scipy stats.kurtosistest () function | python

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. / (sqrt-in-python/">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)