STATISTICA Design of Experiments
Introduction
STATISTICA Design of Experiments features a large selection of DOE, visualisation and other analytic techniques including powerful desirability profilers and extensive residual statistics.
STATISTICA Design of Experiments offers an extremely comprehensive selection of procedures to design and analyse the experimental designs used in industrial (quality) research: 2**(k-p) factorial designs with blocking (for over 100 factors, including unique, highly efficient search algorithms for finding minimum aberration and maximum unconfounding designs, where the user 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. The specific types of available designs, and methods for generating and analysing them, are described in the following sections.