Fishing in the Bay

I recently came upon a piece in Slate Magazine by Steve Landsburg describing a very nice price of research. It concerns the financial costs to women of having childern. I thought this article (reproduced below the fold) might provide a nice class room example.

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Few statistics are more oft-quoted by empirical researchers than r-squared. While applauding the value of an intuitive interpretation in principle, it is pretty clear that the interpretation is wrong. Apart from honesty, the main reason I care about this is that it gets me into trouble with (the more discerning) students.

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Some of my colleagues recently ran in the Melbourne Half Marathon. The good folks who administer the event are good enough to provide an excel spreadsheet listing the finishing times of each competitor with their registered age and gender. You might find it useful in the classroom. There are some interesting patterns in the data but not for the reason you might first think.

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In class, I often explain standard error as the typical error or unreliability of an estimator. Standard deviation is typical deviation of the data around the mean. So I make it a point to interpret the word “standard” in the commonly understood sense. The problem is reconciling this with P-values. If an estimate differs from the null by one standard error then the one-sided P-value is 16% which is fairly unlikey in an informal sense, though not “significant” according to traditional choice of significance levels.

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We’re having a debate, at the University of Melbourne, as to whether or not we should be teaching the interpretation and usage of statistical tables in our service courses.  After all the whiz-bangery of graphics and inference and estimation, it always gives me a little frisson of exasperation when I put up this big slide of numbers, and two hundred eyes gently close.  And mine.
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As a software magpie (anything shiny attracts my attention) I often find myself trawling through obscure reaches of the web, looking for interesting things. I’m especially interested in different ways of organizing and communicating statistical thinking. With this in mind, I’ve been experimenting with some nice software for mapping arguments called Reason!Able. My goal was to see if I could represent all the necessary inferential steps for a simple statistical exercise - a one-way analysis of variance for a four-level factor. Such a representation might be a useful teaching tool, or help communicate statistical results to non-statisticians, and possibly even reveal some of the more obscure corners of how we statisticians think. It turned out to be a little more complicated than I had suspected …
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My title refers to the mechanical decision procedure adopted by the politically-correct animals in George Orwell’s “Animal Farm” for judging the merit of ideas. Similar thinking seems to be employed by New Zealand’s education bureaucrats and their preferred academics who have largely rewritten the assessment process for the country’s secondary schools along the lines of Standards-based Good, Norm-referenced Bad.

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As several commenters to Pt 1 pointed out, there are other spreadsheets besides Excel and I do not endorse Excel or discount the numerical problems with some of the built in routines. If I can find another spreadsheet program, such as Gnumeric or Open Office, which allows me to largely import all my add-ins, then I will change over tomorrow.

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For about 5 years now I have been teaching basic business statistics using Excel as the only computational platform. So I thought I would share with you the main reasons for doings so. Of course, one reason I used Excel is that it is the standard platform for business which will not be relevant to most of you. But at the very least, students can be sure that Excel will be sitting on their desktop for immediate use, even if they use a Mac. A second reason is that there are hundreds of custom built add-ins that are available both free and commercially. For instance I use a funky decision trees add-in called Lumenaut.

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As I mentioned in an earlier post, I used to teach at the University of Idaho. I built a graduate program around forest biometrics, which is the art and science of the application of statistical tools to quantitative problems in forestry. As I was designing this graduate program I had to decide what are the core competencies of forest biometricians, and, more broadly, of applied statisticians. There are obvious ones, like being able to perform a linear regression correctly, but what about more diverse skills?

For example, should applied statisticians be able to dismantle a computer? Should we be able to upgrade RAM, to extract and insert a hard drive, or change a video card?

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