Experimental Design (DOE)

  • 2**(K-p) Standard Design Creation
    Creates experiments (experimental designs) of the standard Box, Hunter, and Hunter 2**(K-p) type.
  • 2-Level Screening (Plackett-Burman) Design Creation
    Creates experimental designs for screening experiments, including standard Plackett-Burman designs and fully saturated 2**(k-p) designs, with many 2-level factors.
  • 2**(k-p) Max. Unconfounded and Min. Aberration Design Creation
    Designing/searching for 2**(k-p) designs that are maximally unconfounded, or of minimum aberration; the program will perform a search of designs with 2-level factors that match certain design criteria (maximum unconfounding, minimum aberration) generally aimed at yielding a set of experimental runs that permit the largest number of main effects and lower order interactions to be uniquely estimated (i.e., to be unconfounded with other main effects or interactions).

 Note that for many factors and few runs, the search for the best design can require a substantial amount of computing resources.
  • Mixed 2 and 3 Level Design Creation
    Creates experimental designs for factors with 2 and 3 levels.
  • 3**(K-p) and Box-Behnken Design Creation
    Creates experimental designs of the Box-Behnken and 3**(K-p) type.
  • Central Composite Design Creation
    Creates standard central composite, non-factorial, response surface designs.
  • Latin Square Design Creation
    Creates Latin square, Greco-Latin square, and Hyper Greco-Latin square designs with 3 to 9 factors.
  • Taguchi Robust Design Creation
    Creates a standard Taguchi robust design experiment from orthogonal arrays.
  • Mixture Design Creation
    Creates experimental designs for mixtures (where the sum of factor values must be constant). STATISTICA will create Simplex-Lattice and Simplex-Centroid designs.
  • D- and A- (T-) Optimal Algorithmic Design Creation
    The D- and A-optimal design procedures provide various options to select from a list of valid (candidate) points (i.e., combinations of factor settings) that will extract the maximum amount of information from the experimental region, given the respective model that you expect to fit to the data.
  • Analysis of 2**(K-p) and Screening Designs
    Analyzes balanced and unbalanced 2**(K-p) designs, Plackett-Burman screening, or other 2-level factorial designs with or without blocking, and with or without center points. The program will compute a complete analysis, estimate factor effects and regression coefficients, produce a large number of additional diagnostic results and graphs to help assess the goodness of fit for the model and model adequacy, etc., and compute various residual statistics for each run (observation). Note: You can also use the General Linear Models facilities to analyze unbalanced and incomplete designs of any complexity.
  • Analysis of Mixed 2 and 3 Level Designs
    Analyzes mixed 2 and 3 level designs with or without blocking. The program will compute a complete analysis, estimate factor effects and regression coefficients, produce a large number of additional diagnostic results and graphs to help assess the goodness of fit for the model and model adequacy, etc., and compute various residual statistics for each run (observation). Note: You can also use the General Linear Models facilities to analyze unbalanced and incomplete designs of any complexity.
  • Analysis of 3**(K-p) and Box-Behnken Designs
    Analyzes balanced and unbalanced 3**(K-p) designs, Box-Behnken designs, or other 3-level factorial designs, with or without blocking. The program will compute a complete analysis, estimate factor effects and regression coefficients, produce a large number of additional diagnostic results and graphs to help assess the goodness of fit for the model and model adequacy, etc., and compute various residual statistics for each run (observation). Note: You can also use the General Linear Models facilities to analyze unbalanced and incomplete designs of any complexity.
  • Analysis of Central Composite Designs
    Analyzes balanced and central composite (response surface) designs, with or without blocking. The program will compute a complete analysis, estimate factor effects and regression coefficients, produce a large number of additional diagnostic results and graphs to help assess the goodness of fit for the model and model adequacy, etc., and compute various residual statistics for each run (observation). Note: You can also use the General Linear Models facilities to analyze unbalanced and incomplete designs of any complexity.
  • Analysis of Latin Square Designs
    Analyzes balanced Latin Square, Greco-Latin Square, and Hyper Greco-Latin Square designs; the program expects the categorical predictor variables to describe a valid balanced Latin Square design; use the General Linear Models (GLM) facilities to analyze balanced and unbalanced designs of any complexity, and to perform residual analyses.
  • Analysis of Taguchi Robust Designs
    Analyzes balanced Taguchi robust design experiments; Statistica expects the categorical predictor variables to describe a valid balanced design; use the General Linear Models (GLM) facilities to analyze balanced and unbalanced designs of any complexity, and to perform residual analyses.
  • Analysis of Mixture Designs
    Analyzes balanced and unbalanced constrained mixture designs; the analysis of mixture experiments amounts to a multiple regression with the intercept set to zero. The mixture constraint - that the sum of all components must be constant - can be accommodated by fitting multiple regression models that do not include an intercept term. See also the General Linear Models (GLM) facilities for options to analyze mixture experiments, and experiments involving mixture and process (non-mixture) variables.