The Lack of Fit test addresses whether there is enough information in the current model or whether more complex terms are needed. This test is sometimes called a goodness-of-fit test. The lack of fit test calculates a pure-error negative log-likelihood by constructing categories for every combination of the model effect values in the data. The Saturated row in the Lack Of Fit table contains this log-likelihood. The Lack of Fit report also contains a test of whether the Saturated log-likelihood is significantly better than the Fitted model.

The Saturated degrees of freedom is m–1, where m is the number of unique populations. The Fitted degrees of freedom is the number of parameters not including the intercept.

The Lack of Fit table contains the negative log-likelihood for error due to Lack of Fit, error in a Saturated model (pure error), and the total error in the Fitted model. The chi-square statistic tests for lack of fit.