IDEAS home Printed from https://ideas.repec.org/a/rsg/littra/2016-023.html

Practical Comparison of Results of Statistic Regression Analysis and Neural Network Regression Analysis

Author

Listed:
  • Marek Vochozka

Abstract

There are many discussions and arguments about accuracy of the results of statistic regression analysis and neural network regression analysis among experts. Of course all of them look for the best method usable in practice. The objective of the contribution is to use a case study to answer the question which of the methods provides better results. As a case study will be used time series of US Gross Domestic Product. It starts in 1966 and ends in 2014. The future development of the variable for the following 20 years will be calculated. Software Statistica 12 developed by Dell corporation will be used for both analyses. The first one will use multiple regression of the software. The second one will use data mining section with its neural networks. Generalized Regression Neural Networks, Multi-layer Perceptron Network, Radial Basis Function Neural Networks, Linear Neural Networks will by calculated. The results are two curves, the first based on statistic regression analysis. The second curve is provided by the best model of neural networks. Both the curves will describe development of the US GDP in 20 next years.

Suggested Citation

  • Marek Vochozka, 2016. "Practical Comparison of Results of Statistic Regression Analysis and Neural Network Regression Analysis," Littera Scripta, VSTE, vol. 9(2), pages 156-168, May.
    • Handle: RePEc:rsg:littra:2016-023
    DOI: 10.36708/LS.2016.I02.011
    as

    Download full text from publisher

    File URL: https://littera-scripta.com/articles/practical-comparison-of-results-of-statistic-regression-analysis-and-neural-netw-263b61d2
    Download Restriction: no
    File URL: https://littera-scripta.com/api/v1/public/files/a85f2e40-a20f-432f-a0ba-a22d5164704a
    Download Restriction: no
    File URL: https://libkey.io/10.36708/LS.2016.I02.011?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Ciurana, J. & Quintana, G. & Garcia-Romeu, M.L., 2008. "Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach," International Journal of Production Economics, Elsevier, vol. 115(1), pages 171-178, September.
    2. Shtub, Avraham & Versano, Ronen, 1999. "Estimating the cost of steel pipe bending, a comparison between neural networks and regression analysis," International Journal of Production Economics, Elsevier, vol. 62(3), pages 201-207, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Quintana, Guillem & Ciurana, Joaquim, 2011. "Cost estimation support tool for vertical high speed machines based on product characteristics and productivity requirements," International Journal of Production Economics, Elsevier, vol. 134(1), pages 188-195, November.
    2. Deng, S. & Yeh, Tsung-Han, 2011. "Using least squares support vector machines for the airframe structures manufacturing cost estimation," International Journal of Production Economics, Elsevier, vol. 131(2), pages 701-708, June.
    3. Eklin, Mark & Arzi, Yohanan & Shtub, Avraham, 2009. "Model for cost estimation in a finite-capacity stochastic environment based on shop floor optimization combined with simulation," European Journal of Operational Research, Elsevier, vol. 194(1), pages 294-306, April.
    4. Wang, Qing, 2007. "Artificial neural networks as cost engineering methods in a collaborative manufacturing environment," International Journal of Production Economics, Elsevier, vol. 109(1-2), pages 53-64, September.
    5. Ciurana, J. & Quintana, G. & Garcia-Romeu, M.L., 2008. "Estimating the cost of vertical high-speed machining centres, a comparison between multiple regression analysis and the neural networks approach," International Journal of Production Economics, Elsevier, vol. 115(1), pages 171-178, September.
    6. Yochanan Shachmurove & Doris Witkowska, "undated". "Utilizing Artificial Neural Network Model to Predict Stock Markets," Penn CARESS Working Papers cae679cdc2e020f74d692ae73, Penn Economics Department.
    7. Kinyua, Johnson D. & Mutigwe, Charles & Cushing, Daniel J. & Poggi, Michael, 2021. "An analysis of the impact of President Trump’s tweets on the DJIA and S&P 500 using machine learning and sentiment analysis," Journal of Behavioral and Experimental Finance, Elsevier, vol. 29(C).
    8. Chou, Jui-Sheng & Tai, Yian & Chang, Lian-Ji, 2010. "Predicting the development cost of TFT-LCD manufacturing equipment with artificial intelligence models," International Journal of Production Economics, Elsevier, vol. 128(1), pages 339-350, November.
    9. Caputo, Antonio C. & Pelagagge, Pacifico M., 2008. "Parametric and neural methods for cost estimation of process vessels," International Journal of Production Economics, Elsevier, vol. 112(2), pages 934-954, April.
    10. Carlos F. A. Arranz & Caleb Kwong & Vania Sena, 2023. "The effect of consumption and production policies on circular economy business models: A machine learning approach," Journal of Industrial Ecology, Yale University, vol. 27(4), pages 1089-1104, August.
    11. C.G. Hart & Z. He & R. Sbragio & N. Vlahopoulos, 2012. "An advanced cost estimation methodology for engineering systems," Systems Engineering, John Wiley & Sons, vol. 15(1), pages 28-40, March.
    12. Verlinden, B. & Duflou, J.R. & Collin, P. & Cattrysse, D., 2008. "Cost estimation for sheet metal parts using multiple regression and artificial neural networks: A case study," International Journal of Production Economics, Elsevier, vol. 111(2), pages 484-492, February.
    13. Ramanathan, Ramakrishnan & Ramanathan, Usha & Zhang, Yubo, 2016. "Linking operations, marketing and environmental capabilities and diversification to hotel performance: A data envelopment analysis approach," International Journal of Production Economics, Elsevier, vol. 176(C), pages 111-122.
    14. Schikora, Paul F. & Godfrey, Michael R., 2003. "Efficacy of end-user neural network and data mining software for predicting complex system performance," International Journal of Production Economics, Elsevier, vol. 84(3), pages 231-253, June.
    15. Arroyabe, Marta F. & Arranz, Nieves & Fdez. de Arroyabe, Juan Carlos, 2015. "R&D partnerships: An exploratory approach to the role of structural variables in joint project performance," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 623-634.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:rsg:littra:2016-023. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Robin Kunju Mol Raj (email available below). General contact details of provider: https://littera-scripta.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.