無母數 ANOVA 與不成對 t 檢定

Python 中的假設檢定

James Chapman

Curriculum Manager, DataCamp

Wilcoxon–Mann–Whitney 檢定

亦稱為 Mann-Whitney U 檢定
對數值輸入 {{1}} 的名次做 t 檢定
適用不成對資料

Wilcoxon–Mann–Whitney 檢定設定

age_vs_comp = stack_overflow[['converted_comp', 'age_first_code_cut']]

age_vs_comp_wide = age_vs_comp.pivot(columns='age_first_code_cut',
                                     values='converted_comp')

age_first_code_cut      adult     child
0                     77556.0       NaN
1                         NaN   74970.0
2                         NaN  594539.0
...                       ...       ...
2258                      NaN   97284.0
2259                      NaN   72000.0
2260                      NaN  180000.0

[2261 rows x 2 columns]

Wilcoxon–Mann–Whitney 檢定

alpha=0.01

import pingouin
pingouin.mwu(x=age_vs_comp_wide['child'], 
             y=age_vs_comp_wide['adult'],
             alternative='greater')

        U-val alternative         p-val       RBC      CLES
MWU  744365.5     greater  1.902723e-19 -0.222516  0.611258

Kruskal–Wallis 檢定

Kruskal–Wallis 檢定 之於 Wilcoxon–Mann–Whitney 檢定，如同 ANOVA 之於 t 檢定

alpha=0.01

pingouin.kruskal(data=stack_overflow, 
                 dv='converted_comp', 
                 between='job_sat')

          Source  ddof1          H         p-unc
Kruskal  job_sat      4  72.814939  5.772915e-15

一起來練習吧！

Python 中的假設檢定