The study of the ordered series of residuals of a fit proved to be useful in evaluating separately the pure experimental error and the model bias leading to a possible improvement of the modeling [J. Mol. Spectrosc. 217 (2003) 32]. In the present work this procedure is extended to homogeneous correlated data. This new method allows a separate estimation of pure experimental error, model bias, and data correlation; furthermore, it brings a new insight into the difference between goodness of fit and model relevance. It can be considered either as a study of 'random systematic errors' or as an extended approach of the Durbin-Watson problem [Biometrika 37 (1950) 409] taking into account the model error. In the present work an empirical approach is proposed for large samples (n>=500) where numerical tests are done showing the accuracy and the limits of the method.
