Which term refers to how strongly two variables move together?

Which term refers to how strongly two variables move together?

• A. Correlation
• B. Causation
• C. Regression
• D. Covariance

Answer: (A)

The term that refers to how strongly two variables move together is correlation. Correlation quantifies the degree to which two variables are related, indicating whether they increase or decrease in tandem. A strong correlation means that as one variable changes, the other variable tends to change in a predictable pattern. This relationship can be positive (both variables increase together) or negative (one variable increases while the other decreases). Causation, while closely related in discussion to correlation, refers to a scenario where one variable directly affects another, rather than simply indicating they move together. Regression deals with predicting one variable based on another and assessing the relationship between them, but it is a broader statistical analysis rather than a direct measure of the strength of the joint movement. Covariance measures the extent to which two variables change together, but it does not provide a standardized measure like correlation does, which is often more useful for comparing the strength of relationships across different datasets.

The term that refers to how strongly two variables move together is correlation. Correlation quantifies the degree to which two variables are related, indicating whether they increase or decrease in tandem. A strong correlation means that as one variable changes, the other variable tends to change in a predictable pattern. This relationship can be positive (both variables increase together) or negative (one variable increases while the other decreases).

Causation, while closely related in discussion to correlation, refers to a scenario where one variable directly affects another, rather than simply indicating they move together. Regression deals with predicting one variable based on another and assessing the relationship between them, but it is a broader statistical analysis rather than a direct measure of the strength of the joint movement. Covariance measures the extent to which two variables change together, but it does not provide a standardized measure like correlation does, which is often more useful for comparing the strength of relationships across different datasets.