Tutorium 3

Tutorium_3.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a8babbab-fd24-4375-b7fa-797d007b61c4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install --upgrade pip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4f47b87d-9127-4127-8230-9246a13f6699",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pip install scipy\n",
+    "pip install pandas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9e9759cd-705a-4a0f-83c7-ae08c84fb333",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from scipy import stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "1edc82b4-4409-4250-9827-c9a217d788b4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Person  Vorher  Nachher\n",
+      "0       1      12       14\n",
+      "1       2      15       16\n",
+      "2       3      13       15\n",
+      "3       4      14       15\n",
+      "4       5      11       13\n",
+      "5       6      16       17\n",
+      "6       7      17       18\n",
+      "7       8      10       11\n",
+      "8       9      13       14\n",
+      "9      10      14       16\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Daten in ein Dictionary umwandeln\n",
+    "data = {\n",
+    "    \"Person\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n",
+    "    \"Vorher\": [12, 15, 13, 14, 11, 16, 17, 10, 13, 14],\n",
+    "    \"Nachher\": [14, 16, 15, 15, 13, 17, 18, 11, 14, 16]\n",
+    "}\n",
+    "\n",
+    "# Erstellen des DataFrames\n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "# Ausgabe des DataFrames\n",
+    "print(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6ca1af33-a8dd-4c83-a8cd-41671f010a2f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "T-Statistik: -8.573214099741122\n",
+      "P-Wert: 6.3409243600676025e-06\n"
+     ]
+    }
+   ],
+   "source": [
+    "t_statistic, p_value = stats.ttest_rel(df['Vorher'], df['Nachher'], alternative='less')\n",
+    "\n",
+    "# Ausgabe der Ergebnisse\n",
+    "print(f\"T-Statistik: {t_statistic}\")\n",
+    "print(f\"P-Wert: {p_value}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}