STAT 704 (Data Analysis I)

Fall 2008

Syllabus

Syllabus (Word document) or Syllabus (pdf format)

Instructor

David Hitchcock, assistant professor of statistics

Office Hours -- Fall 2008

Monday 10:00-11:00 a.m., Tuesday 11:00 a.m.-12:00 noon, Wednesday 10:00-11:00 a.m., Thursday 11:00 a.m.-12:00 noon, Friday 1:30 p.m.-2:30 p.m.
Please feel free to make appointments to see me at other times.

Office: 209A LeConte College
Phone: 777-5346
E-mail: hitchcock@stat.sc.edu

Class Meeting Time

Tue-Thu 12:30-1:45 p.m., LC 201A

Review Material

Before entering the Fall 2008 section of STAT 704, you should have a knowledge of certain basic statistical methods, such as: descriptive statistics, basic graphs and plots, elementary probability, discrete and continuous random variables, sampling distributions, point and interval estimation, and hypothesis testing. If you have had a course in elementary statistics (not "mathematical statistics"), you have probably covered this material. If you need a review of it, you may wish to familiarize yourself with any of the following material:

The first six chapters of the course notes on the following web page:
J. Tebbs STAT 700 notes, Fall 2005

The first six chapters of the course notes on the following web page:
R. Vesselinov STAT 700 course notes, Fall 2006

ALSO PLEASE NOTE:
STAT 704 has a corequisite of STAT 712. If you register for STAT 704 for Fall 2008, please be sure that you are taking STAT 712 in Fall 2008 (or that you have previously taken STAT 712).
If you wish to take an applied statistics sequence without taking STAT 712, please consider STAT 700-701 (or STAT J700-J701), which is designed for graduate students from departments other than statistics.

Textbook:
Required Textbook: "Applied Linear Statistical Models" (5th edition) by Kutner, Nachtsheim, Neter, and Li.
(It is rather expensive, but it is a very nice book and it will also be the book used for STAT 705 in the spring.)

Supplementary Books (NOT required):
"Linear Models with R" by Faraway, J.J.
"Extending the Linear Model with R" by Faraway, J.J.
"An R and S-plus Companion to Applied Regression" by Fox, J.
"Statistical Analysis and Data Display" by Heiberger and Holland.
"Statistical Research Methods in the Life Sciences" by Rao, P.V.

Computer Code for Class Examples

Statistical TopicExample in SAS Code Example in R Code
One-sample t-test and CI SAS example: (Summer temperature data) R example: (Summer temperature data)
Paired-samples t-test and CI SAS example: (Mice data) R example: (Mice data)
Two Independent-samples t-test and CI SAS example: (pollution data)
SAS example: (sleeping data) 		R example: (pollution data)
R example: (sleeping data)
Normal Q-Q plots SAS example: (Normal Q-Q plots) R example: (Normal Q-Q plots)
Sign Test SAS example (Eye relief data) R example (Eye relief data)
Wilcoxon Signed-Rank Test -- R example: (Weather station data)
Wilcoxon Rank-Sum Test SAS example: (dental data) R example: (dental data)
Simple Linear Regression SAS example: (attempts data)
SAS example (Toluca data) 		R example: (attempts data)
R example: (Toluca data)
Correlation Analysis SAS example (Toluca data) R example: (Toluca data)
Multiple Linear Regression SAS example (studio data) R example (studio data)
Transformations SAS example (surgical data) R example (surgical data)
Extra SS F-tests,
Multicollinearity & Interaction Models		 SAS example (body fat data) R example (body fat data)
Polynomial Regression SAS example (cornmeal data) R example (cornmeal data)
Model Building (Variable Selection) SAS example (surgical unit data) R example (surgical unit data)
Diagnostics: Plots; Outlier/Influence Measures
(also Model Validation)		 SAS example (various data sets) R example (body fat data)
Weighted Least Squares SAS example (blood pressure data) R example (blood pressure data)
Ridge Regression -- R example (body fat data)
Robust Regression -- R example (math proficiency data)
Nonlinear Regression SAS example (injured patients data) --
Simple Logistic Regression SAS example (programming task data) R example (programming task data)
Multiple Logistic Regression SAS example (disease outbreak data) R example (disease outbreak data)
Poisson (Count) Regression SAS example (Miller lumber data) R example (Miller lumber data)
Nonparametric Regression:
Kernel Regression		 -- R example (simulated & Old Faithful data)
Nonparametric Regression:
Regression with Splines		 -- R example (simulated & Old Faithful data)

Available Computing Resources: R is available as a free download from the CRAN home page) and students who want SAS can buy a copy from USC Computer Services.
These packages are also available on the computers in the labs in LeConte College (and a few other buildings). Help in using R can be found on the CRAN home page.

Downloading Instructions for R

Computing Tips: Some Review

Homework

Selected Example Homework Solutions

Selected Test Solutions

Data Sets

Review Sheets

Formula Sheets

Information about the Project

Exams