October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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104
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第
3
章
resampled_p_value = sum(perm_chi2 > chi2observed) / len(perm_chi2)
print(f'Observed chi2: {chi2observed:.4f}')
print(f'Resampled p-value: {resampled_p_value:.4f}')
3.9.2
卡方检验
：
统计理论
渐进统计理论表明，卡方统计量的分布可以近似为
卡方分布
（参见
2.10
节）。合适的标准
卡方分布是由
自由度
（参见
3.7
节）决定的
。对于列联表，自由度与行数（
r
）和列数（
c
）
都有关，关系如下所示：
自由度
=
(
r
– 1)
×
(
c
– 1)
卡方分布通常是偏斜的，右侧有一个长尾。自由度分别为
1
、
2
、
5
、
20
的
卡方分布如
图
3-7
所示。观测统计量在卡方分布中的位置越远，
p
值就越小。
chisq.test
函数可以使用卡方分布作为参照计算出
p
值：
> chisq.test(clicks, simulate.p.value=FALSE)
Pearson's Chi-squared test
data: clicks
X-squared = 1.6659, df = 2, p-value = 0.4348
在
Python
中，可以使用函数
scipy.stats.chi2_contingency
：
chisq, pvalue, df, expected = stats.chi2_contingency(clicks)
print(f'Observed chi2: ...
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