BAD 74017 Spring 2009 Booth
BUSINESS ADMINISTRATION
MULTIVARIATE STATISTICS
64017/74017
Instructor: Dr. David Booth
Office: A428 BSA
Phone (Office): 672-1143
Office Hours: To Be Announced
Prerequisites: BAD 6/74023
E-mail: dbooth@kent.edu
Please note that if I am not in my office at these times you will find a note on my door telling you where I am. Please then go to that location to see me. Please feel free to call me or leave a note in my mailbox if you need to contact me.
Textbook: Hair et al, Multivariate Data Analysis, 6th ed.
Course objectives:
At the end of the course the student will have:
1) Learned the basic concepts and techniques of multivariate statistical inference.
2) Learned to apply these basic techniques to real situations
These skills will prepare you for more advanced work in your major, either in college or on the job. Emphasis is on Logit Analysis, Discriminant Analysis, MANOVA(including repeated measures), Canonical Correlation, Factor Analysis and Cluster Analysis.
Attendance and Make-up Policy:
In general, students are expected to attend class and are responsible for any material discussed and/or assigned. With respect to make-up, the general policy is no make-up of missed work (including exams) is allowed, and no late work will be accepted. The only exceptions are:
1) A prearranged situation (e.g., course field trips, athletic trip, etc.)
2) Emergency illness, death in the family, etc., in this case the instructor should be notified as soon as possible.
3) Contact the instructor early.
Performance Evaluation:
The performance evaluation will be based on a series of computer projects as indicated in the course outline. Each will count 100 points. All material handed in is in the public domain. This syllabus is a guide, not an absolute contract. The grading scale is 90%+ A, 80% + B,etc. Exams will have a variable number of points depending on content. Points will be announced prior to the exam.
COURSE OUTLINE
Topic |
Reading |
Assignment (100 pts per data set) |
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Introduction |
Chapter 1 |
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Examination of Data |
Chapter 2 |
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Multiple Regression Models |
Chapter 4,5-logistic |
Vaso-Constriction Data (logit analysis) |
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Discriminant Analysis |
Chapter 5 |
Successful MBA program data |
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MANOVA |
Chapter 6 |
Romantic Relationships DATA |
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MANOVA |
Repeated Measures |
Investment Model |
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Canonical Correlation |
pp 16-19 |
Fitness Club Data |
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Factor Analysis |
Chapter 3 |
Five Socioeconomic Variable Data |
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Cluster Analysis |
Chapter 8 |
Fisher Iris Data
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Path Analysis |
Handout |
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RESERVE LIST FOR MULTIVARIATE STATISTICS
Johnson & Wichern, Applied Multivariate Statistics Analysis, QA278.J63 1982
Srivastava & Carter, An Introduction to Applied Multivariate Statistics, QA278.S687 1983
Green, Mathematical Tools for Applied Multivariate Analysis, QA278.G73
Gnanadesikan, Methods for Statistical Data Analysis of Multivariate Observations, QA278.G6
Mardia, Multivariate Analysis, QA278.M36
Dillon, Multivariate Analysis, QA278.D55 1984
Seber, Multivariate Observations, QA278.S39 1984
Tabachnick, Using Multivariate Statistics, QA278.T3 1989
Booth & Isenhour, “RPDA as a decision making tool with clinical and analytical chemical data.” Computers & Biomedical Research 19, 1 (1986)
Booth et al., “A robust multivariate procedure for the identification of problem S&L Institutions.” Dec. Sci. 20, 320 (1989)
Booth & Montasser, “Robust DA and the periods of modern Egyptian economic development.”
Indust. Math. 35, 81 (1985)
Hu et al., “Robust DA in Marketing Research: Methods and Applications.”
Indust. Math. 38, 181 (1988)
L. Kaufman and P. Rousseeuw, Finding Groups in Data, see Ohio Link
