IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-1-4614-7381-7_4.html
   My bibliography  Save this book chapter

Simulation to Infer Future Performance Levels Given Assumptions

In: Business Statistics for Competitive Advantage with Excel 2013

Author

Listed:
  • Cynthia Fraser

    (University of Virginia, McIntire School of Commerce)

Abstract

Decision makers deal with uncertainty when considering future scenarios. Performance levels depend on multiple influences with uncertain future values. To estimate future performance, managers make assumptions about likely future scenarios and uncertain future values of performance components. To evaluate decision alternatives, the “best” and “worst” case outcomes are sometimes compared. Alternatively, Monte Carlo simulation can be used to simulate random samples using decision makers’ assumptions about performance components, and those random samples can then be combined to produce a distribution of likely future scenarios and outcomes that are less extreme that the “best” and “worst” cases. Inferences from a simulated distribution of outcomes can then be made to inform decision making. “Best” and “worst” case comparisons are contrasted with inferences from Monte Carlo simulation in this chapter.

Suggested Citation

  • Cynthia Fraser, 2013. "Simulation to Infer Future Performance Levels Given Assumptions," Springer Books, in: Business Statistics for Competitive Advantage with Excel 2013, edition 3, chapter 0, pages 85-112, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4614-7381-7_4
    DOI: 10.1007/978-1-4614-7381-7_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    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:spr:sprchp:978-1-4614-7381-7_4. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.