ST 495 Midterm project outline:

Choose a data set of interest (e.g., from https://www.kaggle.com/datasets)

Identify inferential questions of interest about population features you might 
learn about from your data set.

Identify an appropriate statistical model and parameters for representing the
population features of interest.

Determine a statistical method for estimating the parameters of interesting that
represent the population features of interest.

Implement the estimation procedure on your real data set.  Comment on the fitted 
model. 

Construct a simulation study using synthetic data to investigate the performance
of the estimation procedure (see below for an outline of how to do this). 
General outline of the simulation study:

(1) Generate 1 synthetic data set from the statistical model
(2) Implement an estimation procedure (i.e., MLE, EM, MCMC,...)
(3) Evaluate the estimated model (i.e., RMSE, AIC, BIC,... on test set)
(4) Repeat steps (1)-(3) N times (larger N is better)
(5) Use the output from (4) to produce plots and/or tables for inference

Organize the simulation study into 2 script files:
(a) run_file.r  # This script contains code for generating the data and running 
                  the estimation procedure
(b) out_file.r  # This script contains code for producing the plots/tables
 
The output from run_file.r should be saved and then loaded by out_file.r.  And,
the output from out_file.r should be saved, and then loaded (or copy-and-pasted)
into your LaTex file to be compiled into the pdf file of the write-up of your 
project.

Next, your project submission should include a workflow file, which includes 
ALL of the steps for reproducing your computations and plots/tables.  For 
example, it will look something like:

-------------------------------------------------------------------------------
# This is the workflow to reproduce the results of the ST 495 midterm project by
# (student name)

for seed in {1..N}
do
Rscript run_file.r arg_1 arg_2 ...
done

Rscript out_file.r arg_1 arg_2 ...
-------------------------------------------------------------------------------

Note: This will require knowledge of how an interpretable language works 
outside of a GUI environment (i.e., you should know how to run R outside of 
Rstudio).  This is not hard, but is important for understanding how programming 
skills fit into the broader application of problem solving in industry and 
academia.  

Finally, your project write-up will include a 10 page summary describing the
statistical model, estimation procedure, and inference on your simulation study.
You will also need to send me all script files INCLUDING your workflow file.

Visit me periodically during office hours as you progress through your project.  
THERE IS NO REASON THAT YOU SHOULD NOT GET FULL POINTS ON YOUR PROJECT.  If you
learn nothing else in school, learn to be resourceful.  It will make you more 
employable, and a more productive member of society.