UNLV Department of
Mathematical Sciences 

STA 761: Regression Analysis I 

Welcome to the course homepage for STA761. If you
are a student in this class you should check this site frequently for updated
information and announcement. 

I. Outline of the Course 

Instructor: Hokwon A. Cho, Ph.D., Associate
Professor, Office: CDC 1008 (Building #10, Room 1008),
Office phone: 8950393 (Math. Sci. dept. office: 8953567), Email: cho@unlv.nevada.edu. Class Time and Location: Tu, Th
8:30AM  9:45AM, CBC C322. Office Hours: Tu & Th:
1:30PM3:30PM or by appointment. Textbook: Introduction to Regression Analysis
, Revised & updated Ed. (2010), by M. Golberg
and H. Cho, Wessex Institute of Technology, Description of the
Course: This course provides both foundation and
applied perspectives of regression analysis, as a gateway to understanding
statistical modeling. The emphasis will be placed on the theoretical
foundation, statistical rationale, the investigation of the relationship
among variables in a data set, and further advanced topics, models and recent
developments. 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.
Simple linear regression  Regression overview, leastsquares method, GaussMarkov
theorem, ANOVA approach, lack of fit test, assessing model validity,
transformation.
3.
Multiple
regression analysis  Matrix approach, multiple linear regression models, quality
fit and prediction, HAT matrix, extra sum of squares principles.
4.
Residual analysis, diagnostics and remedies  Analysis of residuals, plotting
residuals, PRESS statistics, transformation, correlated errors.
5.
Further models in regression  Polynomial models, indicator
variables, modeling categorical variables, logistic regression, generalized
linear model, selecting models, multicollinearity,
ridge regression, other alternatives.
6.
Selecting and criteria of regressions  Model misspecification, predictive
criteria, methods of selecting models. Homework: All homework assignments will be given in class and expected
to turn in on time. No late homework will be accepted. Some of the problems
will be discussed in class. Exams: Two midterm tests will be most likely on 5th/6th and
10th/11th week and a final exam will be given according to the university calendars and schedules. Grading: The
course grade is based upon the following: (1) homework assignments 20% (2)
two midsemester exams 25% each, and (3) a final exam 30%. Grade Scale: AA⁻: 85% or above, B⁺B: 75% or above, C⁺C: 65% or above, D⁺D: 55% or above, F:
Below 55%. University
Academic Policies on the Academic
Misconduct, Copyright, Disability Resources Center (DRC), Tutoring, Writing
Center, and Religious Holidays: click the next è University
Academic Policies. 

II. Textbook
Information 

Title: Introduction to Regression Analysis, 452 pp
+ xiv. Revised
& updated Edition (2010). Publisher: Wessex
Institute of Technology, WIT Press, Southampton, United Kingdom. Authors: Michael Golberg
and Hokwon Cho ISBN13: 9781853126246 ISBN10: 1853126246 The
book was basically written for the introductory graduate level and for
researchers. The mathematical
level is medium and analytical. Anyone who studied and understand the
undergraduate senior level of linear algebra and mathematical statistics will
have no major difficulties to read the book. è & Textbook
website & Errata: § For the First Edition
(2004), printed in 2004 and 2007: [Errata P2]
§ For the Revised and Updated Edition (2010): [Errata P3] 

III. Bulletin Board 

List of References and Further Readings for the
course: click next è [List of Reference &
Readings] To see the lecture and
homework schedule: click next è [Lecture & HW
schedule] Announcements: 1.
Welcome to STA 761. 

IV. Handouts, Data Sets & Other Resources 

Handouts & Statistical Tables: § [Table for Discrete and Continuous Probability Distributions] § [Table for Confidence Intervals & Testing Hypotheses] Data Sets:


o Statistical Software & Language:  R  SPlus  Minitab
 SAS/JMP  SPSS  STATA  BMDP
 o Search Journal Articles:
JSTOR  MathSciNet  Ingenta  CIS  Thomson  Wikipedia  Ø Journals:  Statistics  Probability  Related Journals  Mathematics
 Ø Societies:  IMS  ASA  IMS Bulletin  SIAM  AMS  MAA 
è More Link? 

© 20062018 Hokwon Cho 