The least squares method for linear regression:
A)minimizes the sum of the errors
B)minimizes the sum of the squared errors
C)maximizes forecasting accuracy
D)minimizes the value of the coefficient of determination R²
E)minimizes the regression equation coefficients

Question

Quizplus · Accepted Answer

The least squares method for linear regression minimizes the sum of the squared errors between the predicted values and the actual values. This is accomplished by minimizing the sum of the squared differences between the observed y-values and the predicted y-values, which is represented by the squared residuals. Therefore, the correct choice is B.

The Least Squares Method for Linear Regression