This is a detailed tutorial of NumPy Chi-Square Distribution. Learn to get Chi-Square Distribution data using NumPy and visualize using Seaborn.

This is a detailed tutorial of NumPy Chi-Square Distribution. Learn to get Chi-Square Distribution data using NumPy and visualize using Seaborn.

Chi-Square Distribution is one of the cases of the gamma distribution, and in most cases, it is helpful in probability distribution and also in the hypothesis testing. It also helps in working up with the construction of confidence intervals. It helps in population variance when the underlying distribution is normal. There is not much application of direct modelling of natural phenomena. There are several hypothesis tests that come under this distribution like the Chi-square test of independence and also Chi-square test of goodness of fit.

Chi-Square distribution takes in two parameters:

`df`

– this depicts a degree of freedom.`size`

– here we will give the shape of the array which will be given to us as output.

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