8 New Regression Books Reshaping the Industry in 2025

What if everything you knew about regression models was challenged by a method that evolves its own equations? This book dives into symbolic regression through the lens of genetic programming, an evolutionary algorithm mimicking natural selection to discover mathematical models without preset assumptions. You’ll get a detailed look at how this approach searches simultaneously for model structure, coefficients, and input variables, which is especially useful when interpretability matters, such as in scientific and engineering contexts. It’s tailored for data scientists and engineers who want to harness transparent, explainable AI models, with practical examples and advanced techniques to apply right away.

Drawing from his background in mathematics and computer science, Jonathan Boyle offers a clear introduction to linear regression using the R programming language. You’ll learn how to prepare data, build models, evaluate their performance, and tackle common challenges like multicollinearity and outliers. Chapter 4’s step-by-step model building and the real-world examples in later chapters make complex concepts approachable. If you want to grasp linear regression fundamentals and apply them confidently with R, this book lays out the essentials without overwhelming jargon.

This tailored book explores the rapidly evolving landscape of regression techniques shaping the data science frontier in 2025. It delves into cutting-edge methods that are transforming how data is analyzed, emphasizing fresh discoveries and emerging insights tailored to your interests and background. The content matches your specific goals, focusing on novel regression approaches, their applications, and the implications for complex data environments. By addressing your unique focus areas, this personalized exploration reveals the latest advancements in regression analysis, helping you navigate and understand new trends without sifting through unrelated material. It offers an engaging journey through innovative techniques that define the future of data science regression.

What started as a need to navigate the volatile financial markets led Hayden Van Der Post and Alice Schwarts to develop a focused guide on regression tailored for finance. You’ll learn how to prepare financial data, construct both linear and nonlinear models, and evaluate their performance with clarity and precision. The book dives into applications like risk management and portfolio optimization, giving you concrete ways to apply regression beyond theory. If you work with financial data and want to sharpen your forecasting skills using Excel and Python, this book offers practical frameworks without overcomplication.

Luc van's extensive experience with statistical data shapes this insightful guide into regression analysis techniques. The book walks you through foundational methods like simple and multiple regression, before advancing to logistic regression and time series analysis, offering a clear view on selecting appropriate models for varied data sets. You gain practical skills in interpreting regression outputs to make predictions, enriched by real-world case studies spanning diverse industries. This approach suits both students aiming to enhance their statistical toolkit and professionals eager to elevate their data-driven decision-making. While not every chapter dives deeply into complex math, it provides a solid, applicable understanding of predictive modeling with regression.

What started as a need to model more than just average outcomes led Mikis D. Stasinopoulos and his co-authors to craft this detailed exploration of generalized additive models for location, scale, and shape (GAMLSS). You’ll learn how to capture the full conditional distribution in your data, moving beyond traditional regression to understand variability and shape parameters. The book covers multiple estimation methods, including penalized likelihood, Bayesian techniques, and boosting, making it valuable if you want to tackle complex data scenarios like fetal ultrasound measurements or social media metrics. If you’re working in statistics or any data-rich field aiming to deepen your regression toolkit, this text offers rich case studies and practical R code to get you hands-on experience.

This tailored book explores the evolving landscape of regression as it moves into 2025, focusing on innovations and emerging trends that match your unique background and goals. It examines the latest discoveries and advanced regression concepts, highlighting how new techniques reshape data interpretation and model construction. You’ll gain insight into cutting-edge topics like adaptive models, AI-driven regression methods, and novel predictive approaches, all tailored to your specific interests. By concentrating on your personal objectives, this book reveals the most relevant advancements and research, making complex developments accessible and immediately applicable to your work. It’s a personalized guide designed to keep you at the forefront of regression analysis in a fast-changing field.

Eric W. Schoon and his co-authors turn the typical approach to regression on its head by shifting focus from variables to the cases—the people, organizations, and nations behind the data. Rather than just crunching numbers, this book guides you through re-analyzing influential studies to gain fresh, nuanced insights into social phenomena. It bridges quantitative techniques with qualitative perspectives, offering a method that’s both intuitive and grounded in real examples you can replicate using provided code and datasets. If you want to deepen your understanding of social science data beyond traditional regression outputs, this book lays out a distinctive path that challenges conventional statistical thinking.

After analyzing numerous machine learning approaches, Perez found that many texts skim over the practical integration of advanced regression techniques within supervised learning. This book dives into specific methods like Support Vector Machine Regression and Gaussian Process Regression, illustrated with MATLAB examples, enabling you to grasp not just theory but implementation. You'll explore ensemble methods such as boosting and bagging, as well as neural network-based regression models, all tailored to enhance predictive accuracy. If you're looking to deepen your technical toolkit with applied regression strategies in machine learning, this resource offers detailed guidance without unnecessary complexity.

Ali S. Hadi, a seasoned statistician, challenges conventional approaches by focusing on practical regression analysis with R in this thoroughly updated sixth edition. You gain a clear grasp of critical topics like regression diagnostics, multicollinearity, logistic regression, and robust regression, all illustrated with real-world data irregularities. The new chapter on R software equips you to implement methods hands-on, while expanded exercises deepen your analytical skills. This book suits upper-level students and practitioners seeking a concise yet rich resource for modern regression techniques without unnecessary complexity.