Güç ve örneklem büyüklüklerini hesaplama

Oyun şirketleri ile reklam şirketleri için yatırım turu başına ortalama fonlamayı karşılaştırmak istiyorsun. Başlangıç olarak iki örneklemli t-testinin gücünü hesaplayacaksın.

Senin için üç şey yüklendi:

ads_acquired_avg_funding - (pandas Series) Satın alınan her bir reklam şirketinin ortalama fonlaması
games_acquired_avg_funding - (pandas Series) Aynısı, ancak oyun şirketleri için
ads_games_cohensd - (Sayı) Reklam şirketleri ve oyun şirketleri karşılaştırması için Cohen's d (standartlaştırılmış etki büyüklüğü)

Gücü hesaplamak için gereken fonksiyon statsmodels.stats.power içinden içe aktarıldı.

Bu egzersiz

Python'da Çıkarımın Temelleri

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

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

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

Kodu Düzenle ve Çalıştır

Bu egzersiz

Python'da Çıkarımın Temelleri

kursunun bir parçasıdır

AvançadoNível de habilidade

4.9+

Kursa Ücretsiz Başlayın

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: Etki büyüklüğü Exercise 2: Ortalamalar için etki büyüklüğü Exercise 3: Korelasyonlar için etki büyüklüğü Exercise 4: Kategorik değişkenler için etki büyüklüğü Exercise 5: Çoklu karşılaştırmalar ve düzeltmeler Exercise 6: Çoklu karşılaştırmalar sorunu Exercise 7: Bonferonni-Holm düzeltmesi Exercise 8: Bir testin gücü Exercise 9: Güç (power) tam olarak nedir?Exercise 10: Deney tasarımı için güç Exercise 11: Güç ve örneklem büyüklüklerini hesaplama

Geçerli Egzersiz

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