SER and MSE

In the regressions given in the theoretical exercises we are using the standard error of the regression (SER), while Stata provides us root mean squared error (rMSE). To clarifly the relationship:

rMSE= sqrt(MSE)=sqrt(SER).

To provide rMSE you take sqrt(MSR) which you find in Your regression output. MSR is the mean squared residuals, which is SSR/degrees of fredom (df).

Note: Stata uses e for error (not explained as in the book) thus you can think of MSE as MSR, mean squared residuals.

This also means that:

SS model = ESS in textbook, explained sum of squares
 

Hope this clarifies any confusion between the language in the book and stata's language. Let me know if there are other sources of confusion.

Siv-Elisabeth