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

[#1]

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.

[#2]

You can do what you desire knowing the context of the data. It is happenstance data and not data originating from a controlled designed experiment which would provide additional information around any interactions.

[#3]

Thank you, Lou.

I really appreciate your help. I see what you did. You just added T & aging time into factors and ran standard least squares/minimal report. I have 3 additional questions on the report.

1. I watched one tutorial video, and a guys mentions that Prob >F should be less than 0.01 as a rule of thumb. Mine is shown as 0.0938. Do I play around with "model effect" and "degree" in "fit model" window to decrease this value?

2. My response ISOD is percentage and it should be in range of 0-100. In prediction profile for 150 C and 15 days, I can understand ISOD being 120 % as 100 %, but is there a way to set so that prediction does not go over 100?

3. According to this prediction model, I get ISOD = 106 at 100 C and 20 days. My actual data has ISOD = 99 at that condition. Is there a way to refine it?

[#4]

YIKES!!!

I would be very dubious about predicting any results outside the experimental space.  You have only one data point at 150+ deg. C and then only 2 days aging.  You also have only one data point with more than 15 days aging and that is at only at 100 deg. C .  Therefore, predicitng at 150 deg C and 15 days aging would be very precarious.  My advice:  Perform more experiments designed around the parameters within which you wish to be able to predict with any confidence/accuracy.

[#5]

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... .

[#6]

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.

[#7]

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