Main Effects Linear Models

Builds a linear model to include main-effects only for categorical predictors (use factorial ANCOVA to include continuous predictors). Both univariate (single continuous dependent variable) and multivariate (multiple continuous dependent variables) designs can be analyzed. Default results include the ANOVA (MANOVA) table and plots of means; set the Level of detail parameter to All results to request tables of means and other statistics.

Model and Estimation

Element Name Description
Parameterization of effects Specifies either the sigma-restricted model or the overparameterized model; The sigma restricted parameterization is the default.
Intercept Specifies whether the intercept (constant) is to be included in the model.
Lack of fit Requests the computation of pure error for testing the lack-of-fit hypothesis.
Sweep delta 1.E- Specifies the negative exponent for a base-10 constant Delta (delta = 10^-sdelta); the default value is 7. Delta is used (1) in sweeping, to detect redundant columns in the design matrix, and (2) for evaluating the estimability of hypotheses; specifically a value of 2*delta is used for the estimability check.
Inverse delta 1.E- Specifies the negative exponent for a base-10 constant Delta (delta = 10^-idelta); the default value is 12. Delta is used to check for matrix singularity in matrix inversion calculations.

Results

Element Name Description
Detail of computed results reported Detail of results reported; if All results is requested, Statistica will report all univariate results (for multivariate designs), descriptive statistics, details about the design terms, the whole-model R, regression coefficients, and the least-squares means for all effects. Residual and predicted statistics (for observations) can be requested as options.
Post Hoc Tests Performs post-hoc comparisons between the means in the design.
Tests homogeneity of variances Tests the homogeneity of variances/covariances assumption. One of the assumptions of univariate ANOVA is that the variances are equal (homogeneous) across the cells of the between-groups design. In the multivariate case (MANOVA), this assumption applies to the variance/covariance matrix of dependent variables (and covariates).
Plots of means vs. std. dev Plots the (unweighted) marginal means (see also the Means tab) for the selected Variables against the standard deviations.
Contrast coefficients Specifies contrasts for least squares means; consult the documentation for syntax details.

Residual Analysis

Element Name Description
Residual analysis In addition to the predicted, observed, and residual values, Statistica will compute the (default) 95% Prediction intervals and 95% Confidence limits, the Standardized predicted and Standardized residual score, the Leverage values, the Deleted residual and Studentized deleted residual scores, Mahalanobis and Cook distance scores, the DFFITS statistic, and the Standardized DFFITS statistic.
Normal probability plot Creates a normal probability plot of residuals.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.

Deployment

Deployment is available if the Statistica installation is licensed for this feature.

Element Name Description
Generates C/C++ code Generates C/C++ code for deployment of predictive model (for a single dependent variable only).
Generates SVB code Generates Statistica Visual Basic code for deployment of predictive model (for a single dependent variable only).
Generates PMML code Generates PMML (Predictive Models Markup Language) code for deployment of predictive model (for a single dependent variable only). This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.
Saves C/C++ code Save C/C++ code for deployment of predictive model (for a single dependent variable only).
File name for C/C code Specify the name and location of the file where to save the (C/C++) deployment code information.
Saves SVB code Save Statistica Visual Basic code for deployment of predictive model (for a single dependent variable only).
File name for SVB code Specify the name and location of the file where to save the (SVB/VB) deployment code information.
Saves PMML code Saves PMML (Predictive Models Markup Language) code for deployment of predictive model (for a single dependent variable only). This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.
File name for PMML (XML) code Specify the name and location of the file where to save the (PMML/XML) deployment code information.