Whenever there is ANY spread about the linear regression line, for any above , the values will increase by less than the increase in .
For example, for one standard deviation above , the corresponding predicted value (y-hat) will be less than one standard deviation above .
Similarly, for one standard deviation less than , the corresponding predicted (y-hat) will decrease by less than one standard deviation below .
This is called the regression effect.
It happens because part of the unexplained variation in is explained by the change of (the regression).
The regression line is ALWAYS flatter than the SD-line. (Both lines pass through the point of averages, .