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Orthogonal Regression and Equivariance [microform] / Eric Blankmeyer
Bib ID 5563576
Format BookBook, MicroformMicroform, OnlineOnline
Author
Blankmeyer, Eric
 
Online Versions
Description [Washington, D.C.] : Distributed by ERIC Clearinghouse, 1993 
12 p. 
Summary

Ordinary least-squares regression treats the variables asymmetrically, designating a dependent variable and one or more independent variables. When it is not obvious how to make this distinction, a researcher may prefer to use orthogonal regression, which treats the variables symmetrically. However, the usual procedure for orthogonal regression is not equivariant. A simple modification is proposed to overcome this serious defect. Illustrative computations involving 15 observations on 5 variables are provided, and a robust version of the method is discussed. The modified orthogonal regression allows a researcher to explore a symmetric, equivariant, and robust linear relationship among a set of variables. (Contains 6 references.) (Author/SLD)

Notes

May also be available online. Address as at 14/8/18: https://eric.ed.gov/

Reproduction Microfiche. [Washington D.C.]: ERIC Clearinghouse microfiches : positive. 
Subjects Equations (Mathematics)  |  Estimation (Mathematics)  |  Least Squares Statistics.  |  Mathematical Models.  |  Regression (Statistics)  |  Research Methodology.  |  Robustness (Statistics)  |  Equivariance Orthogonal Comparison
Form/genre Numerical/Quantitative Data.  |  Reports, Descriptive.
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