The value of the sum of squares for regression SSR can never be smaller than 0.0.

Question

Quizplus · Accepted Answer

This is because the sum of squares for regression (SSR) is calculated by summing the squared differences between the predicted values and the actual values. In other words, it measures the variance in the data that is explained by the regression model. Since the actual values and predicted values can never be further apart than their maximum difference (i.e. the range of the data), the sum of squares for regression can never be negative and is always greater than or equal to 0.

The Value of the Sum of Squares for Regression SSR