About Us
Ravix is a unified statistical modeling and visualization framework for Python designed to support applied regression analysis in business analytics. Developed from years of teaching regression at the graduate level, Ravix emphasizes clarity, interpretation, and workflow consistency over low-level coding complexity. By integrating formula-based modeling, streamlined output, diagnostics, and visualization into a cohesive interface, Ravix allows students and practitioners to focus on statistical relationships and communicating insights rather than managing implementation details. Ravix serves as the companion framework for Applied Linear Regression for Business Analytics with Python: A Practical Guide Using Ravix with Case Studies (Springer, 2026).
Today, Ravix supports graduate and undergraduate analytics education as well as applied business modeling workflows for practitioners seeking clear, reproducible regression analysis in Python.
Contributors
Ravix was developed with the support of undergraduate and graduate students in the Management Science Department at the University of Miami.
Nefeli Zafeiri contributed to the development of Ravix and was instrumental in the design of this website. Mintra Putlek and Kaitlyn Salvi Cruz contributed to the development and testing of Ravix.
Textbook
Applied Linear Regression for Business Analytics with Python
A Practical Guide Using Ravix with Case Studies
Daniel P. McGibney, Springer, 2026
Tailored for graduate students in MSBA and MBA programs and suitable for advanced undergraduates in analytics, math, and statistics. Every chapter uses Ravix throughout.
Author
Daniel P. McGibney is the author of Applied Linear Regression for Business Analytics with Python (Springer) and the core developer of Ravix. His teaching philosophy motivated Ravix's design: code should reinforce statistical concepts, not obscure them.
Contact
- Email: dmcgibney@bus.miami.edu
- GitHub: github.com/danielmcgibney/ravix
- Textbook site: businessregression.com
- PyPI: pypi.org/project/ravix