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Firm The Right Tyres for The Vehicles: An Application of Multiple Regression
Subject area: Management and Commerce · Area of research: Finance and Economics
Abstract
Regression analysis having a single exogenous variable is called simple regression analysis. However, regression analysis having more than one exogenous variable is called multiple regression. The primary difference between the two is the complexities of estimating the model. In multiple regression, mutual covariance among the exogenous variables also plays its part. An additional assumption of multicollinearity needs to be addressed in multiple regression. Suppose the association between the two exogenous variable is high and significant. In that case, one of the best remedies is to remove one of the exogenous variables having a high correlation while maintaining the other model parameters intact.
Keywords
Simple Regression; Multiple Regression; OLS; ML; GMM
References
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How to cite this paper
@article{1703705,
author = {Rahul Singh Gautam, Aashi Rawal},
title = {Firm The Right Tyres for The Vehicles: An Application of Multiple Regression},
journal = {Iconic Research And Engineering Journals},
year = {2022},
volume = {6},
number = {1},
pages = {553-555},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1703705.pdf},
abstract = {Regression analysis having a single exogenous variable is called simple regression analysis. However, regression analysis having more than one exogenous variable is called multiple regression. The primary difference between the two is the complexities of estimating the model. In multiple regression, mutual covariance among the exogenous variables also plays its part. An additional assumption of multicollinearity needs to be addressed in multiple regression. Suppose the association between the two exogenous variable is high and significant. In that case, one of the best remedies is to remove one of the exogenous variables having a high correlation while maintaining the other model parameters intact.},
keywords = {Simple Regression; Multiple Regression; OLS; ML; GMM},
month = {July},
}