Biostatistics Series Module 6: Correlation and Linear
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Biostatistics Series Module 6: Correlation and Linear
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Biostatistics Series Module 6: Correlation and Linear Regression Avijit Hazra, Nithya Gogtay1
Abstract Correlation and linear regression are the most commonly used techniques for quantifying the association between two numeric variables. Correlation quantifies the strength of the linear relationship between paired variables, expressing this as a correlation coefficient. If both variables x and y are normally distributed, we calculate Pearson’s correlation coefficient (r). If normality assumption is not met for one or both variables in a correlation analysis, a rank correlation coefficient, such as Spearman’s rho (ρ) may be calculated. A hypothesis test of correlation tests whether the linear relationship between the two variables holds in the underlying population, in which case it returns a P 0.7 may be regarded as “strong” correlation, values between 0.50 and 0.70 may be interpreted as “good” correlation, between 0.3 and 0.5 may be treated as “fair” or “moderate” correlation, and any value
