8 Regression Books That Separate Experts from Amateurs

Peter H. Westfall's extensive background in statistics shines through in this book, which challenges traditional regression assumptions by treating all models as approximations rather than exact representations of nature. You learn how to apply a unified conditional distribution framework that covers classical, generalized, and modern regression techniques, including neural networks and decision trees. The book offers clear explanations of concepts like p-values aligned with American Statistical Association guidelines and leverages R software for practical examples. If you're engaged in scientific research or advanced statistics, this book equips you to model realistically and critically evaluate regression methods.

Andrew Gelman's extensive experience as a Higgins Professor of Statistics and Political Science shaped this book's focused approach on applying regression to real social research problems rather than just theory. Together with Jennifer Hill and Aki Vehtari, he presents practical methods for comparison, estimation, prediction, and causal inference using regression, emphasizing computation in R and Stan. You'll find detailed examples addressing challenges like sample size and missing data, alongside guidance on assumptions and model fitting that go beyond the usual textbook fare. This work suits you if you want to master regression techniques directly applicable to experiments and observational studies in social sciences.

This tailored book explores the multifaceted world of regression analysis, focusing on your unique background and learning goals. It offers a personalized journey through foundational concepts and advanced topics, illuminating the nuances of regression techniques, assumptions, and interpretations. The content is carefully adjusted to match your experience level, ensuring you engage deeply with areas most relevant to your interests, from linear models to diagnostic assessments. By synthesizing broad expert knowledge, this book reveals how to effectively harness regression for your specific data challenges, fostering a practical and confident understanding that bridges theory and application. The tailored approach ensures you gain clarity and mastery in a way that speaks directly to your objectives.

After decades as a Regents Professor and Fellow of multiple statistical societies, Douglas C. Montgomery brings his extensive expertise to this book, aimed at demystifying linear regression analysis for advanced students and professionals alike. You’ll explore foundational concepts like inference procedures and polynomial regression, while also diving into specialized topics such as time series regression and mixed models with random effects. The inclusion of software examples across JMP, SAS, R, and more makes these methods tangible, especially when examining practical cases like patient satisfaction data. If you’re working in engineering, biological sciences, or social research and want a thorough grounding in both theory and application, this book fits that niche precisely.

Drawing from John Fox's extensive background in sociology and statistics, this book offers a detailed exploration of applied regression within the R programming environment. You’ll gain practical skills in integrating statistical computing with regression analysis, including working with generalized linear models and mixed-effects models. The text balances foundational R programming with advanced applications, supported by updated packages like car and effects, plus coverage of RStudio and R Markdown. If your goal is to deepen your proficiency in R for regression tasks, especially in social sciences or data analysis, this resource provides a solid, methodical approach without overcomplicating programming details.

Simon Sheather’s extensive experience as a professor and head of a statistics department shapes this focused guide on regression modeling. You’ll learn how to build and validate regression models rigorously, with a strong emphasis on understanding residuals and diagnostic plots to detect model misspecification. The book uses real-world data examples and integrates R-generated outputs, helping you grasp both theoretical concepts and practical implementation. It’s particularly useful if you want to deepen your grasp of model validity and the statistical reasoning behind it, rather than just running software commands.

This personalized book explores the essentials of rapid, hands-on regression learning tailored to your unique projects and experience. It covers core regression concepts, guiding you through data preparation, model selection, and interpretation with examples focused on your specific interests. The book delves into practical regression techniques, from linear to advanced models, emphasizing understanding assumptions and diagnostics relevant to your goals. By matching content to your background and desired outcomes, it fosters a rich learning experience that builds confidence and skill efficiently. Through this tailored approach, you engage deeply with regression analysis, mastering application nuances that matter most to your work or study.

When Frank E. Harrell Jr. wrote this book, he drew deeply on his experience as a biostatistics professor and consultant, aiming to tackle real challenges in multivariable predictive modeling beyond textbook examples. You’ll explore how to apply regression techniques like generalized least squares for longitudinal data, logistic and ordinal regression, and survival analysis through detailed case studies using R software. The book’s emphasis on problem-solving strategies and robust analysis equips you to handle complex datasets and avoid common pitfalls, making it especially useful if you’re pursuing advanced graduate studies or working with applied statistics in medical or pharmaceutical research. While dense, its comprehensive approach rewards those seeking to master nuanced regression modeling in practical contexts.

What happens when expert political scientists deeply versed in statistics tackle regression analysis? Colin Lewis-Beck and Michael S. Lewis-Beck bring decades of academic and applied experience to this clear introduction aimed at social scientists and professionals. You’ll explore everything from simple linear regression to more complex topics like multicollinearity and interaction effects, all explained with accessible language and concrete examples, including graphical analyses throughout the chapters. This book suits those seeking to understand how regression models answer real scientific questions, especially in social research contexts, though it may be less ideal if you’re looking for highly technical or purely mathematical treatments.

John Fox, a seasoned sociologist and Professor Emeritus of Sociology at McMaster University, brings his deep expertise in social statistics to this focused examination of regression diagnostics. You’ll learn specific techniques for evaluating how well regression models fit your data, including methods to check assumptions like linearity and normality, with detailed attention to both normal linear regression and generalized linear models. The book also offers practical R code and datasets for hands-on application, making it ideal if you want to sharpen your diagnostic toolkit for statistical modeling. If you’re looking for a rigorous yet accessible guide to understanding and improving your regression analyses, this book will suit your needs well.