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- 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

- 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

- 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 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: 1 (and try the commands in "A Sample Session“) 2 3. The Department of Biostatistics has a free R Clinic every Thursday. Print the R reference card to get a list of the most commonly used commands.
- To download R: Linux Mac Windows
- R Studio is a powerful and easy to use interface for R. I highly recommend it.

- 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 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 Hard to learn and extend, with outdated structure and the worst graphics of any major package.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

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