Detecting Multicollinearity in Regression Analysis
By Noora Shrestha. American Journal of Applied Mathematics and Statistics, 2020
1,589 citations recorded by the source corpus.
Multicollinearity occurs when the multiple linear regression analysis includes several variables that are significantly correlated not only with the dependent variable but also to each other. Multicollinearity makes some of the significant variables under study to be statistically insignificant. This paper discusses on the three primary techniques for detecting the multicollinearity using the questionnaire survey data on customer satisfaction. The first two techniques are the correlation coefficients and the variance inflation factor, while the third method is eigenvalue method. It is observed that the product attractiveness is more rational cause for the customer satisfaction than other predictors. Furthermore, advanced regression procedures such as principal components regression, weighted regression, and ridge regression method can be used to determine the presence of multicollinearity.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/7b1c9c189003154d35b22169450e874a2aa5814b
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In Statistics and Probability
Citations in the corpus
- Factor Analysis as a Tool for Survey Analysis (2021, background): “Multicollinearity makes some of the significant variables in a research study to be statistically insignificant and then the statistical inferences made about the data may not be trustworthy [19,20].”