Which statement best describes Pearson's correlation coefficient?

Which statement best describes Pearson's correlation coefficient?

• A. A nonparametric measure of association.
• B. The standardized measure of the strength and direction of the linear relationship between two variables.
• C. A measure of the proportional variance explained by a predictor.
• D. A test for normality of two variables.

Answer: (B)

Pearson's correlation coefficient measures the strength and direction of the linear relationship between two continuous variables, and it does so in a standardized, unitless way. The value ranges from -1 to 1, with values near 1 indicating a strong positive linear relationship, near -1 indicating a strong negative linear relationship, and around 0 suggesting little or no linear association. Being standardized means the measure is not affected by the units of the variables. It specifically captures linear relationships, so nonlinear patterns may yield a low coefficient even if a relationship exists. It is a parametric statistic, and it does not represent the proportion of variance explained (that’s R^2), nor is it a test for normality.

Pearson's correlation coefficient measures the strength and direction of the linear relationship between two continuous variables, and it does so in a standardized, unitless way. The value ranges from -1 to 1, with values near 1 indicating a strong positive linear relationship, near -1 indicating a strong negative linear relationship, and around 0 suggesting little or no linear association. Being standardized means the measure is not affected by the units of the variables. It specifically captures linear relationships, so nonlinear patterns may yield a low coefficient even if a relationship exists. It is a parametric statistic, and it does not represent the proportion of variance explained (that’s R^2), nor is it a test for normality.