问题描述
我需要在 scipy 中使用 normaltest 来测试数据集是否为正态分布.但我似乎找不到任何好的例子如何使用 scipy.stats.normaltest.
I need to use normaltest in scipy for testing if the dataset is normal distributet. But I cant seem to find any good examples how to use scipy.stats.normaltest.
我的数据集有 100 多个值.
My dataset has more than 100 values.
推荐答案
In [12]: import scipy.stats as stats
In [13]: x = stats.norm.rvs(size = 100)
In [14]: stats.normaltest(x)
Out[14]: (1.627533590094232, 0.44318552909231262)
normaltest 返回卡方统计量的 2 元组和相关的 p 值.给定 x 来自正态分布的原假设，p 值表示看到大(或更大)的卡方统计量的概率.
normaltest returns a 2-tuple of the chi-squared statistic, and the associated p-value. Given the null hypothesis that x came from a normal distribution, the p-value represents the probability that a chi-squared statistic that large (or larger) would be seen.
如果 p-val 非常小，则意味着数据不太可能来自正态分布.例如:
If the p-val is very small, it means it is unlikely that the data came from a normal distribution. For example:
In [15]: y = stats.uniform.rvs(size = 100)
In [16]: stats.normaltest(y)
Out[16]: (31.487039026711866, 1.4543748291516241e-07)
这篇关于Scipy Normaltest 是怎么用的?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！