Title: Discriminating between first and second order linear and nonlinear models for optimality

Authors: Maxwell Azubuike Ijomah; Oyinebifun Emmanuel Biu; Olaide Temitayo Toru

Addresses: Mathematics and Statistics Department, University of Port Harcourt, Choba Port Harcourt, Nigeria ' Mathematics and Statistics Department, University of Port Harcourt, Choba Port Harcourt, Nigeria ' Mathematics and Statistics Department, University of Port Harcourt, Choba Port Harcourt, Nigeria

Abstract: In this paper, an examination of the relationship between a response variable and several explanatory variables was considered for first and second order regression models (with and without interaction). To achieve this, the behaviour of the controllable variables (i.e., reaction time, reaction temperature and moisture content) against response variable (drying rate of bush mango seeds) was examined using ordinary least square method with the aid of Microsoft Excel and Minitab 16. Furthermore, the comparison of the fitted models, using model adequacy criteria procedure and optimality criterion technique was also done. This was to determine the most suitable model that best predicts optimal response variable for given settings of the controllable variables. The result showed that the second order regression model with interaction was the most suitable model, and a new operating region in which a process or product may be improved was identified using optimising multivariable function. This research recommends the extreme points and the identified optimal value for production process.

Keywords: data transformation; optimality criterion; model adequacy criteria; optimising multivariable function; OPM.

DOI: 10.1504/IJMDA.2018.096044

International Journal of Multivariate Data Analysis, 2018 Vol.1 No.4, pp.281 - 307

Available online: 05 Nov 2018 *

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