What is the difference between stepwise methods versus other approaches of selecting independent variables for a linear regression model?

The distinction between stepwise methods and other approaches in selecting independent variables for a linear regression model lies primarily in the criteria and processes employed during variable selection. Stepwise methods leverage statistical criteria, such as p-values and information criteria (like AIC or BIC), to automatically add or remove predictors based on their statistical significance in relation to the response variable. This results in a more data-driven selection process where decisions are made based on the fit of the model to the data.

In contrast, other methods of variable selection often rely on theoretical rationale or subject-matter expertise to decide which variables to include. These can be based on prior research, domain knowledge, or specific hypotheses regarding the relationships between variables without a systematic statistical approach like the one provided by stepwise selection.

This focus on statistical criteria in stepwise selection can streamline the model-building process, particularly in situations where there are many potential predictors. However, it is essential to be cautious with stepwise approaches, as over-reliance on statistical criteria without substantive theoretical backing can lead to models that may not generalize well to other data sets.

In this context, the correct response highlights the fundamental operational differences between stepwise and other variable selection techniques.