The collinearity diagnostics confirm that there are serious problems with multicollinearity. Several eigenvalues are close to 0, indicating that the predictors are highly intercorrelated and that small changes in the data values may lead to large changes in the estimates of the coefficients. The condition indices are computed as the square roots of the ratios of the largest eigenvalue to each successive eigenvalue. Values greater than 15 indicate a possible problem with collinearity; greater than 30, a serious problem. Six of these indices are larger than 30, suggesting a very serious problem with collinearity. Now try to fix the collinearity problems by rerunning the regression using z scores of the dependent variables and the stepwise method of model selection. This is in order to include only the most useful variables in the model.