American Journal of Applied Mathematics and Statistics· 2020
Detecting Multicollinearity in Regression Analysis
- 1,707citations
- 2020year
Short summary
Product attractiveness is a more rational predictor of customer satisfaction than other variables, despite potential multicollinearity issues in regression analysis.
AI-generated from the title and abstract; the full text is not read.
Key points
- Multicollinearity can mask the statistical significance of important variables in regression.
- Correlation coefficients, VIF, and eigenvalues are primary methods for detecting multicollinearity.
- Product attractiveness was identified as a more rational predictor of customer satisfaction in the analyzed data.
- Advanced regression methods (principal components, weighted, ridge) can be used to manage multicollinearity.
AI-generated from the title and abstract; the full text is not read.
Abstract
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.
The authors' abstract, as published at the source. American Journal of Applied Mathematics and Statistics, 2020 · DOI ↗
Continue with a free account
Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.
Continue free on the webSign in with Google or Apple; no card needed. You come back to this paper.
On your phone:
Field: Statistics and Probability
Statistics and ProbabilityMathematics