Yes, your understating is correct.
If so, is there a way I can get the saturated model in JMP?
Yes, you can run such model with some data prep.
(Caution-a saturated model is over-parameterized to the point that it is essentially interpolating the data. It is not a sound modeling practice.)
(1) The screenshot shows a linear regression fit to predict HEIGHT with SEX and WEIGHT as predictors. Although this main effect model doesn't appears to be under-fit, Max R Sq indicates a saturated model would achieve R Sq at 0.8872.
![11883_pastedImage_2.png 11883_pastedImage_2.png](https://community.jmp.com/t5/image/serverpage/image-id/3299i3AC31BE7F93B7F76/image-size/medium?v=v2&px=400)
(2) To do this Combine predictor variables to form a grouping variable, SEX_WEIGHT, so that I can assigns a parameter to each unique combination of the predictors. Use the Combine Columns to get it
![11884_pastedImage_4.png 11884_pastedImage_4.png](https://community.jmp.com/t5/image/serverpage/image-id/3300i467DDBE0EF5A4997/image-size/medium?v=v2&px=400)
(3) Refit the model using SEX_WEIGHT as the predictor . As shown, R Square is indeed 0.8872. There are 32 parameter estimates (plus intercept) in the model.
![11885_pastedImage_23.png 11885_pastedImage_23.png](https://community.jmp.com/t5/image/serverpage/image-id/3301i3E912F8219F7CCB8/image-size/medium?v=v2&px=400)