Stat561  Syllabus   Spring 2008

J.C. Wang, 5503 Everett Tower
e-mail: jung-chao.wang@wmich.edu
Homepage (this page): http://www.stat.wmich.edu/wang/561
Office Hours:
1:30-3:00 MTW, and by appointment

Text:
Applied Multivariate Statistical Analysis by Richard A. Johnson and Dean W. Wichern, 6th edition. ISBN-13: 978-0-13-187715-3; ISBN-10: 0-13-187715-1

Grading Scale:
E D DC C CB B BA A
below 50 50-55 56-60 61-70 71-75 76-84 85-89 90 or more

Grading Policy:
3 1-hour tests 30% (10% each)
final exam 15% (5:00PM --- 7:00PM, Monday, April 21)
homework 45%
computer assignments 10%

Computer use:
We will use statistical softwares: R, SAS, and Minitab for computer works. However, you are not limited to using these softwares.

Regarding homework:
You are encouraged to discuss with classmates general strategies for solving problems or writing computer programs for the computer assignments. However, any assignment turned in should be substantially your own work.

Class notes:
Class notes are in PDF files in which many clickable links are in darkblue color. Note that the class notes as well as this Stat 561 homepage are under construction, please check back regularly.
Attention:
You are responsible for making yourself aware of and understanding the policies and procedures in the Undergraduate (pp. 274-276) [Graduate (pp. 25-27)] Catalog that pertain to Academic Honesty. These policies include cheating, fabrication, falsification and forgery, multiple submission, plagiarism, complicity and computer misuse. If there is reason to believe you have been involved in academic dishonesty, you will be referred to the Office of Student Conduct. You will be given the opportunity to review the charge(s). If you believe you are not responsible, you will have the opportunity for a hearing. You should consult with me if you are uncertain about an issue of academic honesty prior to the submission of an assignment or test.

Tentative Topics

  1. Foundations
    1. Multivariate Descriptive Statistics
    2. A Review of Matrix Algebra
    3. Distribution in Several Dimensions
  2. Elementary Multivariate Statistical Inference
    1. Large Sample Properties of IID Data
    2. Estimation When Data Are Normal
    3. Diagnostics: Checking Normality Assumption
    4. Hypothesis Testing for the Population Mean
    5. Confidence Regions
    6. Large Sample Inference on Proportions
    7. Two-Sample Data
  3. Analysis of the Covariance Structure--Variable Directed R-Techniques
    1. Principal Component Analysis (PCA)
    2. Factor Analysis
    3. Canonical Correlation Analysis
  4. Classification and Grouping Techniques--Observation Directed Q-Techniques
    1. Discrimination and Classification
    2. Clustering Analysis
    3. Correspondence Analysis

2008-04-16