Diagnosis and quantification of the non-essential collinearity
Metadata
Show full item recordEditorial
Springer Nature
Materia
Multicollinearity Multiple linear regression Non-essential multicollinearity Centered variables
Date
2019Referencia bibliográfica
Published version: Salmerón-Gómez, R., Rodríguez-Sánchez, A. & García-García, C. Diagnosis and quantification of the non-essential collinearity. Comput Stat 35, 647–666 (2020). https://doi.org/10.1007/s00180-019-00922-x
Abstract
Marquandt and Snee (1975), Marquandt (1980) and Snee and Marquardt (1984) refer to non-essential multicollinearity as that caused by the relation with the independent term. Although it is clear that the solution is to center the independent variables in the regression model, it is unclear when this kind of collinearity exists. The goal of this study is to diagnose the non-essential collinearity parting from a simple linear model. The collinearity indices, traditionally misinterpreted as variance inflation factors, are reinterpreted in this paper where they will be used to distinguish and quantify the essential and non-essential collinearity. The results can be immediately extended to the multiple linear model. The study also has some recommendations for statistical software such as SPSS, Stata, GRETL or R for improving the diagnosis of non-essential collinearity.