Applied Multivariate (Statistical) Analysis

Mathematical Sciences 807

Instructor: Calvin L. Williams, Ph.D.Course: Applied Multivariate (Statistical) Analysis
Office: 0-323 Martin Hall Class Location: M-307 Martin Hall
Telephone: 656-5241 Class Time: 9:30-10:45 TTh
E-mail: calvinw@math.clemson.eduOffice Hours: TW: 2:00-3:30 or By Appointment
Course Web Page: ~ calvinw/mthsc807.html

Text: Multivariate Statistical Inference and Applications, 1st edition Alvin C. Rencher
     Supplementary Text: Applied Multivariate Statistics with SAS Software, R. Khattree and D. N. Naik
(on reserve in 0-313, you may check out over night)

Course Description: Multivariate data through experimentation and observation occur quite often in engineering, business, social sciences, as well as biological and physical sciences. This is a course in applied multivariate data analysis. It will cover descriptive and graphical methods for continuous multivariate data, the multivariate normal, multivariate tests of means, covariances and equality of distributions, univariate and multivariate regression and their comparisons, multivariate analysis of variance, covariance structure models, and discrimination and classification. Furthermore it should be emphasized that this course and hence the chosen text, is designed around the application of multivariate techniques to continuous data, time allowing we will endeavor to discuss methods of discrete multivariate analysis from prepared class notes. Students will learn how to use statistical software to facilitate the understanding of the foundations of multivariate analysis. Statistical packages will include SAS, S-Plus, and MatLab. Topics to be covered include:

There will be a significant amount of computer analyses conducted to develop the understanding of fundamental concepts in multivariate analysis. Based on the topics mentioned above, we will cover Chapters 1-10 and Rencher(with Chapters 1-3 as basic reference) and some of Khattree and Naik as a computational supplement.

Prerequisites: This course will suit recent students of MTHSC 805 and the equivalence of Mth Sc 403/603 can be considered preparatory for those students interested in multivariate data analysis. Prerequisites are a working knowledge of general linear models, statistical inference concerning these types of models, and hypothesis testing, and elementary matrix operations. Also a working knowledge of SAS and/or S-Plus and any statistical package that would allow descriptive analysis and generalized modeling is required.

Attendance Policy: All classes should be attended, but, if you are ill stay at home. I will accept e-mail or phone messages to that effect. Note that this does not exempt you from turning in homework/projects on time nor taking quizzes at their proposed times. Legitimate excuses must be offered with respect to the day(s) missed. Attendance will be monitored. It is to the instructors discretion whether an excuse is legitimate or not. Accordingly, the university's policy on religious holidays will be acknowledged and honored.

Tardy Professor Policy: If the instructor is more than 15 minutes late for any class you may leave.

Examination Policy: There will be two 60 minutes in class examinations and a final examination. No makeup examinations will be given. Any student who misses an examination without a legitimate excuse,ie, a documented medical excuse, will receive a score of zero for that exam. A student with a legitimate excuse, will receive a final score based on all other class work. More than one missed exam with require withdrawal from the course and/or the receipt of a failing final grade.

Homework and/or Take Home Projects: There will also be several homework sets and/or take home projects assigned from the text as well as from material covered during class. Although it is imperative that each student be completely comfortable with these assigned problems and projects, group study is encouraged.

Grading Policy: The two regular exams will count as 50% of the final grade, homework sets 10%, a multivariate data project 20%, and the final exam 20%. The final exam will cover the more important topics covered during the semester.

Grading Scale:

A 100 - 90
B 89 - 80
C 79 - 70
D 69 - 60
F 59 -

Mathematical Sciences 807
Applied Multivariate Statistical Analysis
Project Description, Fall 2004

This project is an opportunity to use the statistical techniques we have learned in class, to answer real-life questions. Projects should be done individually. Each student should:

You will have about 1 month to perform your experiment, or obtain your data, analyze your data, and report your findings. Plan your time accordingly.

The grade will be based on the final report, which should contain the following items.

Reports should be neatly typed, well-organized and attractive. Graphical displays (either computer-generated or hand-drawn) are encouraged. Generally, graphs are more effective if they are incorporated into the text, rather than hidden at the end of the report. You may also use a computer package to aid in the data analysis. If you do so, the results should be discussed in the text of your report, and the computer output itself may be included in an appendix.

A rough draft of the final report will be due approximately 2 weeks before the final report is due.

The project is worth 100 points. Grades will be based on:
Appropriate and correct procedures 50 pts
Well-written and attractive presentation 20 pts
Grammar, spelling and punctuation 20 pts
Complexity 10 pts

A project proposal (not graded) must be approved before the project is started. An approved proposal must be turned in with the final report. The proposal should state:

Due dates:
Proposal October 21st
Fall Break November 2nd
Rough Draft November 11th
Final report November 18th
Thanksgiving Break November 25th
Last Day of Class December 2nd

File translated from TEX by TTH, version 2.25.
On 17 Aug 1999, 12:32.