Applied Linear Regression for Business Analytics with Python

A Practical Guide Using Ravix with Case Studies
Erscheinungsjahr 2026
ISBN: 978-3-032-23805-4
Verlag: Springer

Buch, Englisch, 327 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 686 g

Reihe: International Series in Operations Research & Management Science

This textbook provides a practical, business-focused introduction to regression analysis using Python. It equips readers with the intuition, coding skills, and statistical tools needed to transform raw data into actionable insights. In today’s data-driven economy, where organizations rely on analytics for pricing, marketing, employee retention, and financial forecasting, regression remains a cornerstone method.

The text bridges theory and application by combining clear explanations, step-by-step coding, and real-world business case studies. A distinguishing feature is the introduction of the Ravix package, a regression modeling and visualization framework developed to streamline regression workflows in Python. Ravix simplifies model building, produces clear and interpretable output, and integrates seamlessly with core scientific Python libraries such as NumPy, Pandas, Statsmodels, and Scikit-learn. By reducing coding complexity and emphasizing interpretation, Ravix makes modern regression techniques accessible to students, analysts, and professionals.

Zielgruppe

Graduate

Autoren/Hrsg.

McGibney, Daniel P.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik Mathematik Stochastik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Inhaltsverzeichnis

Introduction.- Basic Statistics and Functions Using Python.- Regression Fundamentals.- Simple Linear Regression.- Multiple Regression.- Estimation Intervals and Analysis of Variance.- Predictor Variable Transformations.- Model Diagnostics.- Variable Selection.- Appendix.- References.- Index.

Über Autor(innen)

Dr. Daniel McGibney is an Associate Professor of Professional Practice at the University of Miami Herbert Business School, USA. He currently teaches analytics to both graduate and undergraduate students. Over the years, he has taught many analytics and data science classes, ranging from Basic Statistics to Big Data Analytics and Deep Learning. He has taught Applied Linear Regression Analysis to students pursuing their MSBA, MBA, MST, and MAcc. He also actively oversees and mentors graduate capstone projects in Analytics for MSBA students, collaborating with Deloitte, Visa, Carnival, Citi, Experian, and many other companies. Dr. McGibney formerly served as the program director for the Herbert Business School’s MSBA degree program. He advised students, oversaw admissions, expanded industry partnerships, and advanced the program curriculum during his tenure as program director.