COMPARATIVE ANALYSIS ON THE DIFFERENT METHODS OF DETECTING MULTICOLLINEARITY

Joshua C. Guardaquivil, Resa Mae R. Sangco

Abstract


This study explores four methods for detecting multicollinearity: Min-Max and Point-Coordinates approach, Product Moment Correlation, Eigenvalues, and Variance Inflation Factor. Since the Min-Max and Point-Coordinates approach is a new method for detecting multicollinearity, its results were compared and verified against the results obtained from the existing methods. Longley’s Economic Data and Blood Pressure Data were used to evaluate the suitability of the Min-Max and Point-Coordinates approach for detecting multicollinearity. The results indicate that severe multicollinearity can significantly affect model estimation and interpretation, highlighting the importance of detecting it accurately. The study shows that the Min-Max and Point-Coordinates approach is effective in detecting multicollinearity, making it a valuable addition to the existing methods. Overall, this study provides useful insights into the importance of detecting multicollinearity and the efficacy of different methods for doing so.

Keywords


min-max and point-coordinates, multicollinearity, product moment correlation, eigenvalues, variance inflation factor

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References


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