What role does R² play in selecting a regression model?

R², or the coefficient of determination, is a statistical measure that plays a crucial role in evaluating the effectiveness of a regression model. It quantifies the proportion of variance in the dependent variable that can be predicted from the independent variables. A higher R² value indicates that a larger portion of the variance is explained by the model, suggesting a better fit to the data.

When selecting a regression model, R² is particularly valuable because it provides insight into how well the model captures the variability of the target variable based on the predictors included. It is a commonly used criterion for assessing model performance, as it allows practitioners to compare different models and choose the one that explains the data most effectively.

Understanding R² helps in distinguishing between models that may have similar structure but perform differently in explaining the variance within the dataset. While it is not the only metric you should consider—other factors such as residual analysis, adjusted R² (to account for the number of predictors), and model simplicity are also important—R² serves as a foundational benchmark in the model selection process.