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

Question

Quizplus · Accepted Answer

The sum of squares for regression measures how much of the variation in the dependent variable is explained by the regression model, while the sum of squares for error measures how much of the variation in the dependent variable is not explained by the model. Therefore, it is possible for the sum of squares for error to be larger than the sum of squares for regression, indicating that the model is not explaining much of the variation in the dependent variable.

The Value of the Sum of Squares for Regression Can