The High Price of Number Crunching

I do a lot of work in statistics. In fact, almost all of my work involves statistics. I don’t collect data in the field. I don’t do things in a lab. I sit in front of my computer working with data sets and spreadsheets. Lately I’ve been doing a lot of online research for a good Mac statistical package. Right now I use a combination of SPSS and Excel.

SPSS seems like the perfect thing for me (it stands for Statistical Package for the Social Sciences). But it’s, in a word, bad. It’s just bad. I have version 11, which ironically works on Intel while version 13 does not. Still, it follows archaic command line structure under its GUI. Variables can have a name eight letters long or less. No numbers or symbols. What? You can assign labels to variables but that’s killing one bird with two stones. And that’s gross.

Excel… well Excel is easier to use (which says more about SPSS than Excel) but there are several published articles on how you shouldn’t use Excel due to its inaccuracy.

In the statistical package market there are a few alternatives. Aabel and Stata are both up-to-date to work with Intel Macs. The problem, they’re around $500 each, and that’s the student price (it’s almost as expensive as.. a PS3!). If you’re just some guy who wants to do statistics, it’s $1500. Additional feature packages are $300 or so each. Why’re they so expensive? I guess they’re trying to sell to people with grants, but you’d think they would want to aim low and get students to like it first, then sell it to them again when they’re professors with labs.

Open source to the rescue! Kind of. R is free, but it’s strictly command line. And it’s hard. The manuals and tutorials aren’t very good. Lesson 2: Add and subtract. Lesson 3: Multiple regression! I got lost somewhere in there. Also, trying to find outside advice for using R is hard in itself because it’s hard to Google. The internet has a lot of things, and the letter R is one of them.

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