ABSTRACT
Linear regression is one of the most widely studied and applied statistical and econometric techniques. There are numerous reasons for this. First, linear regression is suitable for modeling a wide variety of relationships between variables. In addition, the assumptions of linear regression models are often suitably satisfied in many practical applications. Furthermore, regression model outputs are relatively easy to interpret and communicate to others, numerical estimation of regression models is relatively easy, and software for estimating models is readily available in numerous “non-specialty” software packages. Linear regression can also be overused or misused. In some cases the assumptions are not strictly met, and the correct alternatives are not known, understood, or applied. In particular situations, suitable alternative modeling techniques are not known, or the specialized software and knowledge required to estimate such models are not obtained.
