Model Selection-stepwiseHello,
I have screened a series of biological parameters on a biological response with a DSD. I am doing a forward stepwise regression AICc, and at p value below 0.05, I see only one or maximum two of my parameters become significant, but beyond that (like at around 0.1 or 0.2), several others pop out as significant, and if I remove them, the R square value decreases. In some models I do not see either Cp or R square values, which are mainly the last models the software shows after fitting. So my questions are: 1- Is it correct to exclude those models without Cp and R squared values? 2-The literature lacks enough information about the relationship between parameters for my response, but there might be some that I would like to understand with model. I have checked for multicollinearity, and the VIF value is around 1 to 2 (not high) but I am not clear how I can interpret my results, and whether I should confine my model to p value below 0.05 or above that?
As a beginner, I'd appreciate any help!
AnalyticsDOERe: Model Selection-stepwiseHow many factors do you have?
Did you try to select a model selecting DOE > Definitive Screening > Fit Definitive Screening, or running the Fit Definitive Screening table script?
What is the estimated RMSE for your response with the best model so far? How does that value compare to the expected change in the response from a real effect?
Re: Model Selection-stepwiseTo add a bit to @markbailey 's input, remember you ran a screening design. These types of designs are usually part of a larger strategic pathway to learning or making a decision. One of the primary goals of a screening design is to estimate active or interesting effects that are worthy of further study. Emphasis here on 'further study'. I try to not get too hung up on things like critical p values, or modeling statistical diagnostics to make decisions beyond "Should I include these factors in further study?" So at this stage of your larger problem solving effort, you may want to continue to include factors or effects that are near the critical p values or regression diagnostics you are using. Keep these factors in the study...continue to study via DOE methods. Let the additional elements and analysis guide you in the overall investigation.
