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

Question

Quizplus · Accepted Answer

This is a fundamental property of the decomposition of variance in statistics, where the total sum of squares is broken down into components such as the sum of squares for error or the sum of squares for regression. The sum of squares for error represents the variation in the data that cannot be explained by a model, whereas the total sum of squares represents the total variation in the data. Since the sum of squares for error is a subset of the total sum of squares, it follows that it cannot be larger than the total sum of squares.

The Value of the Sum of Squares for Error Can