Recovered errors and normal diagnostics in regression

Abstract.

Diagnostics for normal errors in regression typically utilize ordinary residuals, despite the failure of assumptions to validate their use. Case studies here show that such misuse may be critical. A remedy invokes recovered errors having the required properties, taking into account that such errors are closer to normality than are disturbances in the observations themselves. Simulation studies show consistent improvement over the usual methods in small samples. In addition, effects on normal diagnostics due to various model violations are examined.