This command in Fit Least Squares is convenient, but all it is doing is centering and scaling the continuous predictors. You can accomplish the same thing yourself with a column formula. It is easy to do:
- Select the predictor columns in the data table.
- Right-click the header for one of them.
- Select New Column Formula > Distributional > Standardize.
Now use these columns in place of the original columns when you set up your modeling in Fit Model dialog. Using the Fitness data table in the sample data folder, I fit a model Oxy versus Age through MaxPulse. I created the standardized version of the predictors and used them for the linear predictor of a second fit. I exported the Parameter Estimates as a data table for each and concatenated them so I could examine them together. Here are the results:
![parm est.JPG parm est.JPG](https://community.jmp.com/t5/image/serverpage/image-id/25771i9BF079AEB444504D/image-size/large?v=v2&px=999)
Notice that the p-values are identical between the two fits. Then I plotted the estimates, where the importance is clearer:
![bar chart.JPG bar chart.JPG](https://community.jmp.com/t5/image/serverpage/image-id/25772iD722B3D642B0D706/image-size/large?v=v2&px=999)
Only Runtime seemed to be important when using the original predictors. Runtime, RunPulse, and MaxPulse seem to be important when using the standardized predictors.
Both models produce identical predictions, of course.
Another technique is built into the Prediction Profiler that might be helpful for your purpose: Variable Importance.