How to perform Chi Square test in python?
This recipe helps you perform Chi Square test in python
Recipe Objective.
How to perform Chi Square test in python?
The Chi-Squared test is a applied math hypothesis test that assumes (the null hypothesis) that the determined frequencies for a categorical variable match the expected frequencies for the specific variable. The test calculates a data point that incorporates a chi-squared distribution.
The number of observations for a class might or might not an equivalent. still, we will calculate the expected frequency of observations in every social group and see whether or not the partitioning of interests.
If Statistic >= Critical Value: reject null hypothesis (Ho), model dependent. If Statistic < Critical Value: fail to reject null hypothesis (Ho), model independent.
Table of Contents
Step 1- Importing Libraries.
# chi-squared test with similar proportions
from scipy.stats import chi2_contingency
from scipy.stats import chi2
import pandas as pd
Step 2- Creating Table.
Creating a sample-2d table to calculate sample stat, p, dof and expected values. Predefining prob as 0.9 to calculate chi values.
# contingency table
data = [[37, 73, 102, 400],
[10, 45, 200, 300]]
print(data)
stat, p, dof, expected = chi2_contingency(data)
# interpret test-statistic
prob = 0.90
chi = chi2.ppf(prob, dof)
chi
Step 3- Printing Result.
Now we will compare the chi value to stat value to know whether we reject the null hypothesis or fail to reject the null hypothesis.
if abs(stat) >= chi:
print('reject Ho')
else:
print('fail to reject Ho')
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