# How to calculate Whites Test in R

**Methods – finnstats**, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

For the latest Data Science, jobs and UpToDate tutorials visit finnstats

The White test is a statistical test that determines whether the variance of errors in a regression model is constant, indicating homoscedasticity.

Halbert White proposed this test, as well as an estimator for heteroscedasticity-consistent standard errors, in 1980.

White’s test is used to determine whether or not a regression model contains heteroscedasticity.

In a regression model, heteroscedasticity refers to the unequal scatter of residuals at different levels of a response variable, which contradicts one of the main assumptions of linear regression, that the residuals are equally scattered at each level of the response variable.

The White test can be used to assess heteroskedasticity, specification error, or both.

This is a pure heteroskedasticity test if no cross-product terms are included in the White test technique. It is a test of both heteroskedasticity and specification bias when cross-products are incorporated into the model.

This tutorial will show you how to run White’s test in R to see if heteroscedasticity is an issue in a given regression model.

To read more visit %%BLOGLINK%%

The post How to calculate Whites Test in R appeared first on finnstats.

**leave a comment**for the author, please follow the link and comment on their blog:

**Methods – finnstats**.

R-bloggers.com offers

**daily e-mail updates**about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.

Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.