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Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations

Author

Listed:
  • Nazifi Morteza

    (University of Bojnord, Iran .)

  • Fadishei Hamid

    (University of Bojnord, Iran .)

Abstract

Two-predictor suppression situations continue to produce uninterpretable conditions in linear regression. In an attempt to address the theoretical complexities related to suppression situations, the current study introduces two different versions of a software called suppression simulator (Supsim): a) the command-line Python package, and b) the web-based JavaScript tool, both of which are able to simulate numerous random two-predictor models (RTMs). RTMs are randomly generated, normally distributed data vectors x1, x2, and y simulated in such a way that regressing y on both x1 and x2 results in the occurrence of numerous suppression and non-suppression situations. The web-based Supsim requires no coding skills and additionally, it provides users with 3D scatterplots of the simulated RTMs. This study shows that comparing 3D scatterplots of different suppression and non-suppression situations provides important new insights into the underlying mechanisms of two-predictor suppression situations. An important focus is on the comparison of 3D scatterplots of certain enhancement situations called Hamilton’s extreme example with those of redundancy situations. Such a comparison suggests that the basic mathematical concepts of two-predictor suppression situations need to be reconsidered with regard to the important issue of the statistical control function.

Suggested Citation

  • Nazifi Morteza & Fadishei Hamid, 2022. "Supsim: a Python package and a web-based JavaScript tool to address the theoretical complexities in two-predictor suppression situations," Statistics in Transition New Series, Statistics Poland, vol. 23(4), pages 177-202, December.
    • Handle: RePEc:vrs:stintr:v:23:y:2022:i:4:p:177-202:n:8
    DOI: 10.2478/stattrans-2022-0049
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    References listed on IDEAS

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    1. Lynn H.S., 2003. "Suppression and Confounding in Action," The American Statistician, American Statistical Association, vol. 57, pages 58-61, February.
    2. Friedman, Lynn & Wall, Melanie, 2005. "Graphical Views of Suppression and Multicollinearity in Multiple Linear Regression," The American Statistician, American Statistical Association, vol. 59, pages 127-136, May.
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