Testing Equality of Variances for Multiple Univariate Normal Populations

Testing equality of variances for multiple univariate normal populations

Testing equality of means and variances of several Weibull populations

The usual practice for testing homogeneity of several populations in terms of means and variances is first to test for the equality of variances and once this assumption is found to be tenable then to test the equality of means. Singh (1986) proposes a very interesting procedure based on two likelihood ratio statistics for testing homogeneity of several normal (μ, σ) populations. Singh uses a m...

Two-Sample Testing for Equality of Variances

To test for equality of variances given two independent random samples from univariate normal populations, popular choices would be the two-sample F test and Levene’s test. The latter is a nonparametric test while the former is parametric: it is the likelihood ratio test, and also a Wald test. Another Wald test of interest is based on the difference in the sample variances. We give a nonparamet...

A Robust Test for Equality of Variances of k Normal Populations

This paper introduces a test based on modified version of Gini’s mean difference for testing equality of scale parameters. If k independent samples of sizes ni (i = 1, 2, . . . , k) are taken from different normal populations, the hypothesis of equality of population variances (H0 : 1 2 = 2 2 = . . . = k ) can be tested using Bartlett’s test. However, it is i) sensitive to non-normality; and ii...

An Alternative Test for the Equality of Variances for Several Populations When the Underlying Distributions are Normal

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