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Identify the cause of nonlinear effects while avoiding confounding any terms up to second order. So not only can you detect nonlinearity, as you might with center points in a traditional screening design, but you can identify the responsible factors.

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All quadratic effects are estimable in models comprised only of main effects and quadratic terms. This enables you to identify the factors that account for nonlinearity. Note that traditional screening designs with added center points do not allow estimation of all quadratic effects in models consisting of main and quadratic effects.

To view the absolute values of the correlations among effects, use the Color Map on Correlations provided as part of the Design Evaluation outline in the Definitive Screening Design window. You can compare the aliasing structure of definitive screening designs to that of other designs by comparing their color maps on correlations. For details, see Color Map on Correlations in Evaluate Designs.
For details, see Structure of Definitive Screening Designs. For information about definitive screening designs with blocks, see Blocking in Definitive Screening Designs. For suggestions on how to analyze data obtained using definitive screening designs, see Analysis of Experimental Data.
After you run a Definitive Screening Design (DSD), analyze your results using the Fit Definitive Screening platform. Standard model selection methods applied to DSDs can fail to identify active effects. To identify active main effects and secondorder effects, the Fit Definitive Screening platform uses an algorithm called Effective Model Selection for DSDs. This algorithm leverages the special structure of DSDs. See The Fit Definitive Screening Platform.