Do so in such a way that your analyses can be replicated.

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Overview: This paper will be your entry point to your project. You’ll then also be picking from a set of sport data sources (three to choose from, FIFA, MLB, Wimbledon). Your project will involve applying (and understanding) the ideas in your selected paper to the dataset that you have chosen.
Bayesian Regression/Testing
• Articles o Gelman, Andrew, Ben Goodrich, Jonah Gabry, and Aki Vehtari. “R-squared for Bayesian regression models.” The American Statistician (2019). o Hahn, P. Richard, Jared S. Murray, and Carlos M. Carvalho. “Bayesian regression tree models for causal inference: Regularization, confounding, and heterogeneous effects (with discussion).” Bayesian Analysis 15, no. 3 (2020): 965-1056.
Project Description: Present the dataset used and the associated analyses performed. Do so in such a way that your analyses can be replicated. Highlight the connection to your entry paper from the topic choices.
Methods: Describe how your results were determined and the mathematical and statistical methods that you used. (3-4 pages).
Results: Provide textural and graphical results of your analyses along with interpretation of those results.
Conclusions: Discuss the results in terms of suitability of the chosen analysis methods for the data, difficulties encountered, and suggestions for alternative types of analyses.
Note: Provide the datasets and the code that you used (commented to facilitate understanding what you did. As always, explicitly use randomization seeds where applicable to permit replicability). This may be, alternatively, posted into Github.
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