Python Scipy Normaltest 是怎么用的？
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Scipy Normaltest how is it used?
提问by The Demz
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.
My dataset has more than 100 values.
我的数据集有 100 多个值。
采纳答案by unutbu
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)
normaltestreturns a 2-tuple of the chi-squared statistic, and the associated p-value. Given the null hypothesis that xcame from a normal distribution, the p-value represents the probability that a chi-squared statistic that large (or larger) would be seen.
normaltest返回卡方统计量的 2 元组和关联的 p 值。给定x来自正态分布的原假设，p 值表示将看到大（或更大）卡方统计量的概率。
If the p-val is very small, it means it is unlikely that the data came from a normal distribution. For example:
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：
In [15]: y = stats.uniform.rvs(size = 100)
In [16]: stats.normaltest(y)
Out[16]: (31.487039026711866, 1.4543748291516241e-07)
回答by The Demz
First i found out that scipy.stats.normaltest is almost the same. The mstats library is used for masked arrays. Arrays where you can mark values as invalid and not taken into the calculation.
首先我发现 scipy.stats.normaltest 几乎是一样的。mstats 库用于掩码数组。可以将值标记为无效且不计入计算的数组。
import numpy as np
import numpy.ma as ma
from scipy.stats import mstats
x = np.array([1, 2, 3, -1, 5, 7, 3]) #The array needs to be larger than 20, just an example
mx = ma.masked_array(x, mask=[0, 0, 0, 1, 0, 0, 0])
z,pval = mstats.normaltest(mx)
if(pval < 0.055):
print "Not normal distribution"
"Traditionally, in statistics, you need a p-value of less than 0.05 to reject the null hypothesis." - http://mathforum.org/library/drmath/view/72065.html
“传统上，在统计学中，你需要一个小于 0.05 的 p 值来拒绝零假设。” - http://mathforum.org/library/drmath/view/72065.html
