Correlation and regression
Department of Medicine, Division of Preventive and Behavioral Medicine
Least-Squares Analysis; Linear Models; *Regression Analysis; Statistics
Life Sciences | Medicine and Health Sciences | Women's Studies
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.
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Citation: Circulation. 2006 Nov 7;114(19):2083-8.Link to article on publisher's site
Crawford, Sybil L., "Correlation and regression" (2006). Women’s Health Research Faculty Publications. 39.