Course Homepage: Fall 2008 (Past Pages: Fall 2005)

Usually Offered: Alternating Fall Semesters

Purpose: To introduce students with a variety of statistical backgrounds to the basic ideas in multivariate statistics. It will cover the assumptions, limitations, and uses of basic techniques such as cluster analysis, principal components analysis, and factor analysis as well as how to implement these methods in R, SAS, and SPSS. Instead of theoretical development, the focus will be on the intuitive understanding and applications of these methods to real data sets by the students.

Current Textbook: An R and S-PLUS Companion to Multivariate Analysis by Brian Everitt, Springer 2005.

 

Topics Covered	Chapters	Time
Introduction to Multivariate Data, R, SAS, and SPSS	1	2.5 weeks
Multivarate Graphical Displays	2	1.5 weeks
Principal Components	3	1.5 weeks
Factor Analysis	4	2 weeks
Multidimensional Scaling	5	1 week
Cluster Analysis	6	1 week
MANOVA and Discriminant Analysis	7	2 weeks
Canonical Correlation Analysis	8	1 week
Survey of Other Methods	-	1 week

The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.  

Contact Faculty: Brian Habing, Don Edwards
(Last Updated: June 9th, 2005)