The total sum of squares (SST)in a regression model will never be greater than the regression sum of squares (SSR).

Question

Quizplus · Accepted Answer

The regression sum of squares (SSR) measures the variation in the dependent variable that is explained by the independent variable(s) in the regression model. On the other hand, the total sum of squares (SST) measures the total variation in the dependent variable. It is possible for SST to be greater than SSR if there is a lot of unexplained variation in the dependent variable. Therefore, SST can be greater than SSR, and the statement is false.

The Total Sum of Squares (SST)in a Regression Model Will