Applied Linear Regression for Business Analytics with Python: A Practical Guide Using Ravix with Case Studies (International Series in Operations Research & Management Science, 378) - Hardcover

McGibney, Daniel P.

Hardcover

ISBN 10: 3032238056 ISBN 13: 9783032238054

Verlag: Springer, 2026

Inhaltsangabe

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.

Über diesen Titel

Über die Autorin bzw. den Autor

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.

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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.

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2026
• Sprache: Englisch
• ISBN 10: 3032238056
• ISBN 13: 9783032238054
• Einband: Gebundene Ausgabe
• Anzahl der Seiten: 346
• Kontakt zum Hersteller: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Tiergartenstr. 17; Heidelberg; 69121; Deutschland