Stat 206: Multivariate Analysis


Multivariate analysis is about handling vector valued data. In ordinary regression modeling we are used to a vector valued predictor. But a vector valued response variable brings new issues. Sometimes we can handle a k dimensional response by treating it as k unrelated 1 dimensional problems. But often that approach will fail to find the key structure. Sometimes we are forced to study the data as an inherently k dimensional thing. It can also pay to reduce the dimension k, sometimes to 3 or 2 where plotting is available, sometimes to k=1 where ordinary methods can then be applied. Also, some of the methods are useful for exploratory work and not just for modelling responses.

This course looks at multivariate methods. The outlook is applied. There will be a midterm and problem sets. Students are expected to use R to do the problem sets.


Art Owen
Sequoia Hall 130
My userid is owenbuzzard on (remember to remove the buzzard s)
Office hour: Mon 2:15


Hewlett 101, MWF 1:15-2:05, starting Wed Jan 11



The main text is "Multivariate Statistical Methods: a Primer" by B.F.J. Manly. The supplementary text is "Modern Applied Statistics with S" by Venables and Ripley. That book explains how to use R. If you already know how to use R you don't need to buy it.



Be sure to give Axess a working email address:
I expect to send a small number of important emails about problem sets and the homework there. Most other announcements will be made in class.
Late penalties apply:
We will count days late on each problem set. Each day late is penalized by 10% of the homework value. Homework more than 3 days late will ordinarily get 0. If you're travelling, you can email a pdf file. For sickness, interviews and other events, up to 3 late days total are forgiven at the end of the quarter. (Work late enough to get zero does not get redeemed though.)

Supplementary materials

Problems (password given in class)