  In this work we introduce a new residual for normal linear models that are
suitable for situations in which we are dealing with heteroskedasticity of
unknown form, they are referred to by principal component analysis (PCA)
residuals. These residuals are obtained through a linear transformation of the
ordinary residuals, by means of a spectral analysis on a
heteroskedasticity-consistent estimator of the covariance matrix. The resulting
residuals are independent and normally distributed. These residuals provide a
simple way to check several assumptions that underlie the normal linear
regression model, as well as model adequacy. Since they are independent and
normally distributed, one may apply several results on independent random
variables directly to these residuals. Finally, we provide an application to
real data to illustrate the usefulness of our residuals.
