In this paper we analyze the bias in a general linear least-squares parameter estimation problem. when it is caused by deterministic variables that have not been included in the model. We propose a method to substantially reduce this bias. under the hypothesis that some a-priori information on the magnitude of the modelled and unmodelled components of the model is known. https://www.itsmajorlook.com/