IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v44y2017i5p882-896.html
   My bibliography  Save this article

A practical method to calculate probabilities: illustrative example from the electronic industry business

Author

Listed:
  • Douglas Moura Miranda
  • Samuel Vieira Conceição

Abstract

The real-life environment is made of probabilistic data by nature and the ability to make decisions based on probabilities is crucial in the business world. It is common to have a set of data and the need of calculating the probability of taking a value greater or less than a specific value. It is also common in many companies the unavailability of a statistical software or a specialized professional in statistics. The purpose of this paper is to present a practical and simple method to calculate probabilities from normal or non-normal distributed data set and illustrate it with an application from the electronic industry. The method does not demand statistical knowledge from the user; there is no need of normality assumptions, goodness test or transformations. The proposed method is easy to implement, robust and the experiments have evidenced its quality. The technique is validated with a large variety of instances and compared with the well-known Johnson system of distributions.

Suggested Citation

  • Douglas Moura Miranda & Samuel Vieira Conceição, 2017. "A practical method to calculate probabilities: illustrative example from the electronic industry business," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(5), pages 882-896, April.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:5:p:882-896
    DOI: 10.1080/02664763.2016.1189517
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2016.1189517
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2016.1189517?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Alexopoulos, Christos & Goldsman, David & Fontanesi, John & Kopald, David & Wilson, James R., 2008. "Modeling patient arrivals in community clinics," Omega, Elsevier, vol. 36(1), pages 33-43, February.
    2. Abbasi, B. & Hosseinifard, S.Z. & Coit, D.W., 2010. "A neural network applied to estimate Burr XII distribution parameters," Reliability Engineering and System Safety, Elsevier, vol. 95(6), pages 647-654.
    3. I. D. Hill & R. Hill & R. L. Holder, 1976. "Fitting Johnson Curves by Moments," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 25(2), pages 180-189, June.
    4. Paranaíba, Patrícia F. & Ortega, Edwin M.M. & Cordeiro, Gauss M. & Pescim, Rodrigo R., 2011. "The beta Burr XII distribution with application to lifetime data," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1118-1136, February.
    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. Hanieh Panahi, 2019. "Estimation for the parameters of the Burr Type XII distribution under doubly censored sample with application to microfluidics data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(4), pages 510-518, August.
    2. Jean-Yves Datey & Genevieve Gauthier & Jean-Guy Simonato, 2003. "The Performance of Analytical Approximations for the Computation of Asian Quanto-Basket Option Prices," Multinational Finance Journal, Multinational Finance Journal, vol. 7(1-2), pages 55-82, March-Jun.
    3. Donald Lien & Christopher Stroud & Keying Ye, 2013. "Comparing VaR Approximation Methods Which Use the First Four Moments as Inputs," Working Papers 0220mss, College of Business, University of Texas at San Antonio.
    4. Kambombo Mtonga & Santhi Kumaran & Chomora Mikeka & Kayalvizhi Jayavel & Jimmy Nsenga, 2019. "Machine Learning-Based Patient Load Prediction and IoT Integrated Intelligent Patient Transfer Systems," Future Internet, MDPI, vol. 11(11), pages 1-24, November.
    5. Lam, William H.K. & Shao, Hu & Sumalee, Agachai, 2008. "Modeling impacts of adverse weather conditions on a road network with uncertainties in demand and supply," Transportation Research Part B: Methodological, Elsevier, vol. 42(10), pages 890-910, December.
    6. Saralees Nadarajah & Bo Zhang & Stephen Chan, 2014. "Estimation methods for expected shortfall," Quantitative Finance, Taylor & Francis Journals, vol. 14(2), pages 271-291, February.
    7. Chen, Huifen & Cheng, Yuyen, 2007. "Non-normality effects on the economic-statistical design of charts with Weibull in-control time," European Journal of Operational Research, Elsevier, vol. 176(2), pages 986-998, January.
    8. Kuiper, Alex & Mandjes, Michel, 2015. "Appointment scheduling in tandem-type service systems," Omega, Elsevier, vol. 57(PB), pages 145-156.
    9. Hanieh Panahi & Abdolreza Sayyareh, 2014. "Parameter estimation and prediction of order statistics for the Burr Type XII distribution with Type II censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 215-232, January.
    10. Arturo Leccadito & Pietro Toscano & Radu S. Tunaru, 2012. "Hermite Binomial Trees: A Novel Technique For Derivatives Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(08), pages 1-36.
    11. De Vuyst, Stijn & Bruneel, Herwig & Fiems, Dieter, 2014. "Computationally efficient evaluation of appointment schedules in health care," European Journal of Operational Research, Elsevier, vol. 237(3), pages 1142-1154.
    12. Tugba Cayirli & Kum Khiong Yang, 2019. "Altering the Environment to Improve Appointment System Performance," Service Science, INFORMS, vol. 11(2), pages 138-154, June.
    13. Stuart Barber & Guy P. Nason & Bernard W. Silverman, 2002. "Posterior probability intervals for wavelet thresholding," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(2), pages 189-205, May.
    14. Karmel S. Shehadeh & Amy E. M. Cohn & Ruiwei Jiang, 2021. "Using stochastic programming to solve an outpatient appointment scheduling problem with random service and arrival times," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 89-111, February.
    15. N. Naguez & J. L. Prigent, 2017. "Optimal portfolio positioning within generalized Johnson distributions," Quantitative Finance, Taylor & Francis Journals, vol. 17(7), pages 1037-1055, July.
    16. Renata Rojas Guerra & Fernando A. Peña-Ramírez & Gauss M. Cordeiro, 2023. "The Logistic Burr XII Distribution: Properties and Applications to Income Data," Stats, MDPI, vol. 6(4), pages 1-20, November.
    17. Naceur Naguez & Jean-Luc Prigent, 2014. "Dynamic Portfolio Insurance Strategies: Risk Management under Johnson Distributions," Working Papers 2014-329, Department of Research, Ipag Business School.
    18. Richard Stevens, 2003. "Evaluation of methods for interval estimation of model outputs, with application to survival models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(9), pages 967-981.
    19. Jiang, Bowen & Tang, Jiafu & Yan, Chongjun, 2019. "A stochastic programming model for outpatient appointment scheduling considering unpunctuality," Omega, Elsevier, vol. 82(C), pages 70-82.
    20. Hainan Guo & David Goldsman & Kwok-Leung Tsui & Yu Zhou & Shui-Yee Wong, 2016. "Using simulation and optimisation to characterise durations of emergency department service times with incomplete data," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6494-6511, November.

    More about this item

    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:taf:japsta:v:44:y:2017:i:5:p:882-896. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.