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 Select the correct answer from above options

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.