In forward selection, the process begins with how many independent variables?

In forward selection, the process starts with zero independent variables. This method is a stepwise regression technique used in model building where no predictors are initially included in the model. The approach involves examining each independent variable separately and progressively adding them to the model based on a specific criterion, such as the statistical significance of their contribution to improving the model's performance. By starting with zero variables, this method carefully considers which predictors should be included at each stage, allowing for a more focused and effective building of the regression model.

The idea is to ensure that only variables that meaningfully contribute to the predictive power of the model are considered, providing a systematic way to arrive at a model that balances simplicity and explanatory power. By incrementally incorporating variables, the process can help identify the most crucial factors influencing the dependent variable while potentially avoiding overfitting.