contracting-hypotheses

Statistical Tests for Federal Contracting Hypotheses

In the Fall/Winter of 2018, OGP Evidence & Analysis prepared an analysis of government-wide contracting data with the objective of identifying the characterisitcs of a high performing contracting office. The results of the analysis were presented 12/3 - 12/4/18 at the Government Contract Management Symposium.

This repository provides a jupyter notebook for each of the statistical analyses involved in that presentation. Each notebook documents:

• the data source
• the unit of analysis
• decisions made to prep/clean the data, including how missing values and/or data quality issues were addressed
• reasoning behind the model selection, including the statistical tests used to validate the model's assumptions
• interpretation of the model summary

Getting Started

Prerequisites

First, you'll need Python 3.7. We recommend using pyenv to get this (as well as other versions of Python).

Then you'll need Jupyter Notebook. You can find directions on how to get that here. They recommend downloading Anaconda to get it, but we suggest using pip if you're already using pyenv to manage your python versions.

Then you'll want to clone this repo and cd in to it:

git clone git@github.com:GSA/contracting-hypotheses.git
cd contracting-hypotheses

We use pipenv for a virtual environment. You can find instructions on installing that here.

Once you've got pipenv installed, you can start up the virtual environment using:

pipenv install

Next, activate the Pipenv shell:

pipenv shell

This will spawn a new shell subprocess, which can be deactivated by using exit.

One of the required packages you just installed is ipykernel. We use this to create a kernel that uses our virtual enivronment for the Jupyter Notebook:

ipython kernel install --user --name=contracting-hypotheses

At this point, you can start jupyter with jupyter notebook. When you open a notebook, be sure you're using the kernel you created(contracting-hypotheses).

Contributing

Please read CONTRIBUTING.md for details on our code of conduct and the process for submitting pull requests to us.

License

This project is licensed under the Creative Commons Zero v1.0 Universal License - see the LICENSE.md file for details.