1. Let $M_p$ denote the null model, which contains no predictors.
2. For k = p, p-1, ..., 1: (a) Consider all k models that contain all but one of the predictors in $M_k$ for a total of k - 1 predictors. (b) Choose the best among these k models, and call it $M_{k-1}$. Here best is defined as having smallest RSS or highest $R^2$.
3. Select a single best model from among $M_0, ..., M_p$ using cross-validated prediction error, $C_p$ (AIC), BIC, or adjusted $R^2$.

Restriction: requires that the number of samples n is larger than the number of variables p so that the full model can be fit.