1.
Open the Bounce Data.jmp sample data table found in the Design Experiment folder.
2.
Select DOE > Evaluate Design.
3.
Assign Stretch to the Y, Response role.
4.
Assign Silica, Sulfur, and Silane to the X, Factor role.
5.
6.
Return to the data table and exclude the rows for Silica=0.7 and Silane=50. These are rows 1 and 2.
8.
Compare the Fraction of Design Space Plot for both reports. See Fraction of Design Space.
Fraction of Design Space
9.
One of the available diagnostics is the maximum prediction variance of the designs. In both reports, select Maximize Desirability from the Prediction Variance Profile red triangle menu.
Prediction Variance Profilers shows the maximum prediction variance for both the complete and reduced designs. For both designs, one of the design points where the maximum prediction variance occurs is Silica=0.7, Sulfur=1.8, and Silane=40. The maximum prediction variance is 1.396 for the complete design, and 3.02 for the reduced design.
Prediction Variance Profilers
10.
Correlations between Model and Alias Terms shows the color maps for both designs.The absolute value of the correlations range from 0 (blue) to 1 (red). Place your cursor over a cell with the mouse to see the value of the correlation. The color map for the reduced design has more cells with correlations that are farther away from 0. For example, the correlation between Sulfur and Silica*Sulfur is <.0001 for the complete design, and 0.577 for the reduced design.
Correlations between Model and Alias Terms
Design Diagnostics
1.
Open the Popcorn DOE Results.jmp sample data table in the Design Experiment folder.
2.
Select DOE > Evaluate Design.
3.
Assign Brand, Time, and Power to the X, Factor role.
4.
Assign Number Popped to the Y, Response role.
5.
The Fraction of Design Space Plot shows that for almost the entire design space, the reduced model has a lower prediction variance than the initial model. See Fraction of Design Space.
Fraction of Design Space
Open the Power Analysis section in both reports. Power Analysis shows the results. For the reduced model, the design produces higher power for every effect. The difference is especially large for the Time*Power effect.
Power Analysis
Open the Design Diagnostics section in both reports. See Design Diagnostics. For the reduced model, the design produces higher efficiency values, except for G efficiency. The A Efficiency (which is related to the variance of the regression coefficients) is a lot larger for the reduced model. The average prediction variance for the reduced model is less than half of that for the initial model.
Design Diagnostics