• DocumentCode
    2854852
  • 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
  • fYear
    2011
  • fDate
    6-9 Dec. 2011
  • Firstpage
    738
  • Lastpage
    741
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Engineering and Engineering Management (IEEM), 2011 IEEE International Conference on
  • Conference_Location
    Singapore
  • ISSN
    2157-3611
  • Print_ISBN
    978-1-4577-0740-7
  • Electronic_ISBN
    2157-3611
  • Type

    conf

  • DOI
    10.1109/IEEM.2011.6118014
  • Filename
    6118014