Proceed with a One-Way ANOVA anyway
Here, the one-way ANOVA is unequal variances as long as the largest variance is no larger(59121.2) than 4 times the smallest variance(52101.43).
59121.2!=208405.72(52101.43*4)
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.