An Improved Robust Regression Model for Response Surface Methodology

• Main Authors: Efosa Edionwe, J. I. Mbegbu, N. Ekhosuehi, H. O. Obiora-Ilouno
• Format: Article
• Language: English
• Published: Croatian Operational Research Society 2018-01-01
• Series: Croatian Operational Research Review
• Online Access: http://hrcak.srce.hr/file/310575

In production, manufacturing and several other allied industries, appropriate tool is applied in the analysis of data in order to enhance the opportunity for product and process optimization. A statistical tool that has successfully been used to achieve this goal is Response Surface Methodology (RSM). A recent trend in the modeling phase of RSM involves the use of semi-parametric regression models which are hybrids of the Ordinary Least Squares (OLS) and the Local Linear Regression (LLR) models. In this paper, we propose a modification in the current structure of the semi-parametric Model Robust Regression 2 (MRR2) with a view to improving its sensitivity to local trends and patterns in data. The proposed model is applied to two multiple response optimization problems from the literature. The results of goodness-of-fits and optimal solutions confirm that the proposed model performs better than the MRR2.