Hi @FISH1,
The Prediction Profiler in Fit Model is exactly what you want -- it is showing the relationship between Y and each predictor that your model fit, and is showing it in a way that lets you see how that relationship changes with respect to the levels of your other variables. Looking at marginal plots (what you will get from Fit Y by X) does not show the same thing -- a bivariate fit is providing the unconditional regression coefficients, whereas multiple regression is providing the conditional, partial regression coefficients (the effect of each variable holding constant the other variables in the model).
Take for instance this example from the Cars 1993 sample data:
Open("$SAMPLE_DATA/Cars 1993.jmp");
Fit Model(
Y( :Name( "City Mileage (MPG)" ) ),
Effects(
:Name( "Engine Size (liters)" ),
:Name( "Weight (pounds)" ),
:Name( "Engine Size (liters)" ) * :Name( "Weight (pounds)" )
),
Personality( "Standard Least Squares" ),
Emphasis( "Minimal Report" ),Run())
The profiler allows you to see how the Y (MPG) changes with respect to each variable, as well as how the relationship between Y and each predictor changes at different levels of the other variable:
So, there is no additional line fitting that is necessary after the model has been run -- the line (or curve etc) that is generated by the model *is* what is shown in the prediction profiler.
Adding points to the prediction profiler isn't standard, so there is no single click way to do it in JMP. However, there is a way using Copy Frame Contents, and Paste Frame Contents. Simply make your bivariate plots with Fit Y by X, right click each plot > Edit > Copy Frame Contents. Then, right click the associated plot in the Profiler > Edit > Paste Frame Contents.
I hope this helps!
@julian