzOOsDJemfkqCp/WryNx5ds59ohXXh6hpW80+yI3hjjscKySt5cs5+3vt7HgZJKEqLDuX54N36Q3o2XVuzib1/t5caR5/HotIE+PzVyoPDalbEtxYreGP9QXVPL59uKeH3NXj7fXsSpOpk5NoVfT+lr58m3IJu90hjTLEKCg5jQP5EJ/RPJPVLOP7/eT8eoMG69INlK3odY0RtjvKJrh0h+MTHV6RjGDXuHxBhjApwVvTHGBDgremOMCXBW9MYYE+Cs6I0xJsBZ0RtjTICzojfGmABnRW+MMQHOJ6dAEJEiwN19aRsjDjjkxTjeYrk8Y7k8Y7k8E4i5uquq29vz+WTRN4WIZJxuvgcnWS7PWC7PWC7PtLZcNnRjjDEBzoreGGMCXCAW/YtOBzgNy+UZy+UZy+WZVpUr4MbojTHG/H+BeERvjDGmHit6Y4wJcH5Z9CLyqogUikjWadaLiDwlIjtFZKOIDPWRXONEpEREMl0fD7ZQrm4iskxEskVks4j8zM02Lb7PGpmrxfeZiESIyFoR+caV62E324SLyNuu/bVGRJJ9JNdtIlJUb3/d2dy56j13sIhsEJGFbta1+P5qZC5H9peI7BGRTa7n/M59U73+86iqfvcBjAWGAlmnWX85sAgQYBSwxkdyjQMWOrC/OgFDXZ9HA9uB/k7vs0bmavF95toHbV2fhwJrgFENtvkJMNv1+XTgbR/JdRvwTEv/P+Z67l8Ab7j77+XE/mpkLkf2F7AHiDvDeq/+PPrlEb2qfgEcPsMm04C/aZ3VQHsR6eQDuRyhqgdUdb3r8zIgG+jSYLMW32eNzNXiXPvgmOvLUNdHw7MWpgGvuT5/F7hUmvkmqY3M5QgR6QpcAbx8mk1afH81Mpev8urPo18WfSN0AfbX+zoXHygQl9GuP70XiciAln5y15/MQ6g7GqzP0X12hlzgwD5z/bmfCRQCS1T1tPtLVauBEiDWB3IBfM/15/67ItKtuTO5PAH8Cqg9zXpH9lcjcoEz+0uBT0RknYjMdLPeqz+PgVr07o4UfOHIZz1181EMBp4G5rXkk4tIW+A94F5VLW242s1DWmSfnSWXI/tMVWtUNQ3oCowQkYENNnFkfzUi1wdAsqoOApbyr6PoZiMiVwKFqrruTJu5Wdas+6uRuVp8f7mMUdWhwBTgbhEZ22C9V/dXoBZ9LlD/N3NXIN+hLN9S1dJTf3qr6kdAqIjEtcRzi0godWX6uqq+72YTR/bZ2XI5uc9cz3kU+ByY3GDVt/tLREKAGFpw2O50uVS1WFWrXF++BAxrgThjgKkisgd4C7hERP7RYBsn9tdZczm0v1DVfNe/hcBcYESDTbz68xioRb8AuMX1zvUooERVDzgdSkSSTo1LisgI6vZ/cQs8rwCvANmq+pfTbNbi+6wxuZzYZyISLyLtXZ+3ASYAWxtstgC41fX5dcBn6noXzclcDcZxp1L3vkezUtVfq2pXVU2m7o3Wz1T1pgabtfj+akwuJ/aXiESJSPSpz4GJQMMz9bz68xhyzmkdJCJvUnc2RpyI5AL/Rd0bU6jqbOAj6t613gmUAz/0kVzXAXeJSDVQAUxv7v/ZXcYANwObXOO7AP8BnFcvmxP7rDG5nNhnnYDXRCSYul8s/1TVhSLyCJChqguo+wX1dxHZSd2R6fRmztTYXP8mIlOBaleu21ogl1s+sL8ak8uJ/ZUIzHUdv4QAb6jqYhGZBc3z82hTIBhjTIAL1KEbY4wxLlb0xhgT4KzojTEmwFnRG2NMgLOiN8aYAGdFb4wxAc6K3hhjAtz/Af6kOYxKP78tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "index = np.linspace(1, 5, 51)\n",
    "std_series_female = pd.Series(1.0, index=index)\n",
    "\n",
    "for num_sigmas in index:\n",
    "    std_series_female[num_sigmas] = estimate_std(height_female, num_sigmas)\n",
    "    \n",
    "std_series_female.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.066807    152.117512\n",
       "0.933193    174.271321\n",
       "Name: HTM4, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ipr_female = height_female.quantile(ps)\n",
    "ipr_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.81866273])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(ipr_male) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7.38460301])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diff(ipr_female) / 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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": [
    "from empiricaldist import Cdf\n",
    "\n",
    "cdf_male = Cdf.from_seq(height_male)\n",
    "cdf_male.plot()\n",
    "\n",
    "cdf_female = Cdf.from_seq(height_female)\n",
    "cdf_female.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}