Minitab obtains the groups that are determined by distinct combinations of predictor values. Within each group with more than one observation, Minitab calculates the contribution to pure error:
Minitab then sums these contributions across groups.
The degrees of freedom for lack of fit is the degrees of freedom for error minus the degrees of freedom for pure error. The sum of squares for lack of fit is the sum of squares for error minus the sum of squares for pure error.
Minitab calculates the mean squares by dividing the sums of squares by their degrees of freedom.
The F statistic equals the mean square for lack of fit divided by the mean square for pure error.
|df||degrees of freedom = degrees of freedom for error minus degrees of freedom for pure error|
|wn||weight for observation n|
|μw||weighted mean within this group|