This paper is concerned with examining the degree of correlation between monthly climatic variables (multicollinearity) within data sets selected for their high quality. Various methods of describing the degree of multicollinearity are discussed and subsequently applied to different combinations of climate data within each site. The results indicate that higher degrees of multicollinearity occur in shorter data sets. Data consisting of 12 monthly variables of a single parameter (temperature or precipitation) have very low degrees of multicollinearity. Data set combinations of two parameters and lagged variables, as commonly used in tree-ring response function analysis, can have significant degrees of multicollinearity. If no preventative or corrective measures are taken when using such multicollinear data, erroneous interpretations of regression results may occur.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/261279 |
| Date | January 1984 |
| Creators | Cropper, John Philip |
| Contributors | ProSight Corporation |
| Publisher | Tree-Ring Society |
| Source Sets | University of Arizona |
| Language | en_US |
| Detected Language | English |
| Type | Article |
| Rights | Copyright © Tree-Ring Society. All rights reserved. |
| Relation | http://www.treeringsociety.org |
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