Perform brown Forsythe test using Levene() function
In this approach to perform the brown Forsythe test, the user needs to first call the Levene() function from the scipy.stats library and pass the required parameter to it and then this function will be returning back the test statistic value to the user.
Syntax: levene(sample1, sample2, …s, center=’median’, proportiontocut=0.05)
Parameters:
- sample1, sample2, …: The sample data, possibly with different lengths. Only one-dimensional samples are accepted.
- center: Which function of the data to use in the test. The default is ‘median’.
- proportiontocutfloat, optional: When the center is ‘trimmed’, this gives the proportion of data points to cut from each end.
Example:
In this example, we have two groups with ten elements each and we are using the Levene() function to get its brown Forsythe test in the python programming language.
Python
# Create dataimport scipy.stats as statsgroup1 = [456, 564, 54, 554, 54, 51, 1, 12, 45, 5]group2 = [65, 87, 456, 564, 456, 564, 564, 6, 4, 564]# conduct the Wilcoxon-Signed Rank Teststats.levene(group1, group2, center='median') |
Output:
LeveneResult(statistic=0.33617324893734357, pvalue=0.5692334858602581)
Output Interpretation
So, here the test statistic value is 0.33617 and the P-value is 0.569233. The p-value of the test seems to be more than .05, so we fail to reject the null hypothesis of the test. However, in the above example, we passed the test but in case we failed we have the apply the following steps:
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.