Which of the following is NOT a method to measure covariation?

• A. Regression
• B. Variance
• C. Covariance
• D. Correlation

Answer: (B)

Covariation describes how two traits vary together across individuals. Covariance directly captures this joint variability by looking at how deviations from each trait’s mean occur together; if both traits tend to be above their means at the same time, covariance is positive, and if one tends to be above its mean while the other is below, it’s negative. Correlation is just a standardized version of covariance, giving a unitless measure of the strength and direction of the linear relationship between the traits. Regression uses covariation to predict one trait from the other, with the slope reflecting how much one trait changes as the other changes.

Variance, on the other hand, measures dispersion of a single variable around its own mean. It does not involve a second variable, so it does not describe how two traits co-vary. That’s why variance is not a method to measure covariation.