The least squares method for determining the best fit minimises:
A)total variation in the dependent variable.
B)the sum of squares for error.
C)the sum of squares for regression.
D)All of these choices are correct.

Question

Quizplus · Accepted Answer

The least squares method minimizes the sum of squares for error, which is the sum of the squared distances between the actual values and the predicted values on the regression line. This is the most common method for determining the best fit line in linear regression. Option A is not correct because minimizing total variation in the dependent variable would mean fitting the line through every data point, making it overly complex and not necessarily the best fit. Option C is not correct because minimizing the sum of squares for regression would mean fitting the line through the mean value of the dependent variable, which is not necessarily the best fit for the data. Option D is not correct because option A and C are incorrect.

The Least Squares Method for Determining the Best Fit Minimises