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Correlation and regression
Women’s Health Research Faculty Publications
  • Sybil L. Crawford, University of Massachusetts Medical School
UMMS Affiliation
Department of Medicine, Division of Preventive and Behavioral Medicine
Publication Date
Document Type
Least-Squares Analysis; Linear Models; *Regression Analysis; Statistics

In many health-related studies, investigators wish to assess the strength of an association between 2 measured (continuous) variables. For example, the relation between high-sensitivity C-reactive protein (hs-CRP) and body mass index (BMI) may be of interest. Although BMI is often treated as a categorical variable, eg, underweight, normal, overweight, and obese, a noncategorized version is more detailed and thus may be more informative in terms of detecting associations. Correlation and regression are 2 relevant (and related) widely used approaches for determining the strength of an association between 2 variables. Correlation provides a unitless measure of association (usually linear), whereas regression provides a means of predicting one variable (dependent variable) from the other (predictor variable). This report summarizes correlation coefficients and least-squares regression, including intercept and slope coefficients.

DOI of Published Version
Circulation. 2006 Nov 7;114(19):2083-8.Link to article on publisher's site
Related Resources
Link to article in PubMed
PubMed ID
Citation Information
Sybil L. Crawford. "Correlation and regression" Vol. 114 Iss. 19 (2006) ISSN: 1524-4539 (Electronic)
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