Nov 30, 2020 · As regards the causes of heteroskedasticity in an econometrics setting: In a regression setting and in econometrics, a fundamental distinction must be made between …

@gung in your comment you put italics on all the words in the phrase minimum variance unbiased estimator. I understand that with heteroscedasticity the estimator will become less efficient …

This isn't a strictly stats question--I can read all the textbooks about ANOVA assumptions--I'm trying to figure out how actual working analysts handle data that doesn't quite meet the …

Aug 8, 2015 · I am plotting a residual plot to test for heteroskedasticity. The Breusch-Pagan test is significant and therefore I am suspecting there is evidence on heteroskedasticity. The question …

Heteroscedasticity literally means "different amounts of scatter." It applies purely descriptively to data, for instance, where one subgroup of data may have a strikingly different spread than …

Nov 12, 2017 · As I understand them, heteroscedasticity is differing variabilities in sub-populations and non-stationarity is a changing mean/variance over time. If this is a correct (albeit …

9 I am looking for any help, advice or tips in how to explain heterogeneity / heteroscedasticity to biologists in my department. In particular I want to explain why its important to look for it and …

Apr 19, 2015 · I list a number of methods of dealing with heteroscedasticity (with R examples) here: . Many of those recommendations would be less ideal because you have a single …

Nov 14, 2019 · The discreteness of the top plot strongly indicates the need for a GLM rather than OLS model. This needs to be taken care of before considering heteroscedasticity.

Dec 4, 2023 · No, they are not equivalent. In fact, they are quite unrelated. Heteroskedasticity is when variance differs between "situations". For instance, in a regression task, the variance of …