Click the All effects/Graphs button on the GLM Results - Quick tab, the GLM Results - Summary tab, or the GLM More Results - Summary tab to display the Table of All Effects dialog. This dialog allows you review (1) the ANOVA (MANOVA) summary for all effects in the model, and (2) the tables of marginal means for the categorical and within-subject (repeated measures) effects in the model. Note that this dialog will also be displayed when you click those button in the General Regression Models and General ANOVA/MANOVA modules.

OK. To review the means specified in the Means group box (see below), first select the respective effect in the Effect list, and then click the OK button.

Cancel.

Close dialog on OK. Select the Close dialog on OK check box to close this dialog when you click the OK button. This is the default setting.

Display. After you click the OK button, STATISTICA will either produce a spreadsheet containing the means specified in the Means group box (see below) or a graph of the means.

Graph. Select the Graph option button to produce a graph of the means.

Spreadsheet. Select the Spreadsheet option button to produce a spreadsheet of the means.

Means. The Means group box allows you to select the type of means to display in a Spreadsheet or a Graph (see the Display options described above). The different types of means computed by GLM are described in the Means tab. Note that if you select a continuous predictor Effect, then meaningful means cannot be computed and this group box will not be available.

Unweighted. Select the Unweighted option button and STATISTICA will compute observed unweighted means (this option is only available in full factorial designs); the standard errors for the unweighted means are computed from the residual mean squares for the respective variables.

Weighted. Select the Weighted option button and STATISTICA will compute weighted observed means (marginal means weighted by the N's in the cells that enter into the computation of the respective marginal means); the standard errors for weighted means are computed from the standard deviations in the respective marginal cell.

Least squares. Select the Least squares option button and STATISTICA will compute least squares (predicted) means. Least squares means are the expected population marginal means, given the current model. Thus, these are usually the means of interest when interpreting significant effects from the ANOVA or MANOVA table. Note that for full factorial designs without missing cells, the least squares means are identical to the observed Unweighted means. Least squares means are also sometimes called predicted means, because they are the predicted values when all factors in the model are either held at their means, or the factor levels for the respective means. If there are continuous predictors (covariates) in the model, the least squares means are computed from the means for those predictors. For details concerning the computation of least squares means refer to Milliken and Johnson (1992), Searle, Speed, and Milliken (1980), or Searle (1987).

Compute std. errors. Select the Compute std. errors check box to display standard errors and confidence limits for the specified means in the Spreadsheet or Graph that is produced when you click the OK button (see above). The graph of means will show the confidence limits as error bars around the respective means. The actual confidence limits are based on the current setting in the Confidence limits field available on the GLM Results - Quick tab, the GLM Results - Summary tab, or the GLM More Results - Summary tab.

Show +/- std errs. Select this check box to show in the tables and plots of means the plus or minus standard error range around each mean. These will only be shown if the Compute std. errors check box is also selected. By default, when the Show +/- std errs check box is cleared, the (95%) confidence intervals will be computed instead (or any other confidence interval, consistent with the specification in the Confidence limits field of the Quick tab).

See also GLM - Index.