STAT 704 (Data Analysis I)

### Syllabus

Syllabus (Word document) or Syllabus (pdf format)

### Instructor

David Hitchcock, associate professor of statistics

### Office Hours -- Fall 2016

Mon 2:15-3:15 pm, Tues 2:00-3:00 pm, Wed 2:15-3:15 pm, Thurs 1:45-2:45 pm, or by appointment

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

### Class Meeting Time

Mon-Wed-Fri, 10:50 am - 11:40 am, Leconte College 201A

### Review Material

Before entering the Fall 2016 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

STAT 704 has a corequisite of STAT 712. If you register for STAT 704 for Fall 2016, please be sure that you are taking STAT 712 in Fall 2016 (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.
(This book is out of print, but is available online, including in the "international version" which is OK. It is rather expensive new, 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.

### Course Notes

Computer Code for Class Examples

 Statistical Topic Example 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) SAS example (rabbit data) R example (cornmeal data) R example (rabbit 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) Regression with Qualitative Predictors SAS example: (Insurance Innovation data & Shirt data) R example: (Insurance Innovation data & Shirt data) Single-Factor ANOVA SAS example: (Kenton Foods data) R example: (Kenton Foods data) Regression Approach to ANOVA model SAS example: (Kenton Foods data) R example: (Kenton Foods data) Power Calculations for One-way ANOVA SAS example: (Power/sample size example) R example (Power/sample size example) Investigation of Treatment Means SAS example: (Kenton Foods data) R example: (Kenton Foods data) Checking ANOVA Model Assumptions SAS example: (Kenton Foods data) R example: (Kenton Foods data) Two-Factor ANOVA SAS example: (Castle Bakery data) R example: (Castle Bakery data) Investigation of Factor Effects (No Interaction) SAS example: (Castle Bakery data) R example: (Castle Bakery data) Investigation of Factor Effects (With Interaction) SAS example: (Melon data) R example: (Melon data) Power Calculations for Two-way ANOVA SAS example: (Power/sample size example) --- Two-Factor ANOVA With One Observation per Cell SAS example: (Insurance data) R example: (Insurance data) Two-Factor ANOVA With Unequal Cell Sample Sizes SAS example: (Growth data) (SAS example includes empty-cell analysis) R example: (Growth data) Three-Factor ANOVA SAS example: (stress data) R example: (stress 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.

### Access Instructions for SAS Studio

After going to the entry page, create a student account. You will receive an enrollment link in an email from the course instructor. Once your account is created, you can access SAS Studio by going to the Control Center:

Data Sets