I have a book called Handbook of Small Data Sets, that you may find helpful for this project. I'm willing to lend it to interested students to browse. The data sets from this book are available on the web. Many other interesting data sets are available on the Web.  I enjoy new and different ideas -- for example, my PhD advisor at one time told me he had found an astrologist who was willing to work with him to do a Bayesian evaluation study of her astrological predictions. Use your imagination and enjoy yourself.  You may feel free to compare a Bayesian and a frequentist analysis on the same data set.

You must hand in a report on your project that:

• Describes the problem context and the question you are asking for which the data set provides information;
• Describes any background information the reader needs to understand the problem;
• Shows summary statistics, exploratory plots, and any useful information that will help the reader understand the data set;
• Describes the prior distribution and likelihood function you are using in your statistical model.
• Describes the posterior distribution.  Include summaries such as credible intervals, hypothesis test results, triplots -- any of the techniques we learned that will be relevant to your analysis.
• Discusses the assumptions underlying your analysis and your justification for those assumptions.
• Reports any conclusions you are drawing from your analysis.
• Describes how the conclusions depend on the assumptions you made.