Analysis of a Central Composite (Response Surface) Experiment - Means Tab

Analyzing Central Composite Designs

Select the Means tab of the Analysis of a Central Composite (Response Surface) Experiment dialog box to access options to review and plot various means from the analysis as well as to perform response desirability profiling. Note that these results are for the currently specified model. You can specify a new model via the Model tab.

Observed design and means. Use the options in the Observed design and means group box to review the design and means for the current model and to specify criteria for the spreadsheet.

Display design and observed means

Click the Display design and observed means button to produce 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 this spreadsheet will be identified by their respective text labels. Note that this option is not available if there are no text labels in the file (for the list of independent variables or factors)

Predicted (estimated) means

Use the option in the Predicted (estimated) means group box to access the facilities for response/desirability profiling. For an overview of response/desirability profiling, see Profiling Predicted Responses and Response Desirability.

Response desirability profiling

Click the Response desirability profiling button to display the Profiler dialog box, which contains options to inspect the predicted values for the dependent variables at different combinations of levels of the independent variables, to specify desirability functions for the dependent variables, and to specify a search for the levels of the independent variables that produce the most desirable response on the dependent variables.