*Rein Taagepera*

- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780199534661
- eISBN:
- 9780191715921
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199534661.003.0005
- Subject:
- Political Science, Comparative Politics, Political Economy

Most physics equations include few variables and at most one freely adjustable constant, which multiply or divide. In contrast, regression equations favored in social sciences often have many ...
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Most physics equations include few variables and at most one freely adjustable constant, which multiply or divide. In contrast, regression equations favored in social sciences often have many variables in additive–subtractive strings, with plenty of freely adjustable constants/coefficients. Physics equations are reversible and transitive; standard regression equations are unidirectional and nontransitive. Physics rarely offers alternate equations for the same phenomenon, with a different set of input variables and constants; this is frequent in social science regression analysis. Physics equations are presented with prediction in mind, while tables of regression coefficients in social sciences reflect postdiction and often preclude even that.Less

Most physics equations include few variables and at most one freely adjustable constant, which multiply or divide. In contrast, regression equations favored in social sciences often have many variables in additive–subtractive strings, with plenty of freely adjustable constants/coefficients. Physics equations are reversible and transitive; standard regression equations are unidirectional and nontransitive. Physics rarely offers alternate equations for the same phenomenon, with a different set of input variables and constants; this is frequent in social science regression analysis. Physics equations are presented with prediction in mind, while tables of regression coefficients in social sciences reflect postdiction and often preclude even that.

*Rein Taagepera*

- Published in print:
- 2008
- Published Online:
- September 2008
- ISBN:
- 9780199534661
- eISBN:
- 9780191715921
- Item type:
- chapter

- Publisher:
- Oxford University Press
- DOI:
- 10.1093/acprof:oso/9780199534661.003.0012
- Subject:
- Political Science, Comparative Politics, Political Economy

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 ...
More

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.Less

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.