Multicollinearity in Regression Analysis; the Problem Revisited

By D. E. Farrar, R. Glauber. 2011

1,522 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/1830c60bc0979cf4f5b6dd270105e33417e84577

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Citations in the corpus

Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt (2020, background): “One of each two highly correlated features (correlation higher than 0.9) were removed to prevent multicollinearity 44 .”
The Impact of Supervisory Board Composition on CSR Reporting. Evidence from the German Two-Tier System (2016, methodology): “According to Studenmund [83] and Farrar et al. [84], the correlation coefﬁcient is thought to be problematic if it exceeds 0.8.”
Some New Diagnostics of Multicollinearity in Linear Regression Model (2019, methodology): “Multicollinearity is lack of independence or the presence of interdependence signified by usually high intercorrelations within a set of explanatory variables (Abdullah 1996; Farrar & Glauber 1967; Gunst 1983; Gunst & Månson 1977; Mason et al. 1975).”
Methods of integrating data to uncover genotype–phenotype interactions (2015, background): “In regression analyses, multi-collinearity (that is, high levels of correlation) might not allow matrix inversion, which is required to estimate reliable regression coefficients 81 .”
Determinants of voluntary CSR disclosure: empirical evidence from Germany (2011, background): “The correlations between the independent variables provide no indication of multicollinearity, as no bivariate correlation exceeds the value of 0.9 (Cheng et al. 2007; Farrar and Glauber 1967; Hair et al. 2010; Peng and Beamish 2008; Roberts 1992).”
Prediction and behavioral analysis of travel mode choice: A comparison of machine learning and logit models (2020, methodology): “After extracting the data from the SP survey, we processed the data and ruled out the concern for multicollinearity (Farrar and Glauber, 1967).”