What does the regression sum of squares measure differ from?

The regression sum of squares specifically measures the variation explained by the regression model, which captures how well the independent variables predict the dependent variable. In contrast, the residual sum of squares quantifies the variation that remains unexplained after fitting the model, indicating how much of the data’s total variance is not captured by the predictors.

The distinction lies in their roles: the regression sum of squares reflects the effectiveness of the model in capturing data patterns, while the residual sum of squares addresses the errors or deviations between the observed values and the values predicted by the model. Therefore, while they are related components within the context of regression analysis, they represent fundamentally different aspects of model performance.