I'm pretty confident the answer is C, Mallows' Cp. The forward selection method aims to minimize Mallows' Cp, which is a measure of model fit that balances goodness of fit and model complexity.
Okay, let me see if I can work this out. The forward selection method starts with an empty model and adds variables one by one based on some selection criterion. I believe the criterion is to choose the variable that gives the largest increase in R-squared, so the answer is A, Adjusted R-Square.
Hmm, I'm a bit unsure about this one. I know the forward selection method is used to build a regression model, but I can't quite recall the specific selection criterion it uses. I'll have to think this through carefully.
I'm pretty sure the answer is B. The forward selection method uses the SLE (Significance Level for Entry) as the selection criterion to determine which variables to add to the model.
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