The Design Evaluation section provides a number of ways to evaluate the properties of the generated design. Open the Design Evaluation section to see the following options:
Power Analysis
Enables you to explore your ability to detect effects of given sizes.
Prediction Variance Profile
Shows the prediction variance over the range of factor settings.
Fraction of Design Space Plot
Shows how much of the model prediction variance lies below (or above) a given value.
Prediction Variance Surface
Shows a surface plot of the prediction variance for any two continuous factors.
Estimation Efficiency
For each parameter, gives the fractional increase in the length of a confidence interval compared to that of an ideal (orthogonal) design, which might not exist. Also gives the relative standard error of the parameters.
Alias Matrix
Gives coefficients that indicate the degree by which the model parameters are biased by effects that are potentially active, but not in the model.
Color Map on Correlations
Shows the absolute correlation between effects on a plot using an intensity scale.
Design Diagnostics
Indicates the optimality criterion used to construct the design. Also gives efficiency measures for your design.
Notes:
• The model used for the design diagnostics contains all main effects and two-factor interactions when all two-factor interactions are estimable. Otherwise, the model contains all main effects.
• The Design Evaluation section is not shown for Cotter designs.
For more details about the Design Evaluation section, see “Design Evaluation Report”.