Which statistic measures the strength of association or relationship between two variables and includes Pearson's correlation coefficient, Spearman's rho, and Kendall's tau?

Which statistic measures the strength of association or relationship between two variables and includes Pearson's correlation coefficient, Spearman's rho, and Kendall's tau?

• A. Regression coefficient
• B. Spearman's rho
• C. Correlation coefficient
• D. Covariance

Answer: (C)

A correlation coefficient measures the strength and direction of the association between two variables, and it includes Pearson's correlation coefficient, Spearman's rho, and Kendall's tau. Pearson's r quantifies the linear relationship between two continuous variables and ranges from -1 to 1. Spearman's rho and Kendall's tau are rank-based measures that assess monotonic relationships, remaining robust to non-normal data and outliers. All of these share the core idea of a standardized measure of association. Covariance, while related, depends on the units of the variables and is not standardized, making it harder to interpret as a universal strength indicator. A regression coefficient, on the other hand, represents the slope in a specific predictive model and is not a symmetric measure of association between the two variables.

A correlation coefficient measures the strength and direction of the association between two variables, and it includes Pearson's correlation coefficient, Spearman's rho, and Kendall's tau. Pearson's r quantifies the linear relationship between two continuous variables and ranges from -1 to 1. Spearman's rho and Kendall's tau are rank-based measures that assess monotonic relationships, remaining robust to non-normal data and outliers. All of these share the core idea of a standardized measure of association. Covariance, while related, depends on the units of the variables and is not standardized, making it harder to interpret as a universal strength indicator. A regression coefficient, on the other hand, represents the slope in a specific predictive model and is not a symmetric measure of association between the two variables.