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The null hypothesis of this chi-squared test is homoscedasticity, and the alternative hypothesis would indicate heteroscedasticity. Since the Breusch–Pagan test is sensitive to departures from normality or small sample sizes, the Koenker–Bassett or 'generalized Breusch–Pagan' test is commonly used instead.
In statistics, Bartlett's test, named after Maurice Stevenson Bartlett, [1] is used to test homoscedasticity, that is, if multiple samples are from populations with equal variances. [2] Some statistical tests, such as the analysis of variance , assume that variances are equal across groups or samples, which can be checked with Bartlett's test.
In R, White's Test can be implemented using the white function of the skedastic package. [5] In Python, White's Test can be implemented using the het_white function of the statsmodels.stats.diagnostic.het_white [6] In Stata, the test can be implemented using the estat imtest, white function. [7]
This is the basis of the Breusch–Pagan test. It is a chi-squared test: the test statistic is distributed nχ 2 with k degrees of freedom. If the test statistic has a p-value below an appropriate threshold (e.g. p < 0.05) then the null hypothesis of homoskedasticity is rejected and heteroskedasticity assumed.
In statistics, Levene's test is an inferential statistic used to assess the equality of variances for a variable calculated for two or more groups. [1] This test is used because some common statistical procedures assume that variances of the populations from which different samples are drawn are equal. Levene's test assesses this assumption.
Homogeneity can be studied to several degrees of complexity. For example, considerations of homoscedasticity examine how much the variability of data-values changes throughout a dataset. However, questions of homogeneity apply to all aspects of the statistical distributions, including the location parameter
In statistics, the Jonckheere trend test [1] (sometimes called the Jonckheere–Terpstra [2] test) is a test for an ordered alternative hypothesis within an independent samples (between-participants) design. It is similar to the Kruskal-Wallis test in that the null hypothesis is that several independent samples are from the same population ...
In SAS, the GODFREY option of the MODEL statement in PROC AUTOREG provides a version of this test. In Python Statsmodels, the acorr_breusch_godfrey function in the module statsmodels.stats.diagnostic [9] In EViews, this test is already done after a regression, at "View" → "Residual Diagnostics" → "Serial Correlation LM Test".