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scipy stats.normaltest () function | python

scipy.stats.normaltest (array, axis = 0) checks if the sample differs from the normal distribution. This function tests the null hypothesis of the population from which the sample was taken.

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

Returns: k2 value and P-value for the hypothesis test on data set.

Code # 1:

# Running a normal test

from scipy.stats import normaltest

import numpy as np 

import pylab as p 

  

x1 = np.linspace ( - 5 , 5 , 1000 )

y1 = 1. / (sqrt-in-python/">np. sqrt ( 2. * np.pi)) * np.exp ( - . 5 * (x1) * * 2  )

 

p.plot (x1, y1, ’.’ )

 

print ( ’Normal test for given data:’ , normaltest (y1))

Output:

 
Normal test for given data: NormaltestResult ( statistic = 146.08066794511544, pvalue = 1.901016994532079e-32)

Code # 2:

# Execute normal test

from scipy.stats import normaltest

import numpy as np 

import pylab as p 

 

x1 = np.linspace ( - 5 , 12 , 1000 )

y1 = 1. / (sqrt-in-python/">np. sqrt ( 2. * np.pi)) * np.exp ( - . 5 * (x1) * * 2  )

  

p.plot (x1, y1, ’.’ )

 

print ( ’Normal test for given data:’ , normaltest (y1))

Output:

 
Normal test for given data: NormaltestResult (statistic = 344.05533061429884, pvalue = 1.9468577593501764e-75)

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