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In Reply to Parent Post by tahirawan11
As the 'P' value for 'Curvature' is more than 0.05. Can i say that there is no curvature in my response variable and a linear model is good enough to describe the relationship between the variables and i do not need to proceed with a Response Surface Modelling DOE?

Or at least you can say there is no conclusive evidence that there is curvature, which is not quite the same thing
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ANOVA also gives me 'Pure error', can anyone tell me what is the practicle meaning of 'Pure error' result in my case. Is it too big or low?

According to minitab help ...
Lack of fit (DOE)
If your design contains replicates (multiple runs at identical, but distinct combinations of factor settings), Minitab may calculate a pure error test for lackoffit. The error term can be partitioned into two parts  pure error (error within replicates) and lackoffit error, which represents degrees of freedom that are not in the model (e.g., higherorder interaction terms). You need both types of error to perform the lackoffit test. Depending on the model, sometimes you only have one of the types of error, but not both.
So pure error is a measure of how well the replicates correspond. In this case, the "pure error" is a pretty noticeable fraction of the total error.
This is backed up by the relatively low adjusted R^2. Only 38% of the variation is explained by your model, which means much of the error is due to the center points and/or the lack or repeatability in the replicates.
I think the main reason that the center point is not more clearly significant is that the replications have so much variabliity themselves.
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I also made 'Main effect plots' and 'Interaction plot' and the figures are attached. I dont understand why Mintab does not connect the line between the 'High value' and 'Low value' of main effect through the 'Centre point'?

I think Minitab is simply trying to show you what the experiment predicts using a linear model (the straight lines on the plots). The fact that the center points do not fall close to the line is another indication that the model does not fully explain the variability.
Tim F