Linear regression is a method for predicting a criterion variable from one or more predictor variable. In simple regression, the criterion is predicted from a single predictor variable and the best-fitting straight line is of the form
Y' = bX + A
where Y' is the predicted score, X is the predictor variable, b is the slope, and A is the Y intercept. Typically, the criterion for the "best fitting" line is the line for which the sum of the squared errors of predcition is minimized. In multiple regression, the criterion is predicted from two or more predictor variables.