@article{MAKHILLJEAS2020151319353,
    title = {Dealing with Multicollinearity in Regression Analysis: A Case in Psychology},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {13},
    pages = {2693-2703},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.2693.2703},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.2693.2703},
    author = {Solly and},
    keywords = {Principal components,ridge regression,stepwise regression,multicollinearity,exploratory},
    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.}
    }