Fitting models to correlated data III: A comparison between residual analysis and other methods

Abstract

Applications of the χ 2 test, the F test, the Durbin–Watson d test, and the f (or Sign) test, to examples of correlated data treatment, show important drawbacks with the d test and (apparently) with the f test. An analytical approach based on residual analysis suggests an improvement in their use that leads to better results at lowest order; it also points out a distinction between goodness-of-fit tests, as the f test, and goodness-of-modeling tests, as the χ 2 and F tests. The residual analysis method is applied to the same examples; it looks faster, simpler, and often more accurate than the classical ones.