You would be best served using the COLLIN option in Proc REG to assess collinearity.
The numbers listed in the "Collinearity Diagnostics" table represent the number
of eigenvalues extracted from the rescaled X`X matrix. They are listed from
largest to smallest.
You can look at the Condition Number to determine if there is any collinearity.
The condition indices are the square roots of the ratio of the largest
eigenvalue to each individual eigenvalue. The largest condition index is the
condition number of the scaled X matrix. Belsey, Kuh, and Welsch (1980) suggest
that, when this number is around 10, weak dependencies may be starting to
affect the regression estimates. When this number is larger than 100, the
estimates may have a fair amount of numerical error (although the statistical
standard error almost always is much greater than the numerical error).
For each variable, PROC REG produces the proportion of the variance of the
estimate accounted for by each principal component. A collinearity problem
occurs when a component associated with a high condition index contributes
strongly (variance proportion greater than about 0.5) to the variance of two or
more variables.
The VIF option in the MODEL statement provides the Variance Inflation Factors
(VIF). These factors measure the inflation in the variances of the parameter
estimates due to collinearities that exist among the regressor (dependent)
variables. There are no formal criteria for deciding if a VIF is large enough
to affect the predicted values, although some authorities (Myers 1990) state
that values exceeding 10 may be cause for concern. The variables with the
larger VIF values may indicate that those variables are the ones involved in
the collinearity.
