Which method of input selection for regression analysis evaluates the statistical significance of the total model to assess improvements as variables are added?

The forward selection method is appropriate for this question as it involves adding variables to the regression model one at a time based on their statistical significance in improving the model. In forward selection, the process begins with an empty model, and variables are evaluated individually to determine if they significantly contribute to explaining the variance in the dependent variable. If the addition of a variable leads to a statistically significant improvement in the overall model fit, that variable is included in the model. This step-by-step approach continues until no additional variables will significantly enhance the model.

The key aspect here is the evaluation of the total model's statistical significance as new variables are incorporated, which is a fundamental characteristic of the forward selection process. This method directly assesses how each variable contributes to improving the model incrementally.

Other options might involve different approaches where variables are either removed from an initial set or combined through more complex reasoning, but in the context of building the model from scratch and assessing the contribution of each new variable, forward selection stands out as the best fit.