Transformations of the response and predictors can improve the fit and correct violations of model assumptions such as nonconstant error variance. We may also consider adding additional predictors that are functions of the existing predictors like quadratic or crossproduct terms. This means we have more choice in choosing the transformations on the predictors than on the response.