Collinearity

Collinearity (Nomenclature) (GWU EMSE-271)
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 * "Collinearity** is a linear relationship between //two// explanatory variables. Two variables are collinear if there is an exact linear relationship between the two. For example, //X//1 and //X//2 are collinear if //X//1 = λ//X//2


 * Multicollinearity** refers to a situation in which two or more explanatory variables in a multiple regression model are highly correlated. We have perfect multicollinearity if the correlation between two independent variables is equal to 1 or -1. In practice, we rarely face perfect multicollinearity in a data set. More commonly, the issue of multicollinearity arises when there is a high degree of correlation (either positive or negative) between two or more independent variables." - [|Wikipedia]

The result of multicollinearity is "small changes in the data will results (sic) in large cnages in the regression coefficinets (instability), which makes interpretation of the coefficinents difficult (to impossible)." - EMSE 271, Fall 2009, Sldie 215


 * Sources:**
 * Multicollinearity. (2009, November 24). In //Wikipedia, The Free Encyclopedia//. Retrieved 15:10, December 8, 2009, from []
 * EMSE 271, Fall 2009