Get the variance of the Data Used
Before performing the Brown-Forsythe test, we need to calculate the variance of each group so that it can be seen that the variances between the groups differ, but to determine if these differences are statistically large we are able to perform the Brown-Forsythe test take a look at.
Python
import numpy as np # Create data group1 = [ 456 , 564 , 54 , 554 , 54 , 51 , 1 , 12 , 45 , 5 ] group2 = [ 65 , 87 , 456 , 564 , 456 , 564 , 564 , 6 , 4 , 564 ] # calculate variance of each group print (np.var(group1), np.var(group2)) |
Output:
52101.43999999999 59121.2
How to Perform a Brown – Forsythe Test in Python
Prerequisites: Parametric and Non-Parametric Methods, Hypothesis Testing
In this article, we will be looking at the approach to perform a brown-Forsythe test in the Python programming language. Brown–Forsythe test is a statistical test for the equality of group variances based on performing an Analysis of Variance (ANOVA) on a transformation of the response variable.
A one-way ANOVA is employed to see whether or not or not there’s a big distinction between the means of 3 or additional independent teams. And the assumption of a one-way ANOVA is that the variances of the populations that the samples come from are equal. Ways to test by using a Brown-Forsythe test is by following hypotheses:
- Ho: The variances among the populations are equal.
- Ha: The variances among the populations are not equal.
Note: If the p-value of the test is less than some significance level (e.g.α = .05) then we reject the null hypothesis and finish that the variances aren’t equal to a few of the exclusive populations.