[Minitab] DOE Output vs. Actual Data



I am running multiple 3 factor DOEs and each one of my model outputs fits very well, all above an 80% fit. The problem I am having is that when I plug our recorded values into my model for parameters x,y and z my model outputs a significantly different/unfeasible value when compared the the actual recorded measure.

Although, when I check using response optimizer I get a value that does resemble the actual recorded output.

I know this is somewhat vague, but my question is regarding if there are any simple fixes or anything that I might be missing in my DOE that would cause my model to appear to have a great fit but not be able to predict values close to my real values. Thanks,
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Bev D

Heretical Statistician
Super Moderator
details and posting your data is the best way to get useful advice. otherwise we are just really guessing.

so my guess would be that your DoE structure is too restricted - your high and low levels aren't far enough apart and/or you don't have enough sample size and replication. Models can have great fits if your data are sparse...


Yeah, I wish I could post the data but its confidential in nature, my assumption was it might be due to the little amount of replications I have.

But just as an example lets say my actual values range from something like .3-.7 and my model is outputing like -100 or something. Clearly, something is wrong here, the model has a fit of .83 but yet spits out these absuredly negative values.

Bev D

Heretical Statistician
Super Moderator
.83 isn't a very good r square value if you don't have many replicates.

no amount of analysis will save a flawed design...if your levels are too close and you don't have enough reps your acuals won't match the model
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