Course Goals

• Learn how to use modern regression methods to answer scientific questions
• Become familiar with statistical concepts including exploratory data analysis, estimation, testing in linear, logistic, and survival models
• Understand how the development of statistical methodology is motivated by biological and medical problems
• Develop data analytic skills including familiarity with several statistical programs
• Develop writing skills needed to communicate the results of a data analysis

Topics of Discussion

• Introduction to Regression Models
• Simple Linear Regression
• A Review of Matrix Algebra and Important Results of Random Vectors
• Precision, Effect Modification, and Confounding
• Specification Issues in Regression Models: ANCOVA, ANOVA, Multicollinearity
• Multivariable Regression
• Model Selection
• Case Studies in Linear Regression
• Logistic Regression
• Generalized Linear Models and Poisson Regression
• Survival models
• Bayesian Regression
• Model Checking: diagnostics, transformations, influential observations, lack-of-fit test

Software

• You can use any software your want other than Excel. We will be demonstrating Stata and R. For Bayesian analysis, R and JAGS will be used.
• See SoftwareRecs for more software recommendations
• Chun Li's Stata notes, Need-to-know commands, and R notes
• Leena Choi's Stata notes for classes, Stata Lab info, and R notes for classes

R information

• R 2.8 (optional, free from http://www.r-project.org): Powerful, versatile, and actively maintained and updated. It may require a longer learning curve than Stata and SPSS, but the effort will pay off later on. To get a feel, look at one of the following: http://cran.r-project.org/doc/manuals/R-intro.pdf 1 (and try the commands in "A Sample Session“) http://cran.r-project.org/other-docs.html 2 3. The Department of Biostatistics has a free R Clinic every Thursday. Print the R http://www.rpad.org/Rpad/Rpad-refcard.pdf reference card to get a list of the most commonly used commands.
• To download R: http://cran.wustl.edu/bin/linux Linux http://cran.wustl.edu/bin/macosx Mac http://cran.wustl.edu/bin/windows/base Windows
• R Studio is a powerful and easy to use interface for R. I highly recommend it.

Comments on Other Software Packages

• Stata: Powerful and good graphics with an SPSS-like menu system. A good support site is at http://www.ats.ucla.edu/stat/stata. Cost is $89 for a year and $145 for life, through http://www.stata.com/order/new/edu/gradplans/gp-campus.html GradPlan. Buy Small Stata for $45 if you have to pay by yourself. Stata also is available at the College of Arts & Science Microcomputer Labs.
• SAS: The oldest survivor, with strong legacy. Hard to learn and extend, with outdated structure and the worst graphics of any major package.
• SPSS: Have “standard” methods and good graphical user interface. However, it is difficult to extend beyond the “standard” methods.
• Honorable mention: Epi Info (free from CDC), S-Plus.
• There is a long list of reasons not to use Excel. See ExcelProblems
• See StatComp for more information about statistical computing including links to several online statistical and probability calculators