8 Linear Regression Books That Separate Experts from Amateurs

Drawing from their extensive backgrounds in statistics and political science, Colin Lewis-Beck and Michael S. Lewis-Beck present regression analysis in a way that demystifies both its theory and application for social scientists. You’ll explore the fundamentals of fitting linear models, then advance through topics like multiple regression, interaction effects, and multicollinearity, all illuminated with clear graphical examples and practical interpretations. The book’s focus on the assumptions and diagnostics of regression equips you to critically evaluate your models beyond mere computation. If your work involves quantitative data in social research, this text offers a solid foundation without overwhelming jargon.

Douglas C. Montgomery's extensive experience in engineering statistics shines through in this detailed guide to regression analysis. You’ll explore foundational principles alongside practical tools like model adequacy checking, transformations, and handling influential data points. The book also dives into specialized topics, such as regression for time series data and mixed models, offering examples from fields like engineering and health sciences. Whether you want to understand patient satisfaction models or improve your statistical inference skills, this text equips you with a solid framework and software applications including R and SAS.

This tailored book explores linear regression theory and application with a focus on your unique goals and background. It delves into fundamental concepts such as model formulation, assumption checking, and coefficient interpretation, while also examining advanced topics like diagnostics, multicollinearity, and model selection. The content is carefully synthesized to align with your interests, providing a clear pathway through complex material and bridging expert knowledge with your specific needs. By addressing your particular areas of focus, this personalized guide facilitates a more engaging and efficient learning experience that deepens your understanding of linear regression's practical and theoretical aspects.

The methods Jim Frost developed while working at a statistical software company shine through in this approachable guide, which breaks down regression analysis into understandable concepts rather than dense equations. You’ll learn how to specify and assess models, interpret statistical significance, and troubleshoot common issues using practical examples and downloadable datasets. Chapters on modeling curvature and interaction effects deepen your grasp beyond basics, making it useful whether you’re just starting or aiming to sharpen your analytical skills. This book suits anyone needing to confidently analyze data without getting lost in technical jargon, especially applied researchers and data analysts.

Damodar Gujarati's decades of teaching and consulting experience culminate in this mathematically focused introduction to linear regression tailored for social science students. You’ll explore regression theory presented with precision yet without overwhelming abstraction, allowing you to grasp core concepts such as model assumptions, estimation techniques, and hypothesis testing. The book includes exercises that reinforce your understanding and appendices that delve deeper into complex topics, making it a solid choice for those seeking a rigorous foundation in regression analysis. If you want to master the mathematical framework behind linear regression rather than just its applications, this book suits you well; it’s less about quick fixes and more about building true analytical skill.

What happens when decades of academic rigor meet practical regression challenges? Samprit Chatterjee, a seasoned statistician and professor emeritus at NYU, crafts this edition to bridge theory and application in regression analysis. You’ll explore real-world examples highlighting regression diagnostics, handling multicollinearity, logistic regression, and nonlinear models, with fresh data sets from engineering and medicine. Chapters like the new multicollinearity detection dive deep but remain accessible, empowering you to balance statistical theory with empirical judgment. This book suits anyone comfortable with elementary statistics who wants to sharpen their regression toolkit with nuanced, example-driven insights.

This tailored book delves into the core principles and practical applications of linear regression with a focus on accelerating your skill development. It explores foundational concepts like model fitting and assumption checking before guiding you through progressively advanced topics such as interaction effects and diagnostic techniques. By aligning content with your background and goals, the book reveals a clear path for rapid enhancement of your regression abilities. Tailored to your interests, it connects theoretical understanding with hands-on activities, enabling you to confidently interpret results and apply linear regression in your field. This personalized approach ensures efficient learning by concentrating on what matters most to you.

Richard B. Darlington, a seasoned psychologist and emeritus professor at Cornell University, brings decades of expertise to this text, aiming to demystify linear regression for social and behavioral scientists. You learn to construct and interpret linear models with clarity, focusing on conceptual understanding rather than heavy mathematics. The book covers a range of topics from moderation and mediation analysis to regression diagnostics, enriched by concrete examples and software code snippets for SPSS, SAS, and STATA, plus R resources. If you're working in social or health sciences and want to deepen your grasp of regression techniques with practical tools, this book suits you well, although those seeking purely mathematical treatments may find it less technical.

Julian J. Faraway’s decades of experience as a statistics professor shine through in this detailed guide to linear modeling with R. You’ll learn how to apply linear models to diverse fields like physical sciences, engineering, and social sciences, gaining skills in prediction, inference, and handling missing data. The book’s updated second edition introduces modern topics such as splines, additive models, and Lasso, while emphasizing interpretation and causality. If you want to deepen your practical understanding of statistical modeling and see clear examples using R’s ggplot2 and base graphics, this book fits that need well.

Michael H. Kutner and his co-authors bring decades of statistical expertise to this edition, distilling complex linear modeling techniques into structured, accessible chapters. You’ll gain a deep understanding of applied linear statistical models, including regression diagnostics, variance analysis, and experimental design, with examples grounded in real data. The book is ideal if you want to master statistical reasoning and modeling skills critical for data analysis in research or industry. While dense, chapters like regression model building and hypothesis testing provide practical frameworks for interpreting data with confidence.