Based on guidelines from Belsley et al. (pp.
112-113;2004) it does appear to me that you do have
significant collinearity among some of your variables.
Large variance-decomposition proportions (>.5)
associated with each condition index (>30) identifies
the offending variables. I'd want to drop some of the
variables causing the collinearity---and re-run the
analysis--with the individual variables.
SR Millis
Belsley, D. A., Kuh, E., & Welsch, R. E. (2004).
Regression diagnostics: Indentifying influential data
and sources of collinearity. Hoboken, NJ: John Wiley &
Sons.
--- georg wernicke <[email protected]>
wrote:
> Hello SR Millis,
>
> the highest condition index is 57.29. regarding the
> variance
> decomposition proportions, there are 6 above 0.5
> when the condition
> index is ~30. . seems a bit weird that the table
> using coldiag2 gives
> me 17 indexes even so i only have 16 variables.
>
> thanks again,
>
> george
>
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