Extended Correlation
| Autor: | Joel H. Levine |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
060101 anthropology
Proper linear model Sociology and Political Science 05 social sciences Regression analysis 06 humanities and the arts Generalized least squares 050905 science studies Joint probability distribution Linear predictor function Statistics Econometrics 0601 history and archaeology 0509 other social sciences Simple linear regression Regression diagnostic Social Sciences (miscellaneous) Partial correlation Mathematics |
| Zdroj: | Sociological Methods & Research. 34:31-75 |
| ISSN: | 1552-8294 0049-1241 |
| DOI: | 10.1177/0049124104267344 |
| Popis: | What is the correlation between two variables? Traditional answers offer summary assessments such as Pearson’s r and regression coefficients. But new computing techniques make it possible to construct conceptually simple hypotheses that describe the full joint distribution of two variables, making it possible to “mine” the correlation for information that was previously unused. This article begins with evidence of systematic anomalies in the empirical joint distribution of height-weight data and follows with a hypothesis that explains these anomalies in terms of a theoretical joint distribution relative to a linear equation. The hypothesis has serious consequences because even in traditional examples, while it offers an improved fit to the data, its estimates of the linear center do not correspond to traditional least squares estimates of the linear relation for the same two variables. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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