Question

Regression and Correlation are two of the most often used and abused tools in research. People are quick to jump to conclusions that if a relationship or correlation exists between two variables, then one must cause (causation) the other. There are many reasons why two variables can be related without causality. Please respond to the following:

1. Comparing the amount of money people spend and the amount people save, your analysis revealed an R-squared = 0.97. Should you use this for predictive purposes and WHY?
2. Comparing the number of cops on our streets and the number of reported crimes, your analysis revealed an R-squared = 0.40. Should you use this for predictive purposes and WHY?
3. How could this apply in your profession?

Answer 1

(1) Yes, I use this for predictive purposes because the R-squared value is high.

(2) No, I do not use this for predictive purposes because the R-squared value is low.

(3) Use Regression analysis.

The most popular use of regression analysis is on investment returns, where the market index is independent while the individual security or mutual fund is dependent on the market. In essence, regression analysis formulates a hypothesis that the movement in one variable (Y) depends on the movement in the other (X).