Snappy Stepwise Regression

Stepwise regression, the technique that attempts to select a smaller subset of variables from a larger set by at each step choosing the ‘best’ or dropping the ‘worst’ was developed back in the late 1950’s by applied statisticians in the petroleum and automotive industries. With an ancestry like this, there’s no wonder that it is often regarded as the statistical version of the early 60’s Chev Corvair, at best only ‘driveable’ by expert careful users, or in Ralph Nader’s immortal words and title of his 1966 book  ‘Unsafe at Any Speed’.

Well maybe. But if used with cross-validation and good sense, it’s an old-tech standby to later model ‘lasso’ and ‘elastic net’ techniques. However, there’s an easy way for a bit of a softshoe shuffle of the old stepwise routine. See how well (preferably on a fresh set of data) forward entry with just one or, maybe two, or at most three variables do, compared with larger models. (SAS and SPSS allow the number of steps to be specified).

Of if you’d like to do some slightly fancier steps it in twotone spats, try a best subset regression (available in SAS, and SPSS through automatic linear, and Minitab and R etc), of all one variable combinations, two variables, three variables.

The inspiration for this is partly from Gerd Gigerenzer’s ‘take the best’ heuristic, taking the best cue or clue often beats more complex techniques including multiple regression etc. ‘Take the best’ is described in Prof Gigerenzer’s great new general book Risk Savvy: How to Make Good Decisions (Penguin, 2014),,9781846144745,00.html as well as his earlier academic books such as Simple Heuristics That Make Us Smart (Oxford University Press, 1999)

See if a good little model can do as well as a good (or bad) big ‘un!.


Further Future Reading

Draper NR, Smith H (1966) Applied regression analysis (and later editions). Wiley: New York.

John and Betty’s Journey into Statistics Packages*

In past days of our lives, those who wanted to learn a stats package, would attend courses, and bail up/bake cakes for statisticians, but would mainly raise the drawbridge, lock the computer lab door and settle down with the VT100 terminal or Apple II or IBM PC and a copy of the brown or update blue SPSS Manual, or whatever.

Nowadays, folks tend to look things up on the web, something of a mixed blessing, and so maybe software consultants will now say LIUOTFW (‘Look It Up On The Flipping Web’) rather than the late, great RYFM (‘Read Your Flipping Manual’).

And yes, there are some great websites, and great online documentation supplied by the software venders, but there are also some great books, available in electronic and print form. A list of three of the many wonderful texts available for each package (IBM SPSS, SAS, Stata, R and Minitab) can be downloaded from the Downloadables section on this site.

IBM SPSS (in particular), R (ever growing), and to a slightly lesser extent SAS, seem to have the best range of primers and introductory texts.
IMHO though, Stata could do with a new colourful, fun primer (not necessarily a Dummies Guide, although there’s Roberto Pedace’s Econometrics for Dummies (Wiley, New York, 2013) which features Stata), perhaps one by Andy Field, who has already done superb books on SPSS, R and SAS.

While up on the soapbox, I reckon Minitab could do with a new primer for Psychologists / Social Scientists, much like that early ripsnorter by Ray Watson, Pip Pattison and Sue Finch, Beginning Statistics for Psychology (Prentice Hall, Sydney, 1993).

Anyway, in memories of days gone by, brew a pot of coffee or tea, unplug email, turn off the phone and the mobile/cell, and settle in for an initial night’s journey, on a set or two of real and interesting data, with a good stats package book, or two!

*(The title of this post riffs off the improbably boring and stereotyped 1950’s early readers still used in Victorian primary (grade) schools in the 1960’s (think Dick and Jane, or Alice and Jerry), as well as the far more entertaining and recent John and Betty’s Journey into Complex Numbers by Matt Bower )