Bayesian Data Analysis Project
You could nd dataset from kaggl
Procedure and Dates
Submit your project via Canvas. The submission consists of four (in some cases,
three) things:
1. An R Markdown le (.Rmd).
2. *Any necessary data le(s) that you used (see below).
3. A Notebook le (.html). I should be able to execute your .Rmd le, with the
submitted data le(s) placed in the same folder, and completely reproduce
this .html le
4. A word document to explain dat
Required Elements
Make clear where the dataset comes from, and include a description of the
dataset. Include more than one predictor variable.
Include trace plots, R6alues, 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.
I will be reading your code, your code should be easy to understand 5se spaces,
don use names like x when you could use variable names like income.
fi
fi
a
e
fi
fi
fi
.
fi
fi
fi
All of your project should be written up in an R Markdown (i.e., .Rmd) le in
which you mix text (outside of the code chunks) and code (inside the code
chunks).
4 Additional 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.
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.
.
fi
fi
Some statistical inference based on the posterior (e.g., the probability that a
regres- sion coef cient is greater than zero, and the practical signi cance of that)

Purchase answer to see full
attachment