Welcome
Welcome to my STAT 535 web site, and to the course.
STAT 535 is being offered for the third time this fall. This course was
created to fill a need for a good course in the analysis of experimental
data for students lacking the time and background necessary for STAT 554
(Applied Statistics) and STAT 656 (Regression Analysis). The course was
designed after consulting with GMU faculty in the fields of biology and enviromental
science. Also, in my many years at GMU, I've helped a lot of graduate
students with their data anlysis for their theses and dissertations, and
so I have a pretty good idea about which statistical methods graduate students
in the sciences frequently use. (I've also witnessed a lot of poor data
analysis (often due to having to use data collected from a poorly
designed experiment), and so I hope to help you prevent from making the
mistakes that others have made.)
The heart of the course will be material on ANOVA (ANalysis Of VAriance)
and regression, since these are important data analysis
techniques that are
typically not covered well (or covered at all)
in one semester undergraduate statistics courses.
But because I'm sure that not all of you have covered the same material
in your introductory statistics course (the prerequisite for STAT 535),
and I fear that many of you have forgotten what you used to know about
statisitcs, about half of the course will cover a lot of the same
material that is typically presented in introductory courses (but
covered much more quickly, and with some advanced topics inserted).
I plan to cover the vast majority of the material in one rather
elementary
book, and also cover some material in a more advanced book (and insert
other advanced material from a variety of sources throughout the
semester), so that the important advanced material will be given decent
coverage, while at the same time building upon a solid foundation of
more elementary material. My lectures will complement the material from
the books, and I'll post a lot of comments about the assigned reading on
this course web site. My belief is that if you do the (great amount of)
assigned reading, and I can help you to understand what you've read,
then you'll be able to leave this course having learned a lot.
(See the
syllabus for the books and software
to be used, and for what will be covered each week (although since this
is just the third offering of this course, the schedule is tentative --- I'm still making adjustments to try to
better balance the coverage of the various topics).)
Please get into the habit of checking this web site regularly.
(By going to the
Announcements page you can keep
track of what I add and what changes I make throughout the web site.)
For now, go over the
syllabus
very carefully in order to get a
good understanding about how the course will be run.
It would be good if you read the material in Ch. 1 and Ch. 3 (at least
up through p. 92) prior to the first lecture.