Variance+Inflation+Factor+(VIF)+for+Checking+Multicollinearity

Variance Inflation Factor (VIF) for Checking Multicollinearity (Statistical Model Validation)
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"In statistics, the **variance inflation factor (VIF)** quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance of an estimated regression coefficient (the square of the estimate's standard deviation) is increased because of collinearity." - [|Wikipedia]

"The square root of the variance inflation factor tells you how much larger the standard error is, compared with what it would be if that variable were uncorrelated with the other independent variables in the equation.
 * "Example**

"If the variance inflation factor of an independent variable were 5.27 (√5.27 = 2.3) this means that the standard error for the coefficient of that independent variable is 2.3 times as large as it would be if that independent variable were uncorrelated with the other independent variables." - [|Wikipedia] "If the VIF < 1, there is no multicollinearity but if the VIF is > 1, predictors may be correlated. Montgomery and Peck suggest that if the VIF is between 5 - 10, the regression coefficients are poorly estimated." - EMSE 271, Fall 2009, Slide 215


 * More**

Lattin identifies another measure of multicollinearity called the Condition Index (CI).


 * Sources:**
 * Variance inflation factor. (2009, November 24). In //Wikipedia, The Free Encyclopedia//. Retrieved 21:42, December 7, 2009, from []
 * EMSE 271, Fall 2009
 * Analyzing Multivariate Data, by James Lattin, Douglas Carroll and Green ([|Amazon]), page 57