I'm transitioning from Minitab to JMP for a course this semester and I'm struggling with the workflow.
Example: I have a 2 factor general factorial experiment with 3 levels each (3^2 design). The variables themselves are continuous (e.g., temperature and pressure). I want to do two things:
1. Evaluate a general factorial effects model with main effects (Temperature, Pressure) and 2-way interaction (Pressure*Temperature) that treats the three factor levels as categorical variables.
2. Fit a response surface including quadratic curvature where the factors are treated as continuous variables.
Note: This data set has quadratic curvature so the responses for the high and low levels are comparatively similar and middle level is the one that's different.
For #1, if I sent the factors up as 3-level continuous but set the 'Design Role' to categorical, the model still appears to be fit as a continuous variable coded -1 and +1 for the low and high values. The middle level (the one that's actually different) is effectively coded 0 and not used in the effect calculation. If it's set up as a 3-level categorical, then the model outputs what I would expect with an effect calculated for each level, recognizing that the middle level is different from the other two.
If I know that I want to do one analysis that's an effects model with categorical variables then transition to fitting a response surface with continuous variables, what is the best practice workflow? Set up two different data table with different data types altogether?