Correlation and Regression

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.