Estimation of Linear Regression with the Dimensional Analysis Method

Luis Pérez-Domínguez (), Harish Garg, David Luviano-Cruz and Jorge Luis García Alcaraz
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Harish Garg: School of Mathematics, Thapar Institute of Engineering & Technology, Deemed University, Patiala 147004, Punjab, India
David Luviano-Cruz: Departamento de Ingeniería Industrial y Manufactura, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32315, Mexico
Jorge Luis García Alcaraz: Departamento de Ingeniería Industrial y Manufactura, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32315, Mexico

Mathematics, 2022, vol. 10, issue 10, 1-13

Abstract: Dimensional Analysis (DA) is a mathematical method that manipulates the data to be analyzed in a homogenized manner. Likewise, linear regression is a potent method for analyzing data in diverse fields. At the same time, data visualization has gained attention in tendency study. In addition, linear regression is an important topic to address predictive models and patterns in data study. However, it is still pending to attack the manipulation of uncertainty related to the data transformation. In this sense, this work presents a new contribution with linear regression, combining the Dimensional Analysis (DA) to address instability and error issues. In addition, our method provides a second contribution related to including the decision maker’s attitude involved in the study. Therefore, the experimentation shows that DA manipulates the regression problem under a complex situation that the outcome may have in the investigation. A real-life case study is used to demonstrate our proposal.

Keywords: linear regression analysis; dimensional analysis (DA); forecast; mean square error; patterns; tendency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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