diff --git a/ukol.ipynb b/ukol.ipynb
index 5eb1773..f1057cd 100644
--- a/ukol.ipynb
+++ b/ukol.ipynb
@@ -1,16 +1,34 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "c88902ee",
+   "metadata": {},
+   "source": [
+    "# Úkol: BI-PST\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "91241ee8",
+   "metadata": {},
+   "source": [
+    "Spolupracovali:\n",
+    "   * Ondřej Hladůvka (reprezentant)\n",
+    "   * Tomáš Kaňka"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "id": "d693757b-4f3a-4162-9300-ec96596a26ec",
+   "execution_count": 70,
+   "id": "334be38a",
    "metadata": {},
    "outputs": [],
    "source": [
     "#import csv\n",
     "#import math\n",
     "#import numpy as np\n",
-    "#import matplotlib.pyplot as plt\n",
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "#np.set_printoptions(precision=3)\n",
     "#from sympy import *\n",
@@ -20,7 +38,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "7c90184c-5f76-4277-b0ad-aeec2ac37d30",
    "metadata": {},
    "outputs": [
@@ -39,6 +57,14 @@
     "print(M)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "129f8693",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 1"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "503f77b6-1c9d-4406-8b30-ec3b792267e7",
@@ -50,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 58,
    "id": "b80d5cec-db0c-42e7-a3b9-296803242269",
    "metadata": {},
    "outputs": [
@@ -58,72 +84,80 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "                ,\"Weight\",\"Status\"\n",
-      "0                1,24.5,\"survived\"\n",
-      "1    2,26.8999996185303,\"survived\"\n",
-      "2    3,26.8999996185303,\"survived\"\n",
-      "3    4,24.2999992370605,\"survived\"\n",
-      "4    5,24.1000003814697,\"survived\"\n",
-      "5                6,26.5,\"survived\"\n",
-      "6    7,24.6000003814697,\"survived\"\n",
-      "7    8,24.2000007629395,\"survived\"\n",
-      "8    9,23.6000003814697,\"survived\"\n",
-      "9   10,26.2000007629395,\"survived\"\n",
-      "10  11,26.2000007629395,\"survived\"\n",
-      "11  12,24.7999992370605,\"survived\"\n",
-      "12  13,25.3999996185303,\"survived\"\n",
-      "13  14,23.7000007629395,\"survived\"\n",
-      "14  15,25.7000007629395,\"survived\"\n",
-      "15  16,25.7000007629395,\"survived\"\n",
-      "16  17,26.2999992370605,\"survived\"\n",
-      "17  18,26.7000007629395,\"survived\"\n",
-      "18  19,23.8999996185303,\"survived\"\n",
-      "19  20,24.7000007629395,\"survived\"\n",
-      "20                21,28,\"survived\"\n",
-      "21  22,27.8999996185303,\"survived\"\n",
-      "22  23,25.8999996185303,\"survived\"\n",
-      "23  24,25.7000007629395,\"survived\"\n",
-      "24  25,26.6000003814697,\"survived\"\n",
-      "25  26,23.2000007629395,\"survived\"\n",
-      "26  27,25.7000007629395,\"survived\"\n",
-      "27  28,26.2999992370605,\"survived\"\n",
-      "28  29,24.2999992370605,\"survived\"\n",
-      "29  30,26.7000007629395,\"survived\"\n",
-      "30  31,24.8999996185303,\"survived\"\n",
-      "31  32,23.7999992370605,\"survived\"\n",
-      "32  33,25.6000003814697,\"survived\"\n",
-      "33                34,27,\"survived\"\n",
-      "34  35,24.7000007629395,\"survived\"\n",
-      "35              36,26.5,\"perished\"\n",
-      "36  37,26.1000003814697,\"perished\"\n",
-      "37  38,25.6000003814697,\"perished\"\n",
-      "38  39,25.8999996185303,\"perished\"\n",
-      "39              40,25.5,\"perished\"\n",
-      "40  41,27.6000003814697,\"perished\"\n",
-      "41  42,25.7999992370605,\"perished\"\n",
-      "42  43,24.8999996185303,\"perished\"\n",
-      "43                44,26,\"perished\"\n",
-      "44              45,26.5,\"perished\"\n",
-      "45                46,26,\"perished\"\n",
-      "46  47,27.1000003814697,\"perished\"\n",
-      "47  48,25.1000003814697,\"perished\"\n",
-      "48                49,26,\"perished\"\n",
-      "49  50,25.6000003814697,\"perished\"\n",
-      "50                51,25,\"perished\"\n",
-      "51  52,24.6000003814697,\"perished\"\n",
-      "52                53,25,\"perished\"\n",
-      "53                54,26,\"perished\"\n",
-      "54  55,28.2999992370605,\"perished\"\n",
-      "55  56,24.6000003814697,\"perished\"\n",
-      "56              57,27.5,\"perished\"\n",
-      "57  58,31.1000003814697,\"perished\"\n",
-      "58  59,28.2999992370605,\"perished\"\n"
+      "Prvních 5 řádků:\n",
+      "      Weight    Status\n",
+      "0  24.500000  survived\n",
+      "1  26.900000  survived\n",
+      "2  26.900000  survived\n",
+      "3  24.299999  survived\n",
+      "4  24.100000  survived\n",
+      "Info\n",
+      "Počet řádků: 59\n",
+      "Datové typy sloupců: Weight    float64\n",
+      "Status     object\n",
+      "dtype: object\n"
      ]
     }
    ],
    "source": [
-    "df = pd.read_csv(\"/data.csv\", sep = \";\", decimal = \",\")\n",
-    "print(df)"
+    "# načtení dat\n",
+    "df = pd.read_csv(\"/data.csv\")\n",
+    "df = df.drop(df.columns[0], axis=1)\n",
+    "# informace\n",
+    "print(\"Prvních 5 řádků:\")\n",
+    "print(df.head())\n",
+    "print(\"Info\")\n",
+    "print(\"Počet řádků:\", df.shape[0])\n",
+    "print(\"Datové typy sloupců:\",df.dtypes)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "41e43831",
+   "metadata": {},
+   "source": [
+    "Tento datový soubor zkoumá, zda hmotnost dospělých samců vrabců hraje roli v jejich přežití během extrémních klimatických podmínek. Cílem je zjistit, zda vrabci, kteří přežili, měli významně jinou průměrnou hmotnost ve srovnání se vrabci, kteří zahynuli. \n",
+    "\n",
+    "Tabulak má dva sloupce weight a status, kde\n",
+    "**weight** udává hmotnost dospělých samců vrabců v gramech a\n",
+    "**status**  udává, zda vrabec přežil nebo zahynul během zimní bouře. Může mít hodnoty \"survived\" (přežil) nebo \"perished\" (zahynul).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "717d3775",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Index(['Weight', 'Status'], dtype='object')\n",
+      "     Status       mean       var     median\n",
+      "0  perished  26.275000  2.168043  26.000000\n",
+      "1  survived  25.462857  1.584756  25.700001\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Zobrazení názvů sloupců\n",
+    "print(df.columns)\n",
+    "\n",
+    "groups = df.groupby('Status')\n",
+    "summary_stats = groups['Weight'].agg(['mean', 'var', 'median']).reset_index()\n",
+    "\n",
+    "# Zobrazení výsledků\n",
+    "print(summary_stats)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b370bf22",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 2"
    ]
   },
   {
@@ -134,6 +168,167 @@
     "(1b) Pro každou skupinu zvlášť odhadněte hustotu a distribuční funkci pomocí histogramu a empirické distribuční funkce."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "9c8f12d3",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Výsledky pro skupinu: perished ---\n",
+      "       Weight       CDF\n",
+      "0   24.600000  0.041667\n",
+      "1   24.600000  0.083333\n",
+      "2   24.900000  0.125000\n",
+      "3   25.000000  0.166667\n",
+      "4   25.000000  0.208333\n",
+      "5   25.100000  0.250000\n",
+      "6   25.500000  0.291667\n",
+      "7   25.600000  0.333333\n",
+      "8   25.600000  0.375000\n",
+      "9   25.799999  0.416667\n",
+      "10  25.900000  0.458333\n",
+      "11  26.000000  0.500000\n",
+      "12  26.000000  0.541667\n",
+      "13  26.000000  0.583333\n",
+      "14  26.000000  0.625000\n",
+      "15  26.100000  0.666667\n",
+      "16  26.500000  0.708333\n",
+      "17  26.500000  0.750000\n",
+      "18  27.100000  0.791667\n",
+      "19  27.500000  0.833333\n",
+      "20  27.600000  0.875000\n",
+      "21  28.299999  0.916667\n",
+      "22  28.299999  0.958333\n",
+      "23  31.100000  1.000000\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Výsledky pro skupinu: survived ---\n",
+      "       Weight       CDF\n",
+      "0   23.200001  0.028571\n",
+      "1   23.600000  0.057143\n",
+      "2   23.700001  0.085714\n",
+      "3   23.799999  0.114286\n",
+      "4   23.900000  0.142857\n",
+      "5   24.100000  0.171429\n",
+      "6   24.200001  0.200000\n",
+      "7   24.299999  0.228571\n",
+      "8   24.299999  0.257143\n",
+      "9   24.500000  0.285714\n",
+      "10  24.600000  0.314286\n",
+      "11  24.700001  0.342857\n",
+      "12  24.700001  0.371429\n",
+      "13  24.799999  0.400000\n",
+      "14  24.900000  0.428571\n",
+      "15  25.400000  0.457143\n",
+      "16  25.600000  0.485714\n",
+      "17  25.700001  0.514286\n",
+      "18  25.700001  0.542857\n",
+      "19  25.700001  0.571429\n",
+      "20  25.700001  0.600000\n",
+      "21  25.900000  0.628571\n",
+      "22  26.200001  0.657143\n",
+      "23  26.200001  0.685714\n",
+      "24  26.299999  0.714286\n",
+      "25  26.299999  0.742857\n",
+      "26  26.500000  0.771429\n",
+      "27  26.600000  0.800000\n",
+      "28  26.700001  0.828571\n",
+      "29  26.700001  0.857143\n",
+      "30  26.900000  0.885714\n",
+      "31  26.900000  0.914286\n",
+      "32  27.000000  0.942857\n",
+      "33  27.900000  0.971429\n",
+      "34  28.000000  1.000000\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Funkce pro výpočet hustoty a CDF\n",
+    "def calculate_density_cdf(group):\n",
+    "    sorted_weights = group.sort_values('Weight')\n",
+    "    \n",
+    "    # Výpočet CDF\n",
+    "    cdf = pd.Series(range(1, len(sorted_weights) + 1)) / len(sorted_weights)\n",
+    "    \n",
+    "    # Přidání CDF k seřazeným váhám\n",
+    "    results = pd.DataFrame({'Weight': sorted_weights['Weight'].values, 'CDF': cdf.values})\n",
+    "    \n",
+    "    return results\n",
+    "\n",
+    "# Vytvoření hustoty a CDF pro každou skupinu\n",
+    "groups = df.groupby('Status')\n",
+    "density_cdf_results = {}\n",
+    "\n",
+    "for name, group in groups:\n",
+    "    density_cdf_results[name] = calculate_density_cdf(group)\n",
+    "\n",
+    "# Zobrazení výsledků\n",
+    "for name, results in density_cdf_results.items():\n",
+    "    plt.figure(figsize=(10, 6))\n",
+    "    plt.hist(df[df['Status'] == name]['Weight'], bins=20, density=True, alpha=0.5, label=f'Hustota {name}')\n",
+    "    \n",
+    "    # CDF\n",
+    "    plt.plot(results['Weight'], results['CDF'], label=f'CDF {name}', color='red')\n",
+    "    \n",
+    "    # Nastavení g\n",
+    "    plt.title(f'Hustota a CDF pro skupinu: {name}')\n",
+    "    plt.xlabel('Hmotnost')\n",
+    "    plt.ylabel('Hustota / Kumulativní pravděpodobnost (CDF)')\n",
+    "    plt.legend()\n",
+    "    plt.grid()\n",
+    "    plt.show()\n",
+    "    print(f\"\\n--- Výsledky pro skupinu: {name} ---\")\n",
+    "    print(results)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f98f6ad6",
+   "metadata": {},
+   "source": [
+    "Vytvořili jsme funkci calculate_density_cdf, která seřadila data podle hmotnosti a spočítala kumulativní distribuční funkci. Tato funkce vrátila DataFrame se seřazenými hmotnostmi a odpovídajícími hodnotami CDF."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9f9b7946",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 3"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "626d8bce-65af-4659-96e9-c1fbe4ccb3cc",
@@ -144,6 +339,14 @@
     "Zaneste příslušné hustoty s odhadnutými parametry do grafů histogramu. Diskutujte, které z rozdělení odpovídá pozorovaným datům nejlépe."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "424b6dc3",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 4"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "6226456c-fdf3-4537-830c-05f8ee7022c5",
@@ -154,6 +357,14 @@
     "Porovnejte histogram simulovaných hodnot s pozorovanými daty."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d43bff73",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 5"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "1c5f7d31-ca21-42b4-9a23-1111bbf599b9",
@@ -162,6 +373,14 @@
     "(1b) Pro každou skupinu zvlášť spočítejte oboustranný 95% konfidenční interval pro střední hodnotu."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "1c7cf77b",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 6"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "53a61e4f-fc67-4237-ab38-f5f9fb7767c5",
@@ -172,6 +391,14 @@
     "Můžete použít buď výsledek z předešlého bodu, nebo výstup z příslušné vestavěné funkce vašeho softwaru."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "617cf82f",
+   "metadata": {},
+   "source": [
+    "## Úloha č. 7"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "7007c195-97a3-4cd4-8427-dcc8417eedf8",