Different Parameter Estimates in Fit Defintive Screening and Standard least squares

Danijel_V

Hi!

 

I want to find significant factors in the process of spherical crystallization. I did Definitive Screening Design with 6 factors on three levels, with four additional runs (2 fake factors). I am measuring several different responses.

 

I analysed the data with Fit Definitive Screening and got the results which make sense. When I went on and made a model, using Standard Least Squares, I noticed, that the Parameter Estimates are different in a model made with Standard Least Squares and in Fit Definitive Screening . p- values are the same for some factors and very close for others. It worries me also, that in one factor (V ratio) the Estimate got the minus sign (See the photo below) . That changes the model a lot. (+ sign is more logical, based on the understanding of the process). As I know, the Parameter Estimates should be the same. How can I get the same Parameter Estimates? 

 

When I analysed my other responses, I also got different Parameter Estimates, but the sign (+/-) stayed the same. However, some results in Standard Least Squares report in Prediction Profiler were very unlogical (I got the negative number for the mean diameter of a particle). That makes me think that there is something wrong with the Standard Least Squares report, hence I also can't trust the data in the Actual by Predicted Plot, etc. Is there any better way to evaluate the model fitting?

 

I also noticed, that in the Standard Least Squares report, the Prediction Profiler has Yield on the Y axis, despite the fact, that I entered Root Square Yield as a response. On the axis of Actual by Predicted Plot, Root Square Yield is used, as it should. How can I fix that problem?

 

Thank you

Fit Defintive Screening Report (left) and Standard Least Squares Report (right).png

Mark_Bailey

Did you design the experiment for Yield and then create a new column for Root Square Yield? The table scripts won't capture that change so you might be comparing 'apples to oranges.'

Also, did you use the Model script in the data table to get Fit Least Squares or did you click Run Model at the bottom of the Fit Definitive Screening platform? Again, it might make a difference. The fact that you observe Yield, not Root Square Yield, that makes me wonder.

On the other hand, did you enter Yield at the Y variable and the apply a transformation in the Fit Model dialog box? That action would also provide the untransformed response in the prediction profiler.

I know I am wrong but I am just trying to find where the discrepancy comes from.