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While you are not expected to build a computationally complex model, your work needs
to show logical flow, and demonstrates the Bayesian analysis concepts discussed in the
course. This includes the following:
1. Description of the problem: What is the problem you are trying to solve? What is the
motivation and significance behind this? Why might your approach be useful here?
2. Description of your data: What are the variables of interest and their summary? What
are some caveats of the data (such as data quality issues) that we need to be aware of,
if any?
3. Formulation of your analysis approach: How is the model or estimation algorithm
defined?
4. Computational approach: What methods are you using to analyze the data? You are
encouraged to use existing R packages.
5. Results and conclusion: What is the takeaway from your analysis? What makes your
approach advantageous (or challenging) in your problem? What are the next steps in
your analysis?
