o Loans made and intended to be sold by the originating lender or creditor to FNMA, GNMA, or FHLMC (or its successor); 7 or o Loans that are the subject of a home equity conversion mortgage or reverse mortgage issued by a lender or creditor subject to the regulation. 4
1 Draw the max line -- the one with as large a slope as you think reasonable (taking into account error bars), while still doing a fair job of representing all the data. Measure the slope of this line.  Calculate the uncertainty in the slope as one-half of the difference between max and min slopes.
Dividing the sum of squares of the residual (851.469) by its degrees of freedom (72) yields 11.826. That is the mean sum of squares. If you further take a square root, you'll get Root MSE (3.4289 in the output).Basically, it's a measurement of accuracy. The more accurate model would have less error, leading to a smaller error sum of squares, then MS, then Root MSE. Square them. 2  Add them up, this will give you the Error sum of squares, SS in Stata output. 3  Divide it by the error's degrees of freedom, this will give you the Mean error sum of squares, MS in Stata output. 4  Take a square root of it, and this is the Root MSE.