The value of the sum of squares for regression SSR can never be larger than the value of sum of squares for error SSE.

Question

Quizplus · Accepted Answer

The value of the sum of squares for regression (SSR) can indeed be larger than the sum of squares for error (SSE) because SSR reflects the variation explained by the regression model, while SSE represents the unexplained variation. The total variation in the data (SST) is the sum of SSR and SSE, and there's no inherent constraint that SSR must be smaller than SSE.

The Value of the Sum of Squares for Regression SSR