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Undoing Statistical Advice

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Book cover Advances in Intelligent Data Analysis (IDA 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1642))

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Abstract

Statistical advisors are small pieces of code that are run in response to user actions which provide statistical support for the user, especially users who lack statistical sophistication. These could provide advice in the form of a message, or could automatically perform supplemental actions which change the model according to the advice. However, when the original action is undone, the supplemental actions need to be undone as well. This paper describes an implementation of an intelligent advisory system, NAEPVUE, for analyzing data from the National Assessment of Educational Progress (NAEP). NAEPVUE uses an object-oriented data dictionary for storing statistical advisors. Built in the Amulet user interface development environment, NAEPVUE is able to take advantage of the Myers and Kosbie hierarchical undo model to undo the actions of statistical advisors.

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Author information

Authors and Affiliations

  1. Educational Testing Service, Research Statistics Group, 15-T, Princeton, NJ, 08541, USA

    Russell Almond

Authors
  1. Russell Almond
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Editor information

Editors and Affiliations

  1. Department of Mathematics, Imperial College, Huxley Building 180 Queen’s Gate, London, SW7 2BZ, UK

    David J. Hand

  2. Leiden Institute for Advanced Computer Science, Leiden University, 2300, RA Leiden, The Netherlands

    Joost N. Kok

  3. Berkeley Initiative in Soft Computing, University of California at Berkeley, 329 Soda Hall, Berkeley, CA, 94720, USA

    Michael R. Berthold

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© 1999 Springer-Verlag Berlin Heidelberg

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Almond, R. (1999). Undoing Statistical Advice. In: Hand, D.J., Kok, J.N., Berthold, M.R. (eds) Advances in Intelligent Data Analysis. IDA 1999. Lecture Notes in Computer Science, vol 1642. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48412-4_30

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  • DOI: https://doi.org/10.1007/3-540-48412-4_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66332-4

  • Online ISBN: 978-3-540-48412-7

  • eBook Packages: Springer Book Archive

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