1´
PROC GLM;
	CLASS A B;
	MODEL A B A*B;
Using the JMP Fit Model command and requesting a factorial model for columns A and B produces the following design matrix. Note that A13 in this matrix is A1–A3 in the previous matrix. However, A13B13 is A13*B13 in the current matrix.
JMP implements the effective hypothesis tests described by Hocking (1985, 80–89, 163–166), although JMP uses structural rather than cell-means parameterization. Effective hypothesis tests start with the hypothesis desired for the effect and include “as much as possible” of that test. Of course, if there are containing effects with missing cells, then this test will have to drop part of the hypothesis because the complete hypothesis would not be estimable. The effective hypothesis drops as little of the complete hypothesis as possible.
Note that this shows that a test on the β1 parameter is equivalent to testing that the least squares means are the same. But because β1 is not estimable, the test is not testable, meaning there are no degrees of freedom for it.
a1
a2
b1
ab11
ab21
4.
The tests are whole marginal tests, meaning they always go completely across other effects in interactions.
Comparison of GLM and JMP Hypotheses, shows the test of the main effect A in terms of the GLM parameters. The first set of columns is the test done by JMP. The second set of columns is the test done by GLM Type IV. The third set of columns is the test equivalent to that by JMP; it is the first two columns that have been multiplied by a matrix:

Help created on 9/19/2017