r is the **correlation coefficient** and will be a number between -1 and +1. The sign of r tells you whether the regression line is decreasing (r is negative) or increasing (r is positive). If r is close to +1 or -1, then the linear relationship between the two variables is strong. If r is close to 0, then the linear relationship is weak.

r^2 is the **coefficient of determination** and will be a number between 0 and 1. Unlike r, you can not tell just from r^2 whether the slope of the regression line will be positive or negative. You can however interpret r^2 as the percentage of variation in the y-values that is explained by the linear relationship with the x-values. For example, if r^2 = 0.81 then 81% of the variation in the y-values is explained by the linear relationship with the x-values (i.e. the straight-line model with one independent variable is explaining most of what is going on).