Condition Index and Multicollinearity

In the case of Multicollinearity, a less common, but more satisfactory, way of detecting Multicollinearity is through the condition index, or number, of the data, the square root of the ratio of the largest to the smallest characteristic root of X'X. A high condition index reflects the presence of collinearity.
When there is no collinearity at all, the eigenvalues, condition indices and condition number will all equal one. As collinearity increases, eigenvalues will be both greater and smaller than 1, and the condition indices and the condition number will increase. An informal rule of thumb is that if the condition number is 15, multicollinearity is a concern; if it is greater than 30 multicollinearity is a very serious concern. (But again, these are just informal rules of thumb.) In SPSS, you get these values by adding the COLLIN parameter to the Regression command; in Stata you can use COLLIN. In SAS, you can use COLLIN option in Model statement of PROC REG.

Here are two more rules of thumb in dealing with multicollinearity:
Don't worry about multicollinearity if the R^2 from the regression exceeds the R^2 of any independent variable regressed on the other independent variables.
Don't worry about multicollinearity if the t statistics are all greater than 2.