Correlation measures the strength and direction of relationship between two variables. Correlation can range from −1.00 to +1.00. A correlation of +1.00 indicates that changes in one variable are always matched by changes in the other; a correlation of −1.00 indicates that increases in one variable are matched by decreases in the other; and a correlation close to zero indicates little linear relationship between two variables.
In a simple linear regression y=ax+b, correlation coefficient (r) is close to -1, and the square of correlation coefficient is 0.65.
Is this a good linear regression equation for us in forecasting? Why?