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数据科学中的实用统计学(第2版)
book

数据科学中的实用统计学(第2版)

by Peter Bruce, Andrew Bruce, Peter Gedeck
October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Posts & Telecom Press
Content preview from 数据科学中的实用统计学(第2版)
92
3
Python
中,可以使用函数
scipy.stats.ttest_ind
res = stats.ttest_ind(session_times[session_times.Page == 'Page A'].Time,
session_times[session_times.Page == 'Page B'].Time,
equal_var=False)
print(f'p-value for single sided test: {res.pvalue / 2:.4f}')
备择假设是页面
A
的会话时间均值小于页面
B
p
值是
0.1408
,非常接近于置换检验中的
p
0.121
0.126
(参见
3.3.2
)。
在重抽样模式下,我们可以静心设计解决方案,用来反映观测数据和要检验的假设,而不
用考虑数据是数值型的还是二元的、样本容量是否平衡、样本方差以及各种其他因素。如
果使用公式,就要了解公式的各种变化形式,这有时很令人困惑。统计学家需要了解公
式,并说明其中的道理,但数据科学家无须如此,他们不用花费力气搞清楚假设检验和置
信区间的具体细节——研究者在准备发表论文时却必须这么做。
本节要点
在计算机出现之前,重抽样检验是不现实的,统计学家使用的是标准参照分布。
检验统计量可以在标准化之后与参照分布进行比较。
t
统计量就是一种广泛使用的标准化统计量。
扩展阅读
所有统计学导论教科书都会介绍
t
计量及其用法。两本比较好的教科书是
David
Freedman
Robert Pisani ...
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Publisher Resources

ISBN: 9787115569028