In statistics, multicollinearity, or collinearity, is a phenomenon in which one independent variable (predictor variable) in a multiple regression model can be linearly predicted from the others with a substantial degree of accuracy. Collinearity tends to inflate the variance of at least one estimated regression coefficient. This could cause some regression coefficients to have the wrong sign. Those issues would make our regression results unreliable. Therefore, how can we detect the potential problem? One way is that we could simply look at the correlation between each pair of independent variables. If their correlation is close to ±1, then we might have such an issue:
>con<-url("http://canisius.edu/~yany/RData/ff3monthly.RData") ...
