The primary difference in analyzing combined models versus normal models is in the Fit Summary tables. An example is given below:
This table provides a matrix of p-values values for all combinations of mixture and process models. Find the combination where both models are significant A p-value less than 0.05 is a good rule of thumb.
A Lack of Fit column will appear only if there are replicates. The lack of fit should be insignificant (p-value > .10 is desirable).
Several combinations of mixture and process models may be significant. In that case, use the follow-up columns, Adjusted and Predicted R-squared to make the selection.
Stat-Ease will try to identify and suggest a model combination that is significant in both the mixture and process order, insignificant in the lack of fit, with high adjusted and predicted R-squared values. This is a good starting model, but can be tweaked and modified as desired. If two models are suggested, Stat-Ease will default to the larger model.
Click on the Model tab next. Stat-Ease uses the suggested model as the default. You may change it if you wish.