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Stepwise regression (often called forward or backward regression) involves fitting a regression model and adding or removing predictors based on $t$ statistics, $R^2$ or information criteria to arrive in a *stepwise* manner at a final model. This tag can also be used for forward selection, backward elimination & best subsets variable selection strategies.
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Should stepwise regressions also be avoided for exploratory (hypothesis generating) modelling?
I think the assertion that stepwise selection is always unreliable is a bit too strong.
I think stepwise selection procedure can probably be amended to remove some of its weaknesses. One example might …