Ridge Regression for Addressing of the Multicollinearity Problem with Application in Cost of Production

Ali Sadig Mohommed Bager (), Bahr KADHIM Mohammed () and Meshal HARBI Odah ()
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Ali Sadig Mohommed Bager: The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Iraq, Muthanna University
Bahr KADHIM Mohammed: The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Iraq, University of AL-Qadisiyah
Meshal HARBI Odah: The Bucharest University of Economic Studies, Department of Statistics and Econometrics, Iraq, Muthanna University

Chapter 4 in New Approaches in Social and Humanistic Sciences, 2018, vol. 3, pp 56-63 from Editura Lumen

Abstract: The regression analysis is statistical method of extensive use, which illustrates the relationship between the explanatory variables and the dependent variable in the form of a model useful in the interpretation of scientific phenomenon, bringing also benefits to society. In this paper we study the most important factors affecting the cost production of cement (Muthanna Factory) by using the ridge regression. The factors are described as follows: we consider the cost of production amount as response variable and factors that affect or may affect the explanatory variables are labor, Price per ton, Electric power, Quantity consumed. They all suffer from high correlation, indicating a problem of multicollinearity .The data analysis is included in the study of the ridge regression as the best approach in case of a multicollinearity problem in the context of financial and economic data being associated with each other often. We used R packages (MASS).

Keywords: Ridge regression; Mullticollinearity problem; Production of cement (search for similar items in EconPapers)
JEL-codes: A3 I2 I3 M0 (search for similar items in EconPapers)
Date: 2018
ISBN: 978-1-910129-15-9
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Persistent link: https://EconPapers.repec.org/RePEc:lum:prchap:03-04

DOI: 10.18662/lumproc.nashs2017.4

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