Advanced Linear and Nonlinear Models

  • General Linear Models
  • Generalized Linear and Nonlinear Models
  • General Regression Models
  • General Partial Least Squares Models
  • NIPALS Algorithm (PCA/PLS)
  • Variance Components
    Creates variance components for ANOVA/ANCOVA designs with random effects using various estimation techniques, including ANOVA (and denominator synthesis), Minimum Variance Quadratic Unbiased Estimators method (MIVQUE(0)), Restricted Maximum Likelihood (REML), and Maximum Likelihood (ML) estimation. Note the assignment of variables: Continuous dependent - Dependent Categorical dependent - Random effects Categorical predictors - Fixed effects Continuous predictors - Covariates The General Linear Models (GLM) procedures will also analyze and compute the ANOVA results with synthesized error terms. The aforementioned analysis more specifically involves ANOVA/ANCOVA designs of arbitrary complexity with random effects and estimate variance components (using the ANOVA method)
  • Survival Analysis
  • Nonlinear Estimation
  • Log-Linear Analysis
    Complete implementation of log-linear modeling procedures for multi-way frequency tables; you can analyze up to 7-way tables, and automatically find the best model.
  • Time Series and Forecasting
  • Structural Equation Modeling
    Complete implementation of Structural Equation Modeling techniques for analyzing correlation, covariance, and moment matrices (structured means, models with intercepts). Simple or complex factor or path models can be specified via a simple path-language (which can also be created via dialogs or step-by-step wizards in the Statistica application). The program will compute, using constrained optimization techniques, the appropriate standard errors for standardized models, and for models fitted to correlation matrices. The results options include a comprehensive set of diagnostic statistics including the standard fit indices as well as noncentrality-based indices of fit, reflecting the most recent developments in the area of structural equation modeling. You can fit models to multiple samples (groups) and specify for each group fixed, free, or constrained (to be equal across groups) parameters. When analyzing moment matrices, these facilities allow you to test complex hypotheses for structured means in different groups. STATISTICA also includes powerful Monte Carlo simulation options: you can generate data files for predefined models, based on normal or skewed distributions. Bootstrap estimates can be computed, as well as distributions for various diagnostic statistics, parameter estimates, etc. over the Monte Carlo trials. Numerous flexible graphing options are available to visualize the results (e.g., distributions of parameters) from Monte Carlo runs.