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.edu Office Hours: TW: 2:00-3:30 or By Appointment Course Web Page: http://www.math.clemson.edu/ ~ 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:

• 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;
• discrimination methods; and
• classification methods.

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.

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:

• Choose a question that is of interest to them, and that can be answered via a designed experiment or an observational study.
• Design and perform an experiment, gathering data to answer the question. Published data are generally not acceptable. Although, if it is published in a different context from statistics, it may be used. Data that were gathered for a project in another class are acceptable, provided the guidelines for this project are met.
• Analyze the data in whatever way is appropriate.
• Report the findings.

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

• A description of the question, and the team's reasons for wanting to know the answer,
• A description of the techniques used for gathering the data, including how randomization was performed and how the sample size was chosen,
• Analysis and illustration of the findings and conclusions.
• A listing of all the data, and example of a data-collection form (if used) and the details of any unusual calculations.

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:

• The question and its motivation
• Plan for collecting data, details of how randomness will be achieved, planned sample size and reason for it.
• Proposed analysis.

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.