Of the values of the coefficient of determination listed below, which one implies the greatest value of the sum of squares for regression, given that the total variation in y is 1800?
A)0.69.
B)0.96.
C)96.
D)-100.

Question

Quizplus · Accepted Answer

The coefficient of determination (R-squared) is the proportion of the variance in the dependent variable (y) that is predictable from the independent variable (x). The sum of squares for regression measures the amount of variance in y that can be explained by the independent variable. It is calculated as the difference between the total sum of squares (SS Total) and the sum of squares error (SS Error). Thus, the greater the R-squared value, the greater the sum of squares for regression. Of the given choices, only B has a value greater than 0.69, implying the highest sum of squares for regression. Therefore, B is the best choice.

Of the Values of the Coefficient of Determination Listed Below