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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统计实验与显著性检验
｜
85
据的所有方式。这种方法仅适用于比较小的样本。通过大量的打乱重排，随机置换检验
的结果会接近穷尽置换检验的结果，并逼近它的极限。因为穷尽置换检验具有一种统计
特性，能保证零模型在检验时不会比
α
水平更显著（参见
3.4
节）
，所以它有时也称为
精
确检验
。
在
Bootstrap
置换检验中，
随机置换检验的第二步和第三步是通过
有放回
方式进行的，而不
是无放回方式。这样，重抽样过程的建模不仅包括了将处理分配给被试者时的随机因素，
还包括了从总体中选择被试者时的随机因素。在统计学中，这两种过程我们都会遇到，它
们的区别比较复杂，但在数据科学实践中不是那么重要。
3.3.4
置换检验
：
数据科学的底线
置换检验是一种非常有用的启发式过程，可以用来探索随机变动的作用。这种过程很容易
编码、理解和解释。它提供了一种有用的变通方案，可以解决基于公式的统计学中的形式
主义和“错误决定论”，其中根据公式得出的精确答案往往意味着不能保证其确定性。
与公式方法不同，重抽样的一个优点是，它是一种几乎适用于任何情况的推断方法。数据
可以是数值型的，也可以是二元的；样本容量可以相同，也可以不同；也不需要数据符合
正态分布的假设。
本节要点
•
在置换检验中，多个样本被组合在一起，然后打乱重排。
•
重排后的值被分割为重抽样，并重新计算相应的统计量。
•
重复这个过程，并将重抽样统计量列成表格。
•
比较统计量的观测值和重抽样分布，就可以判断样本之间的观测差异是否是由随机
性造成的。
3.3.5
扩展阅读
• Eugene Edgington ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.