Geometric characterization of planar regression |
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Authors: | Brian J. McCartin |
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Affiliation: | Applied Mathematics , Kettering University , 1700 West Third Avenue, Flint, MI, 48504-4898, USA |
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Abstract: | The geometric characterization of linear regression in terms of the ‘concentration ellipse’ by Galton [Galton, F., 1886, Family likeness in stature (with Appendix by Dickson, J.D.H.). Proceedings of the Royal Society of London, 40, 42–73.] and Pearson [Pearson, K., 1901, On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2, 559–572.] was extended to the case of unequal variances of the presumably uncorrelated errors in the experimental data [McCartin, B.J., 2003, A geometric characterization of linear regression. Statistics, 37(2), 101–117.]. In this paper, this geometric characterization is further extended to planar (and also linear) regression in three dimensions where a beautiful interpretation in terms of the concentration ellipsoid is developed. |
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Keywords: | Orthogonal regression Generalized least squares Total least squares |
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