7 Best-Selling Regression Books Millions Trust and Use

What began as Jacob Cohen's effort to demystify statistical methods for behavioral scientists has become a cornerstone text that blends clarity with practical application. You gain a strong grasp of multiple regression through verbal explanations and numerous examples, including how to specify models tailored to your specific research questions. The book's chapters are accessible independently, making it easier to focus on topics like effect size measures and confidence intervals without getting bogged down. Whether your focus is psychology, education, or political science, this book offers a solid foundation to interpret and apply regression analysis effectively.

The methods William D. Berry developed while studying public policy and research methodology shine through in this concise guide to multiple regression analysis. Berry and Feldman tackle common pitfalls in regression, such as assumption violations, and offer practical detection and correction strategies without relying on complex matrix algebra. You’ll find concrete examples drawn from political science, sociology, and economics, which clarify how to apply these techniques in social science research. This book suits anyone aiming to deepen their grasp of regression analysis nuances, especially those engaged in empirical policy or social research who want a clear, focused resource without heavy mathematical jargon.

This tailored book explores advanced methods and practical applications in regression analysis, focusing on your specific interests and goals. It examines key concepts such as model diagnostics, nonlinear regression, and categorical data techniques, providing a personalized learning journey that matches your background. By integrating reader-validated knowledge and proven approaches, the book reveals how to apply regression insights effectively in real-world scenarios. Its tailored content enables you to deepen your understanding of complex regression topics, empowering you to confidently interpret results and refine your analytical skills.

The methods Norman R. Draper and Harry Smith developed while teaching and researching have shaped this detailed guide to regression analysis. You’ll gain a solid understanding of fitting and evaluating both linear and nonlinear models, including complex topics like multicollinearity and robust regression, presented with clarity that assumes only basic stats knowledge. The book’s inclusion of exercises, data sets, and computational techniques bridges theory and practice effectively, making it a practical tool if you’re involved in statistical analysis or applied sciences. While dense, it suits anyone ready to deepen their grasp of regression beyond surface-level concepts.

Paul D. Allison's decades of experience in sociology and statistics shine through this approachable primer on multiple regression. Written in a question-and-answer format, the book demystifies concepts like interpreting coefficients and model assumptions without heavy math jargon. You’ll find clear explanations on handling common issues such as multicollinearity and model specification, making it accessible whether you’re a graduate student or researcher refining your analysis skills. This primer works best if you want a solid conceptual grasp rather than advanced technical depth, providing foundational insights that underpin more complex statistical modeling.

Unlike most regression books that focus purely on theoretical model fitting, this work by Chatterjee and Hadi dives into the practical challenges of applying linear regression to real-world data sets. It highlights how measurement errors can skew coefficient estimates, something often overlooked in standard least squares methods. You'll find detailed examples showing how to assess the robustness of regression fits both qualitatively and quantitatively, offering you tools to critically evaluate your model's reliability. This book suits statisticians, data analysts, and researchers who need to understand the subtle nuances affecting regression outcomes beyond basic assumptions.

This tailored book explores rapid skill development in regression, focusing precisely on your background, interests, and goals. It examines fundamental concepts and advanced techniques, blending widely validated knowledge with your specific learning needs. The personalized content covers essential regression methods, model interpretation, diagnostics, and practical applications, ensuring you build confidence quickly and effectively. By concentrating on what matters most to you, this book reveals how to accelerate your understanding and application of regression analysis in real-world scenarios. Its tailored nature ensures an efficient learning journey that matches your pace and aspirations, making complex ideas accessible and relevant.

John Scott Long's extensive experience as a sociology and statistics professor shines through in this book, where he presents a cohesive framework for understanding models dealing with categorical and limited dependent variables. You will learn how these models connect to traditional linear regression, gaining clarity on interpretation, testing, and practical derivation methods. The text carefully bridges complex statistical techniques with social science applications, making it particularly useful if your work involves analyzing non-continuous outcomes. While the book assumes some statistical background, its structured approach benefits those aiming to deepen their understanding of regression beyond standard linear models.

Leo H. Kahane’s Regression Basics takes a straightforward, approachable path into regression analysis, designed for anyone with a foundation in introductory statistics. Rather than overwhelming you with jargon, Kahane builds your understanding step-by-step, starting with simple two-variable models and gradually introducing more complexity. The book’s strength lies in its rich, real-world examples—from analyzing baseball salaries to exploring crime rates—that show how regression can illuminate diverse social and economic issues. If you’re studying economics, political science, or sociology, this book offers clear explanations and practical software guidance, making it a solid choice to grasp the essentials of regression techniques.