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Click the button to display the Design & Analysis of Experiments (Startup Panel). Experimental design techniques apply analysis of variance principles to product development. The primary goal is usually to extract the maximum amount of unbiased information regarding the factors affecting a production process from as few (costly) observations as possible.

The Design of Experiments module offers an extremely comprehensive selection of procedures to design and analyze the experimental designs used in industrial (quality) research: 2(k-p) factorial designs with blocking (for more than 100 factors, including unique, highly efficient search algorithms for finding minimum aberration and maximum unconfounding designs, where you can specify the interaction effects of interest that are to be unconfounded), screening designs (for over 100 factors, including Plackett-Burman designs), 3(k-p) factorial designs with blocking (including Box-Behnken designs), mixed-level designs, central composite (or response surface) designs (including small central composite designs), Latin square designs, Taguchi robust design experiments via orthogonal arrays, mixture designs and triangular surfaces designs, vertices and centroids for constrained surfaces and mixtures, and D- and A-optimal designs for factorial designs, surfaces, and mixtures.

STATISTICA includes an extremely large number of other computational methods for analyzing data collected in experiments, and for fitting ANOVA/ANCOVA-like designs to continuous or categorical outcome variables. Specifically, the STATISTICA family of products includes complete implementations of General Linear Models (GLM) and General Regression Models (GRM) with sophisticated model-building procedures (stepwise and best-subset selection of predictor effects), Generalized Linear/Nonlinear Models (GLZ), General Discriminant Analysis (GDA), General Classification/Regression Trees Models (GTrees), and General CHAID Models.