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An F-test or X2-test formally tests how well the model fits the data.
When the model fitted is correct the residual (model error) mean square provides and unbiased estimate of the true variance. If the model is wrong, then the mean square is larger than the true variance. It is possible to test for lack of fit by comparing the model error mean square to the true variance.
When the true variance is known, a X2-squared test formally tests whether the model error is equal to the hypothesized value.
When the true variance is unknown, and there are multiple observations for each set of predictor values, an F-test formally tests whether there is a difference between pure error and the model error. The pure error is the pooled variance calculated for all unique sets of predictor values. It makes no assumptions about the model but does assume that the variance is the same for each set of predictor values. An analysis of variance table shows the pure error, model error, and the difference
between them called the lack of fit.
The null hypothesis states that the model error mean square is equal to the hypothesized value/pure error, against the alternative that it is greater than. When the test p-value is small, you can reject the null hypothesis and conclude that there is a lack of fit.