scipy stats.normaltest() function | Python
Last Updated :
11 Feb, 2019
scipy.stats.normaltest(array, axis=0)
function test whether the sample is different from the normal distribution. This function tests the null hypothesis of the population that the sample was drawn from.
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:
from scipy.stats import normaltest
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 ( '\nNormal test for given data :\n' , normaltest(y1))
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Output :
Normal test for given data :
NormaltestResult(statistic=146.08066794511544, pvalue=1.901016994532079e-32)
Code #2:
from scipy.stats import normaltest
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 ( '\nNormal test for given data :\n' , normaltest(y1))
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Output :
Normal test for given data :
NormaltestResult(statistic=344.05533061429884, pvalue=1.9468577593501764e-75)
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