I am setting up a custom experimental design with three factors, two continuous and one categorical. I want to look at main effects, one way interactions and a squared term for one of the continuous factors, this is all fairly straightforward. What I also want to look at is the interaction between the main terms and the squared term. To me this means how the level of a given factor affects the form of the curvature described by the squared term.
For the following factors I therefore have the subsequent dialogue in the custom design view.
A: Continuous
B: Continuous
Cat: Categorical, 2 level
![david_gillespie_1-1637676937467.png david_gillespie_1-1637676937467.png](https://community.jmp.com/t5/image/serverpage/image-id/37876i3E42FE6CF541D252/image-dimensions/608x271?v=v2)
However, when I construct the design, no matter how many runs I decide on the correlation map shows very high (~0.8) correlations between:
B and A*A*B
Cat and A*A*Cat
![david_gillespie_2-1637677408440.png david_gillespie_2-1637677408440.png](https://community.jmp.com/t5/image/serverpage/image-id/37877i90DC94E64AD9D272/image-size/large?v=v2&px=999)
Can anyone help me out as to why this is, whether it's an issue and if there is a workaround. My initial thought is that as A*A will always have a positive sign the sign of A*A*B will always have the same sign as B (and equivalently for A*A*Cat and Cat).
Many thanks,
Dave