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Making Social Sciences More ScientificThe Need for Predictive Models$
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Rein Taagepera

Print publication date: 2008

Print ISBN-13: 9780199534661

Published to Oxford Scholarship Online: September 2008

DOI: 10.1093/acprof:oso/9780199534661.001.0001

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Why We Should Shift to Symmetric Regression

Why We Should Shift to Symmetric Regression

(p.154) 12 Why We Should Shift to Symmetric Regression
Making Social Sciences More Scientific

Rein Taagepera (Contributor Webpage)

Oxford University Press

When data are scattered, Ordinary Least-Squares (OLS) regression produces two quite distinct regression lines – one for y versus x and another for x versus y – and both may differ appreciably from what your eyes tell you. If data are scattered, OLS regression of y against x will disconfirm a model that actually fits; thus good statistics can be death of good science. Standard OLS equations cannot form a system of interlocking models, because they are unidirectional and nontransitive. Scale-independent symmetric regression avoids these problems of OLS, offering a single reversible and transitive equation.

Keywords:   interlocking models, Ordinary Least-Squares regression, reversible equations, scattered data, symmetric regression, transitive equations, unidirectional equations

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