PST_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><h1> Domácí úloha BI-PST  ZS 2017/18</h1></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Vypracovali: \n",
    "* Jan Horák \n",
    "* Jaroslava Sydorenko "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Požadavky na spuštění notebooku\n",
    "* Nainstalovat Python knihovny: `pip install numpy pandas scipy matplotlib seaborn rpy2`\n",
    "* Nainstalovat R, a nainstalování knihovny Sleuth2: `install.packages(\"Sleuth2\")`\n",
    "\n",
    "Nic jiného není potřeba.  \n",
    "Využívám Python knihovnu rpy2, která umožňuje provádět R příkazy přímo zde v Jupyter notebooku."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import knihoven a nastavení notebooku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from math import sqrt\n",
    "import rpy2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext rpy2.ipython"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.set_printoptions(precision=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 1)\n",
    "*(1b) Načtěte datový soubor a rozdělte sledovanou proměnnou na příslušné dvě pozorované\n",
    "skupiny. Data stručně popište a pro každu skupinu zvlášť odhadněte střední\n",
    "hodnotu, rozptyl a medián příslušného rozdělení.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zvolíme správný dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11\n"
     ]
    }
   ],
   "source": [
    "K=29 # 29.9.1995\n",
    "L=len(\"horak\")\n",
    "M=(K*L*47)%(12)\n",
    "print(M)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Získali jsme 11, tedy náš **dataset je: ex0222** - hladina cholesterolu dle prostředí.\n",
    "\n",
    "Provedem načtení datasetu pomocí R, a přiřadíme do Pythonovského DataFramu df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "%R library('Sleuth2');\n",
    "%R -o df df = ex0222;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podíváme se na začátek a konec datasetu:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>133.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>134.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>155.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>170.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>175.0</td>\n",
       "      <td>Urban</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>204.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>220.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>223.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>226.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>231.0</td>\n",
       "      <td>Rural</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cholesterol  Group\n",
       "1         133.0  Urban\n",
       "2         134.0  Urban\n",
       "3         155.0  Urban\n",
       "4         170.0  Urban\n",
       "5         175.0  Urban\n",
       "90        204.0  Rural\n",
       "91        220.0  Rural\n",
       "92        223.0  Rural\n",
       "93        226.0  Rural\n",
       "94        231.0  Rural"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df.head(), df.tail()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vidíme, že dataset obsahuje dvě skupiny o celkovém počtu 94 hodnot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rozdělíme dataset na Urban a Rural subsety:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_urban = df[df.Group == \"Urban\"]\n",
    "df_rural = df[df.Group == \"Rural\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urban popis\n",
    "Využijeme metody dataframu **describe**, a zjistíme rozptyl."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cholesterol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>45.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>216.866667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>39.920148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>133.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>196.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>206.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>239.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>330.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cholesterol\n",
       "count     45.000000\n",
       "mean     216.866667\n",
       "std       39.920148\n",
       "min      133.000000\n",
       "25%      196.000000\n",
       "median   206.000000\n",
       "75%      239.000000\n",
       "max      330.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rozptyl 1593.6181818181815\n"
     ]
    }
   ],
   "source": [
    "df_urban.describe().rename({\"50%\": \"median\"})\n",
    "print(\"Rozptyl {}\".format(list(df_urban.var())[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rural popis\n",
    "Využijeme metody dataframu **describe**, a zjistíme rozptyl."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cholesterol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>49.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>157.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>31.756233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>95.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>136.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median</th>\n",
       "      <td>152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>174.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>231.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Cholesterol\n",
       "count     49.000000\n",
       "mean     157.000000\n",
       "std       31.756233\n",
       "min       95.000000\n",
       "25%      136.000000\n",
       "median   152.000000\n",
       "75%      174.000000\n",
       "max      231.000000"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Rozptyl 1008.4583333333334\n"
     ]
    }
   ],
   "source": [
    "df_rural.describe().rename({\"50%\": \"median\"})\n",
    "print(\"Rozptyl {}\".format(list(df_rural.var())[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Našli jsme **minimum, maximum, střední hodnotu (mean), rozptyl a medián** pro obě pozorované skupiny."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 2)\n",
    "*(1b) Pro každou skupinu zvlášť odhadněte hustotu a distribuční funkci pomocí histogramu\n",
    "a empirické distribuční funkce.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rural histogram a ECDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nZmZq7969I21ntV9wcdX+TfPdHYCZ18tmy7zZ8sJwmTfqCy6W65H1uJ/H+Z2TDFXoY73L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\ndElqxP8Djr38PILUl3gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cf1c72320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rural.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Využijeme funkci ECDF z jazyka R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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4F4fbplhyI6oWJZfvok1AeXl5nTp18ng8DRo0GD9+fGZmpuyOAP3SIKDNTtUl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\n0CkCGgB0ioAGAJ36/w2jOHjDDMFTAAAAAElFTkSuQmCC\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R -i df_rural plot(ecdf(df_rural$Cholesterol))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urban histogram a ECDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mzQBMTEwwOjq64O2s3HBFBykXbv2qKTZu7eRMVO+YbfHqmgv2ZNt2znED2f58z6le77d2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\nsNAlqRAWuiQVwkKXpEL8P3FQRIuibRsDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cf1e9cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_urban.hist();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Využijeme funkci ECDF z jazyka R."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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He18//fTTH330kdlsltsSEHWY4kBE+NLZ67e//a2sToDoRUAj/L7//vuAypYt\nW1RVldIMEL0IaIRfy5YtAyp9+vRRFEVKM0D0IqARfoqiOBwO/8rUqVNlNQNEL04SIiKmTp3auXPn\n4uLi+vXrP/bYYz169JDdERB9CGiE2bFjx/7nf/6nVatW6enpGRkZstsBohhTHAin9957r23btllZ\nWb179x4wYMCpU6dkdwREMQIaYXP69OnHHnvMt7hlyxaurgPqgoBG2HzxxRcBleXLl+/du1dKM0AM\nIKARNm3btg2odO3aNSUlRUozQAwgoBE2BoNh2LBh/pVf//rXCQn8GwNuE1dxIGwURfnzn/88d+7c\n7du3t2jRYsqUKenp6bKbAqIYAY1wunDhwrhx41588cXExETZvQBRjz8/ER4XL14cO3Zs69atjUbj\nI4888vHHH8vuCIh6BDTC4+WXX16+fLn3dWlpqclkunz5styWgGhHQCM8Pvnkk4DK7t27pXQCxAwC\nGuFRr169gErTpk2ldALEDAIa4TFq1Cj/xX79+hkMBlnNALGBgEZ4TJo0ad68eX369BFCTJgwYenS\npVwBDdQRl9nhllVUVIQM3ylTpkyZMkX7foBYxRgHt+Czzz4bMGBAYmJi8CP5AYQdI2jU1g8//NC9\ne3ffYn5+frt27caMGSOxJSC2EdCorR07dgRUHA5H8FxHWlrav/3bv2nVFBDLCGjUVkVFRUDl4sWL\nJ0+eDCheuHBBq46AGEdAo7Z69eoVUMnPz3/iiSekNAPEA04SorbuuOOO0tJS74V0QohXXnmFdAYi\nihE0boHJZPrrX/965syZJk2acJkzEGn8H0OtqKr6xhtvpKen9+3bt6Cg4Pz587I7AmIfI2jUit1u\nf/bZZ72vS0pKDhw48O6778ptCYh5jKBRK2vWrPFfXL58+enTp2U1A8QJTQLa4zArXmaHx1d12RSb\nS4vNIwxKSkoCKqdOnZLSCRA/NAhojyM3P82pqqqqqotFbrWQRrQIuGDDbDZ37NhRVjNAnNAgoN3l\nZdZsi/e1Ia9UfancyNA56syZM6d///7e12az2W63y+0HiAcanCQ0ppqKil2FlsqMFpZC1e0wK2Zh\nEmnZkd88wqJ169YbNmzweDznzp3r3Llz8OP5AYSdBiNoQ16pU2RWm9kw5JW6R4uyyG8bYXHs2LEZ\nM2YMHTr0zTffbNOmDekMaEOby+wshaoaUMj2/GsAAAy9SURBVDLklap5mmwcdXP69OmcnJyyssrf\np//xH/9x7Nix1q1by+0KiAf6vczu6tWrJ4NcunRJdl9x54MPPvCls9eyZctkNQPEFYk3qrhsSnG2\nWmip4e3Vq1fPnz8/oLhv3z6j0RjpzuDv22+/DagcOnRISidAvJEY0JZCtaZwFkKI4cOHDx8+PKA4\nbdq0I0eORLIrBHrggQcCKj169JDSCRBv9DvFAZ2wWCxWq9W3OH78+NGjR0vsB4gfGt9J6I9roaNG\nYWHh3//+9yVLlnzyySfLli3jOXaANjSY4qi8k7A0YD7DZVPMDndpniHyHeCWHDp0aOnSpadOnerb\nt+/AgQO9xe7du/t/ICEADWgQ0P53EvqxZFszi91CENC68vnnn3fr1s37+k9/+tPzzz//7//+73Jb\nAuKWBn+reu8kDCq7iotMqVyQoTcvv/yy/+Ls2bOPHz8uqxkgzmkwgjbklTptiqIE1q1OlfkN3Vm5\ncmVAZfDgwS1atBBC3HPPPYWFhTKaAuKUdncS8j87KowaNWrFihX+FZfL1apVK1n9APGM0/Go5pVX\nXvFf/P3vf086A7LwkVeo5r777jt69Ojy5ctPnTqVkZFhNptldwTELwIaQlXVkpISj8fTpUuX3r17\nt2nTZurUqbKbAkBAx72rV6+OHDnyww8/9C4++eSTb7/9ttyWAHgxBx3vlixZ4ktnIcQ777yzYcMG\nif0A8CGg491nn30WUPl//+//9e/f/89//rOUfgD4MMUR7+66666Ayssvvzxu3DgpzQDwxwg63k2Y\nMMF/sU+fPjk5OZJ6AVANI+h496Mf/ei77777r//6L7fb3aVLl1/+8peNGjWS3RQAIQjoOLdnz57j\nx4937dq1oKBAdi8AAjHFEacuXbo0fPjw1NTURx55JDk5+d1335XdEYBABHSc+tOf/rRq1Srf4rhx\n444dOyaxHwDBCOg4FfBB3UKImTNnSukEQE0I6DiVnJwcUBkxYoSUTgDUhICOU7/4xS/8F/v27du/\nf39ZzQAIias44lR6evqGDRtee+21s2fPdu3a9Te/+U1SEv8YAH3h/2QcOXnyZFJSUrNmzbyLAwYM\nGDBggNyWANwAUxxx4cCBAxaLpWXLls2bNx8xYsT3338vuyMAN8cIOi5Mnjx5/fr13tcrV65s0qTJ\nkiVL5LYE4KYI6Nh39uxZp9PpX1m6dGlqaqr3g3x79uyZnp4uqTUAN0JAx76QZ/86derkfdGmTRtt\n2wFQWwR07GvYsOGYMWPee+89X2Xq1KmjRo2S2BKA2uAkYVyYP3/+U0895X09bdq02bNny+0HQG0w\ngo4LycnJCxYsWLBggexGANwCRtAx5cKFC88++6yiKIqiPPPMMz/88IPsjgDcPkbQMWX69Omvv/66\n9/X8+fPPnDmzdOlSuS0BuG0EdEzxpbPXsmXLxo8f738VR48ePVq0aKF5XwBuBwEdOyoqKoKL//u/\n/1uvXj3f4r333ktAA9GCgI4dCQkJjz322PLly32VrKysl156SWJLAOqCk4QxxeFwZGVleV8PHDjw\njTfekNsPgLpgBB1T2rRps2bNmpMnT1ZUVLRq1Up2OwDqhBG0XmzdunXIkCF9+/bNz8+v49PmkpOT\nSWcgBjCC1oW//vWvGRkZ3tclJSVffPHF+vXr/U/uAYhDBLQuvPXWW/6LW7duXb16dffu3W97hR06\ndGjYsGGd+wIgEwGtC8FzGm+99VbHjh1ve4WTJk3q0aNH3ZoCIBkBrQsmk8n3QH2vhQsXtm/fXlY/\nAPSAk4S6MH369GHDhvkWly5dSjoDYAStCw0bNiwuLi4vLz969Gi3bt1atmwpuyMA8hHQtVJSUrJ8\n+fKKiophw4b57gQJu9TU1NTU1AitHEDUIaBvbvHixRMmTPC+XrBgwZw5c371q19J7QhAXGAO+ube\nfvtt/8XnnntOVVVZzQCIH4ygb0JV1e3btwcU09PTI3QXSXZ29pQpUyKxZgBRh4C+CUVRMjIytmzZ\n4l/ctm2bpHYAxBGmOG6uoKDAfzHggmUAiBBG0DfXq1evY8eOrV+//sqVK4MGDerQoYPsjgDEBQL6\nJq5cufLee+99/fXXaWlpOTk5CQn8zQFAIwT0jVy8eDErK8s3AT106NCVK1cmJibK7QpAnGA8eCOL\nFy/2Pz24evXq1atXS+wHQFzRJKA9DrMSzObSYtt1smfPnoDKl19+KaUTAHFIg4D2OHLz05xqIKfI\nNDs8kd98XRiNxoBKSkqKlE4AxCENAtpdXmbNtgSVLdnWsnJ35DdfFxMmTHj44Yd9i/3798/JyZHY\nD4C4osFJQmOqqajYVWgJyGhXcZEpdXrkN18XTZo0Wb9+/cKFC91ud5cuXR5//HE+hgqAZjQIaENe\nqdOmKEpg3epU8wyR33wdNW7cmHuvAUihzWV2lkJVLdRkSwAQM7jMDgB0SuKNKi6bUpytFgafPvRa\nvXr1smXLAoq7du0aNWrUvHnzhBB79+49duxYcnJyly5dvO9GrnLhwoXp06efOXNm37593so999yT\nnJxMhQqVOK/06NGjoqJCRIai20cbX7ly5ezZswHFlStXnjhxwvv4/MTEREVRVFW9du2a993IVRo3\nbtykSRNVVa9eveqtJCUleb+GChUq8Vx55plnnnvuuU6dOokI0O+t3vXq1UtOTg4oNmvW7NKlS61b\nt5bSkqIoAVdxUKFCJc4rV65cERHDnYQAoFPcSQgAOsWdhACgUxoEtPdOwqCyq7jIlBr4qAsAQBXu\nJAQAneJOQgDQKe4kBACdIqABQKcIaADQKQIaAHRKv7d6h9SiRYs//OEPq1atkt1IXZWVlTVs2FB2\nFxo5f/5848aNZXehkQsXLjRq1Eh2Fxq5dOlSUlJSnHzO/ZUrVzp06HDnnXcG1A8ePBi5I67fhyXF\ntvT09JKSEtldaCSudjYnJ2fhwoV33HGH7Ea08MILLwwbNqxXr16yG9HCokWLhBDeJ7VphikOANAp\nAhoAdIqABgCdIqABQKcIaADQqSi7zC5mJCXF0U8+rnY2ISEhISFexj1xtbNSribkMjs5zpw506xZ\nM9ldaISdjVXnzp1r3LhxiEdVxqLLly8LIerXr6/lRgloANCpePnzBACiDgENADpFQAOAThHQAKBT\nBDQA6BQBDQA6RUADgE4R0JHncZgVm8t/6Tpf3WXzFswOj6Q268pvx67vbej9is2djdkjW7UH1Q5s\nrB5ZEXp/ZR1cAjrCXDbFmF/mV3CXl1mdapVCixBCeBzmTOFUVVV1j37f6P+/IGq4bMb3R7tVVVVV\nt313pvcfbMj9itWdjdEje30PVLd9d+aNDmL076yoaX/lHVwVEeO0CiFMdqfdJHwH1203mezu4C+s\n+gK3/xdHD7898FsKuV+xurMxemT93eQgxtbOqv57Ie3gMoKOIEuhqqqleUb/mru8rCzfWP0vJc++\n3abUyq8ypKSJ3fui7u9DS2HlqEIIIVzFRaZUYw37Fas7G6tH1o9n3ftl1myLiN0jW931/ZV3cAlo\nbXn27Ra+X8Xu1AKzwyOEu7zs5t8ZLTwOc+Zu++I8Qw37Fas7G9tH1uMwK4oxX9inV+VV8NfEzM6K\noP2Vd3AJaG0Z8krV0jxD5UJKWlm5WwhjqkluV2HjsinG90e7K/cw5H7F6s7G9pE15JWqqqq+VG40\nOzwxf2SD9lfewSWgdaDqiAvh/V2dlmKQ29Bt8TjMSqZwXv+HHHq/YnVnQ4qNnb3Okm0tK3fH9JGt\npmp/Q9JmfyMwr43q/E8gOK3X/1RS3XaT7xREZTlaT6647SYR3HjI/YrRnY3VI1ttv3wLMXpk1Rr2\nV97BJaAjr/rRc9t9fxj5nxd2WoNr0cNvpyr5/UYK3q/Y3NmYPLLqre1X1O+sWsP+yjq4PLAfAHSK\nOWgA0CkCGgB0ioAGAJ0ioAFApwhoANApAhoAdIqABgCdIqABQKcIaADQKQIaAHSKgAYAnSKgAUCn\nCGgA0CkCGgB0ioAGAJ0ioAFApwhoANApAhoAdIqABgCdIqABQKcIaADQKQIaccdlU8wOjxDC4zAr\nNpfsdoAaKaqqyu4B0JjLphRnq9nFSnG2WmiR3Q1QEwIa8cjjMBvzy0x2d2meQXYvQI2Y4kA8Mgwe\nbRKm0YNJZ+gaI2jEI5dNKRbWIsEMB3SNgEb88TjMuWJx6eB15lyxmEkO6BcBjXjjcZiN5S+phRYh\nXDalIJV5aOgWAQ0AOsVJQgDQKQIaAHSKgAYAnSKgAUCnCGgA0CkCGgB0ioAGAJ0ioAFApwhoANAp\nAhoAdIqABgCdIqABQKcIaADQKQIaAHSKgAYAnSKgAUCnCGgA0CkCGgB06v8DTPI1rqcd1MUAAAAA\nSUVORK5CYII=\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%R -i df_urban plot(ecdf(df_urban$Cholesterol))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 3)\n",
    "*(3b) Pro každou skupinu zvlášť najděte nejbližší rozdělení. Odhadněte parametry normálního,\n",
    "exponenciálního a rovnoměrného rozdělení a zaneste příslušné hustoty s odhadnutými\n",
    "parametry do grafů histogramu. Diskutujte, které z rozdělení odpovídá pozorovaným\n",
    "datům nejlépe.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rural subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odhad parametrů normálního rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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INMTpIN9SyP0zJUL5V8wk4tnNUP+t7KOS1wGZkhJXGbpNhQO74bVBTr+FKXfy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\nZZ3PXip5HWahWKIIJTvbSQq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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cf1e56048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rural = df_rural[\"Cholesterol\"]\n",
    "plt.hist(rural, normed=True)\n",
    "\n",
    "# najdeme pocatecni a max hodnoty krivky\n",
    "# odtud budeme pocitat rozdeleni\n",
    "xt = plt.xticks()[0]  \n",
    "xmin_rural, xmax_rural = min(xt), max(xt)  \n",
    "lnspc = np.linspace(xmin_rural, xmax_rural, len(rural))\n",
    "\n",
    "# vyzkousime odhad parametru normalniho rozdeleni\n",
    "m_rural, s_rural = stats.norm.fit(rural) # ziskame odhadnutou stredni hodnotu a standartni odchylku\n",
    "pdf_norm = stats.norm.pdf(lnspc, m_rural, s_rural) # teoreticke hodnoty pro nas interval\n",
    "plt.plot(lnspc, pdf_norm, label=\"Normální rozdělení\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Odhad střední hodnoty: 157.0\n",
      "Odhad standardní odchylky: 31.43051942014971\n"
     ]
    }
   ],
   "source": [
    "print(\"Odhad střední hodnoty: {}\\nOdhad standardní odchylky: {}\".format(m_rural, s_rural))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odhad parametrů exponenciálního rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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2zQD6FbP7iarayX0tdo/XHhgMdHDbPe3GUP0EsiG+lvO+593Q7Bx4/Xb4aau3cRljTJhI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\niOwqrq07G+ouoKeqFiz5JiK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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cee471b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rural, normed=True);\n",
    "\n",
    "# zkusíme odhadnout parametry pro exponenciální rozdělení\n",
    "loc, lambd = stats.expon.fit(rural) # získaný odhad lambdy\n",
    "pdf_exp = stats.expon.pdf(lnspc, loc, lambd)  \n",
    "plt.plot(lnspc, pdf_exp, label=\"Exponenciální rozdělení\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lambda: 62.005136153873025\n"
     ]
    }
   ],
   "source": [
    "print(\"Lambda: {}\".format(lambd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odhad parametrů uniformního rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SNK6o2nRgTPqZBywq2PYdYForu34c+KOIeD/wMnBTwbZXI2JC+pmf81jMzKwL5LmimATU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cee55c978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rural, normed=True)\n",
    "\n",
    "# zkusíme odhadnout parametry pro uniformního rozdělení\n",
    "a, b = stats.uniform.fit(rural) # získaný odhad lambdy\n",
    "pdf_uniform = stats.uniform.pdf(lnspc, a, b)\n",
    "plt.plot(lnspc, pdf_uniform, label=\"Uniformní rozdělení\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: 94.99995275447364\n",
      "b: 136.00008516372432\n"
     ]
    }
   ],
   "source": [
    "print(\"a: {}\\nb: {}\".format(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nakonec ještě vykreslíme všechny odhady nad jeden histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LefHFF+nXrx/Lli0jJSWF448/njvuuAOADRs28O677zJ58mSuvPJKPvzwQwYOHHhMzxRK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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cef613e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rural, normed=True);\n",
    "plt.plot(lnspc, pdf_norm, label=\"Normální\");\n",
    "plt.plot(lnspc, pdf_exp, label=\"Exponenciální\");\n",
    "plt.plot(lnspc, pdf_uniform, label=\"Uniformní\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urban subset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odhad parametrů normálního rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NtCCosHW/6zve9Y5hm6nAA4lD+Z1SufOLy8TBY0uhZBWY8RCsnpQ7v1epLNKCoMLSY+7P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\nlHOl264AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cef7024e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "urban = df_urban[\"Cholesterol\"]\n",
    "plt.hist(urban, normed=True)\n",
    "\n",
    "# najdeme pocatecni a max hodnoty krivky\n",
    "# odtud budeme pocitat rozdeleni\n",
    "xt = plt.xticks()[0]  \n",
    "xmin_urban, xmax_urban = min(xt), max(xt)  \n",
    "lnspc = np.linspace(xmin_urban, xmax_urban, len(urban))\n",
    "\n",
    "# vyzkousime odhad parametru normalniho rozdeleni\n",
    "m_urban, s_urban = stats.norm.fit(urban) # ziskame odhadnutou stredni hodnotu a standartni odchylku\n",
    "pdf_norm = stats.norm.pdf(lnspc, m_urban, s_urban) # teoreticke hodnoty pro nas interval\n",
    "plt.plot(lnspc, pdf_norm, label=\"Normální rozdělení\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Odhad střední hodnoty: 216.86666666666667\n",
      "Odhad standardní odchylky: 39.474098399386456\n"
     ]
    }
   ],
   "source": [
    "print(\"Odhad střední hodnoty: {}\\nOdhad standardní odchylky: {}\".format(m_urban, s_urban))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odhad parametrů exponenciálního rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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S4LWb9NoEpVSVaUKIFZUZMipS7yg46x+waTF8Mb964lJKJY2wEoKI9BWR9SKyUUTGlbJd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\nYb+/Anilim1RSilVBRX2EIw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      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cef6496a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(urban, normed=True);\n",
    "\n",
    "# zkusíme odhadnout parametry pro exponenciální rozdělení\n",
    "loc, lambd = stats.expon.fit(urban) # získaný odhad lambdy\n",
    "pdf_exp = stats.expon.pdf(lnspc, loc, lambd)  \n",
    "plt.plot(lnspc, pdf_exp, label=\"Exponenciální rozdělení\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Lambda: 83.86668186323696\n"
     ]
    }
   ],
   "source": [
    "print(\"Lambda: {}\".format(lambd))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Odhad parametrů uniformního rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ERKQHOm0huHuzmc0HVgN5wHJ332Jmc8PxJUANcAFQC+wHZneUN5z6auA+MxsIHCCME4iI\nSHZEeqayu9eQ+NJP3bckZduBeVHzhv2/Bj7TlcKKiEjmaKayiIgACggiIhIoIIiICKCAICIigQKC\niIgACggiIhIoIIiICKCAICIigQKCiIgACggiIhIoIIiICKCAICIigQKCiIgACggiIhIoIIiICKCA\nICIigQKCiIgACggiIhIoIIiICKCAICIigQKCiIgAEQOCmU03s21mVmtmC9o4bmZ2fzi+2cwmRMlr\nZv9iZm+Z2RYzu7vn1RERke4a2FkCM8sDFgHnA3XABjOrdvc3U5LNAMrC6xxgMXBOR3nN7AtAJXCm\nux80sxHprJiIiHRNlBbCRKDW3Xe4+yFgJYkv8lSVwApPWA8MM7PiTvL+M7DQ3Q8CuHt9GuojIiLd\nFCUgjATeTXlfF/ZFSdNR3rHAZ83sJTN7wczO7krBRUQkvTrtMsrwZx8PTALOBlaZ2Unu7qmJzGwO\nMAdgzJgxvV5IEZH+IkoLYRcwOuX9qLAvSpqO8tYB/xm6mV4GjgAntP5wd1/q7hXuXlFUVBShuCIi\n0h1RAsIGoMzMSs1sEHA5UN0qTTUwK9xtNAn40N13d5L3aeALAGY2FhgE7OlxjUREpFs67TJy92Yz\nmw+sBvKA5e6+xczmhuNLgBrgAqAW2A/M7ihvOPVyYLmZvQEcAqpadxeJiEjviTSG4O41JL70U/ct\nSdl2YF7UvGH/IeCKrhRWREQyRzOVRUQEyO5dRiKSI0oWPJPR8+9ceGFGzy/RqIUgIiKAAoKIiAQK\nCCIiAiggiIhIoIAgIiKAAoKIiAS67bSvcIcXfgB/ab1MVG64a+C7nSeSzKl+LqOnz/j1zXD5M+Ks\nr8HoidkuRVopIPQVf9kFv7wLhnwS8o/Jdmm67At5B7JdhP5t+1sZPX3Gr2+Gy592jfVwaL8CgmRI\nY3g+0JeXwKcvyG5ZumFShicuScd23pjZiV2Zvr6ZLn/aPXA+NDVkuxRppzGEviL5w1WoJ4mK9HmF\nIxQQJIOSLYQCPfNBpM8rKPrb72yMKCD0FU0KCCI5o6AI9u+Fw83ZLklaKSD0FY0NMKgQBuXegLJI\nv1M4AvBEUIgRBYS+oqlBrQORXJH8XY3ZOIICQl/RVK8BZZFckfxdbYrXOIICQl/RqBaCSM4oCAGh\nUS0EyYSmegUEkVxRcELiX7UQJO0ON8P+feoyEskVQz4JeYNid+upAkJfsH8v4GohiOQKs0S3UdOe\nbJckrRQQ+oJks1MtBJHcUVjUP7uMzGy6mW0zs1ozW9DGcTOz+8PxzWY2oQt5bzQzN7MTelaVHNYy\nS1kBQSRnFIzof11GZpYHLAJmAOOAmWY2rlWyGUBZeM0BFkfJa2ajgWnAH3tck1yWvJdZXUYiuaOg\nqF/OQ5gI1Lr7Dnc/BKwEKlulqQRWeMJ6YJiZFUfI+x/ATYD3tCI5LflXRqECgkjOKAwB4ciRbJck\nbaIEhJFA6tMx6sK+KGnazWtmlcAud3+ti2WOn6YGyBsMgz+R7ZKISFQFI+BIMxz4c7ZLkjZZeR6C\nmR0DfIdEd1FnaeeQ6IZizJgxGS5ZljQ1JAaUzbJdEhGJqmW2cgMcc3x2y5ImUVoIu4DRKe9HhX1R\n0rS3/2SgFHjNzHaG/a+Y2T+0/nB3X+ruFe5eUVQU0y6VRk1KE8k5yd/ZGA0sRwkIG4AyMys1s0HA\n5UB1qzTVwKxwt9Ek4EN3391eXnd/3d1HuHuJu5eQ6Eqa4O7vp6tiOUWzlEVyT8sCd/EJCJ12Gbl7\ns5nNB1YDecByd99iZnPD8SVADXABUAvsB2Z3lDcjNclljQ1QfGa2SyEiXVEYv/WMIo0huHsNiS/9\n1H1LUrYdmBc1bxtpSqKUI5aOHIH9ezQHQSTXDD0eLC9Wt55qpnK2Hfhz4k4FzVIWyS0DBiQWuYtR\nl5ECQrbpWcoiuatgRKy6jBQQsk3PUhbJXWohSFo1amE7kZxVqBaCpFNy+VwNKovknuR6Rh6P1XcU\nELKtqT5xp8LQ47JdEhHpqsIR0PwRHGrMdknSQgEh25KzlAfoUojknJZnK8djHEHfQtnW1KABZZFc\n1TJbOR7jCAoI2dZYr2WvRXJVYbzWM1JAyLYmzVIWyVkFKSuexoACQja5JwaV1UIQyU0F4cm/CgjS\nYwf/Cs0H1EIQyVV5+Yk1jdRlJD2mZymL5L6CotjMVlZAyCY9S1kk98VotrICQja1tBDUZSSSs5Kz\nlWNAASGbmrSOkUjOKxyhgCBpkGxmHnNCdsshIt1XUAQH/wIfH8h2SXpMASGbmuoTdyjkRXpwnYj0\nRTF6trICQjY11qu7SCTXxejZygoI2dS0R7eciuS6GM1WVkDIpia1EERyXmF8uozUeZ1NjQ0tf12U\nLHgmy4URkW4piM8Cd5FaCGY23cy2mVmtmS1o47iZ2f3h+GYzm9BZXjO7x8zeCun/y8yGpadKOeLj\nj+DQX/+2FoqI5Kb8oTDo2P7RZWRmecAiYAYwDphpZuNaJZsBlIXXHGBxhLxrgNPc/QzgbeDbPa5N\nLtGzlEXio7Co37QQJgK17r7D3Q8BK4HKVmkqgRWesB4YZmbFHeV19/9x9+aQfz0wKg31yR16lrJI\nfBTEY3JalIAwEng35X1d2BclTZS8AFcCz7b14WY2x8w2mtnGhobc/w9v0aR1jERiozAey1dk/S4j\nM7sFaAYea+u4uy919wp3rygqitGXZ7J5qRaCSO4rGBGLLqModxntAkanvB8V9kVJk99RXjP7OnAR\nMNXdPXKp4yDZQtA8BJHcV1AEH+2Dwx8nnpGQo6K0EDYAZWZWamaDgMuB6lZpqoFZ4W6jScCH7r67\no7xmNh24CbjY3fenqT65o7EBBn8C8odkuyQi0lMtcxH2ZLccPdRpC8Hdm81sPrAayAOWu/sWM5sb\nji8BaoALgFpgPzC7o7zh1D8CBgNrzAxgvbvPTWfl+rSmBrUOROIidbbyJ4qzW5YeiDQxzd1rSHzp\np+5bkrLtwLyoecP+f+xSSeOmqUG3nIrERfJ3OcdnK2d9ULnfaqxXC0EkLlpmK+f2nUYKCNnSpIAg\nEhsxWQJbASEbDn8MH/1JXUYicTH4WBg4JOdvPVVAyIaWWcpqIYjEglmYrZzbdxkpIGSDnqUsEj+F\nReoykm5IDjxplrJIfBSM0KCydEPLLGUtfS0SGwUnqIUg3aClr0XipzCMIRw5ku2SdJuemJYNTQ0w\ncCgMKsx2SSQm9MS9jmX6/2fnwgsTXUZ+OHEHYcHwjH5epqiFkA1NDYkBqMSSHSISBzF4trICQjY0\n1mtAWSRukr/TOTwXQQEhG7SwnUj8tMxWzt07jRQQsqGxXk9KE4mbQrUQpKuOHIH9e9RlJBI3Q4bB\ngIFqIUgXfLQP/IhuORWJmwEDEt1GGlSWyBr16EyR2CooyunZygoIvU3PUhaJL7UQpEuSfz2oy0gk\nfgpzez0jBYTelhxwUgtBJH4KihK/4+7ZLkm3KCD0tqZ6GJAPQ4/LdklEJN0KR8Dhg3DwL9kuSbco\nIPS2xjApTctWiMRPy2zl3Ow2ihQQzGy6mW0zs1ozW9DGcTOz+8PxzWY2obO8Zna8ma0xs+3h3/7x\nJ3NTvZa9Fomr5O92jg4sdxoQzCwPWATMAMYBM81sXKtkM4Cy8JoDLI6QdwGw1t3LgLXhffw11mtA\nWSSucny2cpQWwkSg1t13uPshYCVQ2SpNJbDCE9YDw8ysuJO8lcDDYfth4Ms9rEtuaNIsZZHYSv5u\n5+hs5SjPQxgJvJvyvg44J0KakZ3k/ZS77w7b7wOfiljmrnvuO/DKw52n6w2HGrWOkUhcHTMcbACs\n/g48f3t6z/2VR+Dk89J7zlb6xANy3N3NrM37tMxsDoluKIBGM9vWzY85AdjTzbxp9n/DK+P6UJ17\njeqcg+wHXc7Sp+rcjfJ33S1Te1LnE6MkihIQdgGjU96PCvuipMnvIO8HZlbs7rtD91KbnW7uvhRY\nGqGcHTKzje5e0dPz5BLVuX9QnfuH3qhzlDGEDUCZmZWa2SDgcqC6VZpqYFa422gS8GHoDuoobzVQ\nFbargP/uYV1ERKQHOm0huHuzmc0HVgN5wHJ332Jmc8PxJUANcAFQC+wHZneUN5x6IbDKzK4C3gEu\nS2vNRESkSyKNIbh7DYkv/dR9S1K2HZgXNW/YvxeY2pXC9lCPu51ykOrcP6jO/UPG62yeo2tuiIhI\nemnpChERAWIUEMxsuZnVm9kbKfvaXR7DzL4dltPYZmb/Jzul7pl26ny7me0ys1fD64KUYzldZzMb\nbWb/a2ZvmtkWM/vXsD+217mDOsf5Og8xs5fN7LVQ5zvC/jhf5/bq3LvX2d1j8QI+B0wA3kjZdzew\nIGwvAH4QtscBrwGDgVLg90BetuuQpjrfDvxbG2lzvs5AMTAhbB8LvB3qFdvr3EGd43ydDSgM2/nA\nS8CkmF/n9urcq9c5Ni0Ed18H7Gu1u73lMSqBle5+0N3/QOLuqIm9UtA0aqfO7cn5Orv7bnd/JWz/\nFdhKYjZ8bK9zB3VuTxzq7O7eGN7mh5cT7+vcXp3bk5E6xyYgtKO95THaW2ojLv4lrDq7PKVZHas6\nm1kJcBaJv6T6xXVuVWeI8XU2szwze5XEhNU17h7769xOnaEXr3PcA0ILT7Sz+sMtVYuBk4ByYDfw\n79ktTvqZWSHwFHC9ux/1JJK4Xuc26hzr6+zuh929nMTqBhPN7LRWx2N3ndupc69e57gHhA/Cshi0\nWh4jynIcOcndPwg/WEeAZfytGRmLOptZPokvxsfc/T/D7lhf57bqHPfrnOTufwb+F5hOzK9zUmqd\ne/s6xz0gtLc8RjVwuZkNNrNSEs9xeDkL5Uu75C9M8E9A8g6knK+zmRnwILDV3f9fyqHYXuf26hzz\n61xkZsPC9lDgfOAt4n2d26xzr1/nbI+up+sFPE6iSfUxif60q4DhJB6+sx14Hjg+Jf0tJEbmtwEz\nsl3+NNb5EeB1YHP4oSmOS52Bc0l0E2wGXg2vC+J8nTuoc5yv8xnA70Ld3gBuDfvjfJ3bq3OvXmfN\nVBYRESD+XUYiIhKRAoKIiAAKCCIiEiggiIgIoIAgIiKBAoKIiAAKCCIiEiggiIgIAP8f1XucgJHL\nHxQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cee4e4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(urban, normed=True)\n",
    "\n",
    "# zkusíme odhadnout parametry pro uniformního rozdělení\n",
    "a, b = stats.uniform.fit(urban) # získaný odhad lambdy\n",
    "pdf_uniform = stats.uniform.pdf(lnspc, a, b)\n",
    "plt.plot(lnspc, pdf_uniform, label=\"Uniformní rozdělení\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a: 132.99991832442385\n",
      "b: 197.00011274489498\n"
     ]
    }
   ],
   "source": [
    "print(\"a: {}\\nb: {}\".format(a, b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nakonec ještě vykreslíme všechny odhady nad jeden histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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c7nWHq9aF1GHley0/N027iVwsahiqUiG86aU7ANQu9DjJs82bfcwXOPZ24EHP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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cef716be0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(urban, normed=True);\n",
    "plt.plot(lnspc, pdf_norm, label=\"Normální\");\n",
    "plt.plot(lnspc, pdf_exp, label=\"Exponenciální\");\n",
    "plt.plot(lnspc, pdf_uniform, label=\"Uniformní\");\n",
    "plt.legend(loc='upper right');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Z porvnání všech odhadů rozdělení vidíme, že ze zvolených, náš dataset nejvíce odpovídá **normálnímu rozdělení**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Poznámka - při použití knihovny seaborn, lze grafický odhad provést rychleji (viz graf), avšak nezjistíme odhad parametrů.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PodVq+ec//2mT6zXW92d+RJr1TO5a9+ZCJpOJnd+cxD/Im8QBress2xgajeAOfRvQlPFd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\nHtswnmUgxQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cee3326d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(urban, kde=True, fit=stats.norm);\n",
    "sns.distplot(urban, kde=False, fit=stats.expon);\n",
    "sns.distplot(urban, kde=True, fit=stats.uniform);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 4)\n",
    "*(1b) Pro každou skupinu zvlášť vygenerujte náhodný výběr o 100 hodnotách z rozdělení\n",
    "zvoleného jako nejbližší v předchozím bodě, s parametry odhadnutými shora. Porovnejte\n",
    "histogram simulovaných hodnot s pozorovanými daty.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rural - Srovnání náhodného výběru a pozorovaných dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu, sigma = m_rural, s_rural\n",
    "s = np.random.normal(mu, sigma, 100) # náhodný sample 100 hodnot z n.rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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SMTOzzGSaZCQNkbRG0jpJ4ytZL0l3putXSupbU1tJEyVtlrQi/VyYs25CWn+N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\nTjJmZpaZ/w9+hDtc9cbIsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cef6db320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(rural, normed=True, alpha=0.50, label=\"Zadané\");\n",
    "plt.hist(s, 10, normed=True, alpha=0.50, label=\"Simulované\",  facecolor='red');\n",
    "plt.legend(loc='upper right');\n",
    "plt.xlabel('Vzorky');\n",
    "plt.ylabel('Pravděpodobnost');\n",
    "plt.title('Rural');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Urban - Srovnání náhodného výběru a pozorovaných dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mu, sigma = m_urban, s_urban\n",
    "s = np.random.normal(mu, sigma, 100) # náhodný sample 100 hodnot z n.rozdělení"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IOKSgzT7AfwHVwIvAGRHxep7HYZbFkVOuTRb+Z9+m+9Jx45ruu6zi5dYjkdQauB4YCvQG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\nzDL5/xB7CwAHvZudAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x26cef82e4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(urban, normed=True, alpha=0.50, label=\"Zadané\");\n",
    "plt.hist(s, 10, normed=True, alpha=0.50, label=\"Simulované\",  facecolor='red');\n",
    "plt.legend(loc='upper right');\n",
    "plt.xlabel('Vzorky');\n",
    "plt.ylabel('Pravděpodobnost');\n",
    "plt.title('Urban');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Ze srovnání lze vidět, že vygenerované hodnoty z odhadnutého normální rozdělení hezky korelují se zadanými daty**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 5)\n",
    "*(1b) Pro každou skupinu zvlášť spočítejte oboustranný 95% konfidenční interval pro\n",
    "střední hodnotu.*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Budeme vycházet z následujícího vyjádření oboustraného intervalu spolehlivosti,  \n",
    "který pro dostatečně velké $n$, můžeme určit stejně, jako u výběru z normálního rozdělení:**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$ \\large\\displaystyle\n",
    "\\left(\n",
    "\\overline{X}_n-{{z}_{\\tfrac{\\alpha }{2}}}\\frac{\\sigma}{\\sqrt{n}}, \n",
    "\\overline{X}_n+{{z}_{\\tfrac{\\alpha }{2}}}\\frac{\\sigma}{\\sqrt{n}}\n",
    "\\right) $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 95% CI pro Rural subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Počet: n = 49\n",
      "Směrodatná odchylka: s = 31.43051942014971\n",
      "Výběrový průměr: m = 157.0\n",
      "\n",
      "95% CI = [148.19945456235808, 165.80054543764192]\n"
     ]
    }
   ],
   "source": [
    "m, s, n = m_rural, s_rural, len(rural)\n",
    "\n",
    "print(\"Počet: n = {}\".format(n))\n",
    "print(\"Směrodatná odchylka: s = {}\".format(s))\n",
    "print(\"Výběrový průměr: m = {}\".format(m))\n",
    "\n",
    "L = (m - 1.96 * (s/sqrt(n)))\n",
    "U = (m + 1.96 * (s/sqrt(n)))\n",
    "\n",
    "print(\"\\n95% CI = [{}, {}]\".format(L, U))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 95% CI pro Urban subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Počet: n = 45\n",
      "Směrodatná odchylka: s = 39.474098399386456\n",
      "Výběrový průměr: m = 216.86666666666667\n",
      "\n",
      "95% CI = [205.33314239678495, 228.4001909365484]\n"
     ]
    }
   ],
   "source": [
    "m, s, n = m_urban, s_urban, len(urban)\n",
    "\n",
    "print(\"Počet: n = {}\".format(n))\n",
    "print(\"Směrodatná odchylka: s = {}\".format(s))\n",
    "print(\"Výběrový průměr: m = {}\".format(m))\n",
    "\n",
    "L = (m - 1.96 * (s/sqrt(n)))\n",
    "U = (m + 1.96 * (s/sqrt(n)))\n",
    "\n",
    "print(\"\\n95% CI = [{}, {}]\".format(L, U))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 6)\n",
    "*(1b) Pro každou skupinu zvlášť otestujte na hladině významnosti 5% hypotézu, zda je\n",
    "střední hodnota rovná hodnotě K, proti oboustranné alternativě. Můžete použít jak\n",
    "výsledek z předešlého bodu, tak výstup z příslušné vestavěné funkce vašeho softwaru\n",
    "a výsledky porovnat.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nulová hypotéza: střední hodnota je rovna $K = 29$.  \n",
    "Provedeme T-test u ubou skupin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=28.214933441626243, pvalue=1.5409956491730866e-31)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Ttest_1sampResult(statistic=31.569219777685191, pvalue=7.282542597108422e-32)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_1samp(rural, K)\n",
    "stats.ttest_1samp(urban, K)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jelikož u obou subsetů je p-hodnota menší, než naše hladina významnosti (tj. 0.05),  \n",
    "**nulovou hypotézu zamítáme, tj. ani jedna skupina nemá střední hodnotu $K = 29$**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Úkol 7)\n",
    "*(2b) Na hladině spolehlivosti 5% otestujte, jestli mají pozorované skupiny stejnou\n",
    "střední hodnotu. Typ testu a alternativy stanovte tak, aby vaše volba nejlépe korespondovala\n",
    "s povahou zkoumaného problému.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nejdříve je třeba zjistit, zda jsou rozptyly obou subsetů závislé.  \n",
    "**Provedeme Bartlettův test:**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BartlettResult(statistic=2.3725507139289452, pvalue=0.12348513814434653)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.bartlett(rural, urban)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Z Bartlettova testu vidíme, že p-hodnota je $ 0.123 > 0.05 $, nulovou hypotézu nezamítáme a můžeme tvrdit že **rozpytly jsou homogenní**.\n",
    "\n",
    "Provedeme nyní **oboustraný T-test**, pro nulovou hypotézu, že obě skupiny mají **shodou střední hodnotu**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-8.0781890581123843, pvalue=2.4758798190491828e-12)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(rural, urban, equal_var=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "P-hodnota **T-testu je $ < 0.05$**, nulovou hypotézu zamítáme, a tedy **střední hodnoty pozorovaných skupin nejsou stejné**."
   ]
  }
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