The correlation coefficient measures what?

The correlation coefficient measures what?

• A. Linear correlation between two variables, ranging from -1 to +1
• B. Nonlinear relationship strength
• C. Sample size
• D. Causation

Answer: (A)

The correlation coefficient measures the strength and direction of a linear relationship between two variables. It yields a value between -1 and +1. A value close to +1 means a strong positive linear association: as one variable increases, the other tends to increase in a proportional way. A value close to -1 indicates a strong negative linear association: as one variable increases, the other tends to decrease. A value near 0 suggests little or no straight-line relationship. It's important to recognize that this statistic specifically captures linear patterns. A strong nonlinear relationship can exist even if the correlation is near zero, because the measure is about straight-line fit, not any curve. Also, the correlation coefficient does not convey information about how many data points were used (sample size), and it does not imply causation—two variables can be correlated without one causing the other.

The correlation coefficient measures the strength and direction of a linear relationship between two variables. It yields a value between -1 and +1. A value close to +1 means a strong positive linear association: as one variable increases, the other tends to increase in a proportional way. A value close to -1 indicates a strong negative linear association: as one variable increases, the other tends to decrease. A value near 0 suggests little or no straight-line relationship.

It's important to recognize that this statistic specifically captures linear patterns. A strong nonlinear relationship can exist even if the correlation is near zero, because the measure is about straight-line fit, not any curve. Also, the correlation coefficient does not convey information about how many data points were used (sample size), and it does not imply causation—two variables can be correlated without one causing the other.