ANOVA/MANOVA Button

Click the button to display the General ANOVA/MANOVA Startup Panel. The purpose of analysis of variance (ANOVA) is to test for significant differences between means in different groups or variables (measurements), usually arranged by an experimenter in order to evaluate the effects of different treatments or experimental conditions, or combinations of treatments or conditions, on one (ANOVA) or more (MANOVA) outcome measures (dependent variables).

The ANOVA/MANOVA module is a subset of the General Linear Models (GLM) module and can perform univariate (ANOVA) and multivariate (MANOVA) analysis of variance of factorial designs with or without a repeated measure. STATISTICA will use, by default, the sigma restricted parameterization for factorial designs, and apply the effective hypothesis approach (see Hocking, 1985) when the design is unbalanced or incomplete. Type I, II, III, and IV hypotheses can also be computed, as can Type V and Type VI hypotheses that will perform tests consistent with the typical analyses of fractional factorial designs in industrial and quality improvement applications (see also the Experimental Design module). Results include summary ANOVA tables, univariate and multivariate results for repeated measures factors with more than 2 levels, the Greenhouse-Geisser and Huynh-Feldt adjustments, plots of interactions, detailed descriptive statistics, detailed residual statistics, planned and post-hoc comparisons, testing of custom hypotheses and custom error terms, detailed diagnostic statistics and plots (e.g., histogram of within-cell residuals, homogeneity of variance tests, plots of means versus standard deviations, etc.)

See also the General Linear Models (GLM) module to analyze any kind of linear model with categorical and/or continuous predictor variables, random effects, and multiple repeated measures factors; stepwise and best-subset selection of predictor effects is available in the General Regression Models (GRM) module; to fit nonlinear models see the Generalized Linear/Nonlinear Models (GLZ) and Nonlinear Estimation; see also Generalized Additive Models (GAM), Partial Least Squares Models (PLS), and General Discriminant Analysis Models (GDA) module for more specialized procedures.