Statistics 501 - Multivariate Statistical Methods

Statistics 501 - Multivariate Statistical Methods


This year I am experimenting with WebCT to deliver the course.


The final exam is Wednesday May 7 2:15-4:15pm Howe 1304.


Syllabus | Introductory Notes
Data Sets To download xml format files to your computer: with netscape by holding the shift key down and left-clicking the mouse, with internet explorer with the right mouse button. A manual for S-Plus can be found here.
Course description: In this course students will learn about
statistical methods for data that contains more than one
variable. Topics include dimension reduction using principal
components analysis and factor analysis, case reduction using cluster
analysis, supervised classification with neural networks and trees.
Inferential methods based on the assumption of multivariate normally
distributed population will be discussed. Graphics will be heavily
utilized in all topics.

Schedule of topics:

Introduction 
Graphics 
Multivariate Normal 
Inference for the population mean 
Handling Missing Values 
Comparing several population means 
Principal Component Analysis 
Supervised Classification 
Unsupervised Classification 
Factor Analysis 
Multidimensional scaling
Canonical Correlation

Course Goals: The objectives of the course are to help students:
1. grasp the concepts and develop critical thinking in multivariate
statistical analysis. 2. learn about multivariate problems. 3. compute
analyses using standard statistical software. 4. learn sufficient
vocabulary to read further about new methodology. 5. apply the
methodology to new problems.

Course date: Jan 13, 2002 through May 2, 2002 

Location: Howe 1304

Meeting days: MWF 1:10-2:00pm

Textbook: Applied Multivariate Statistical Analysis, 
          Johnson and Wichern, 2001 (5th ed)

Instructor Information:
                 Dr Dianne Cook 
                 dicook@iastate.edu 
                 325 Snedecor Hall 
                 515 294 8865 
                 Office hours MWF 9:30-10:30am or by appointment 

Teaching Assistant: Wuyan Zhang, Office hours: Tues 2:00-3:00,
 Snedecor 305

Computing: Software used will be S-Plus and GGobi on PCs. Information
on using each of the packages for particular exercises and sample code
will be provided by the instructor. The packages have online
help. GGobi is free software for multivariate graphics. S-Plus
provides student copies at discount prices but there is also a free
version called R. The lab PCs have zip drives. Your own personal
zip disk ensures that you have the data available after the lab work
for additonal exercises or subsequent labs. You will need to buy zip
disk to use in labs for saving data and analysis code for use in
subsequent labs. 

Course Grades: Computed from a total of 200pts made up of bi-weekly
homeworks (40pts), small review quizzes, (3@10pts=30pts), one mid-term
exam (60pts) and a final exam (70pts). Review quizzes will be randomly
given. Homeworks will require some computing.



Dianne Cook, Dept of Statistics, ISU, 323 Snedecor Hall, Ames, IA 50011-1210
Tel: (515) 294 8865, Fax: (515) 294 4040
email: dicook@iastate.edu
http://www.public.iastate.edu/~dicook/

Last revision: Tue Nov 5 16:16:44 CST 2002