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+ + + diff --git a/Material/wise_24_25/lernmaterial/10.Data_Analysis.ipynb b/Material/wise_24_25/lernmaterial/10.Data_Analysis.ipynb new file mode 100644 index 0000000..c6f07ed --- /dev/null +++ b/Material/wise_24_25/lernmaterial/10.Data_Analysis.ipynb @@ -0,0 +1,367 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0362a1eb-1e46-462a-b22c-f972c398741d", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-e3006ef8308b2f34", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# 10. Programmierübung: Datenanalyse\n", + "\n", + "
\n", + "
\n", + " Willkommen zur zehnten Programmierübung Einführung in Python 3.\n", + "
\n", + " \n", + "
\n", + "\n", + "Wenn Sie Fragen oder Verbesserungsvorschläge zum Inhalt oder Struktur der Notebooks haben, dann können sie eine E-Mail an Phil Keier ([p.keier@hbk-bs.de](mailto:p.keier@hbk-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) oder Martin Le ([martin.le@tu-bs.de](mailto:martin.le@tu-bs.de?subject=[SigSys]%20Feedback%20Programmierübung&)) schreiben.\n", + "\n", + "Link zu einem Python Spickzettel: [hier](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf)\n", + "\n", + "Der Großteil des Python-Tutorials stammt aus der Veranstaltung _Deep Learning Lab_ und von [www.python-kurs.eu](https://www.python-kurs.eu/python3_kurs.php) und wurde für _Signale und Systeme_, sowie _Einführung in die Programmierung für Nicht Informatiker_ angepasst.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "0a97a824-bc2f-4f9d-a730-42ff05828cc8", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-a5071e148ad2227c", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Data Analysis\n", + "\n", + "In dieser & letzter Aufgabe ist dir das Datenset `survey.csv` gegeben. Diese Übung dient als Vorbereitung auf die abschließende Prüfung und wird dieshingehend auch streng bewertet. Jede Aufgabe setzt sich aus 6 Punkten zusammen von denen jeweils 4 auf den Schriftteil und 2 Punkte auf den Programmatischen Teil fallen. \n", + "\n", + "Gegeben ist immer eine Hypothese, beweise/widerlege die Hypothese und zeige auch die Gegenhypothese auf. Stelle zu jeder Aufgabe Annahmen an, Erkläre kurz dein Vorgehen, Interpretiere dein Ergebniss, Schließe mit einem angemessenen Fazit ab und erkläre jede von dir selbst erstellte Grafik. Gebe auch alle verwendeten Quellen die benutzt wurden an. \n", + "\n", + "Da zahlen nicht Aussagen gilt auch, dass sofern der Schriftteil nicht beantwortet wurde die Aufgabe mit 0 Punkten bewertet wird.\n", + "\n", + "Antworten sind in Englisch & Deutsch möglich andere Sprachen werden nicht akzeptiert.\n", + "\n", + "Die Daten stammen aus folgender Umfrage [Survey](https://forms.gle/JVKq6FrSUE8kN7Jq6).\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "ad9d17d9-dd59-4766-bb46-de4df822ce0f", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-3da446eb91bdd35d", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Hypothesis 1\n", + "\n", + "Der Kurs besteht überwiegend aus männlichen Individuum, welche überwiegend Windows verwenden und sich überpropotional als gute Programmierer einschätzen." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8e24289e-a738-4ae4-a6d4-a936263120fc", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-a6a23c7c132e44d9", + "locked": false, + "points": 2, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "80cf1c27-644a-4538-abbc-7d5f524251d7", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-9cbb6c488a06eb18", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "source": [] + }, + { + "cell_type": "markdown", + "id": "171340b9-0302-4c7c-ac85-f81d1e57a9da", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-3fc34b3f943dfa4b", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Hypothesis 2\n", + "\n", + "Geisteswissenschaftliche Studierende neigen dazu das Betriebsystem Mac Os zu verwenden. Unteranderem wird auch angenommen, dass diese Gruppe überwiegend das Smartphone Betriebsystem Android verwendet. Daraus ableitend ist anzunehmen das diese Gruppe sich selbst als mittelmäßige Python Programmierer einschätzt." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "98fb0abc-0d50-4f4c-aeb0-16b056aeeabf", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-b28aca63b8048f70", + "locked": false, + "points": 2, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "7ae161df-8dd8-4828-99cc-3ba28d2bf9dd", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-016f2b30651bea18", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "source": [] + }, + { + "cell_type": "markdown", + "id": "3b77bda1-5851-4394-81c7-fd649cb980e5", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-fae767f8aab85e92", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Hypothesis 3\n", + "\n", + "Studierende aus gut situierten Haushalten neigen dazu Gruppenarbeiten an der Uni als schlecht einzuschätzen. Insbesondere dann wenn Sie sich selbst als Gute Programmierer einschätzen." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "0d74bd0d-e83e-4211-bc58-b9615e36b848", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-34a9cdeebcf2d7b9", + "locked": false, + "points": 2, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "b9bb8c23-e161-492c-b1ee-9cc4c21c2325", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-81264718d58ae0f6", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "source": [] + }, + { + "cell_type": "markdown", + "id": "3fa617bb-77dd-4c43-b30b-b422eac770ef", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-d532947366781d90", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "# Hypothesis 4\n", + "\n", + "Personen die bereits strukturelle Benachteiligung erfahren haben sind meistens älter als der Durschnitt der Samplegruppe und in ärmlicheren Verhältnissen aufgewachsen. Ihr bevorzugtes Smartphone ist ein Samsung." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "18467418-586b-4153-9066-0de730994e8e", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-5284bedc957436b3", + "locked": false, + "points": 2, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "3f5dace2-5a13-4806-946c-0d0e4c9bc307", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-90f0108fe83268c7", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "source": [] + }, + { + "cell_type": "markdown", + "id": "c6fdcfa2-5507-4c85-b262-c83e6b1074ee", + "metadata": { + "nbgrader": { + "grade": false, + "grade_id": "cell-6ace0ffb9a32d3b1", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + } + }, + "source": [ + "## Hypothesis 5\n", + "\n", + "Jüngere Studierende betrachten die zurzeitige Politische Situation in Deutschland als schlecht. Unteranderem deshalb bewerten Sie Gruppenarbeiten als etwas gutes, da so ihr sozialer Zusammenhalt gestärkt wird" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5a76e273-a649-4bdf-ba8d-bc1351d0ec08", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-18c2e79244ca1beb", + "locked": false, + "points": 2, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "### BEGIN SOLUTION\n", + "### END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "8bd2c8fa-801a-4c0a-8754-61bdc2f8127b", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-a421f0cc1a31c697", + "locked": false, + "points": 4, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "source": [] + } + ], + "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": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Material/wise_24_25/lernmaterial/9.Lösungen_Statistical_Test_Methods.ipynb b/Material/wise_24_25/lernmaterial/9.Lösungen_Statistical_Test_Methods.ipynb new file mode 100644 index 0000000..2504f44 --- /dev/null +++ b/Material/wise_24_25/lernmaterial/9.Lösungen_Statistical_Test_Methods.ipynb @@ -0,0 +1,439 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "990b6558-1145-4024-8492-882b5d1358c1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "# Lösungen Statistical Test Methods" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7fc345df-f487-44ed-a2d2-0162736cab85", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "skip" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from scipy import stats\n", + "import pandas as pd\n", + "import seaborn as sb" + ] + }, + { + "cell_type": "markdown", + "id": "7958f96d-3762-40dd-9573-59936a406433", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "## Umfrage für nächste Aufgabe\n", + "\n", + "https://forms.gle/JVKq6FrSUE8kN7Jq6" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "dda1bf9e-6ab2-4794-832e-79cba819b9fa", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/jpeg": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import qrcode\n", + "qrcode.make(\"https://forms.gle/JVKq6FrSUE8kN7Jq6\")" + ] + }, + { + "cell_type": "markdown", + "id": "b3645176-f2bc-42b9-b9f0-ed460adf55e6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "### Aufgabe T-Test\n", + "\n", + "*6 Punkte - Programmieren*\n", + "\n", + "*6 Punkte - Schriftteil*\n", + "\n", + "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Nominallohn zu verstehen ist. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an.\n", + "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", + "\n", + "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**" + ] + }, + { + "cell_type": "markdown", + "id": "0ab1d35e-f6a4-40e3-afca-4008077e0444", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "Dem Notebook mit beigelegt ist das Datenset `nominallohn_deutschland_2023-2024_nach_geschlecht.csv` (Quelle: [DESTASIS Nominallohnindex: Deutschland, Monate, Geschlecht](https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0003)).\n", + "\n", + "Analysiere das Datenset, gehen dabei von der Nullhypothese aus das es keinen Signifikanten Unterschied in den Nominallöhnen zwischen den Geschlechtern gibt.\n", + "\n", + "Gehe dabei wie folgt vor:\n", + "1. Stelle die Nullhypothese prüfbar auf.\n", + "2. Importiere das Datenset und filtere angemessen (Kommentare helfen mir später bei der Korrektur diesen Schritt nachzuvollziehen)\n", + "3. Berechne den von der Nullhypothese geforderten Populations Parameter $\\mu$.\n", + "4. Bestimme das Signifikanzlevel $\\alpha$ unter der $H_0$ verworfen wird.\n", + "5. Bestimme mittels t-test den p-wert zur Nullhypothese.\n", + "6. Stelle dein Ergebnis dar." + ] + }, + { + "cell_type": "markdown", + "id": "ca2cad52-220e-40f7-9ba8-8d8c9a800314", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## Nullhypothese\n", + "\n", + "$$H_0 = \\varnothing\\text{Nominallohn Männer} = \\varnothing\\text{Nominallohn Frauen}$$" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b013f02a-e4aa-4870-80c9-a9f61d1ae44a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dataset: pd.DataFrame = pd.read_csv('nominallohn_deutschland_2023-2024_nach_geschlecht.csv', delimiter=',')\n", + "male = dataset.loc[dataset[\"Geschlecht\"] == \"männlich\"]\n", + "female = dataset.loc[dataset[\"Geschlecht\"] == \"weiblich\"]" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "9b7a1f0f-fb91-4159-8692-32606feed847", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "mu = male[\"Nominallohn\"].mean()\n", + "alpha = 0.05" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "543c338d-53f7-410b-bfce-eb77832db104", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " Fail to reject the null hypothesis;\n", + " there is no significant difference between the sample mean\n", + " and the hypothesized population mean.\n", + " \n", + "P-Value: 0.95\n" + ] + } + ], + "source": [ + "t_stat, p_value = stats.ttest_1samp(female[\"Nominallohn\"], mu)\n", + "\n", + "if p_value < alpha:\n", + " print('''\n", + " Reject the null hypothesis;\n", + " there is a significant difference between the sample mean\n", + " and the hypothesized population mean.\n", + " '''\n", + " )\n", + "else:\n", + " print('''\n", + " Fail to reject the null hypothesis;\n", + " there is no significant difference between the sample mean\n", + " and the hypothesized population mean.\n", + " '''\n", + " )\n", + "\n", + "print(f\"P-Value: {p_value:.2}\")" + ] + }, + { + "cell_type": "markdown", + "id": "b42d406d-e79c-494c-9b50-7660785b8a58", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "slide" + }, + "tags": [] + }, + "source": [ + "### Aufgabe\n", + "\n", + "*6 Punkte - Programmieren*\n", + "\n", + "*6 Punkte - Schriftteil*\n", + "\n", + "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Bruttoverdienst zu verstehen ist. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an.\n", + "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", + "\n", + "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**" + ] + }, + { + "cell_type": "markdown", + "id": "94f5f238-281b-4884-bf18-ffbac035f621", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "Analysiere das Datenset `bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv` (Quelle: [Destatis]( https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0030))\n", + "\n", + "Gehe dabei von der Hypothese aus das einen keinen Bruttoeinkommensunterschied der Geschlechter in Deutschland für den Zeitraum 2022-2023 gibt.\n", + "\n", + "Gehe dabei wie folgt vor:\n", + "1. Lies das Datenset ein und schaue dir die vorhandenen Werte an. Achte auf `Nan` Werte.\n", + "2. Filter aus der Spalte \"Geschlecht\" die benötigten Werte.\n", + "3. Führe einen Korrelationstest zwischen den Geschlechtern & dem Bruttoverdienst durch.\n", + "4. Berechne den Bruttoeinkommensunterschied.\n", + "5. Finde eine geeignete Darstellung für dein Ergebnis." + ] + }, + { + "cell_type": "markdown", + "id": "9d28b23e-868b-4dce-9efe-d9a509673812", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "source": [ + "## Nullhypothese\n", + "\n", + "$$H_0 = \\varnothing\\text{Bruttoeinkommen Männer} - \\varnothing\\text{Bruttoeinkommen Frauen} = 0$$" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2bb73d80-edc5-4256-b52e-2095f52395e8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df: pd.DataFrame = pd.read_csv('bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv')\n", + "df_cleaned: pd.DataFrame = df.dropna()\n", + "df_cleaned = df_cleaned.drop(\n", + " df_cleaned[df_cleaned['Geschlecht'] == 'Insgesamt'].index\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "478dbd4a-91cb-4ebc-9baa-a554b65aa3ca", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Pearson test\n", + "f = lambda x: 1 if x=='männlich' else 0\n", + "df_cleaned[\"Geschlecht numerisch\"] = df_cleaned[\"Geschlecht\"].apply(f)\n", + "pearsoncorr = df_cleaned.corr(method='pearson', numeric_only=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "9377f08f-d9c7-4c03-97e7-8fdd8c3b8e56", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sb.heatmap(pearsoncorr, \n", + " xticklabels=pearsoncorr.columns,\n", + " yticklabels=pearsoncorr.columns,\n", + " cmap='RdBu_r',\n", + " annot=True,\n", + " linewidth=0.5)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "e3bd5f36-390d-4be9-a009-b0ce555e497f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "subslide" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "df_women = df_cleaned[df_cleaned['Geschlecht'] == 'weiblich']\n", + "df_men = df_cleaned[df_cleaned['Geschlecht'] == 'männlich']" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f2d19be6-d402-48fe-b7cd-1518bb591200", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "fragment" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Differenz Bruttoeinkommen 313.25€\n" + ] + } + ], + "source": [ + "diff = abs(df_men['Verdienst'].mean() - df_women['Verdienst'].mean())\n", + "print(f\"Differenz Bruttoeinkommen {diff:.2f}€\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26415fd9-7150-4844-8187-fd382d14db3e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "skip" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "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": "3.12.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Material/wise_24_25/lernmaterial/9.Statistical_Test_Methods.ipynb b/Material/wise_24_25/lernmaterial/9.Statistical_Test_Methods.ipynb index 935bf1c..68e4ae7 100644 --- a/Material/wise_24_25/lernmaterial/9.Statistical_Test_Methods.ipynb +++ b/Material/wise_24_25/lernmaterial/9.Statistical_Test_Methods.ipynb @@ -39,10 +39,18 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 1, "id": "483f1609-fd3a-4154-a059-7e5d26562bcb", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-2885f7f5d6e48f91", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -78,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 2, "id": "76813bd6-6efe-439f-b48c-30da670b84ab", "metadata": { "editable": true, @@ -424,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "id": "10d3b6f1-2c8d-4999-9f02-30496db96297", "metadata": { "editable": true, @@ -607,10 +615,18 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 7, "id": "4f7c2990-3f68-47e3-93e9-f4c919f1d93c", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-5294192c404de210", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -663,7 +679,7 @@ "Nominallohn 0.172602 1.000000" ] }, - "execution_count": 38, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -679,6 +695,14 @@ "id": "d012e5ff-7e5f-4bb6-9d2d-9993dd4b1947", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-9cbe48bc8062ccae", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -690,10 +714,18 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 8, "id": "84ad50bb-b8d9-4f69-a2ae-905395a7456a", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-69937404c2eb9a0e", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -726,6 +758,14 @@ "id": "22e6a3f3-b402-4364-a418-70a6e4001d32", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-d81e230add7ed3f2", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -740,6 +780,14 @@ "id": "b60b2a4d-611d-4da4-9309-9150d500878e", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-94a5452d5be14bae", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -785,10 +833,18 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 9, "id": "e4b77f20-493d-4424-a3c2-a7fa140a78e2", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-b8c069ec23d35f28", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -841,7 +897,7 @@ "Doktor 2 0.466667 1.000000" ] }, - "execution_count": 42, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -860,6 +916,14 @@ "id": "d2ff93fc-dedb-4dd1-8969-db9fcdc63c66", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-732bc023d7ac4739", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -874,6 +938,14 @@ "id": "0cedcad4-0834-43cb-a253-b9d009018a6c", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-818c093a4e3ba72e", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -890,10 +962,18 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 10, "id": "2bb83c7d-f3af-42c9-ae93-3655868d12e9", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-ec1bbf166d417dc7", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -994,7 +1074,7 @@ "9 15 20" ] }, - "execution_count": 50, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -1010,10 +1090,18 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 11, "id": "5c223094-8d54-4cd8-a3cc-4cb56595ca78", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-9a98a7086c0bd404", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -1066,7 +1154,7 @@ "Reaktionsgeschwindigkeit 0.509356 1.000000" ] }, - "execution_count": 51, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -1081,6 +1169,14 @@ "id": "c52137a3-20dc-46d3-9e30-6b0d49a62964", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-008a676a06ecdd3e", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -1095,6 +1191,14 @@ "id": "53345351-2494-4142-b550-a047fd7a7849", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-be05fea16294ca00", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -1123,6 +1227,14 @@ "id": "4e82abfb-41d9-4133-9e08-29494b8dab50", "metadata": { "editable": true, + "nbgrader": { + "grade": false, + "grade_id": "cell-5fcf92745c0c6744", + "locked": true, + "schema_version": 3, + "solution": false, + "task": false + }, "slideshow": { "slide_type": "" }, @@ -1131,10 +1243,66 @@ "source": [ "### Aufgabe\n", "\n", - "Führe einen Pearson Test durch. Forme dabei das Datenset so um das die geschlechter ihren eigenen Spalten haben.\n", + "*6 Punkte - Programmieren*\n", "\n", - "Quelle: https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0030\n", - "bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv" + "*6 Punkte - Schriftteil*\n", + "\n", + "Für diese Aufgabe ist extra ein Feld für den Schriftteil reserviert. **Erkläre** dort dein Vorgehen, die Nullhypothese, dein Ergebnis und wie der Bruttoverdienst zu verstehen ist. **Interpretiere** dein Ergebnis und Stelle ein **Fazit** auf. Gebe auch alle verwendeten Quellen (samt link) korrekt an.\n", + "Jupyter unterstütz die Markdown Syntax ein Guide hierfür ist unter [The Ultimate Markdown Guide](https://medium.com/analytics-vidhya/the-ultimate-markdown-guide-for-jupyter-notebook-d5e5abf728fd) zu finden.\n", + "\n", + "**!!Wird der Schriftteil nicht oder nur unzureichend ausgefüllt führt dies zu einer Bewertung von 0 Punkten für die gesamte Aufgabe!!**\n", + "\n", + "Analysiere das Datenset `bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv` (Quelle: [Destatis]( https://www-genesis.destatis.de/datenbank/online/statistic/62361/table/62361-0030))\n", + "\n", + "Gehe dabei von der Hypothese aus das einen keinen Bruttoeinkommensunterschied der Geschlechter in Deutschland für den Zeitraum 2022-2023 gibt.\n", + "\n", + "Gehe dabei wie folgt vor:\n", + "1. Lies das Datenset ein und schaue dir die vorhandenen Werte an. Achte auf `Nan` Werte.\n", + "2. Filter aus der Spalte \"Geschlecht\" die benötigten Werte.\n", + "3. Führe einen Korrelationstest zwischen den Geschlechtern & dem Bruttoverdienst durch.\n", + "4. Berechne den Bruttoeinkommensunterschied.\n", + "5. Finde eine geeignete Darstellung für dein Ergebnis." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "8bc69bfc-96aa-4fdf-b540-a956ff4c1baf", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-78d2ec73dc266600", + "locked": false, + "points": 6, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "outputs": [], + "source": [ + "# BEGIN SOLUTION\n", + "dataset: pd.DataFrame = pd.read_csv('bruttoverdiensterhebung_deutschland_april_22-23_nach_geschlecht.csv')\n", + "# END SOLUTION" + ] + }, + { + "cell_type": "markdown", + "id": "d6871b8f-5eda-4cad-a482-7c0bb88dff0d", + "metadata": { + "nbgrader": { + "grade": true, + "grade_id": "cell-8fb2067332715dfa", + "locked": false, + "points": 6, + "schema_version": 3, + "solution": true, + "task": false + } + }, + "source": [ + "# BEGIN SOLUTION\n", + "# END SOLUTION" ] } ],