Question - What happens when some of the assumptions required for linear regression are violated?
Answer -
These assumptions may be violated lightly (i.e., some minor violations) or strongly (i.e., the majority of the data has violations). Both of these violations will have different effects on a linear regression model.
Strong violations of these assumptions make the results entirely redundant. Light violations of these assumptions make the results have greater bias or variance.