Which term describes the measure of how well two factors vary together and predict each other?

The correct term for describing the measure of how well two factors vary together and predict each other is correlation. Correlation quantifies the relationship between two variables, indicating both the strength and direction of their association. A correlation close to +1 implies a strong positive relationship, where as one variable increases, the other also tends to increase. Conversely, a correlation close to -1 indicates a strong negative relationship, where as one variable increases, the other tends to decrease. A correlation of 0 suggests no relationship between the two variables.

This concept is essential in statistical analysis, as it helps researchers understand how two variables may influence one another, which is foundational for various analyses, including regression and other predictive modeling. The coefficient of determination, while related to this topic, specifically measures the proportion of variance in one variable that can be explained by the other and is derived from the square of the correlation coefficient. Causation implies a direct cause-and-effect relationship, which correlation does not establish. Statistical significance refers to the likelihood that a relationship observed in data is not due to random chance, rather than measuring how two factors predict each other.