To test one or more custom hypotheses involving any model parameters, select Custom Test from the Estimates menu. In this window, you can specify one or more linear functions, or contrasts, of the model parameters.
The results include individual tests for each contrast and a joint test for all contrasts. See Figure 2.26. The report for the individual contrasts gives the estimated value of the specified linear function of the parameters and its standard error. A t ratio, its p-value, and the associated sum of squares are also provided. Below the individual contrast results, the joint test for all contrasts gives the sum of squares, the numerator degrees of freedom, the F ratio, and its p-value.
Note: Select Estimates > Custom Test repeatedly to conduct several joint custom tests.
Figure 2.26 shows an example of the specification window with three contrasts, using the Cholesterol.jmp sample data table. Note that the constant is set to zero for all three tests. The report for these tests is shown in Figure 2.27.
The Cholesterol.jmp sample data table gives repeated measures on 20 patients at six time periods. Four treatment groups are studied. Typically, this data should be properly analyzed using all repeated measures as responses. This example considers only the response for June PM.
1.
Select Help > Sample Data Library and open Cholesterol.jmp.
2.
Select Analyze > Fit Model.
3.
Select June PM and click Y.
4.
Select treatment and click Add.
5.
Click Run.
6.
From the red triangle next to Response June PM, select Estimates > Custom Test.
7.
In the Custom Test specification window, click Add Column twice to create three columns.
Figure 2.26 Custom Test Specification Window for Three Contrasts
9.
Click Done.
The results shown in Figure 2.27 indicate that all three hypotheses are individually, as well as jointly, significant.
Figure 2.27 Custom Test Report Showing Tests for Three Contrasts

Help created on 7/12/2018