scipy stats.kurtosistest () function | python

NumPy | Python Methods and Functions

What is kurtosis?
This is the fourth central point divided by the square of the variance. This is a measure of "closeness", that is, a descriptor of the form of the probability distribution of a real random variable. Simply put, it can be said to be a measure of how a heavy tail compares to a normal distribution.

Its formula is —

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

Returns: Z-score (Statistics value) and P-value for the normally distributed data set.

Code # 1:

# Graph using numpy.linspace ()
# finding kurtosa

 

from scipy.stats import kurtosistest

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 ( ` Kurtosis for normal distribution: ` , kurtosistest (y1))

Output:

 
Kurtosis for normal distribution: KurtosistestResult (statistic = -2.2557936070461615, pvalue = 0.024083559905734513)




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