RMA_Model_doc

# RMA_Model_doc - Donald G. Hill, Ph.D. Consulting...

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Donald G. Hill, Ph.D. Consulting Petrophysicist The World is My District California Registered Geophysicist 170 California Registered Geologist 6043 Kentucky Registered Professional Geologist 1624 Texas Licensed Professional Geophysicist 6289 Petrophysics, Borehole Geophysics, and Subsurface Geology Planning, Oversight, and Interpretation 1012 Hillendale Ct. Office: (510) 450-6102 Walnut Creek, CA 94596 FAX: (781) 240-6208 e-mail: [email protected] Home: (925) 947-1541 http://www.hillpetro.com Cell: (925) 437-5748 DRAFT DRAFT - - Memorandum Memorandum Subject: RMA Model Fitting Date: January 10, 2005 ————————————————————————————————————————————————————————— ABSTRACT In contrast to Least Squares regression (LSQ), which minimize errors in only one variable, of a bi-variate distributions, Reduced Major Axis (RMA) models simultaneously reduce errors in both variables of a bi-variate data distributions. Like LSQ regression, RMA models can be used to fit data distributions which are linear in semi-logarithmic and bi-logarithmic space. LEAST SQUARE REGRESSION, MAJOR AXIS MODELS, AND REDUCED MAJOR AXIS MODELS Most engineering students have encountered the data fitting technique known as "least squares" (LSQ) regression. This approach is appropriate if all parameters of a relationship, except one, are well known. In the case of the two variable, linear model: y = mx + b 1 ) where: y is the unknown (dependent, or measured) variable, x is the known (independent, or known) variable. m is the slope of the linear model Regression line.

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## This note was uploaded on 02/27/2008 for the course PTE 461 taught by Professor Donhill during the Fall '07 term at USC.

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RMA_Model_doc - Donald G. Hill, Ph.D. Consulting...

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