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]]

