Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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A Systematic View on Residual Plots in Linear Regression

  • Myung-Wook (Associate Professor, Department of Statistics, Sookmyung Women's University) ;
  • YoungIl (Professor Department of Information System ChungAng University) ;
  • Chul H. (Associate Professor, Department of Applied Mathematics, Sejong University)
  • Published : 2000.04.01
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Abstract

We investigate some properties of commonly used residual plots in linear regression and provide some systematic insight into the relationships among the plots. We discuss three issues of linear regression in this stream of context. First of all we introduce two graphical comparison methods to display the variance inflation factor. Secondly we show that the role of a suppressor variable in linear regression can be checked graphiclly. Finally we show that several other types of standardized regression coefficients besides the ordinary one can be obtained in residual plots and the correlation coefficients of one of these residual plots can be used in ranking the relative importance of variables.

Keywords

References

  1. Plots, Transformations, and Regression Atkinson, A.C.
  2. Technometrics v.37 Seeing a curve in multiple regression Berk, K.N.;Booth, D.E.
  3. The American Statistician v.48 How to standardize regression coefficients Bring, J.
  4. Technometrics v.35 Exploring partial residual plots Cook, R.D.
  5. Residuals and influence in regression Cook, R.D.;Weisberg, S.
  6. Inftroduction to Regression Graphics Cook, R.D.;Weisberg, S.
  7. Applied Regression Analysis(3rd ed.) Draper, N.R.;Smith, H.
  8. Journal of the American Statistical Association v.19 A method for handing curvilinear correlation for any numbers of variables Ezekiel, M.
  9. Linear Models: An Introduction Guttmann, I.
  10. The American Statistician v.41 Sometimes R²>$γ^2_yx^1$+γ^2_yx^2$ Hamilton, D.
  11. Technometrics v.28 Augmented parial residual plots Mallows, C.L.
  12. Applied Linear Statistical Model(4th ed.) Neter, J.;Kutner, M.H.;Nachtsheim, C.J.;Wasserman, W.
  13. The American Statistician v.51 The relationship among sums of squares, correlation coefficients, and suppression Sharpe, N.R.;Roberts, R.A.
  14. Linear Statical Models Stapleton, J.H.
  15. The American Statistician v.49 Graphical interpretation of variance inflation factor Stine, R.A.
  16. Applied Linear Regression(2nd ed.) Weisberg, S.