如何计算统计“t-test"与麻木 [英] How to calculate the statistics "t-test" with numpy

问题描述

我希望生成有关我在 Python 中创建的模型的一些统计信息.我想对其进行 t 检验，但想知道是否有一种简单的方法可以使用 numpy/scipy 来做到这一点.周围有什么好的解释吗?

例如，我有三个相关的数据集，如下所示:

[55.0, 55.0, 47.0, 47.0, 55.0, 55.0, 55.0, 63.0]

现在，我想对他们进行学生的 t 检验.

解决方案

在 scipy.stats 包有几个 ttest_... 函数.请参阅此处的示例:

<预><代码>>>>打印 't-statistic = %6.3f pvalue = %6.4f' % stats.ttest_1samp(x, m)t 统计量 = 0.391 pvalue = 0.6955

I'm looking to generate some statistics about a model I created in python. I'd like to generate the t-test on it, but was wondering if there was an easy way to do this with numpy/scipy. Are there any good explanations around?

For example, I have three related datasets that look like this:

[55.0, 55.0, 47.0, 47.0, 55.0, 55.0, 55.0, 63.0]

Now, I would like to do the student's t-test on them.

解决方案

In a scipy.stats package there are few ttest_... functions. See example from here:

>>> print 't-statistic = %6.3f pvalue = %6.4f' %  stats.ttest_1samp(x, m)
t-statistic =  0.391 pvalue = 0.6955

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