how to get two prediction formula if i have one categorical variable and many continuous variables

[#1]

if i have one categorical variable in my data，is there anaway to get two prediction formula in different level of categorical variable in one analysis report

[#2]

The prediction formula in this example would just be the mean of the different categorical levels.

From the Oneway analysis (Fit Y by X), click red triangle -> Save -> Save Predicted.

[#3]

the situation i mentioned is a multivariable model with one categorical variable and many continuous variables.and i want to get two model prediction equation which is in different categorical variable level.you konwn because of the interaction between categorical variable and continuous variable，the effect of continuous maybe have a little different  in different categorical variable，like this：

this report is generated by design expert，i wnat to know if JMP could do it

[#4 · Accepted solution]

I cannot think of an easy way to get the two formulas into a single window like this report, but there is a way to get the two models. First, I think fitting a single model is the way to go, but to get the report the way you want do this: When you are fitting your model put the categorical variable in the BY role rather than in the model. This will give you two separate models, one for each level of the categorical variable. Then, for each level, go to Estimates > Show Prediction Expression to get each model that you wish to see.

[#5]

OK！thank you very much.i thought of the same thing，cuting my data to two part then fit models respectively.

just want to know if there are more easy way to achieve this.now i known

[#6]

You have a solution to your question, but I wonder about your motivation for this request. You can use the Prediction Profiler to explore the model, which includes the categorical factor. You can alternate between the two levels of this factor and visualize the change in the effects of the continuous factor.

[#7]

yes ，the practical significance of this question is little important.this is a problem in my text book，i just to finish my homework