ABSTRACT
In a multiple regression there are two or more explanatory variables. At times, some of these variables will be highly linearly related - a condition known as multicollinearity. When this occurs, ordinary least-squares has trouble discerning how much of the impact on the dependent variable can be attributed to each explanatory variable since changes in one explanatory variables are mirrored in the other.
