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Linear Regression Diagnostics

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  • Roy E. Welsch
  • Edwin Kuh

Abstract

This paper attempts to provide the user of linear multiple regression with a battery of diagnostic tools to determine which, if any, data points have high leverage or influence on the estimation process and how these possibly discrepant data points differ from the patterns set by the majority of the data. The point of view taken is that when diagnostics indicate the presence of anomolous data, the choice is open as to whether these data are in fact unusual and helpful, or possibly harmful and thus in need of modifications or deletion. The methodology developed depends on differences, derivatives, and decompositions of basic regression statistics. There is also a discussion of how these techniques can be used with robust and ridge estimators. An example is given showing the use of diagnostic methods in the estimation of a cross-country savings rate model.

Suggested Citation

  • Roy E. Welsch & Edwin Kuh, 1977. "Linear Regression Diagnostics," NBER Working Papers 0173, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:0173
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    References listed on IDEAS

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    1. David A. Belsley, 1976. "Multicollinearity: Diagnosing its Presence and Assessing the Potential Damage It Causes Least Squares Estimation," NBER Working Papers 0154, National Bureau of Economic Research, Inc.
    2. Fisher, Franklin M, 1970. "Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note," Econometrica, Econometric Society, vol. 38(2), pages 361-366, March.
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