Certainly, multiple regression is capable of analyzing data with correlated independent variables. However, problems can arise from situations in which two or more variables are highly intercorrelated. When two variables are highly correlated. Can be detected by looking at the zero order correlations. Multicollinearity Involves combinations of more than two variables.
Uncorrelated variables are orthogonal.
As correlations increase the determinant of the correlation matrix decreases. Because of the zero determinant it is impossible to compute the inverse of the correlation matrix, R -1 . Computer Indicators of Collinearity R i 2 is the squared multiple correlation of independent variable i with the other IVs. Variance Proportions are the proportion of variance of the intercept and each of the regression coefficients associated with each of the condition indices. In Stata Use the vif command after the regress command.