Most books that discuss regression modeling start out and often finish with Ordinary Least Squares (OLS) as the technique to use; Generalized
Least Squares (GL S) sometimes get a mention near the back. This is all well and good if the readers’ data has the characteristics required for OLS to be an applicable technique. A lot of data in the social sciences has these characteristics, or so I’m told; lots of statistics books are written for social science students, as a visit to a bookshop will confirm.
Software engineering datasets often range over several orders of magnitude or involve low value count data, not the kind of data that is ideally suited for analysis using OLS. For this kind of data GL
S is probably the correct technique to use (the difference in the curves fitted by both techniques is often small enough to be ignored for many practical problems, but the confidence bounds and p-values often differ in important ways).
The target audience for my book, Empirical Software Engineering with R, are working software developers who have better things to do that learn lots of statistics. However, there is no getting away from the fact that I am going to have to make extensive use of GL
S, which means having to teach readers about the differences between OLS and GL S and under what circumstances OLS is applicable. What a pain.
Then I had a brainwave, or a moment of madness (time will tell). Why bother covering OLS? Why not tell readers to always use GL
S, or rather use the R function that implements it,
glm. The default
glm behavior is equivalent to
lm (the R function that implements OLS); the calculation is not being done by hand but by a computer (i.e., who cares if it is more complicated).
Perhaps there is an easy way to explain this to software developers:
glm is the generic template that can handle everything and
lm is a specialized template that is tuned to handle certain kinds of data (the exact technical term will need tweaking for different languages).
There is one user interface issue, models built using
glm do not come with an easy to understand goodness of fit number (
lm has the R-squared value). AIC is good for comparing models but as a single (unbounded) number it is not that helpful for the uninitiated. Will the demand for R-squared be such that I will be forced for tell readers about
lm? We will see.
How do I explain the fact that so many statistics books concentrate on OLS and often don’t mention GL
S? Hey, they are for social scientists, software engineering data requires more sophisticated techniques. I will have to be careful with this answer as it plays on software engineers’ somewhat jaded views of social scientists (some of whom have made very major contribution to CRAN).
All the software engineering data I have seen is small enough that the performance difference between
lm is not a problem. If performance is a real issue then readers will search the net and find out about
lm; sorry guys but I want to minimise what the majority of readers need to know.