Statistics 575: Multivariate Analysis
- Course information, assessment procedures,
- The textbook is Applied
Multivariate Statistical Analysis (6th edition), by Richard
A. Johnson and Dean W. Wichern. It has now been around for a while and
there are inexpensive copies available from a variety of sources - Google
notes. These are constantly under revision – both before and after
- It is assumed that
students have studied mathematics, and in particular linear algebra, to
the level of STAT 512.
- Academic integrity – see
the entry in course information, and in
particular see “Don’t
and using the R package:
- For a quick start you
can obtain R here and
install it on a PC by double-clicking on this .exe file and following the
instructions. There is a Mac version of R too; you'll need to consult the
'R home page' link below for this.
- What is R?
- The R home page
- R manuals: small, medium, big.
- Two primers (1, 2) on multivariate
analysis using R.
and code for R examples discussed in class: You should find it useful to
download and run these R scripts before the classes in which they are to
used in the text (all are here as a .zip file).
- Lecture 1: An introduction to the use
- Lectures 5, 6: Analysis of
a single mean.
- Lecture 7:
Paired samples and repeated measures examples.
- Lecture 8: Profile
- Lecture 9: One way MANOVA.
- Lecture 12: Multivariate
regression. See also this useful reference
to multivariate regression in R.
- Lectures 15: Principal components.
- Lectures 16, 17: Factor analysis;
- Lecture 19: Canonical
- Lectures 20,
21, 22: Discrimination; help.
In addition, a function to do logistic discrimination in more than two
populations is here, with an
example here and a
- Lectures 23,
24, 25: Clustering and correspondence analysis; clustering
scaling help and correspondence
analysis help. See also a paper describing the model-based
clustering method in R, and another describing the correspondence
- The importance of
clarity of exposition, and of grammatical correctness, in technical writing
cannot be over-emphasized. The best way to determine if you understand
what you are doing is to try to write it down in a form that another
reader can understand. Some comments along these lines are included with
the course information; for some helpful
resources see "Writing Aids" on my homepage.
Statistics resources at U of A:
Statistics Centre Home Page
of Doug Wiens
of Mathematical and Statistical Sciences Home Page