Your model looses precision. We’ll explain why. — In the previous article, we saw how leaving out important variables causes the regression model’s coefficients to become biased. In this article, we’ll look at the converse of this situation namely, the damage caused to your regression model from stuffing it with variables that are entirely superfluous. What are irrelevant and superfluous variables?