Stats 201A: Research
Design, Sampling and Data Management
Syllabus (Fall 2007)
Course Homepage: http://www.stat.ucla.edu/~hqxu/stat201A/
Instructor: Hongquan Xu <hqxu AT
stat.ucla.edu> Phone: (310) 206-0035
Lecture Hours: MWF
2-2:50pm MS 5128
Office Hours: MWF
1-1:50pm at MS 8955
Textbooks (1-2)/References (3-4):
- Stephen K. Thompson (2002). “Sampling, Second Edition,” John
Wiley.
- Douglas C. Montgomery (2005). "Design and Analysis of
Experiments," 6th ed., John Wiley. (5th ed. is okay)
- Julian J. Faraway (2005). "Linear Models with R," Chapman &
Hall.
- Sharon L. Lohr (1999). "Sampling: Design and Analysis," Duxbury
Press.
Overview:
This course begins with a brief review of linear
regression, then sampling and research design. Topics include
- Regression: Linear Model, Estimation, and Inference.
[Chapter 10 of Montgomery (2005)]
- Sampling: Simple random sample, Confidence Intervals,
Sample Size Estimation, Estimation of Various Other Parameters
(Proportions, Ratios and Subpopulation Means), Unequal Probability
Sampling (Hansen-Hurwitz
and Hovitz-Thompson Estimators), Regression Estimation, Stratified
Sampling, Cluster and Systematic Sampling, Multistage designs.
[Chapter 1-6,
8, 11-13 of Thompson (2002)]
- Design: Basic principles, ANOVA, randomized block
designs, factorial designs, blocking and confounding in
factorial designs, fractional factorial designs, minimum
aberration designs, supersaturated designs, experiments with random
factors, and nest designs. [Chapters 1-8, 13-14 of Montgomery
(2005)]
Grading
- Homework 30%
- Group Project 30%
- Final Exam 40%
Group Project: due Friday, December 14, 11:30am.
You can work with other 1-2 students, either on a sampling or design
project.
Final Exam: Friday, December 14, 11:30-2:30.
The final exam is comprehensive and includes topics on design, sampling
and analysis.
Statistical Software:
You are expected to be familar with one statistical software (e.g., R,
S+, SAS, Stata, etc.). You are expected to know how to perform
linear regression and make various plots. I will use R commands
and outputs in the lecture, but I won't teach how to use R. I
will provide some sample R commands and outputs on the web.
R is a language and environment for statistical computing and
graphics. You can download R and get information from the R Home Page
(http://www.r-project.org/).