Multiplying an estimating equation by a correcting factor to correct heteroskedasticity may cause extra correlation to enter the model, which raises the R^2. This renders our ultimate regression results meaningless. True or false, or uncertain? Please provide an explanation.© BrainMass Inc. brainmass.com October 9, 2019, 7:21 pm ad1c9bdddf
The answer is false. A regression model is useless until all the regression assumptions are satisfied. So a regression model where Heteroskedasticity is not satisfied is a useless model. We must make necessary correcting adjustments to remove Heteroskedasticity.
Multiplying a regression ...
Explains why a regression model where Heteroskedasticity is not satisfied is a useless model.