Can someone please explain the difference between SSE and SEE as they both seem to be measuring the unexplained variation between predicted Y and actual Y in a regression equation. Thanks.

I have not reviwed it since i read it but as i recall, SSE is the sum of squared errors while SEE is the Sq Root of MSE. The point is that like R^2 it is a measure of fit since its the st dev of the unexplained errors. As a result, unlike R^2 you want a low SEE since that indicates a small amount of unexplained error

SEE=square(SSE/n-k-1))

you mean to say the square root rather than the square SSE is already squared units. Thats why its called sum of squared errors

Thank you Alladin! This was driving me insane, great explanation by David Harper. I will check out some of his other tutorials.

- Mike

Hey Mike,glad you found it useful, the bionicturtule on youtube is a great resource in my opinion!

One day you’ll all look back and say, oh yeah I studied that but I don’t remember what it was

5 mins after the exam i forget what it was all about