Produktivität innerhalb von Blöcken nach Anreiz visualisieren

Beim Beispiel zur Produktivität von Arbeitskräften untersuchst du, ob die Produktivitätswerte im Datensatz so verteilt sind, wie man es bei einer zufälligen Zuweisung der Behandlung erwarten würde. Beachte: Das ist ein Vorsichtsschritt – die eigentliche Auswertung zum Effekt der drei Behandlungen folgt noch nicht!

seaborn und matplotlib.pyplot sind als sns bzw. plt geladen.

Diese Übung ist Teil des Kurses

<Kurs>Versuchsplanung mit Python</Kurs>

Übungsanweisungen

Visualisiere die Produktivitätswerte innerhalb der Blöcke nach Behandlung mit einem Boxplot: Verwende 'block' für x, 'productivity_score' für y und 'Treatment' für hue.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Make a plot showing how productivity_score varies within blocks
sns.____(x='____', 
            y='____', 
            hue='____', 
            data=prod_df)

plt.show()

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Versuchsplanung mit Python</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

Building knowledge in experimental design allows you to test hypotheses with best-practice analytical tools and quantify the risk of your work. You’ll begin your journey by setting the foundations of what experimental design is and different experimental design setups such as blocking and stratification. You’ll then learn and apply visual and analytical tests for normality in experimental data.

Exercise 1: Setting up experiments Exercise 2: Non-random assignment of subjects Exercise 3: Random assignment of subjects Exercise 4: Experimental data setup Exercise 5: Blocking experimental data Exercise 6: Stratifying an experiment Exercise 7: Which was stratified?Exercise 8: Normal data Exercise 9: Visual normality in an agricultural experiment Exercise 10: Analytical normality in an agricultural experiment

You'll delve into sophisticated experimental design techniques, focusing on factorial designs, randomized block designs, and covariate adjustments. These methodologies are instrumental in enhancing the accuracy, efficiency, and interpretability of experimental results. Through a combination of theoretical insights and practical applications, you'll acquire the skills needed to design, implement, and analyze complex experiments in various fields of research.

Exercise 1: Faktorielles Design: Grundlagen und Anwendungen Exercise 2: Marketing-Kampagnenwirksamkeit verstehen Exercise 3: Heatmap der Kampagnen-Interaktionen Exercise 4: Faktorielle Designs und randomisierte Blockdesigns Exercise 5: Randomisierte Blockanlage: Varianz kontrollieren Exercise 6: Randomized-Block-Design implementieren Exercise 7: Produktivität innerhalb von Blöcken nach Anreiz visualisieren

Aktuelle Übung

Exercise 8: ANOVA innerhalb von Mitarbeiter-Blöcken Exercise 9: Kovariatenanpassung im Versuchsdesign Exercise 10: Wichtigkeit von Kovariaten Exercise 11: Kovariatenanpassung bei Kükenwachstum

Master statistical tests like t-tests, ANOVA, and Chi-Square, and dive deep into post-hoc analyses and power analysis essentials. Learn to select the right test, interpret p-values and errors, and skillfully conduct power analysis to determine sample and effect sizes, all while leveraging Python's powerful libraries to bring your data insights to life.

Exercise 1: Choosing the right statistical test Exercise 2: Choosing the right test: petrochemicals Exercise 3: Choosing the right test: human resources Exercise 4: Choosing the right test: finance Exercise 5: Post-hoc analysis following ANOVA Exercise 6: Anxiety treatments ANOVA Exercise 7: Applying Tukey's HSD Exercise 8: Applying Bonferoni correction Exercise 9: P-values, alpha, and errors Exercise 10: Analyzing toy durability Exercise 11: Visualizing durability differences Exercise 12: Role of significance levels Exercise 13: Power analysis: sample and effect size Exercise 14: Effect size purpose Exercise 15: Estimating required sample size for energy study

Hop into the complexities of experimental data analysis. Learn to synthesize insights using pandas, address data issues like heteroscedasticity with scipy.stats, and apply nonparametric tests like Mann-Whitney U. Learn additional techniques for transforming, visualizing, and interpreting complex data, enhancing your ability to conduct robust analyses in various experimental settings.

Exercise 1: Synthesizing insights from complex experiments Exercise 2: Visualizing loan approval yield Exercise 3: Exploring customer satisfaction Exercise 4: Effectively communicating experimental data Exercise 5: Addressing complexities in experimental data Exercise 6: Check for heteroscedasticity in shelf life Exercise 7: Exploring and transforming shelf life data Exercise 8: Applying nonparametric tests in experimental analysis Exercise 9: Visualizing and testing preservation methods Exercise 10: Further analyzing food preservation techniques Exercise 11: Congratulations!