I will use the Big Class example that is installed with JMP. I want to model the linear relationship between :weight and :height. There are also the :age and :sex factors. I enter these terms in the model to test their effect I cross :age and :sex with :height to produce the interaction terms. These terms test if the :height effect depends on the :age or :sex.
![dialog.PNG dialog.PNG](https://community.jmp.com/t5/image/serverpage/image-id/37525i13933B52B6179FCA/image-size/large?v=v2&px=999)
Here are the initial results:
![initial.PNG initial.PNG](https://community.jmp.com/t5/image/serverpage/image-id/37526i25CBA24F8EA8F637/image-size/large?v=v2&px=999)
We would then follow the Q1A and Q1E guidance and test higher order terms first. We eliminate the least significant term (i.e., highest p-value) first and proceed one step at a time. Then we might arrive at a model like this one for interpretation and prediction:
![final.PNG final.PNG](https://community.jmp.com/t5/image/serverpage/image-id/37527i9C7BB394ABC8C298/image-size/large?v=v2&px=999)
Such a result is evidence that the data across :sex and :age groups may be pooled, but there is a difference across :age groups.