Can Scatterplot Matrix be used to identify the optimal region?

[#1]

I generated an Output Random Table (10,000 runs) from the Prediction Profiler and then used the resulting data to create a Scatterplot Matrix with the Nonpar Density option enabled.

By applying a Local Data Filter to the Scatterplot Matrix to screen for the most desirable response regions, would this approach provide an effective indication of the optimal operating region?

[#2]

Hi @erichujh,

Yes, the option to generate an output random table can help you identify interesting area that match your specifications/objectives. As stated in JMP Help related to this section: "The new data table contains scripts that can be used to visualize the in-spec regions of the factors or responses." And "Suppose you want to see the locus of all factor settings that produce a given range to desirable response settings. By selecting and hiding the points that do not qualify (using graphical brushing or the Data Filter), you see the possibilities of what is left: the opportunity space yielding the result that you want.

Some rows might appear selected and marked with a red dot. These represent the points on the multivariate desirability Pareto Frontier - the points that are not dominated by other points with respect to the desirability of all the factors. The selected rows correspond to rows that have a value of 1 in the Dominant column."

Another alternative could be to use the Design Space Profiler to determine the factor ranges that allow you to maximize the in-spec proportion of samples.

Hope this answer will help you,