Chi-Square Test of Independence
Explanation with an Example:-
The chi-square test is one of the most common ways to examine relationships between two or more categorical variables. Not surprisingly, It involves calculating a number, called the chi-square statistic - χ2. Which follows a chi-square distribution. The chi-square test relies on the difference between observed and expected values.
Our hypotheses will be:
Let’s understand a chi-square test by taking an example :
We can find p-value by online distribution calculator. We can also calculate both chi-square statistic and p-value with the help of an online calculator.
At Significance Level:0.05, The chi-square statistic is 0.09 and the p-value is .764177. If we look at the Chi-square Distribution Table at the degree of freedom 1 and at Significance Level (Alpha) 0.05, The critical value is 3.84
Reject H0 if χ2 ≥ 3.84 and retain H0 if χ2 < 3.84. Finally, We will check p-value= value of chi-square>0.09. If p≤0.05, we reject the null hypothesis at level 0.05.
As per our test results, χ2 is 0.09 and the p-value is .764177. So, We will retain the null hypothesis i.e.There is no relationship between 2 categorical variables. ‘Gender’ and ‘Like Shopping?’ both variables are independent.
Python Code:-
Please, find the code here: Kaggle or Github
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