For the first time, I will be teaching a section of the course which is the pre-requisite for my spring advanced data analysis class. This is an introduction to linear regression modeling for our third-year undergrads, and others from related majors; my section is currently eighty students. Course materials, if you have some perverse desire to read them, will be posted on the class homepage twice a week.
This course is the first one in our undergraduate sequence where the students have to bring together probability, statistical theory, and analysis of actual data. I have mixed feelings about doing this through linear models. On the one hand, my experience of applied problems is that there are really very few situations where the "usual" linear model assumptions can be maintained in good conscience. On the other hand, I suspect it is usually easier to teach people the more general ideas if they've thoroughly learned a concrete special case first; and, perhaps more importantly, whatever the merits of (e.g.) Box-Cox transformations might actually be, it's the sort of thing people will expect statistics majors to know...
Addendum, later that night: I should have made it clear in the
first place that my syllabus is, up through the second exam,
off borrowed with gratitude
from Rebecca Nugent, who
401 outstandingly for many years.
Update, since people have asked for it, links here (see the course page for the source files for lectures):
Posted at August 31, 2015 13:52 | permanent link