scipy stats.skewtest()函数| Python

scipy.stats.skewtest(array, axis=0)函数测试偏斜是否与正态分布不同。此函数检验零假设，即抽取样本的总体偏度与相应正态分布的偏度相同。

它的公式——

Parameters :
array : Input array or object having the elements.
axis : Axis along which the skewness test is to be computed. By default axis = 0.

Returns : Z-score (Statistics value) and P-value for the hypothesis test on data set.

代码#1：

# Performing skewtest
from scipy.stats import skewtest
import numpy as np 
import pylab as p 
  
x1 = np.linspace( -5, 5, 1000 )
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  )
  
p.plot(x1, y1, '*')
  
  
print( '\nSkewness test for given data :\n', skewtest(y1))

输出 ：

给定数据的偏度测试：SkewtestResult(statistic=11.874007880556805, pvalue=1.6153913086650964e-32)

代码#2：

# Performing skewtest
from scipy.stats import skewtest
import numpy as np 
import pylab as p 
  
x1 = np.linspace( -5, 12, 1000 )
y1 = 1./(np.sqrt(2.*np.pi)) * np.exp( -.5*(x1)**2  )
  
p.plot(x1, y1, '.')
  
  
print( '\nSkewness for data :\n', skewtest(y1))

输出 ：

数据偏度：SkewtestResult(statistic=16.957642860709516, pvalue=1.689888374767126e-64)