The Design outline shows the runs for a design that is optimal, given the conditions that you have specified. The runs might not appear to be randomized. You can select Run Order options in the Output Options panel before generating your design table.
The Design Evaluation outline provides a number of ways to evaluate the properties of the generated design. Open the Design Evaluation outline 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 idealized (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. You specify the terms representing potentially active effects in the Alias Terms table. See The Alias Matrix in the Technical Details section.
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. See Optimality Criterion in Custom Design Options and Optimality Criteria.
Note: The Design Diagnostics outline does not provide the following statistics when the model includes factors with Changes set to Hard or Very Hard or with Estimability set to If Possible: D Efficiency, G Efficiency, A Efficiency.
For more information about the Design Evaluation outline, see Design Evaluation in the Evaluate Designs section.