Normallik testi

Çok sayıda yaygın hipotez testini içeren güçlü bir istatistiksel araç seti, alttaki verinin normal dağıldığı varsayımına dayanır. Histogram, verinin yaklaşık olarak normal dağılıp dağılmadığına dair ipuçları verebilirken, çeşitli hipotez testleri bu varsayımı doğrudan sınamamıza olanak tanır. Ayrıca, özellikle örneklem boyutları küçükken, histogramlar kutu (bin) sayısına karşı oldukça hassastır.

Bu egzersizde salary_df içinde Austin Şehri çalışanlarının maaş verileriyle çalışacaksın. Özellikle Hispanik itfaiyecilerle ilgileneceksin. Anderson-Darling hipotez testini kullanarak çalışma yıllarının yaklaşık olarak normal dağılıp dağılmadığını analiz edeceksin.

Bu egzersiz

Python'da Çıkarımın Temelleri

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Çalışanların Years of Employment değerlerini gösteren bir histogram çiz.
Years of Employment değerlerinin yaklaşık olarak normal dağılıp dağılmadığını görmek için Anderson-Darling normallik testi uygula.
Test statistic değerinin hangi critical_values değerlerinden büyük olduğunu bul.
Sıfır hipotezinin reddedileceği significance_level(leri) yazdır.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Plot a histogram of the employees' "Years of Employment"
____.plot(kind="____")
plt.show()

# Conduct an Anderson-Darling test using the years of employment from salary_df
result = stats.____(____)

# Print which critical values the test statistic is greater than the critical values
print(result.____ > result.____)

# Print the significance levels at which the null hypothesis is rejected
print(result.____[result.____ > result.____])

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: Normallik testleri Exercise 2: Normallik testi

Geçerli Egzersiz

Exercise 3: Hataların dağılımı Exercise 4: Normal dağılım uydurma Exercise 5: Korelasyon testleri Exercise 6: Korelasyon testi Exercise 7: Otokorelasyon Exercise 8: Açıklanan varyans Exercise 9: Parametrik testler Exercise 10: Eşit varyans Exercise 11: Grupların normalliği Exercise 12: ANOVA Exercise 13: Parametrik olmayan testler Exercise 14: Sıralamaları karşılaştırma Exercise 15: Medyanları karşılaştırma

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: Effect size Exercise 2: Effect size for means Exercise 3: Effect size for correlations Exercise 4: Effect size for categorical variables Exercise 5: Multiple comparisons and corrections Exercise 6: Multiple comparisons problem Exercise 7: Bonferonni-Holm correction Exercise 8: Power of a test Exercise 9: What is power anyway?Exercise 10: Power for experimental design Exercise 11: Computing power and sample sizes

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