Which regression procedure identifies the best regression equation with a specified number of independent variables?

Best-subsets regression is a method used to identify the most effective regression equation by considering all possible combinations of independent variables. It evaluates every combination and selects the subset that yields the best performing model based on a specified criterion, such as the adjusted R-squared or the Akaike Information Criterion (AIC). This allows for the determination of the optimal number of independent variables to include in the model, leading to the most accurate predictions while controlling for overfitting.

In contrast, other methods such as stepwise regression, backward elimination, and forward selection systematically add or remove variables based on statistical criteria, but they do not necessarily consider all possible combinations. Best-subsets regression stands out because it explores the full landscape of variable combinations to identify the most effective model that incorporates the desired number of independent variables. This comprehensive approach often leads to more robust and reliable regression models.