STT843   Multivariate Analysis, Spring 2009


                   Yijun Zuo
                   Office: A440 Wells Hall
                   Tel: 517-432-5413, Email:
Office Hours:
                   MW 2:00pm-3:00am and by appointment
Class Time:
                   MWF 3:00pm-3:50pm  C306 WH
                   Applied Multivariate Statistical Analysis, 6th Edition
                   By Richard A. Johnson and Dean W. Wichern,      


                   (Reference book, recommended) Learning SAS in the Computer Lab, 3rd Edition

                    Rebecca J. Elliott - Statistically Significant

                    Christopher H. Morrell - Loyola University Maryland  

                   STT 442 or STT 862 (or approval of instructor)
                   Exams 60% (  Exam I , 100 points ; Final Exam, 200 points)       
                   Homework 40% (about 8 sets of assignments )
                   4.0(>=90%),  3.5(85%-89%), 3.0(80%-84%), 2.5(75%-79%), 2(<=74%)

                   Assignments will be due at the beginning of the lecture on the days indicated
                    Late homework is not accepted


               SAS or R. You are encouraged to use other statistical software

                    packages, e.g. SPlus, Minitab, Excel, etc.  

Important Dates:

                     Monday, Jan 11               First day of classes

                     Monday, Jan 18                Martin Luther King Day, no classes

                     Thursday, February 4      Last day to drop a course and receive 100% refund

                    Wednesday, March 3       Last day to drop course with no grade reported (middle of semester)

                     Mon-Fri, March 8-12        Spring Break, No classes

                     Friday, April 30                  Last day of classes

                     Mon-Fri, May 3-7              Final exam week


                   1. Introduction to multivariate analysis and matrix algebra (Chapters 1 & 2)
                   2. Sample geometry and random sampling (Chapter 3)
                   3. Multivariate normal distribution (Chapter 4)

                   4. Principal components (Chapter 8) (Eaxm I, )

                   5. Factor analysis (Chapter 9)

                   6. Classification and discriminant analysis (Chapter 11)
                   7. Clustering (Chapter 12)

                   8. Canonical correlations analysis, Logistic Regression, MANOVA (Chapters 10, 5-7) (if time permits)

The instructor reserves the right to make any changes deemed academically necessary