How do I make a prediction model based on my old experiment data?

Question

I am new to JMP software ( version 11.2.0).

I have a question on prediction model. Before having JMP, I ran 2 experiments shown below. ISOD is the response, and temperature & aging time are both continuous. Using these 2 tables, can I make a prediction model for  ISOD yield in temperature range 100 to 180 & aging time 1 to 20 together? For an example, what will ISOD be at like 150 C with 15 days of aging time? I was not able to do it. I know that I can make matrix with DOE for prediction model, but that means I have to do experiments again.

experiment 1

​temperature (C)​aging time (days)​ISOD​100​2​14​140​2​79​180​2​67

experiment 2

​temperature (C)​aging time (days)​ISOD​100​1​0.3​100​10​75​100​20​99

Thank you for your time, and I would appreciate your help.

hlrauch · Answer

A couple more ideas:

Instead of creating a totally new designed experiment or repeating the same experimental runs, you can add experiments to your current data using the "Augment" feature. With your current data table open, select DOE > Augment Design from the menu bar. Enter ISOD as the Y and the other two variables as X's.In the Augment Design window, click on the Augment button. For a standard model that is useful for prediction (interactions and curvature effects), click on the RSM button. The new designed experiment will consist of your 6 experimental runs plus several more runs to complete.

Regarding predicted values above 100: one could do a Logitpct transform on the response data. I would want to know more about the nature of the response before transforming it, but this would guarantee that the model predictions stay between 0 and 100. In the model dialog, put ISOD in the Y role, click on it to select it, and then click on the Transform red triangle and select LogitPct.

Howard

Kevin_Anderson · Answer

Your original experiment looks like an OFAT...one in which One-Factor-at-A-Time is varied.  It's not a very efficient way to generate information.

Like Lou Valente and smoore2 recommend, you might consider running a designed experiment.  "More data points" is not all you need to form a more accurate prediction model.

sushiz · Answer

Hi smoore2,

You are right. The error in prediction model is probably too big to be useful. I need more data points to have more accurate prediction model... .