Homepage of Professor Hokwon A. Cho

UNLV Department of Mathematical Sciences

STA 764 – Regression & Multivariate II

(1) Spring 2005 Syllabus (in PDF)

I. Outline of the Course

Instructor: Hokwon A. Cho, Ph.D., Associate Professor, CBC B-506, Office phone: 895-0393 (Math. Sci. dept. office: 895-3567), E-mail: cho@unlv.nevada.edu.

Class Time and Location: T, Th 8:30 a.m. - 9:45 a.m., CBC C-319.

Office Hours: T, Th 3:00 p.m. - 5:00 p.m., or by appointment, or by appointment.

Textbook: Applied Multivariate Statistical Analysis, 5th Edition by R. Johnson and D. Wichern. Prentice Hall, New Jersey.

Description of the Course: The main goal of this course is to provide an understanding of the methodology and applications including data analysis, the available computational tools and the fundamental limitations, based on the theoretical infrastructure about the multivariate data. Several major topics covering will be

1. Introduction to basic concepts and background - random vectors, inferences on mean vector, multivariate normal distribution theory, Wishart distribution and Hotelling's T² statistic.

2. General linear models - multivariate regression models, MANOVA.

3. Analysis of covariance Structure - principal components, factor analysis and canonical correlation analysis.

4. Classification and grouping techniques- clustering, discrimination and multidimensional scaling.

5. Some selected topics - categorical data analysis, log-linear models; model selection and validation, robust fits, inspection of residuals, bioassay, survival curves.

Homework: There will be weekly/biweekly assignments will be given in class and expected to turn in on time. Some of them will be discussed in class. The usage of statistical packages (e.g., SAS, MINITAB or SPSS etc) or programmable languages (R, S-Plus, or C+ etc) is strongly recommended.

Grading: The course grade is based upon the following: Homework & assignments - 20%, Two tests (in class) - 25% each, Final Exam - 30%.

Exams: There will be two midterm exams most likely on 5th and 10th week. Final exam is scheduled on Thursday, May 6 from 7:30 a.m. to 9:50 a.m. in classroom.

II. Lecture Schedule

The tentative schedule is given in chronological order with topics to be covered:

Week	Topics	Remarks
1	Review: Matrix Algebra for data matrix and random vector
2	Multivarite normal distribution
3-4	sampling theory, multivariate inference - Hotelling T
5	Multiple comparisons, ANOVA, MANOVA
6	[Test 1 and review]
7-8	Multivariate linear regression & generalized linear models
9-10	Spectral analysis, Principal components, Factor anlysis,
11	[Test 2 and review]
12	canonical correlation analysis
13-14	Fisher’s discriminant function and classification
15	Clustering, multidimensional scaling

III. Homework Assignments

The homework assignments are three parts: (1) Reading assignments (2) Exercises (3) Problems.

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