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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.
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