What does R² indicate in statistical modeling?

R², or the coefficient of determination, serves as a crucial metric in statistical modeling, particularly in the context of linear regression. It quantifies the proportion of variance in the dependent variable that can be explained by the independent variables in the model. This means that a higher R² value signifies that the model can account for a larger proportion of the variability, making it a useful indicator of how well the model explains the data.

When interpreting R², it provides insight into the effectiveness of the model in terms of its explanatory power regarding the dependent variable. For instance, an R² of 0.70 suggests that 70% of the variance in the dependent variable can be predicted from the independent variables, which indicates a strong relationship between the variables. Therefore, the answer directly reflects R²'s primary purpose in assessing how much of the variability in the outcome is captured by the model, thereby making it the most appropriate choice in this context.

Other possible interpretations, such as correlation strength or goodness of fit, while related, do not capture the essence of R² as clearly as the concept of explained variance. R² is not a measure of mean error of predictions, which assesses the average difference between predicted and actual values.