Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach
DOI:
https://doi.org/10.17533/udea.redin.14469Keywords:
system dynamics, causality, model predictive, explanatory model, mean square error, linear regressionAbstract
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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