Homepage of Professor Hokwon A. Cho

UNLV Department of Mathematical Sciences

STA 763 – Regression & Multivariate I

(1) Spring 2007 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: M, W 11:30 a.m. - 12:45 p.m., CBC C-323.

Office Hours: M W 2:00 p.m.-4:00 p.m., Th 10:00 a.m.-12:00 p.m., or by appointment.

Textbook: Introduction to Regression Analysis by M. Golberg and H. Cho, Wessex Institute of Technology, Southampton, UK.

Description of the Course: The main goal of this course is to provide an understanding of the methodology and applications in linear regression analysis. The emphasis is placed on methodological infrastructure to explain the relationship among variables in data and the fundamental limitations. Among topics to be covered will be

1. Basic concepts and background - probability space, random variables (or vectors), normal distribution and related distributions, estimation, testing hypothesis, matrix algebra.

2. Regression analysis - simple linear regression, least-square method, Gauss-Markov theorem, ANOVA approach, assessing model validity, multiple linear regression models.

3. Further topics in regression - selecting regression models, polynomial models, modeling categorical variables, logistic regression, generalized linear model.

4. Diagnostics and remedies - residual analysis, residual plots, transformation, multicollinearity, ridge regression.

Homework: There will be roughly weekly assignments will be given in class (on a Tuesday) and expected to turn in on time. Some of them will be discussed in class.

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 6th and 11th week. The final exam is scheduled on Wednesday, May 9 from 10:10 a.m. to 12:10 p.m.

II. Lecture Schedule

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

Week	Topics	Remarks
1	Review: probability space, random variables, expectations
2	Simple linear regression, LSE, Gauss-Markov theorem
3	ANOVA approach, model validity, transformations
4-5	Model validity, Matrix Algegra, geometry of vectors
6	[Test 1 and review]
7-8	Multple regression, LSE, extra sum of squares, inferences
9	Residual analysis, diagnostics,
10	Transformation: Box-Tidwell & Box-Cox, autocorrelation
11	[Test 2 and review]
12-13	Polynomial regression, logistic regression, criteria function
14	Methods of model selection
15	Multicollinearity, biased estimation

III. Homework Assignments

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

IV. Data Sets

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