BAYESIAN DATA ANALYSIS FINAL PROJECT FALL 2021
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BAYESIAN DATA ANALYSIS
FINAL PROJECT
FALL 2021
1. PRoJEcT
For the final project for the course, your assignment is essentially to perform a complete Bayesian data analysis on data of your choice. The final project is worth 8% of the final grade, so think of it as the equivalent of about a week and a half of course work.
2. PRocEpuREs ANp DATEs
Submit your project via Canvas. Submit your R Markdown file (not an html file) along with any necessary data file.
The project itself is due on the last day of classes, December 13 at 11:59 pm.
Do not use a dataset that you have used in this or any other course. This is an individual, not a group, project.
I expect that you will discuss this project with others, but please avoid using datasets in common (I realize that might still happen by coincidence). All of the work submitted must be your own. Be sure to credit the sources of your data and any other material—it is better to over-credit than to under-credit. If you have any questions about properly crediting others’ work just contact me about it.
The last lines of your R Markdown file should be “On my honor, I have neither received nor given any unauthorized assistance on this project” followed by your name.
3. REouIREp ELEMENTs
Make clear where the dataset comes from and include a description of the dataset.
Include more than one predictor variable.
Include trace plots, rhat values (whether traditional or new), and effective sample sizes to demonstrate that the MCMC process has worked well. Summarize posteriors, both numerically and graphically.
Include a posterior predictive check.
Use your model to make predictions at some particular set of predictor values.
Include some interpretation of your results.
Your code should be easy to understand—use spaces, don’t be shy about using new lines, and don’t use names like x when you could use variable names like income.
All of this should be in an R Markdown (i.e., .Rmd) file, in which you mix text (outside of the code chunks) and code (inside the code chunks).
4. AppITIoNAL ELEMENTs
The following will elements are not required, but will favorably affect your grade. An A on the project is not possible if none of these elements are included.
A binary or count variable as the outcome.
A hierarchical model.
A model outside those discussed in the course.
Use of priors other than the defaults (along with an explanation of your choices).
Comparison among more than one model.
The use of loo as a diagnostic.
A prior predictive check.
Some inference (e.g., the probability that a regression coefficient is greater than zero).
Other aspects that go beyond a vanilla analysis.
5. A FINAL NoTE
You will both enjoy the project more and you will do better if you pick something that you are genuinely interested in. You may also find yourself expanding the project somewhat so that you can brag about it in a job interview—it’s hard to make someone interested in a project that you are not interested in yourself.
2021-12-17