IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v30y2003i6p683-696.html
   My bibliography  Save this article

Evaluation of three lack of fit tests in linear regression models

Author

Listed:
  • Daniel Wang
  • Michael Conerly

Abstract

A key diagnostic in the analysis of linear regression models is whether the fitted model is appropriate for the observed data. The classical lack of fit test is used for testing the adequacy of a linear regression model when replicates are available. While many efforts have been made in finding alternative lack of fit tests for models without replicates, this paper focuses on studying the efficacy of three tests: the classical lack of fit test, Utts' (1982) test, Burn & Ryan's (1983) test. The powers of these tests are computed for a variety of situations. Comments and conclusions on the overall performance of these tests are made, including recommendations for future studies.

Suggested Citation

  • Daniel Wang & Michael Conerly, 2003. "Evaluation of three lack of fit tests in linear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(6), pages 683-696.
  • Handle: RePEc:taf:japsta:v:30:y:2003:i:6:p:683-696
    DOI: 10.1080/0266476032000053763
    as

    Download full text from publisher

    File URL: http://www.tandfonline.com/doi/abs/10.1080/0266476032000053763
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/0266476032000053763?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Daniel Wang & Michael Conerly, 2008. "Evaluating the power of Minitab's data subsetting lack of fit test in multiple linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(1), pages 115-124.

    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:30:y:2003:i:6:p:683-696. 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: 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.