Application of Bivariate Regression Model Data Comparison Using Information Measures

P.A. Esemokumo¹ , J. Opara² , R. Bekesuoyeibo³

Section:Research Paper, Product Type: Journal-Paper
Vol.9 , Issue.2 , pp.23-27, Apr-2022

Online published on Apr 30, 2022

Copyright © P.A. Esemokumo, J. Opara, R. Bekesuoyeibo . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
This study is on the application of bavariate regression model data comparison using information measures. Data set for the study was extracted from Esemokumo and Opara (2018), which was on weight of soap (Y) and the number of days it had been used (Z). Ten Regression models: Linear, Quadratic, Polynomial, Logarithmic, Hyperbolic, Power, S-curve, Growth, Exponential and Compound were studied. The E-views package was employed in the analysis of data. Three information measures for model selection known as; AIC, SIC, and HQIC were employed for best model selection. The results revealed that the Growth and Exponential regression models outperformed the other eight regression models utilized, while the least performed regression model is the hyperbolic. Hence, future studies should look at a similar work with different bivariate data sets with the assumptions fully examined.

Key-Words / Index Term :
AIC, SCI, HQIC, Regression models, Model Comparison

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