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Biplots of Multivariate Data Guided by Linear and/or Logistic Regression
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 Title & Authors
Biplots of Multivariate Data Guided by Linear and/or Logistic Regression
Huh, Myung-Hoe; Lee, Yonggoo;
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Linear regression is the most basic statistical model for exploring the relationship between a numerical response variable and several explanatory variables. Logistic regression secures the role of linear regression for the dichotomous response variable. In this paper, we propose a biplot-type display of the multivariate data guided by the linear regression and/or the logistic regression. The figures show the directional flow of the response variable as well as the interrelationship of explanatory variables.
Data visualization;biplot graph;linear regression;logistic regression;dimensional reduction;
 Cited by
SVM-Guided Biplot of Observations and Variables,Huh, Myung-Hoe;

Communications for Statistical Applications and Methods, 2013. vol.20. 6, pp.491-498 crossref(new window)
Global and Local Views of the Hilbert Space Associated to Gaussian Kernel,Huh, Myung-Hoe;

Communications for Statistical Applications and Methods, 2014. vol.21. 4, pp.317-325 crossref(new window)
Brownlee, K. A. (1965). Statistical Theory and Methodology in Science and Engineering, Second Edition, Wiley, New York.

Gabriel, K. R. (1971). The biplot display of matrices with the application to principal component analysis, Biometrika, 58, 453-467. crossref(new window)

Gower, J. C. and Hand, D. J. (1996). Biplots, Chapman and Hall, London.

Greenacre, M. (2010). Biplots in Practice, BBVA Foundation, Madrid.

Huh, M. H. (2011a). Exploratory Multivariate Data Analysis, Freedom Academy, Seoul.

Huh, M. H. (2011b). Statistical Concepts, Methods and Applications Using R, Freedom Academy, Seoul.

Huh, M. H. and Park, H. M. (2009). Visualizing SVM classification in reduced dimensions, Communications of the Korean Statistical Society, 16, 881-889. crossref(new window)

Lebart, L., Morineau, A. and Warwick, K. M. (1984). Multivariate Descriptive Statistical Analysis: Correspondence Analysis and Related Techniques for Large Matrices, Wiley, New York.

SAS Inc. (2009). SAS/STAT V9.2 Users Guide, Second Edition. NC: Cary.