TIBCO Statistica® Multiple Regression

Last updated:
12:43pm Apr 03, 2020

The Multiple Regression module is an implementation of linear regression techniques that includes:

  • simple
  • multiple
  • stepwise: forward, backward, or in blocks
  • hierarchical
  • nonlinear including polynomial, exponential, log, etc.
  • Ridge regression, with or without intercept (regression through the origin)
  • weighted least squares models.

This module will calculate a comprehensive set of statistics and extended diagnostics including the complete regression table. This includes:

  • standard errors for B, Beta and intercept
  • R-square and adjusted R-square for intercept and non-intercept models
  • ANOVA table for the regression
  • part and partial correlation matrices
  • correlations and covariances for regression weights
  • sweep matrix (matrix inverse)
  • Durbin-Watson d statistic
  • Mahalanobis and Cook's distances
  • deleted residuals
  • confidence intervals for predicted values
  • ... and others

Predicted And Residual Values

The extensive residual and outlier analysis features a large selection of plots, including a variety of scatterplots, histograms, normal and half-normal probability plots, detrended plots, partial correlation plots, different casewise residual and outlier plots and diagrams, and others. The scores for individual cases can be visualized via exploratory icon plots and other multidimensional graphs integrated directly with the results Spreadsheets. Residual and predicted scores can be appended to the current data file. A forecasting routine allows the user to perform what-if analyses, and to interactively compute predicted scores based on user-defined values of predictors.

By-Group Analysis & Related Procedures

Large regression designs can be analyzed. An option is also included to perform multiple regression analyses broken down by one or more categorical variable (multiple regression analysis by group). Additional add-on procedures include a regression engine that supports models with thousands of variables, a Two-stage Least Squares regression, as well as Box-Cox and Box-Tidwell transformations with graphs.

Related Modules

Nonlinear Estimation, Generalized Linear Models (GLZ), and Partial Least Squares models (PLS) modules can estimate practically any user-defined nonlinear model, including Logit, Probit, and others.

SEPATH, the general Structural Equation Modeling and Path Analysis module, allows the user to analyze large correlations, covariances, and moment matrices for intercept models.

Additional advanced methods are provided in the General Regression Models (GRM) module. This module includes best subset regression, multivariate stepwise regression for multiple dependent variables, for models that may include categorical factor effects; statistical summaries for validation and prediction samples, custom hypotheses, etc.).