Which method does the Dmine Regression Node utilize for model computation?

The Dmine Regression Node in SAS Enterprise Miner employs forward stepwise least squares regression for model computation. This method is particularly effective in selecting a subset of predictor variables that contribute significantly to the model's predictive power. Forward stepwise regression begins with no predictors in the model and iteratively adds variables based on specific criteria, such as the improvement of the overall model fit or significance levels.

This approach is advantageous when dealing with multiple potential predictors, as it helps in building a more interpretable and parsimonious model. The method checks each variable individually and selects those that provide the most significant increase in predictive capability. This stepwise addition continues until no further improvements can be made, which is why it is a popular choice in regression modeling.

By contrast, other methods mentioned either focus on different modeling strategies, such as regression with interaction terms or logistic regression for categorical outcomes, or use polynomial regression analysis that extends linear models but does not align with the stepwise method of variable selection. Each of these alternatives serves distinct purposes but does not reflect the core methodology utilized by the Dmine Regression Node.