Which selection method compares each variable included in a regression model to remove non-significant variables?

Which selection method compares each variable included in a regression model to remove non-significant variables?

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

Answer: (C)

The stepwise selection method is a systematic way to build a regression model by adding or removing predictors based on their statistical significance. It begins with no variables in the model and assesses which predictors have a statistically significant contribution to the outcome variable. In this technique, variables are evaluated one at a time, and non-significant variables can be removed from the model as needed. This ongoing assessment can include adding variables to the model that meet a significance criterion and removing those that do not—thereby optimizing the model for predictive accuracy while ensuring that only the most relevant predictors are retained. This approach is particularly useful because it balances the need for simplicity and interpretability with the goal of model accuracy. As a result, stepwise selection is a versatile choice when dealing with many potential predictors, as it carefully considers and adjusts for significance iteratively throughout the modeling process.

The stepwise selection method is a systematic way to build a regression model by adding or removing predictors based on their statistical significance. It begins with no variables in the model and assesses which predictors have a statistically significant contribution to the outcome variable.

In this technique, variables are evaluated one at a time, and non-significant variables can be removed from the model as needed. This ongoing assessment can include adding variables to the model that meet a significance criterion and removing those that do not—thereby optimizing the model for predictive accuracy while ensuring that only the most relevant predictors are retained.

This approach is particularly useful because it balances the need for simplicity and interpretability with the goal of model accuracy. As a result, stepwise selection is a versatile choice when dealing with many potential predictors, as it carefully considers and adjusts for significance iteratively throughout the modeling process.