Multicollinearity and misleading statistical results

Datos Bibliográficos

ID23344648
AutoresJonghae Kim (0000-0003-1222-0054, Daegu Catholic University, autor de correspondencia)
Año2019
Volumen72
Número6
Páginas558-569
Fecha de publicación2019-12-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaKorean Journal of Anesthesiology (JOURNAL)
Identificadores de la revistaISSN: 2005-6419 • E-ISSN: 2005-7563
EditorialThe Korean Society of Anesthesiologists (PUBLISHER)
DOI10.4097/kja.19087
PMID31304696
OpenAlexW2957606078
IdiomaEN
Citas recibidas294
Referencias citadas5

Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression analyses. Diagnostic tools of multicollinearity include the variance inflation factor (VIF), condition index and condition number, and variance decomposition proportion (VDP). The multicollinearity can be expressed by the coefficient of determination (Rh2) of a multiple regression model with one explanatory variable (Xh) as the model's response variable and the others (Xi [i ≠ h]) as its explanatory variables. The variance (σh2) of the regression coefficients constituting the final regression model are proportional to the VIF. Hence, an increase in Rh2 (strong multicollinearity) increases σh2. The larger σh2 produces unreliable probability values and confidence intervals of the regression coefficients. The square root of the ratio of the maximum eigenvalue to each eigenvalue from the correlation matrix of standardized explanatory variables is referred to as the condition index. The condition number is the maximum condition index. Multicollinearity is present when the VIF is higher than 5 to 10 or the condition indices are higher than 10 to 30. However, they cannot indicate multicollinear explanatory variables. VDPs obtained from the eigenvectors can identify the multicollinear variables by showing the extent of the inflation of σh2 according to each condition index. When two or more VDPs, which correspond to a common condition index higher than 10 to 30, are higher than 0.8 to 0.9, their associated explanatory variables are multicollinear. Excluding multicollinear explanatory variables leads to statistically stable multiple regression models.

Bayesian multivariate linear regression · Econometrics · Economics · Explained variation · Linear predictor function · Linear regression · Multicollinearity · Proper linear model · Regression · Regression analysis · Regression diagnostic · Statistics · Variables · Variance (accounting) · Variance inflation factor · Advanced Statistical Methods and Models · Computational Drug Discovery Methods · Mathematics · Spectroscopy and Chemometric Analyses