Introduction to Numpy Chi-square Distribution

In This Article, You Will Learn About Numpy chi-square Distribution.

Numpy Chi-square Distribution – Before moving ahead, let’s know a bit of Python Exponential Distribution

Chi-square distribution is a squared distribution that is used for statistical tests. In other words, it is used to test statistical tests where the test statistic follows Chi-squared distribution.

It includes two parameters –

df - Degree of freedom.

size - Shape of the returned array.

Example – Creating an array of random numbers of size 3×3 for Chi-square distribution.

from numpy import random

z = random.chisquare(df=2, size=(3, 3))

print(z)
Output -

[[3.17431893 0.31158006 1.36799217]
 [0.76598932 1.20045585 0.82961162]
 [0.0807137  3.39885294 5.11447682]] 

As shown above, it returned an array of shape 3×3 containing random numbers.

Note: Later you will learn more in our Python Chi-square Distribution Tutorial.

Visualization of Chi Square Distribution

Example – Visualizing the Chi-square Distribution graph without histogram.

from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.chisquare(df=2.2, size=5000), hist=False)

plt.show()
numpy-chi-square-distribution

As a result, it returned visualized the Chi-square Distribution graph without histogram.

Example – Visualizing the Chi-square Distribution graph with histogram.

from numpy import random
import matplotlib.pyplot as plt
import seaborn as kl

kl.distplot(random.chisquare(df=2.2, size=5000))

plt.show()
numpy-chi-square-distribution

As a result, it returned visualized the Chi-square Distribution graph without histogram.

If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.

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