Abstract
The present investigation is concerned with a review of the foundations upon which regression methodology is based, taking into account a study of regression diagnostics conducted by Belsey et al. (1980). It is found that one of the most difficult and controversial problems facing data analysts is related to redundant predictor variables in a regression analysis. Collinearity diagnostics are only meaningful when interpreted in terms of 'basic variables' which are 'structurally interpretable'. Conflicting perspectives are discussed, giving attention to the role of centering when diagnosing collinearity. The definition of collinearity and collinearity measures are considered along with questions regarding 'structural interpretability' as a universally accepted principle in model formulation, and the importance of the detection of collinearity with the constant term.
Keywords
CollinearityInterpretabilityEconometricsRegression analysisLinear regressionRegressionStatisticsMulticollinearityMathematicsComputer scienceArtificial intelligence
Affiliated Institutions
Related Publications
Demeaning Conditioning Diagnostics through Centering
Abstract It is often thought that regression data should be mean-centered before being diagnosed for collinearity (ill conditioning). This view is shown not generally to be corr...
CONFRONTING MULTICOLLINEARITY IN ECOLOGICAL MULTIPLE REGRESSION
The natural complexity of ecological communities regularly lures ecologists to collect elaborate data sets in which confounding factors are often present. Although multiple regr...
Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
Contents: Preface. Introduction. Bivariate Correlation and Regression. Multiple Regression/Correlation With Two or More Independent Variables. Data Visualization, Exploration, a...
Detecting Multicollinearity in Regression Analysis
Multicollinearity occurs when the multiple linear regression analysis includes several variables that are significantly correlated not only with the dependent variable but also ...
Multicollinearity and misleading statistical results
Multicollinearity represents a high degree of linear intercorrelation between explanatory variables in a multiple regression model and leads to incorrect results of regression a...
Publication Info
- Year
- 1984
- Type
- article
- Volume
- 38
- Citations
- 13
- Access
- Closed
External Links
Citation Metrics
13
OpenAlex
Cite This
Richard F. Gunst
(1984).
Toward a balanced assessment of collinearity diagnostics.
The American Statistician
, 38
.