Polynomial parametric and non-parametric B-Splines regression models. An Engineering Application.

Authors

  • Byron Toalombo Universidad Técnica de Ambato, Facultad de Sistemas, Electrónica e Industrial, Ambato, Ecuador. Quito, Ecuador.
  • Antonio Meneses Universidad Nacional de Chimborazo, Facultad de Ingeniería, Carrera de Ingeniería en Telecomunicaciones, Riobamba, Ecuador.
  • Lourdes Zúñiga Escuela Superior Politécnica de Chimborazo, Facultad de Informática y Electrónica, Carrera Ingeniería en Telecomunicaciones, Riobamba, Ecuador.
  • Ricardo Espín Universidad Técnica de Ambato, Facultad de Sistemas, Electrónica e Industrial, Ambato, Ecuador. Quito, Ecuador.

DOI:

https://doi.org/10.47187/perf.v1i28.185

Keywords:

Vehicle crash simulation, polynomial regression models, B-Splines regression models, goodness of fit, confidence interval, normality

Abstract

A study of the polynomial parametric and nonparametric B-splines regression models applied to a simulation of an impact of a car against a bus body was performed. A non-experimental design, cross-sectional, and correlational was established. The R software was used and the following criteria were considered: the rejection of the nullity of the coefficients of the models through the Student's t-hypothesis test, the validity of the models using the Snedecor F-test of the ANOVA table, the goodness of fit, 95% confidence intervals, and compliance with the assumptions of normal distribution, non-autocorrelation and homoscedasticity of the residuals. The Wilcoxon nonparametric test was applied to select the appropriate regression model based on the lengths of the confidence intervals. The parametric polynomial regression models were fitted to curves with parabolic shape or curvature without abrupt changes, fitting better to the relationships of the vehicle impact simulation, whose explanatory variable is the speed of the impacting vehicle. While the nonparametric B-splines regression models provided a better fit to bell-shaped curves with more abrupt curvature changes.

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Published

2022-10-11

How to Cite

Toalombo, B., Meneses, A., Zúñiga, L., & Espín, R. (2022). Polynomial parametric and non-parametric B-Splines regression models. An Engineering Application. Perfiles, 1(28), 75-86. https://doi.org/10.47187/perf.v1i28.185