STT843   Multivariate Analysis, Spring 2009

 

Instructor:
                   Yijun Zuo
                   Office: A440 Wells Hall
                   Tel: 517-432-5413, Email: zuo@msu.edu
Office Hours:
                   MW 2:00pm-3:00am and by appointment
Class Time:
                   MWF 3:00pm-3:50pm  C306 WH
Textbook:
                   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  

Prerequisite:
                   STT 442 or STT 862 (or approval of instructor)
Grading:
                   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:
                   Assignments will be due at the beginning of the lecture on the days indicated
                    Late homework is not accepted

Computing:

               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

 

Topics:
                   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