What is a measure that indicates the strength of dependence between two variables, producing a value between +1 and -1?

The Pearson correlation coefficient is a statistical measure that indicates the strength and direction of a linear relationship between two continuous variables. It produces a value between -1 and +1, where a value of +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship at all. This coefficient is widely used in data analysis to assess how closely two variables move in relation to each other, providing insights that are crucial for predictive modeling and understanding relationships in data.

In contrast, the regression coefficient measures the change in the dependent variable for a unit change in the independent variable but does not necessarily indicate correlation strength on a standardized scale. The standard error is a measure that indicates the accuracy with which a sample represents a population, and variance is a measure of the dispersion of a set of values, indicating how spread out the data is. These concepts, while important in statistics, do not specifically capture the strength of dependence between two variables in the same way the Pearson correlation coefficient does.