Power und Stichprobengrößen berechnen

Du möchtest den durchschnittlichen Finanzierungsbetrag pro Investitionsrunde von Spielefirmen mit dem von Werbefirmen vergleichen. Du beginnst damit, die Power eines Zweistichproben-t-Tests zu berechnen.

Drei Dinge wurden für dich geladen:

ads_acquired_avg_funding – (pandas Series) Die durchschnittliche Finanzierung jeder übernommenen Werbefirma
games_acquired_avg_funding – (pandas Series) Dasselbe, aber für Spielefirmen
ads_games_cohensd – (Zahl) Cohen’s d (standardisierte Effektstärke) für Werbung versus Spielefirmen

Die zum Berechnen der Power benötigte Funktion wurde aus statsmodels.stats.power importiert.

Diese Übung ist Teil des Kurses

<Kurs>Grundlagen der Inferenz in Python</Kurs>

Interaktive praktische Übung

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

# Compute the ratio of games to advertising companies
games_ads_ratio = ____ / ____

# Compute the power of the test
____().____(effect_size=____, 
            nobs1=____,
            alpha=____,
            ratio=____)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Grundlagen der Inferenz in Python</Kurs>

Hohe SchwierigkeitSchwierigkeitsgrad

4.9+

Kurs kostenlos starten

In this chapter, we'll explore the relationship between samples and statistically justifiable conclusions. Choosing a sample is the basis of making sound statistical decisions, and we’ll explore how the choice of a sample affects the outcome of your inference.

Exercise 1: Statistical inference and random sampling Exercise 2: Sampling and point estimates Exercise 3: Repeated sampling, point estimates and inference Exercise 4: Sampling and bias Exercise 5: Visualizing samples Exercise 6: Inference and bias Exercise 7: Confidence intervals and sampling Exercise 8: Normal sampling distributions Exercise 9: Calculating confidence intervals Exercise 10: Drawing conclusions from samples

Learn all about applying normality tests, correlation tests, and parametric and non-parametric tests for sound inference. Hypothesis tests are tools, and choosing the right tool for the job is critical for statistical decision-making. While you may be familiar with some of these tests in introductory courses, you'll go deeper to enhance your inferential toolkit in this chapter.

Exercise 1: Normality tests Exercise 2: Testing for normality Exercise 3: Distribution of errors Exercise 4: Fitting a normal distribution Exercise 5: Correlation tests Exercise 6: Testing for correlation Exercise 7: Autocorrelation Exercise 8: Explained variance Exercise 9: Parametric tests Exercise 10: Equal variance Exercise 11: Normality of groups Exercise 12: ANOVA Exercise 13: Non-parametric tests Exercise 14: Comparing rankings Exercise 15: Comparing medians

In this chapter, you'll measure and interpret effect size in various situations, encounter the multiple comparisons problem, and explore the power of a test in depth. While p-values tell you if a significant effect is present, they don't tell you how strong that effect is. Effect size measures how strong an effect a treatment has. Master the factors underpinning effect size in this chapter.

Exercise 1: Effektstärke Exercise 2: Effektstärke für Mittelwerte Exercise 3: Effektstärke für Korrelationen Exercise 4: Effektstärke für kategoriale Variablen Exercise 5: Mehrfachvergleiche und Korrekturen Exercise 6: Problem der multiplen Vergleiche Exercise 7: Bonferroni-Holm-Korrektur Exercise 8: Teststärke Exercise 9: Was ist eigentlich Power?Exercise 10: Power für Versuchsplanung Exercise 11: Power und Stichprobengrößen berechnen

Aktuelle Übung

You’ll expand your inferential statistics toolkit further with a look at bootstrapping, permutation tests, and methods of combining evidence from p-values. Bootstrapping will provide you with a first look at statistical simulation. In the lesson meta-analysis, you’ll learn all about combining results from multiple studies. You’ll end with a look at permutation tests, a powerful and flexible non-parametric statistical tool.

Exercise 1: Bootstrapping Exercise 2: Bootstrap confidence intervals Exercise 3: Bootstrapping vs. normality Exercise 4: Combining evidence from p-values Exercise 5: Fisher's method in SciPy Exercise 6: Inference using Fisher's method Exercise 7: Summarizing Fisher's method Exercise 8: Permutation tests Exercise 9: Permutation tests for correlations Exercise 10: Permutation tests and bootstrapping Exercise 11: Analyzing skewed data with a permutation test Exercise 12: Course wrap-up video