# while calculating the slope of a regression line, why do we multiply the slope by the correlation coefficient when it can only decrease the steepness?

Given that that correlation coefficient can only be between -1 and 1, it can never increase the steepness of the slope when calculating the steepness of a slope in a regression line.

Say correlation coefficient r = 0.946, standard deviation along the x-axis Sx = 0.816, and standard deviation along y-axis is Yx = 2.160 (values same as in reference Khan Academy video to follow along with example) then slope m = r(Sx/Sy). Here Sx/Sy will be the slope if the correlation coefficient is 1 and all the data plots match perfectly to the line. When we multiply it by r=0.946 however, the steepness of the slope decreases. My question is, how do we know that the only possibility here is where the slope decreases and not increases? The correlation coefficient only indicates how accurate the data plot is compared with the regression line, it says nothing about in which direction (more steep or less steep) the inaccuracy points to. So why are we multiply r with Sx/Sy when we know a r with a absolute value of less than 1 can only decrease the steepness of the slope but never increase it?

Edit: This is how Sal Khan explains the concept in his lesson video, that the slope decreases after multiplying by the correlation coefficient.

• Trying to self-study statistics. Sorry guys if my question is a bit confusing. May 29 at 11:28
• There is a story to tell here about regression to the mean... May 30 at 10:29
• If you can provide another answer, that would be great! I did checkmark the answer but I still don't completely understand this. May 30 at 22:57