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cochran Q 測(cè)試用于非參數(shù)檢驗(yàn)，特別是對(duì)分，二分

成功或失敗

有效或無效

活著或死了

有缺陷的或無缺陷的

python代碼

無顯著差異，結(jié)論一致

# -*- coding: utf-8 -*-

# Import standard packages

import numpy as np

import pandas as pd

# additional packages

from statsmodels.sandbox.stats.runs import cochrans_q

obs = np.array([[1,1,0,1,0,1,1,1,0,0,1,1],

[1,1,1,1,0,1,1,1,0,1,1,1],

[1,1,0,0,0,1,1,0,1,0,1,1]])

def cochranQ(obs):

'''Cochran's Q test: 12 subjects are asked to perform 3 tasks. The outcome of each task is "success" or

"failure". The results are coded 0 for failure and 1 for success. In the example, subject 1 was successful

in task 2, but failed tasks 1 and 3.

Is there a difference between the performance on the three tasks?

'''

# I prefer a DataFrame here, as it indicates directly what the values mean

df = pd.DataFrame(obs.T, columns = ['Diet1', 'Diet2', 'Diet3'])

# --- >>> START stats <<< ---

(Q, pVal) = cochrans_q(df)

# --- >>> STOP stats <<< ---

print('\nCOCHRAN\'S Q -----------------------------------------------------')

print('Q = {0:5.3f}, p = {1:5.3f}'.format(Q, pVal))

if pVal < 0.05:

print("H1 wins,There is a significant difference between the three tasks.")

else:

print("H0 wins,There was no significant change")

cochranQ(obs)

測(cè)試，12個(gè)對(duì)象參加三種考試，檢驗(yàn)三種考試難度是否一樣。

H0，三種考試難度相同

H1，三種考試難度不同，至少有一個(gè)簡(jiǎn)單或難

三種考試有顯著差異

總結(jié)

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