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
Hours: TW: 2:00-3:30 or By Appointment
|Course Web Page:
Multivariate Statistical Inference and Applications, 1st
Alvin C. Rencher
- Supplementary Text:
Applied Multivariate Statistics with SAS Software, R. Khattree and D. N.
(on reserve in 0-313, you may check out over
- 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
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
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
- the multivariate normal;
- multivariate tests of means, covariances and equality of
- 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
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.
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.
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.
be monitored. It is to the instructors discretion whether an excuse is
legitimate or not. Accordingly, the
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.
There will be two 60 minutes in class examinations and a
No makeup examinations will be given. Any student who misses an
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.
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
- 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
- Analyze the data in whatever way is appropriate.
- Report the findings.
You will have about 1 month to perform your experiment, or obtain
analyze your data, and report your findings. Plan your time
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
- 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
|Well-written and attractive presentation ||20
|Grammar, spelling and punctuation ||20
|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.
|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,