dataset added
This commit is contained in:
parent
784926e111
commit
909ceda055
14
Dockerfile
14
Dockerfile
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@ -1,7 +1,15 @@
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FROM python:3.10-slim
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RUN pip install --upgrade pip \
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&& pip install jupyter notebook
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RUN apt-get update && apt-get install -y --no-install-recommends \
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r-base && \
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R --quiet -e "install.packages('Sleuth2')" && \
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R --quiet -e "library(Sleuth2); write.csv(ex0221, file = \"data.csv\")" && \
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apt-get purge -y r-base && \
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apt-get autoremove -y && \
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apt-get clean && \
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rm -rf /var/lib/apt/lists/*
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RUN pip install --upgrade pip && \
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pip install jupyter notebook pandas numpy matplotlib
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CMD ["jupyter", "notebook", "--ip=0.0.0.0", "--allow-root", "--no-browser"]
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179
ukol.ipynb
179
ukol.ipynb
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@ -2,11 +2,184 @@
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7c90184c-5f76-4277-b0ad-aeec2ac37d30",
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"execution_count": 1,
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"id": "d693757b-4f3a-4162-9300-ec96596a26ec",
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"metadata": {},
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"outputs": [],
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"source": []
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"source": [
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"#import csv\n",
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"#import math\n",
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"#import numpy as np\n",
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"#import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"#np.set_printoptions(precision=3)\n",
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"#from sympy import *\n",
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"#from scipy.stats import norm, uniform, expon, t\n",
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"#from scipy.optimize import minimize"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "7c90184c-5f76-4277-b0ad-aeec2ac37d30",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"10\n"
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]
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}
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],
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"source": [
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"K = 28\n",
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"L = 8\n",
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"M = (((K + L) * 47) % 11) + 1\n",
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"print(M)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "503f77b6-1c9d-4406-8b30-ec3b792267e7",
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"metadata": {},
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"source": [
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"(1b) Načtěte datový soubor a rozdělte sledovanou proměnnou na příslušné dvě pozorované skupiny.\n",
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"Stručně popište data a zkoumaný problém. Pro každou skupinu zvlášť odhadněte střední hodnotu, rozptyl a medián příslušného rozdělení."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "b80d5cec-db0c-42e7-a3b9-296803242269",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" ,\"Weight\",\"Status\"\n",
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"0 1,24.5,\"survived\"\n",
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"1 2,26.8999996185303,\"survived\"\n",
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"2 3,26.8999996185303,\"survived\"\n",
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"3 4,24.2999992370605,\"survived\"\n",
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"4 5,24.1000003814697,\"survived\"\n",
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"5 6,26.5,\"survived\"\n",
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"6 7,24.6000003814697,\"survived\"\n",
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"7 8,24.2000007629395,\"survived\"\n",
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"8 9,23.6000003814697,\"survived\"\n",
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"9 10,26.2000007629395,\"survived\"\n",
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"10 11,26.2000007629395,\"survived\"\n",
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"11 12,24.7999992370605,\"survived\"\n",
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"12 13,25.3999996185303,\"survived\"\n",
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"13 14,23.7000007629395,\"survived\"\n",
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"14 15,25.7000007629395,\"survived\"\n",
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"15 16,25.7000007629395,\"survived\"\n",
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"16 17,26.2999992370605,\"survived\"\n",
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"17 18,26.7000007629395,\"survived\"\n",
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"18 19,23.8999996185303,\"survived\"\n",
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"19 20,24.7000007629395,\"survived\"\n",
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"20 21,28,\"survived\"\n",
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"21 22,27.8999996185303,\"survived\"\n",
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"22 23,25.8999996185303,\"survived\"\n",
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"23 24,25.7000007629395,\"survived\"\n",
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"24 25,26.6000003814697,\"survived\"\n",
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"25 26,23.2000007629395,\"survived\"\n",
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"26 27,25.7000007629395,\"survived\"\n",
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"27 28,26.2999992370605,\"survived\"\n",
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"28 29,24.2999992370605,\"survived\"\n",
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"29 30,26.7000007629395,\"survived\"\n",
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"30 31,24.8999996185303,\"survived\"\n",
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"31 32,23.7999992370605,\"survived\"\n",
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"32 33,25.6000003814697,\"survived\"\n",
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"33 34,27,\"survived\"\n",
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"34 35,24.7000007629395,\"survived\"\n",
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"35 36,26.5,\"perished\"\n",
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"36 37,26.1000003814697,\"perished\"\n",
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"37 38,25.6000003814697,\"perished\"\n",
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"38 39,25.8999996185303,\"perished\"\n",
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"39 40,25.5,\"perished\"\n",
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"40 41,27.6000003814697,\"perished\"\n",
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"41 42,25.7999992370605,\"perished\"\n",
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"42 43,24.8999996185303,\"perished\"\n",
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"43 44,26,\"perished\"\n",
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"44 45,26.5,\"perished\"\n",
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"45 46,26,\"perished\"\n",
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"46 47,27.1000003814697,\"perished\"\n",
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"47 48,25.1000003814697,\"perished\"\n",
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"48 49,26,\"perished\"\n",
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"49 50,25.6000003814697,\"perished\"\n",
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"50 51,25,\"perished\"\n",
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"51 52,24.6000003814697,\"perished\"\n",
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"52 53,25,\"perished\"\n",
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"53 54,26,\"perished\"\n",
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"54 55,28.2999992370605,\"perished\"\n",
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"55 56,24.6000003814697,\"perished\"\n",
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"56 57,27.5,\"perished\"\n",
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"57 58,31.1000003814697,\"perished\"\n",
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"58 59,28.2999992370605,\"perished\"\n"
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]
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}
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],
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"source": [
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"df = pd.read_csv(\"/data.csv\", sep = \";\", decimal = \",\")\n",
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"print(df)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "29eef015-c103-4e7f-89e8-2b59526f4b1e",
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"metadata": {},
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"source": [
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"(1b) Pro každou skupinu zvlášť odhadněte hustotu a distribuční funkci pomocí histogramu a empirické distribuční funkce."
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]
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},
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{
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"cell_type": "markdown",
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"id": "626d8bce-65af-4659-96e9-c1fbe4ccb3cc",
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"metadata": {},
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"source": [
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"(3b) Pro každou skupinu zvlášť najděte nejbližší rozdělení: \n",
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"Odhadněte parametry normálního, exponenciálního a rovnoměrného rozdělení.\n",
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"Zaneste příslušné hustoty s odhadnutými parametry do grafů histogramu. Diskutujte, které z rozdělení odpovídá pozorovaným datům nejlépe."
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]
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},
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{
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"cell_type": "markdown",
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"id": "6226456c-fdf3-4537-830c-05f8ee7022c5",
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"metadata": {},
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"source": [
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"(1b) Pro každou skupinu zvlášť vygenerujte náhodný výběr o 100 hodnotách z rozdělení, \n",
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"které jste zvolili jako nejbližší, s parametry odhadnutými v předchozím bodě.\n",
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"Porovnejte histogram simulovaných hodnot s pozorovanými daty."
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]
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},
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{
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"cell_type": "markdown",
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"id": "1c5f7d31-ca21-42b4-9a23-1111bbf599b9",
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"metadata": {},
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"source": [
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"(1b) Pro každou skupinu zvlášť spočítejte oboustranný 95% konfidenční interval pro střední hodnotu."
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]
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},
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{
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"cell_type": "markdown",
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"id": "53a61e4f-fc67-4237-ab38-f5f9fb7767c5",
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"metadata": {},
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"source": [
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"(1b) Pro každou skupinu zvlášť otestujte na hladině významnosti 5 % hypotézu,\n",
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"zda je střední hodnota rovná hodnotě K (parametr úlohy), proti oboustranné alternativě.\n",
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"Můžete použít buď výsledek z předešlého bodu, nebo výstup z příslušné vestavěné funkce vašeho softwaru."
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]
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},
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{
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"cell_type": "markdown",
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"id": "7007c195-97a3-4cd4-8427-dcc8417eedf8",
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"metadata": {},
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"source": [
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"(2b) Na hladině významnosti 5 % otestujte, jestli mají pozorované skupiny stejnou střední hodnotu.\n",
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"Typ testu a alternativy stanovte tak, aby vaše volba nejlépe korespondovala s povahou zkoumaného problému."
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]
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}
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],
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"metadata": {
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