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An Improved Boxplot for Univariate Data

Author

Listed:
  • M. L. Walker
  • Y. H. Dovoedo
  • S. Chakraborti
  • C. W. Hilton

Abstract

The boxplot is an effective data-visualization tool useful in diverse applications and disciplines. Although more sophisticated graphical methods exist, the boxplot remains relevant due to its simplicity, interpretability, and usefulness, even in the age of big data. This article highlights the origins and developments of the boxplot that is now widely viewed as an industry standard as well as its inherent limitations when dealing with data from skewed distributions, particularly when detecting outliers. The proposed Ratio-Skewed boxplot is shown to be practical and suitable for outlier labeling across several parametric distributions.

Suggested Citation

  • M. L. Walker & Y. H. Dovoedo & S. Chakraborti & C. W. Hilton, 2018. "An Improved Boxplot for Univariate Data," The American Statistician, Taylor & Francis Journals, vol. 72(4), pages 348-353, October.
    • Handle: RePEc:taf:amstat:v:72:y:2018:i:4:p:348-353
    DOI: 10.1080/00031305.2018.1448891
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    Cited by:

    1. Bing Dai & Danli Li & Lei Zhang & Yong Liu & Zhijun Zhang & Shirui Chen, 2022. "Rock Mass Classification Method Based on Entropy Weight–TOPSIS–Grey Correlation Analysis," Sustainability, MDPI, vol. 14(17), pages 1-18, August.
    2. Zhu, Bangzhu & Wan, Chunzhuo & Wang, Ping, 2022. "Interval forecasting of carbon price: A novel multiscale ensemble forecasting approach," Energy Economics, Elsevier, vol. 115(C).

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