GLM Results - Quick Tab

Select the Quick tab of the GLM Results dialog box to access options to display the main results for the current analysis.

Element Name Description
All effects/Graphs Click the All effects/Graphs button to display the Table of All Effects dialog box. This dialog box shows the summary ANOVA (MANOVA) table for all effects; you can then select an effect and produce a spreadsheet or graph of the observed unweighted, observed weighted, and least squares means. Refer also to the description of the options on the Means tab for details concerning the different means computed by STATISTICA, and their standard errors. This button is only available if 1) the current design includes categorical predictor variables or within-subject (repeated measures) effects, and 2) if there are random effects in the current design, there is only a single dependent variable (multivariate results for mixed-model designs cannot be computed).
All effects Click the All effects button to display a spreadsheet with the ANOVA (MANOVA) table for all effects. If the design is univariate in nature (involves only a single dependent variable), then the univariate results ANOVA table will be displayed; the univariate results ANOVA table is also displayed for univariate repeated measures designs (where appropriate, multivariate tests for repeated measures can be computed via the Within effects options available on the GLM Results - Summary tab); if the design is multivariate in nature, then the multivariate results MANOVA table will be displayed, showing the statistics as selected in the Multiv. tests group box available on the GLM Results - Summary tab; if the design includes random effects and multiple dependent variables, then multiple univariate ANOVA tables (spreadsheets) will be displayed, one for each dependent variable (in that case, the tests reported in the ANOVA table will use synthesized error terms). For a discussion of the different types of designs, and how the respective ANOVA/MANOVA tables are computed, see the Introductory Overview.
Effect sizes Click the Effect sizes button to display a spreadsheet with the ANOVA (MANOVA) table for all effects and the effect sizes and powers (i.e., Partial eta-squared, Non-centrality, and Observed power). Partial eta-squared is the proportion of the variability in the dependent variables that is explained by the effect. The Non-centrality value is the main statistic used to compute power, and the Power column contains the power values of the significant test on the effect. The ANOVA (MANOVA) table is described above, see All effects.
Between effects Use the options in the Between effects group box to review, as appropriate for the given design, various results statistics for the between-group design.
Design terms Click the Design terms button to display a spreadsheet of all the labels for each column in the design matrix (see Introductory Overview); this spreadsheet is useful in conjunction with the Coefficient option (available on the GLM Results - Summary tab) to unambiguously identify how the categorical predictors in the design where coded, that is, how the model was parameterized, and how, consequently, the parameter estimates can be interpreted. The Introductory Overview discusses in detail the overparameterized and sigma-restricted parameterization for categorical predictor variables and effects, and how each parameterization can yield completely different parameter estimates (even though the overall model fit, and ANOVA tables are usually invariant to the method of parameterization).

If in the current analysis the categorical predictor variables were coded according to the sigma-restricted parameterization, then this spreadsheet will show the two levels of the respective factors that were contrasted in each column of the design matrix; if the overparameterized model was used, then the spreadsheet will show the relationship of each level of the categorical predictors to the columns in the design matrix (and, hence, the respective parameter estimates).

Whole model R Click the Whole model R button to display a series of spreadsheets, summarizing the overall fit of the model.
Overall fit of the model First, a spreadsheet will be displayed reporting the R, R-square, adjusted R-square and overall model ANOVA results, for each dependent variable. The statistics reported in this spreadsheet, thus, test the overall fit of all parameters in the current model.
Lack of fit If you selected the Lack of fit option on the Quick Specs Dialog - Options tab or via the LACKOFFIT keyword in the GLM (STATISTICA) syntax, another spreadsheet will be displayed that compares, for each dependent variable, the residual sums of squares for the current model against the estimate of pure error. The pure error is computed from the sums of squares within each unique combination of treatment levels (for categorical predictors) or values (for continuous predictors). If this test is statistically significant, it can be concluded that the current model does not satisfactorily explain all (random) error variability in the data, and hence, that the current model exhibits an overall lack-of-fit (models that provide a good fit to the data will explain most variability in the data, except for random or pure error). For additional details, see also the discussion on replicated design points and pure error in Experimental Design.
Overall fit of the model vs. pure error Since the pure error provides an estimate of the random error variability in the data, you can test the overall fit of the model (see also above) against this estimate. If you selected the Lack of fit option on the Quick Specs Dialog - Options tab or via the LACKOFFIT keyword in the GLM (STATISTICA) syntax, a third spreadsheet will come up reporting the results for this test.
Test of whole model, adjusted for the mean If the current model does not include an intercept term, then another spreadsheet will be displayed, reporting the results (for each dependent variable) for the test of the overall fit of the model, using the sums of squares residuals adjusted for the means as the error term. When the current model does not include an intercept, you can compute the multiple R-square value either based on the variability around the origin (zero), or based on the variability around the mean. The default R-square value reported in the Overall fit of the model spreadsheet (see above) pertains to the former, that is, it is the proportion of variability of the dependent variables around 0 (zero) that is accounted for by the predictor variables. In this spreadsheet, STATISTICA will report the ANOVA tables (for each dependent variable), including the sums of squares and R-square value, based on the proportion of variability around the mean for the dependent variables, explained by the predictor variables. These computations are common in the analysis of mixtures (see also the discussion of mixture designs and triangular surfaces in Experimental Design). For various other alternative ways for computing the R-square value, refer to Kvalseth (1985).
Alpha values Use the Alpha values group box to specify Confidence limits and Significance level values. These values are used in all results spreadsheets and graphs whenever a confidence limit is to be computed or a particular result is to be highlighted based its statistical significance.
Confidence limits Enter the value to be used for constructing confidence limits in the respective results spreadsheets or graphs (e.g., spreadsheet of parameter estimates, graph of means) in the Confidence limits field. By default 95% confidence limits will be constructed.
Significance level Enter the value to be used for all spreadsheets and graphs where statistically significant results are to be highlighted (e.g., in the All effects spreadsheet) in the Significance level field. By default all results significant at the p<.05 level will be highlighted.

See also GLM - Index.