Question

What is meant by the term "least squares" in linear regression?

Answer 1

All this means is the minimum between the sum of the difference between the actual y value and the predicted y value.

`min sum_(i=1)^n(y_i-haty)^2`

Explanation:

Just means the minimum between the sum of all the resuidals

`min sum_(i=1)^nhatu_i^2`

all this means is the minimum between the sum of the difference between the actual y value and the predicted y value.

`min sum_(i=1)^n(y_i-haty)^2`

This way by minimizing the error between the predicted and error you get the best fit for the regression line.