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International Conference on Theory and Application of Diagrams

Diagrams 2018: Diagrammatic Representation and Inference pp 734–738Cite as

Euler Box Diagrams to Represent Independent and Non-independent Events

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10871))

Abstract

Venn and Euler diagrams are valuable tools for representing the logical set relationships among events. Proportional Euler diagrams add the constraint that the areas of diagram regions denoting various compound and simple events must be proportional to the actual probabilities of these events. Such proportional Euler diagrams allow human users to visually estimate and reason about the probabilistic dependencies among the depicted events. The present paper focuses on the use of proportional Euler diagrams composed of rectangular regions and proposes an enhanced display format for such diagrams, dubbed “Euler boxes”, that facilitates quick visual determination of the independence or non-independence of two events and their complements. It is suggested to have useful applications in exploratory data analysis and in statistics education, where it may facilitate intuitive understanding of the notion of independence.

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Author information

Authors and Affiliations

  1. Teachers College, Columbia University, New York, NY, 10027, USA

    James E. Corter

Authors
  1. James E. Corter
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Correspondence to James E. Corter .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, UK

    Peter Chapman

  2. University of Brighton, Brighton, UK

    Gem Stapleton

  3. Tallinn University of Technology, Tallinn, Estonia

    Amirouche Moktefi

  4. Mitre Corporation, McLean, VA, USA

    Sarah Perez-Kriz

  5. University of Bologna, Bologna, Italy

    Francesco Bellucci

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Corter, J.E. (2018). Euler Box Diagrams to Represent Independent and Non-independent Events. In: Chapman, P., Stapleton, G., Moktefi, A., Perez-Kriz, S., Bellucci, F. (eds) Diagrammatic Representation and Inference. Diagrams 2018. Lecture Notes in Computer Science(), vol 10871. Springer, Cham. https://doi.org/10.1007/978-3-319-91376-6_70

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  • DOI: https://doi.org/10.1007/978-3-319-91376-6_70

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-91375-9

  • Online ISBN: 978-3-319-91376-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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