1. If the SRM is used to model data that do not have constant variance, then 95% prediction intervals produced by this model are longer than needed.
2. When data do not satisfy the similar variances condition, the regression predictions tend to be too high on average, over predicting most observations.
3. A common cause of dependent error terms is the presence of a lurking variable.
4. The Durbin-Watson test quantifies deviations from a normal population that are seen in the normal quantile plot.
5. A leveraged outlier has an unusually large or small value of the explanatory variable.
6. The presence of an outlier in the data used to ft a regression causes the estimated model to have a lower r2 than it should.