Least Squares Line
The Simple Linear Regression model is materialized by a straight line, called the "Least Squares Line". This line is a condensed graphic representation of the distribution of the sample in the (x, y) plane.
This line is called the regression line or least squares line, because it is determined such that the sum of the squared distances of all the data points from the line is the lowest possible.
Such estimation can be performed by using least squares linear regression or by fitting a Box-Jenkins autoregressive (AR) model.
Many common statistics, including t-tests, regression models, design of experiments, and much else, use least squares Linear models theory, which is based on the Taguchi loss function.
[edit] See also ...
 See also: Regression, Variance, Distribution, Estimation, Standard Deviation
  
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