I'll start with question #2. The general best practice for validating your 'old' model is to partition the original data set into a training, validation, and test categories for each row in the data table. Then examine the various fit oriented statistics and visualizations. What you see in the platform report will depend in large measure on the nature of the response...is it categorical or numeric continuous? Ultimately, it's up to you to say whether the model is 'validated'...whatever that means to you. You'll need JMP Pro to effectively use this pathway. Here's a link to the 'how to' create a validation column in JMP Pro Creating a Validation Column I'm assuming you've got lots and lots of observations. This is required to get the most benefit from this pathway. If you don't have lots of data, then other methods like Kfold can be useful. Then once you get to the modeling platform you are using, make sure to place the Validation column name in the Validation specification window. If you don't have JMP Pro...this general pathway can still be recreated in part...but it's gonna be real clunky wrt to the workflow and beyond the scope of what can be described here in a forum such as this.
As for question #1 make sure you store the predicting equation in a column unto itself...then just add your new observations and compare the predictions to the actual responses.
