Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach

Keywords: Linear regression, system dynamics, causality, model predictive, explanatory model, mean square error


One of the main assumptions of the linear regression analysis is the existence of a causal relationship between the variables analyzed, which the regression analysis does not demonstrate. This paper demonstrates the causality between the variables analyzed through the construction and analysis of the feedback from the variables under study, expressed in a causal diagram and validated through  dynamic  simulation.  The  major  contribution  of  this  research  is  the  proposal of the use of the system dynamics approach to develop a method of transition from a multiple regression predictive model to a simpler nonlinear regression explanatory model, which increases the level of prediction of the model. The mean square error (MSE) is taken as a criterion for prediction. The validation in the transition model was performed with three linear regression models obtained experimentally in a textile company, showing a method for increasing the reliability of prediction models.

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Author Biographies

Roberto Baeza-Serrato, Instituto Tecnológico Superior del Sur de Guanajuato

Director de Vinculación y Extensión 

José Antonio Vázquez-López

Dr. C. José Antonio Vázquez López

Coordinador de Investigación Instituto Tecnológico de Celaya


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How to Cite
Baeza-Serrato R., & Vázquez-López J. A. (2014). Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach. Revista Facultad De Ingeniería Universidad De Antioquia, 71(71), 59-71. Retrieved from