Class meets: MH3030, Wed 1-5.
Phil Ender (X63195, firstname.lastname@example.org)
Office Hours: MH2005B, W 12-1 and by appointment.
Special Reader: Jeff Forrest (MH2005B, X53157, email@example.com)
Office Hours: TTH 12:00-1:45
The purpose of this course is to introduce various topics in multivariate analysis and to provide some practical experience in their applications and interpretation. The Stata software package will be used throughout the course. Ten matrix algebra and data analysis exercises will be completed and turned in by each student. There will also be a take-home final exam. These exercises and the final will provide the basis for assigning final grades.
Computer-Aided Multivariate Analysis (4th Edition)
by Afifi, Clark and May
Publisher: Chapman & Hall/CRC
You can view textbook examples for this book using several different statistical software packages at the ATS website: Afifi, Clark & May -- Textbook Examples.
Homework and Tests:
There will be 10 homework assignments involving programming using a matrix programming language or a multivariate data analysis. Each student is required to write their own programs. The programs are to be written so as to be as general as possible. The programs shall have sufficient comments and labels so as to make the inner workings of the prgram clear.
There are no tests.
Students will be required to have network access by one of the following means: 1) GSE&IS Computer Labs, 2) their own departmental computer labs with Internet access, or 3) their own personal computer at home with Internet access.
World Wide Web:
Course information, including assignments, datasets, examples, helpsheets, computer printouts, class discussion forums, and lecturers, is available over the Internet on the World Wide Web at the following URL: www.philender.com/courses/multivariate/.
Lecture notes will be on the Ed231A Web site. You will not anything other then a web browser to read the lecture notes.
Hypothetical Course Schedule
Week Topic 1 Matrix Algebra I, Multivariate Normal Distribution 2 Multiple Regression, Probit Analysis 3 Hotelling's T2 4 Multivariate Analysis of Variance 5 Matrix Algebra II 6 Discriminant Analysis 7 Canonical Correlation Analysis 8 Factorial Manova 9 Principal Components and Factor Analysis 10 Cluster Analysis Readings from Afifi, Clark & MayWeek Chapter 1 Chap 1 & Chap 2 2 Chap 3 & Chap 4 3 4 Chap 6 & Chap 7 5 Chap 12 6 Chap 11 7 Chap 10 8 9 Chap 14 & chap 15 10 Chap 16
Phil Ender, jul07, 29sep05