530—Applied Multivariate Statistics and Data Mining (3) (Prereq: STAT 515 or PSYC 228 or MGSC 391 or equivalent) Introduction to fundamental ideas in multivariate statistics using case studies. Descriptive, exploratory, and graphical techniques; introduction to cluster analysis, principal components, factor analysis, discriminant analysis, Hotelling's T²and other methods.

Sample Course Homepage: Recent Semester, Another Recent Semester

Usually Offered: Fall Even Years

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/or 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
Multivariate 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,  David Hitchcock