Question

In practice, Line of best fit or regression line is found when _____________

(a) Sum of residuals (∑(Y – h(X))) is minimum

(b) Sum of the absolute value of residuals (∑|Y-h(X)|) is maximum

(c) Sum of the square of residuals ( ∑ (Y-h(X))2) is minimum

(d) Sum of the square of residuals ( ∑ (Y-h(X))2) is maximum

This question was addressed to me during an internship interview.

This question is from Linear Regression in section Commands, Packages, Visualizing Data and Linear Regression of R Programming

Answer 1

The correct option is (c) Sum of the square of residuals ( ∑ (Y-h(X))2) is minimum

The explanation is: Here we penalize higher error value much more as compared to the smaller one, such that there is a significant difference between making big errors and small errors, which makes it easy to differentiate and select the best fit line.