Regression Techniques for Data Analysis

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.

List of contents

Linear Regression.- Non-linear regression.- Generalized linear models.- Linear mixed models.- Non-parametric regression.

About the author

Dr. Mike Nguyen is a professor of marketing and data science whose research is deeply informed by active collaborations with firms in telecommunications, financial institutions, and digital platforms. He teaches data analytics and causal inference at both undergraduate and graduate levels, with an emphasis on business applications in marketing, finance, and strategic decision-making. His work integrates econometrics, machine learning, and experimental design to tackle real-world problems. Dr. Nguyen designs research-based solutions in partnership with industry to inform both theory and practice.

Product details

• Authors: Mike Nguyen
• Publisher: Springer, Berlin
• Languages: English
• Product format: Hardback
• Release: 20.10.2025, delayed
• EAN: 9783032018342
• ISBN: 978-3-0-3201834-2
• No. of pages: 490
• Illustrations: X, 490 p.
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Financial Modeling, Multivariate Analysis, Linear Models and Regression, Non-parametric Inference, predictive modeling, generalized linear models, Business Statistics, Linear Models, Nonparametric Regression, nonlinear regression, Linear Mixed Models, Generalized Linear Mixed Models, Nonlinear Mixed Models, GLMs, Marketing Analytics, Model Diagnostics, Mixed Models