Applied Linear Regression with Python by Daniel P. McGibney (.ePUB)+
File Size: 44.8 MB
Applied Linear Regression for Business Analytics with Python: A Practical Guide Using Ravix with Case Studies by Daniel P. McGibney
Requirements: .ePUB, .PDF reader, 44.8 MB | True PDF, True EPUB
Overview: 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.
Genre: Non-Fiction > Tech & Devices
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