Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
In regression analysis, the main interest is to predict the response variable using the exploratory variables by estimating parameters of the linear model. However, in reality, the exploratory variables may share similar characteristics. This interdependency between the exploratory variables is called multicollinearity and causes parameter estimation in regression analysis to be unreliable. Different approaches to address the multicollinearity problem in regression modelling include variable selection, principal component regression and ridge regression. In this study, the performances of these techniques in handling multicollinearity in simulated data are compared. Out of the four regression models compared, principal regression model produced the best model to explain the variability and its parameter estimates were precise and addressing multicollinearity.
How to cite this article:
Solly Matshonisa Seeletse and Motlalepula Grace Phalwane. Dealing with Multicollinearity in Regression Analysis: A Case in Psychology.
DOI: https://doi.org/10.36478/jeasci.2020.2693.2703
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2020.2693.2703