Perform a Kruskal-Wallis Test
If the ratio of the largest variance to the smallest variance is greater than 4, we may choose to perform a Kruskal-Wallis test. This is taken into consideration the non-parametric equivalent to the one-way ANOVA. The ratio of the largest to smallest variance is 59121.2/ 52101.43 = 1.13, which is extremely less than 4, so we can proceed with the one-way ANOVA even if the Brown-Forsythe test indicated that the variances had been no longer equal.
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.