Multicollinearity within Selected Western North American Temperature and Precipitation Data Sets
| dc.contributor.author | Cropper, John Philip | |
| dc.date.accessioned | 2012-12-12T22:00:15Z | |
| dc.date.available | 2012-12-12T22:00:15Z | |
| dc.date.issued | 1984 | |
| dc.identifier.citation | Cropper, J.P. 1984. Multicolinearity within selected western North American temperature and precipitation data sets. Tree-Ring Bulletin 44:29-37. | en_US |
| dc.identifier.issn | 0041-2198 | |
| dc.identifier.uri | http://hdl.handle.net/10150/261279 | |
| dc.description.abstract | 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. | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Tree-Ring Society | en_US |
| dc.relation.url | http://www.treeringsociety.org | en_US |
| dc.rights | Copyright © Tree-Ring Society. All rights reserved. | en_US |
| dc.subject | Dendrochronology | en_US |
| dc.subject | Tree Rings | en_US |
| dc.subject | Dendroclimatology | en_US |
| dc.subject | Statistical Analysis | en_US |
| dc.title | Multicollinearity within Selected Western North American Temperature and Precipitation Data Sets | en_US |
| dc.type | Article | en_US |
| dc.contributor.department | ProSight Corporation | en_US |
| dc.identifier.journal | Tree-Ring Bulletin | en_US |
| dc.description.collectioninformation | This item is part of the Tree-Ring Research (formerly Tree-Ring Bulletin) archive. It was digitized from a physical copy provided by the Laboratory of Tree-Ring research at The University of Arizona. For more information about this peer-reviewed scholarly journal, please email the Editor of Tree-Ring Research at editor@treeringsociety.org. | en_US |
| refterms.dateFOA | 2018-08-26T22:59:21Z | |
| html.description.abstract | 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. |
