The regression sum of squares (SSR)can never be larger than the total sum of squares.

Question

Quizplus · Accepted Answer

This is true because the total sum of squares (SST) is the sum of the squared deviations of each observation from the mean of the dependent variable, while the regression sum of squares (SSR) is the sum of the squared deviations of the predicted values from the mean of the dependent variable. Since the predicted values are based on the regression line, which minimizes the sum of squared errors, the sum of squared errors for the regression line (SSR) will always be smaller than the sum of squared deviations from the mean (SST). Therefore, SSR <= SST.

The Regression Sum of Squares (SSR)can Never Be Larger Than