The least squares regression line minimizes the sum of the:
A)Differences between actual and predicted Y values.
B)Absolute deviations between actual and predicted Y values.
C)Absolute deviations between actual and predicted X values.
D)Squared differences between actual and predicted Y values.
E)Squared differences between actual and predicted X values.

Question

Quizplus · Accepted Answer

The least squares regression line is based on minimizing the sum of the squared differences between the actual and predicted Y values. This is because the squares penalize larger errors more heavily, and the goal is to find the line that has the smallest overall errors in order to make the best predictions. Hence, option D, squared differences between actual and predicted Y values, is the best choice.

The Least Squares Regression Line Minimizes the Sum of The