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

Apr 22, 2018

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

$\min {\sum}_{i = 1}^{n} {\left({y}_{i} - \hat{y}\right)}^{2}$

#### Explanation:

Just means the minimum between the sum of all the resuidals

$\min {\sum}_{i = 1}^{n} {\hat{u}}_{i}^{2}$

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

$\min {\sum}_{i = 1}^{n} {\left({y}_{i} - \hat{y}\right)}^{2}$

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