#### Abstract

A brief overview of the area that you’ll be investigating, the research question(s) of interest, your approach to analysis, and the general conclusions.

### Introduction

Overview of the setting of the data, existing theories/models, and your research questions.

### The Data

Where does the data come from? How many observations? How many variables? What does each observation refer to (what is the observational unit)? What sorts of data processing was necessary to get the data in shape for analysis?

### Exploratory Data Analysis

Explore the structure of the data through graphics. Here you can utilize both traditional plots as well as methods from unsupervised learning.

### Modeling

Contruct (descriptive and/or predictive) (classification and/or regression) models that address your research questions. You are encouraged to fit many different classes of models and see how they compare in terms the bias/variance tradeoff (do you have a Rashomon effect going on?). Also be sure to guard against overfitting through cross-validation or shrinkage/penalization (don’t forget about ridge regression and the lasso).

This will be the most extensive section and will include your results as well.

### Discussion

Review the results generated above and sythensize them in the context from which the data originated. What do the results tell your about your original research question? Are there any weaknesses that you see in your analysis? What additional questions would you explore next?

### References

At minimum, this will contain the full citation for your data set. If you reference existing analyses, they should be cited here as well.