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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

Chapter:
(p.154) 12 Why We Should Shift to Symmetric Regression
Source:
Making Social Sciences More Scientific
Author(s):

Rein Taagepera (Contributor Webpage)

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199534661.003.0012

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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