I've been teaching an intro stats class to engineering students (who are better in calculus and math than med students, I would imagine), and use of R has never been received very warmly. I might not be teaching it right, but their (quite valid, from their standpoint) concerns were that they would have to learn a tool that they will never use (so they might have been better off with statistics toolbox from Matlab say, as they use the latter in their DiffEq, Circuits and other classes), and that did not get enough credit points for doing those (and indeed I was suggesting using R as an extra credit, essentially as a bypass so as not to use the tables in the end of the book). With health sciences people, I would expect they would want to learn the tool that they would use for life -- at least that's my impression with the applied researchers that I've interacted with: their computer literacy is often limited to a small number of software titles, but they know each of them quite well. R might be just too dynamic for them. Again, it's not terribly clear whether they will use it at all if that's the only statistics class they take for breadth requirement. If anything, I would expect SAS and Stata to be more widely used in biostatistics, so teaching any of those might be of greater service and use to your students. Training researchers of tomorrow might be great, but ifyour students get on the market in the end of the semester, they won't have the luxury of waiting until R becomes THE package of choice.
-- Stas Kolenikov, also found at http://stas.kolenikov.name Small print: Please do not reply to my Gmail address as I don't check it regularly. [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
