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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.
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Collinearity Diagnostics |