There are no replicated points with respect to the X variables, so it is impossible to calculate a pure error sum of squares.
The model is saturated, meaning that there are as many estimated parameters as there are observations. Such a model fits perfectly, so it is impossible to assess lack of fit.
The difference between the error sum of squares from the model and the pure error sum of squares is called the lack of fit sum of squares. The lack of fit variation can be significantly greater than pure error variation if the model is not adequate. For example, you might have the wrong functional form for a predictor, or you might not have enough, or the correct, interaction effects in your model.
The Pure Error DF is pooled from each replicated group of observations. In general, if there are g groups, each with identical settings for each effect, the pure error DF, denoted DFPE, is given by:
where ni is the number of replicates in the ith group.
The Total Error SS is the sum of squares found on the Error line of the corresponding Analysis of Variance table.
where SSi is the sum of the squared differences between each observed response and the mean response for the ith group.
Shows the ratio of the Mean Square for Lack of Fit to the Mean Square for Pure Error. The F Ratio tests the hypothesis that the variances estimated by the Lack of Fit and Pure Error mean squares are equal, which is interpreted as representing “no lack of fit”.
Lists the p-value for the Lack of Fit test. A small p-value indicates a significant lack of fit.

Help created on 9/19/2017