The sum of squared errors is the sum of
A)each score on the criterion variable minus the predicted score,squared.
B)each squared score on the criterion variable minus each squared predicted score.
C)each score on the predictor variable minus the predicted score,squared.
D)each squared score on the predictor variable minus each squared predicted score.

Question

Quizplus · Accepted Answer

The sum of squared errors is a statistical measure used to determine the accuracy of a regression analysis. It is calculated by subtracting each actual score from the predicted score, then squaring this difference, and finally adding up all of the squared differences. Choice A correctly describes this process, whereas choices B, C, and D describe different calculations that are not relevant to the sum of squared errors.

The Sum of Squared Errors Is the Sum of