Detection and elimination of multicollinearity in regression analysis

Article type: Research Article

Authors: Singh, Preeti^* | Singh, Sarvpal | Paprzycki, Marcin

Affiliations: Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India

Correspondence: [*] Corresponding author: Preeti Singh, Madan Mohan Malaviya University of Technology, Gorakhpur, Uttar Pradesh, India. E-mail: singh.preeti294@gmail.com.

Abstract: Multicollinearity occurs when there comes a high level of correlation between the independent variables. This correlation creates the problem because the independent variables should be independent. Higher the degree of correlation means more complex problems you will face while fitting the model and interpreting the results. In this paper, we have eliminated the problem of multicollinearity on the basis of Hatvalues. The variables with higher Hatvalues will be removed from the data before fitting the model. This paper presents the comparison of results achieved by the proposed technique and state of the art methods.

Keywords: Regression model, multicollinearity, correlation

DOI: 10.3233/KES-221622

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 27, no. 1, pp. 105-111, 2023