Authors
Crawford, Sybil L.UMass Chan Affiliations
Department of Medicine, Division of Preventive and Behavioral MedicineDocument Type
Journal ArticlePublication Date
2006-11-07Keywords
Least-Squares AnalysisLinear Models
*Regression Analysis
Statistics
Life Sciences
Medicine and Health Sciences
Women's Studies
Metadata
Show full item recordAbstract
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.Source
Circulation. 2006 Nov 7;114(19):2083-8.Link to article on publisher's siteDOI
10.1161/CIRCULATIONAHA.105.586495Permanent Link to this Item
http://hdl.handle.net/20.500.14038/50858PubMed ID
17088476Related Resources
Link to article in PubMedae974a485f413a2113503eed53cd6c53
10.1161/CIRCULATIONAHA.105.586495