a type of
regression analysis in which the researcher strives to develop a
least-squared-errors solution or
least squares solution to describe the relationship between an outcome or
dependent variable and one or more predictors or
independent variables. That is, one develops a model that is the best fit for the data because it yields the smallest squared differences between the actual observations and their values as predicted by the model. Also called
least squares estimation;
ordinary least squares (
OLS)
regression. See
least squares criterion.