Analysis of a Mixture Experiment - Display Design Tab

Select the Display design tab of the Analysis of a Mixture Experiment dialog to access options to view various aspects of the experiment. Note that these results are for the currently specified model. You can specify a new model on the Model tab.

Display design and observed means

Click the Display design and observed means button to display a spreadsheet showing the unique runs (those with unique combinations of factor settings) in the experiment. In addition, for each unique run, Statistica computes the mean, standard deviation, and standard error of the mean (if there is more than one run for the respective unique combination of factor settings).

Show text labels instead of factor values

If the Show text labels instead of factor values check box is selected, the factor settings in the spreadsheet will be identified by their respective text labels. If there are no text labels in the file (for the list of independent variables or factors), then this option will not be available.

Review factor names and settings

Click the Review factor names and settings button to display a spreadsheet with the factor low and high settings. The low factor values are used to transform the original factor values in the computation of the parameter estimates for the pseudo-components (see the Summary: Estimates, pseudo-components option on the Quick tab or the ANOVA/Effects tab; see also the Introductory Overview); pseudo-components are also used in the computation of the Correlation matrix of design variables and Correlations of effects (see below).

Correlations

Use the Correlations group box to access the options described here.

Corr. matrix of design variables (X'X).

Click the Corr. matrix of design variables (X'X) button to display a correlation matrix of the columns of the current design matrix. Thus, the number of effects displayed in this matrix depend on the current choice of the model on the Model tab. Note that these correlations are computed from the transformed pseudo-component values (for details, see the Summary: Estimates, pseudo-components option on the Quick tab or the ANOVA/Effects tab). Also, the entries in this table are not the standard Pearson product-moment correlations; instead they are the standardized cross-products for the pseudo-component values. To estimate the parameters for mixture models, usually the polynomial model is rewritten into the so-called canonical form. In short, given the overall mixture constraint (that all component values must add to a constant), the standard linear, quadratic, and cubic models can be rewritten into equivalent no-intercept regression models. For more details, refer to the Introductory Overview.

Correlations of effects (X'X inverse). Click the Correlations of effects (X'X inverse) button to display the standardized inverse of the correlation matrix. This matrix can be interpreted as the correlation matrix of effects; that is, it is the standardized variance/covariance matrix of the parameter estimates for the current model. The greater the absolute value of a correlation between effects in this matrix, the more redundant are the respective effects.

Display matrices in compressed format

If the Display matrices in compressed format check box is selected, the width for the columns in the spreadsheet with the correlations are set to 4 (4 characters, including the decimal point, show in each cell of the correlation matrix, e.g., value 0.31). Use this compressed format to review large matrices efficiently. Clear this check box to display the cells in the spreadsheet in the usual default width of 8 characters per cell (e.g., to display 0.312345). Of course, you can always use the standard spreadsheet option Format - Cells to change the display format for all spreadsheets; (remember that, regardless of display format, values in spreadsheets are always stored in their highest precision).