Nguyen M. Regression Techniques for Data Analysis 2026

Textbook in PDF format

This volume explores regression analysis as the backbone of statistical modeling, offering readers a versatile and robust framework for analyzing relationships in data. From foundational linear models to more complex non-linear, generalized linear, mixed, and nonparametric approaches, this volume provides a comprehensive toolkit for real-world applications. Designed for both first-time learners and experienced analysts seeking a refresher, the book emphasizes model diagnostics, interpretation, and predictive accuracy. Its intuitive explanations, paired with hands-on examples, make it ideal for students studying for exams, professionals updating their skills, or researchers applying models in practice. With applications across marketing analytics, financial forecasting, and management science, this volume bridges theory and practice to support data-driven decision-making in diverse fields.
Introduction.
Linear Regression.
Nonlinear Regression.
Generalized Linear Models.
Linear Mixed Models.
Nonlinear and Generalized Linear Mixed Models.
Nonparametric Regression.
Bibliography