You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
260 lines
7.4 KiB
260 lines
7.4 KiB
{
|
|
"cells": [
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"## Bevezetés a valószínűségszámításba és statisztikába\n",
|
|
"## Feladat\n",
|
|
"\n",
|
|
"Ebben a feladatban a cukorbeteg páciensek adathalmazát fogjuk használni, amely [innen származik](https://www4.stat.ncsu.edu/~boos/var.select/diabetes.html).\n"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"source": [
|
|
"import pandas as pd\n",
|
|
"import numpy as np\n",
|
|
"\n",
|
|
"df = pd.read_csv(\"../../data/diabetes.tsv\",sep='\\t')\n",
|
|
"df.head()"
|
|
],
|
|
"outputs": [
|
|
{
|
|
"output_type": "execute_result",
|
|
"data": {
|
|
"text/plain": [
|
|
" AGE SEX BMI BP S1 S2 S3 S4 S5 S6 Y\n",
|
|
"0 59 2 32.1 101.0 157 93.2 38.0 4.0 4.8598 87 151\n",
|
|
"1 48 1 21.6 87.0 183 103.2 70.0 3.0 3.8918 69 75\n",
|
|
"2 72 2 30.5 93.0 156 93.6 41.0 4.0 4.6728 85 141\n",
|
|
"3 24 1 25.3 84.0 198 131.4 40.0 5.0 4.8903 89 206\n",
|
|
"4 50 1 23.0 101.0 192 125.4 52.0 4.0 4.2905 80 135"
|
|
],
|
|
"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>AGE</th>\n",
|
|
" <th>SEX</th>\n",
|
|
" <th>BMI</th>\n",
|
|
" <th>BP</th>\n",
|
|
" <th>S1</th>\n",
|
|
" <th>S2</th>\n",
|
|
" <th>S3</th>\n",
|
|
" <th>S4</th>\n",
|
|
" <th>S5</th>\n",
|
|
" <th>S6</th>\n",
|
|
" <th>Y</th>\n",
|
|
" </tr>\n",
|
|
" </thead>\n",
|
|
" <tbody>\n",
|
|
" <tr>\n",
|
|
" <th>0</th>\n",
|
|
" <td>59</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>32.1</td>\n",
|
|
" <td>101.0</td>\n",
|
|
" <td>157</td>\n",
|
|
" <td>93.2</td>\n",
|
|
" <td>38.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>4.8598</td>\n",
|
|
" <td>87</td>\n",
|
|
" <td>151</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>1</th>\n",
|
|
" <td>48</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>21.6</td>\n",
|
|
" <td>87.0</td>\n",
|
|
" <td>183</td>\n",
|
|
" <td>103.2</td>\n",
|
|
" <td>70.0</td>\n",
|
|
" <td>3.0</td>\n",
|
|
" <td>3.8918</td>\n",
|
|
" <td>69</td>\n",
|
|
" <td>75</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>2</th>\n",
|
|
" <td>72</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>30.5</td>\n",
|
|
" <td>93.0</td>\n",
|
|
" <td>156</td>\n",
|
|
" <td>93.6</td>\n",
|
|
" <td>41.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>4.6728</td>\n",
|
|
" <td>85</td>\n",
|
|
" <td>141</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>3</th>\n",
|
|
" <td>24</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>25.3</td>\n",
|
|
" <td>84.0</td>\n",
|
|
" <td>198</td>\n",
|
|
" <td>131.4</td>\n",
|
|
" <td>40.0</td>\n",
|
|
" <td>5.0</td>\n",
|
|
" <td>4.8903</td>\n",
|
|
" <td>89</td>\n",
|
|
" <td>206</td>\n",
|
|
" </tr>\n",
|
|
" <tr>\n",
|
|
" <th>4</th>\n",
|
|
" <td>50</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>23.0</td>\n",
|
|
" <td>101.0</td>\n",
|
|
" <td>192</td>\n",
|
|
" <td>125.4</td>\n",
|
|
" <td>52.0</td>\n",
|
|
" <td>4.0</td>\n",
|
|
" <td>4.2905</td>\n",
|
|
" <td>80</td>\n",
|
|
" <td>135</td>\n",
|
|
" </tr>\n",
|
|
" </tbody>\n",
|
|
"</table>\n",
|
|
"</div>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"execution_count": 13
|
|
}
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"Ebben az adathalmazban az oszlopok a következők:\n",
|
|
"* Az életkor és nem magától értetődőek\n",
|
|
"* A BMI a testtömeg-index\n",
|
|
"* A BP az átlagos vérnyomás\n",
|
|
"* Az S1-től S6-ig különböző vérvizsgálati eredmények\n",
|
|
"* Az Y a betegség egyéves előrehaladásának kvalitatív mértéke\n",
|
|
"\n",
|
|
"Vizsgáljuk meg ezt az adathalmazt a valószínűség és statisztika módszereivel.\n",
|
|
"\n",
|
|
"### Feladat 1: Számítsuk ki az átlagértékeket és a szórást minden értékre\n"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"source": [],
|
|
"outputs": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### 2. feladat: Készítsen boxplotokat a BMI, BP és Y értékekről nemek szerint\n"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"source": [],
|
|
"outputs": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"source": [],
|
|
"outputs": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [
|
|
"### Feladat 4: Vizsgáld meg a különböző változók és a betegség előrehaladása (Y) közötti korrelációt\n",
|
|
"\n",
|
|
"> **Tipp** A korrelációs mátrix nyújtja a leghasznosabb információt arról, hogy mely értékek függnek egymástól.\n"
|
|
],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"source": [],
|
|
"metadata": {}
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"\n---\n\n**Felelősségkizárás**: \nEz a dokumentum az [Co-op Translator](https://github.com/Azure/co-op-translator) AI fordítási szolgáltatás segítségével készült. Bár törekszünk a pontosságra, kérjük, vegye figyelembe, hogy az automatikus fordítások hibákat vagy pontatlanságokat tartalmazhatnak. Az eredeti dokumentum az eredeti nyelvén tekintendő hiteles forrásnak. Kritikus információk esetén javasolt professzionális, emberi fordítást igénybe venni. Nem vállalunk felelősséget a fordítás használatából eredő félreértésekért vagy téves értelmezésekért.\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"orig_nbformat": 4,
|
|
"language_info": {
|
|
"name": "python",
|
|
"version": "3.8.8",
|
|
"mimetype": "text/x-python",
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"pygments_lexer": "ipython3",
|
|
"nbconvert_exporter": "python",
|
|
"file_extension": ".py"
|
|
},
|
|
"kernelspec": {
|
|
"name": "python3",
|
|
"display_name": "Python 3.8.8 64-bit (conda)"
|
|
},
|
|
"interpreter": {
|
|
"hash": "86193a1ab0ba47eac1c69c1756090baa3b420b3eea7d4aafab8b85f8b312f0c5"
|
|
},
|
|
"coopTranslator": {
|
|
"original_hash": "6d945fd15163f60cb473dbfe04b2d100",
|
|
"translation_date": "2025-09-06T17:49:19+00:00",
|
|
"source_file": "1-Introduction/04-stats-and-probability/assignment.ipynb",
|
|
"language_code": "hu"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
} |