Title :
Comparison between regression analysis and artificial neural network in project selection
Author :
Olanrewaju, O.A. ; Jimoh, A.A. ; Kholopane, P.A.
Author_Institution :
Dept. of Ind. Eng., Tshwane Univ. of Technol., Pretoria, South Africa
Abstract :
A common problem faced by managers is that of project selection, to decide which project out of the lots should be undertaken. This paper aims at comparing results of the application of two approaches - respectively regression analysis, a parametric method and artificial neural network, a non-parametric technique. To demonstrate these methods, the models were illustrated using Oral, Kettani and Lang´s data on 37 R&D projects for their success. From statistical analysis, it was discovered that artificial neural network showed superiority to deciding how projects should be ranked and selected.
Keywords :
neural nets; production engineering computing; project management; regression analysis; artificial neural network; parametric method; project selection; regression analysis; statistical analysis; Artificial neural networks; Biological neural networks; Biological system modeling; Economics; Mathematical model; Neurons; Regression analysis; Project selection; artificial neural network; regression analysis;
Conference_Titel :
Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4577-0740-7
Electronic_ISBN :
2157-3611
DOI :
10.1109/IEEM.2011.6118014