Koreksi Bonferroni

Mari terapkan pengujian hipotesis ganda menggunakan pendekatan koreksi Bonferroni yang telah kita bahas di slide. Anda akan menggunakan fungsi multipletests() yang sudah diimpor untuk melakukannya.

Gunakan tingkat signifikansi untuk satu uji sebesar 0,05 dan amati bagaimana koreksi Bonferroni memengaruhi daftar p-value sampel yang sudah dibuat.

Latihan ini adalah bagian dari kursus

Berlatih Pertanyaan Wawancara Statistik dengan Python

Petunjuk latihan

Hitung daftar p-value yang disesuaikan dengan Bonferroni menggunakan fungsi multipletests() yang telah diimpor.
Cetak hasil pengujian hipotesis ganda yang dikembalikan pada indeks 0 dari variabel p_adjusted Anda.
Cetak p-value itu sendiri yang dikembalikan pada indeks 1 dari variabel p_adjusted Anda.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

from statsmodels.sandbox.stats.multicomp import multipletests
pvals = [.01, .05, .10, .50, .99]

# Create a list of the adjusted p-values
p_adjusted = multipletests(____, alpha=____, method='bonferroni')

# Print the resulting conclusions
print(____)

# Print the adjusted p-values themselves 
print(____)

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Berlatih Pertanyaan Wawancara Statistik dengan Python

SkillTag.level.advancedSkillTag.label

4.7+

Mulai Kursus Gratis

This chapter kicks the course off by reviewing conditional probabilities, Bayes' theorem, and central limit theorem. Along the way, you will learn how to handle questions that work with commonly referenced probability distributions.

Exercise 1: Conditional probabilities Exercise 2: Setting up problems Exercise 3: Bayes' theorem applied Exercise 4: Central limit theorem Exercise 5: Samples from a rolled die Exercise 6: Simulating central limit theorem Exercise 7: Probability distributions Exercise 8: Bernoulli distribution Exercise 9: Binomial distribution Exercise 10: Normal distribution

In this chapter, you will prepare for statistical concepts related to exploratory data analysis. The topics include descriptive statistics, dealing with categorical variables, and relationships between variables. The exercises will prepare you for an analytical assessment or stats-based coding question.

Exercise 1: Descriptive statistics Exercise 2: Mean or median Exercise 3: Standard deviation by hand Exercise 4: Categorical data Exercise 5: Encoding techniques Exercise 6: Exploring laptop prices Exercise 7: Two or more variables Exercise 8: Types of relationships Exercise 9: Pearson correlation Exercise 10: Sensitivity to outliers

Prepare to dive deeper into crucial concepts regarding experiments and testing by reviewing confidence intervals, hypothesis testing, multiple tests, and the role that power and sample size play. We'll also discuss types of errors, and what they mean in practice.

Exercise 1: Interval kepercayaan Exercise 2: Interval kepercayaan secara manual Exercise 3: Menerapkan interval kepercayaan Exercise 4: Pengujian hipotesis Exercise 5: Uji z satu ekor Exercise 6: Uji t dua ekor Exercise 7: Daya dan ukuran sampel Exercise 8: Dampak pada galat Tipe II Exercise 9: Menghitung ukuran sampel Exercise 10: Memvisualisasikan hubungan Exercise 11: Pengujian berganda Exercise 12: Menghitung laju kesalahan Exercise 13: Koreksi Bonferroni

Latihan Saat Ini

Wrapping up, we'll address concepts related closely to regression and classification models. The chapter begins by reviewing fundamental machine learning algorithms and quickly ramps up to model evaluation, dealing with special cases, and the bias-variance tradeoff.

Exercise 1: Regression models Exercise 2: Linear regression Exercise 3: Logistic regression Exercise 4: Evaluating models Exercise 5: Regression evaluation Exercise 6: Classification evaluation Exercise 7: Missing data and outliers Exercise 8: Handling null values Exercise 9: Identifying outliers Exercise 10: Bias-variance tradeoff Exercise 11: Test and training error Exercise 12: Visualizing the tradeoff Exercise 13: Wrapping up