MGT 718: Multivariate Analysis
Fall, 2009

Yasuaki Sakamoto, Ph.D.
Assistant Professor, Howe School of Technology Management
Office: Babbio 632
Office hours: By appointment
Email: yasuaki.sakamoto@stevens.edu

Undergraduate level statistics

Multivariate analysis is concerned with datasets that have more than one response variable for each observational or experimental unit. This course focuses on understanding the basic methods underlying multivariate analysis through computer applications. We will learn how to analyze data in R, which is a free statistical computing environment. R is used by many researchers and is an attractive environment for learning statistics (Why use R? and Follow up).

Topics covered will include principal components analysis, factor analysis, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, multiple regression, and canonical correlation, as well as basic concepts in statistics such as experimental design, statistical estimation, and hypothesis testing. The general goal of the course is to teach students how to think critically about data analysis and research findings.

We will use articles as reading materials. These articles will be available online (http://cog.mgnt.stevens-tech.edu/~yasu/courses/718/ and http://personal.stevens.edu/~ysakamot/718/). The following books may be helpful:

T. W. Anderson (2003). An Introduction to Multivariate Statistical Analysis, Third Edition, Wiley.
Abdelmonem A. Afifi, Virginia Clark, Susanne May (2004). Computer-Aided Multivariate Analysis, Fourth Edition, CRC Press.

Here is the breakdown for the grading purposes:

Midterm exam: 30%

Final exam: 30%

Final Paper: 40%

The following statement is printed in the Stevens Graduate Catalog and applies to all students taking Stevens courses, on and off campus.

"Cheating during in-class tests or take-home examinations or homework is, of course, illegal and immoral. A Graduate Academic Evaluation Board exists to investigate academic improprieties, conduct hearings, and determine any necessary actions. The term 'academic impropriety' is meant to include, but is not limited to, cheating on homework, during in-class or take home examinations and plagiarism."

Reference: The Graduate Student Handbook, Academic Year 2003-2004 Stevens Institute of Technology, page 10.

Consequences of academic impropriety are severe, ranging from receiving an "F" in a course, to a warning from the Dean of the Graduate School, which becomes a part of the permanent student record, to expulsion.

Consistent with the above statements, all homework exercises, tests and exams that are designated as individual assignments MUST contain the following signed statement before they can be accepted for grading:

I pledge on my honor that I have not given or received any unauthorized assistance on this assignment/examination. I further pledge that I have not copied any material from a book, article, the Internet or any other source except where I have expressly cited the source. Signature __________ Date __________

Please note that assignments in this class may be submitted to www.turnitin.com, a web-based anti-plagiarism system, for an evaluation of their originality.

Continuous improvement can only occur with feedback based on comprehensive and appropriate surveys. Your feedback is an important contributor to decisions to modify course content/pedagogy which is why we strive for 100% class participation in the survey.

All course teacher evaluations are conducted on-line. You will receive an e-mail one week prior to the end of the course informing you that the survey site (https://www.stevens.edu/assess) is open along with instructions for accessing the site. Login using your Campus Pipeline (email) 'CPIPE' username and password. This is the same username and password you use for WebCT. Simply click on the course that you wish to evaluate and enter the information. All responses are strictly anonymous. We especially encourage you to clarify your position on any of the questions and give explicit feedbacks on your overall evaluations in the section at the end of the formal survey which allows for written comments. We ask that you submit your survey prior to the last class.

Course schedule will be posted online (http://cog.mgnt.stevens-tech.edu/~yasu/courses/718/ and http://personal.stevens.edu/~ysakamot/718/). Make sure to regularly consult the web page for an updated schedule.

Here are some data files you can use for the final paper:

Social learning: in1ob2ex3.xlsx

Food and thought: food.xlsx

Digg influentials: digg.xls