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Fungible regression coefficients

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  • Phil Ender

    (UCLA Stat Consulting)

Abstract

Ordinary least-squares regression (OLS) estimates coefficients such that the residual sum of squares (RSS) is a minimum. Further, the R-squared between the response variable and the predictors is a maximum. The solution for these OLS coefficients is unique; that is, there is only one set of coefficients that minimizes the residuals. But what if we estimated coefficients that come within one percent (0.01) or less of the maximum value of R-squared. There can be multiple sets of coefficients that yield the same R-squared. These are the fungible regression coefficients (FRCs). How many different fungible regression coefficients are possible? What do these FRCs look like? Are these FRCs of any use whatsoever? This presentation will address these questions.

Suggested Citation

  • Phil Ender, 2024. "Fungible regression coefficients," 2024 Stata Conference 03, Stata Users Group.
    • Handle: RePEc:boc:usug24:03
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    File URL: http://repec.org/usug2024/US24_Ender.pdf
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    References listed on IDEAS

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    1. Niels Waller, 2008. "Fungible Weights in Multiple Regression," Psychometrika, Springer;The Psychometric Society, vol. 73(4), pages 691-703, December.
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