1.
Select DOE > Custom Design.
2.
Double-click Y under Response Name and type Rating.
Figure 3.2 shows the completed Responses outline.
1.
First, add the blocking factor, Rater. Click Add Factor > Blocking > 8 runs per block.
2.
Type Rater over the default Name of X1.
3.
Click Add Factor > Categorical > 2 Level.
4.
Type Variety over the default Name of X2.
6.
Click Add Factor > Categorical > 4 Level.
7.
Type Field over the default Name of X3.
9.
Click Add Factor > Categorical > 2 Level.
10.
Type De-Stem over the default Name of X4.
12.
Type 6 next to Add N Factors, and then click Add Factor > Categorical > 2 Level.
Yeast (Cultured and Wild)
Temperature (High and Low)
Press (Hard and Soft)
Barrel Age (New and Two Years)
Barrel Seasoning (Air and Kiln)
Filtering (No and Yes)
Figure 3.2 Completed Responses and Factors Outlines
14.
Click Continue.
1.
Select Help > Sample Data Library and open Design Experiment/Wine Factors.jmp.
Figure 3.3 Model Outline
The Alias Terms outline specifies the effects to be shown in the Alias Matrix, which appears later. See Alias Matrix. The Alias Matrix shows the aliasing relationships between the Model terms and the effects listed in the Alias Terms outline. Open the Alias Terms outline node to verify that all two-factor interactions are listed.
Figure 3.4 Partial View of the Alias Terms Outline
3.
5.
6.
1.
Under Number of Runs, type 40 in the User Specified box.
2.
Click Make Design.
Figure 3.5 Design for Wine Experiment
Figure 3.6 Color Map on Correlations
The only red in Figure 3.6 is on the main diagonal. The color indicates absolute correlations of one, reflecting that each term is perfectly correlated with itself. It follows that no main effect is completely confounded with any two-way interaction. In fact, the absolute values of the correlations of main effects with two-way interactions are fairly low. This means that estimates of main effects might be only slightly biased by the presence of active two-way interactions.
Figure 3.7 Partial View of Alias Matrix
For example, consider the model effect Barrel Seasoning. If Variety*Press is active, then the expected value of the estimate for the Barrel Seasoning effect differs from an unbiased estimate of that effect. The amount by which it differs is equal to 0.4 times the effect of Variety*Press. Therefore, what appears to be a significant Barrel Seasoning estimated effect could in reality be a significant Variety*Press effect.
Figure 3.8 Design Diagnostics Outline
Specify the order of runs in your data table using the Output Options panel. The default selection, Randomize within Blocks, is appropriate for this example. Simply click Make Table.
Figure 3.9 Custom Design Table

Help created on 7/12/2018