ANOVA

Sekarang setelah Anda menetapkan kesamaan varians menggunakan uji Levene dan menilai secara visual kenormalan perkiraan dari gaji yang ditransformasikan log, saatnya melakukan uji ANOVA! Ingat bahwa tujuan uji ANOVA adalah untuk menentukan apakah perusahaan biotech, perangkat lunak enterprise, dan layanan kesehatan memiliki rata-rata pendanaan yang sama. Karena ketiga kelompok ini memenuhi prasyarat uji ANOVA, Anda tahu bahwa kesimpulan dari uji tersebut akan valid.

DataFrame yang Anda buat (biotech_df, enterprise_df, dan health_df) telah dimuat untuk Anda. Paket pandas sebagai pd, NumPy sebagai np, Matplotlib sebagai plt, dan paket stats dari SciPy juga telah dimuat. Hasil transformasi log dari nilai pendanaan yang Anda hitung pada latihan sebelumnya sudah disediakan.

Latihan ini adalah bagian dari kursus

Dasar-dasar Inferensi di Python

Petunjuk latihan

Lakukan uji ANOVA satu arah menggunakan masing-masing dari tiga data pendanaan yang ditransformasikan log dengan urutan argumen berikut: Biotechnology, Enterprise Software, Health Care.
Cetak jika p-value signifikan pada 5%.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

biotech_log_funding = np.log(biotech_df['funding_total_usd'])
enterprise_log_funding = np.log(enterprise_df['funding_total_usd'])
health_log_funding = np.log(health_df['funding_total_usd'])

# Conduct a one-way ANOVA test to compare the log-funding
s, p_value = ____

# Print if the p-value is significant at 5%
____

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Dasar-dasar Inferensi di Python

SkillTag.level.advancedSkillTag.label

4.9+

Mulai Kursus Gratis

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: Uji normalitas Exercise 2: Menguji kenormalan Exercise 3: Distribusi galat Exercise 4: Menyesuaikan sebaran normal Exercise 5: Uji korelasi Exercise 6: Menguji korelasi Exercise 7: Autokorelasi Exercise 8: Varians terjelaskan Exercise 9: Uji parametrik Exercise 10: Varians sama Exercise 11: Kenormalan per kelompok Exercise 12: ANOVA

Latihan Saat Ini

Exercise 13: Uji nonparametrik Exercise 14: Membandingkan pemeringkatan Exercise 15: Membandingkan median

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