Which method of input selection evaluates statistical significance after every input is added in regression analysis?

Which method of input selection evaluates statistical significance after every input is added in regression analysis?

• A. Backward
• B. Forward
• C. Stepwise
• D. Simple

Answer: (C)

The method of input selection that evaluates statistical significance after every input is added during regression analysis is the Stepwise method. This approach is particularly useful for building a regression model in a systematic way, allowing for the inclusion and exclusion of variables based on their statistical importance. In Stepwise regression, the process begins either with no variables (in the case of forward selection) or a full model (for backward elimination). At each step, the method assesses the significance of the variables and either adds a new variable or removes an existing one based on specific criteria, typically involving p-values. This iterative process continues until no further significant improvements can be made, ensuring that only the most relevant predictors are included in the final model. The ability to evaluate significance after each input allows for a more nuanced model that can adapt to the data being analyzed, which helps in identifying the best predictors while maintaining model efficiency.

The method of input selection that evaluates statistical significance after every input is added during regression analysis is the Stepwise method. This approach is particularly useful for building a regression model in a systematic way, allowing for the inclusion and exclusion of variables based on their statistical importance.

In Stepwise regression, the process begins either with no variables (in the case of forward selection) or a full model (for backward elimination). At each step, the method assesses the significance of the variables and either adds a new variable or removes an existing one based on specific criteria, typically involving p-values. This iterative process continues until no further significant improvements can be made, ensuring that only the most relevant predictors are included in the final model.

The ability to evaluate significance after each input allows for a more nuanced model that can adapt to the data being analyzed, which helps in identifying the best predictors while maintaining model efficiency.