in regression analysis and similar statistical procedures, the principle that one should estimate the values of parameters in a way that will minimize the squared error of predictions from the model. That is, one should strive to build models that minimize the squared differences between actual scores (observed data) and expected scores (those predicted by the model). Also called least squares principle.