Statistics using R programming - Test of Multicollinearity in Regression with R

• Statistics using R pro...
Multicollinearity refers to a situation where the independent variables have an high linear relationship (correlation).
Multicollinearity can lead to skewed or misleading results when the model attempts to determine how well each independent variable can be used most effectively to predict or understand the dependent variable in a multiple linear regression.
Multicollinearity can be detected using a statistic called the variance inflation factor (VIF). For any predictor variable, the square root of the VIF indicates the degree to which the confidence interval for that variable’s regression parameter is expanded relative to a model with uncorrelated predictors.
As a general rule, a VIF larger than 10 indicates a multicollinearity problem.
In R, with car package.
vif()
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