7 Logistic Regression Books That Separate Experts from Amateurs

David W. Hosmer Jr. and his coauthors bring decades of biostatistical expertise to this book, focusing on logistic regression applications especially in health sciences. You’ll learn how to build, interpret, and assess logistic regression models through examples drawn from real research, including correlated data and Bayesian approaches. The book’s clear explanations of model fit and performance equip you to handle both nominal and ordinal outcomes confidently. If you’re working with health data or social sciences research, this text offers practical guidance grounded in modern software use, though it’s best suited for readers with some statistical background rather than novices.

Fred C. Pampel, a seasoned sociology researcher with decades of experience studying health behaviors and social disparities, delivers an accessible guide to the logic behind logistic regression without overwhelming jargon. You’ll find clear explanations of interpreting logistic coefficients across different statistical software like Stata and SPSS, plus a thoughtful overview of complications that arise in multicategory outcomes. For example, the appendix breaks down the role of logarithms, helping you grasp their function in regression modeling. This primer suits social science students and practitioners looking to demystify logistic regression with practical, example-driven clarity rather than dense mathematical theory.

This tailored book explores logistic regression through a personalized lens, focusing on your unique background and objectives. It examines tailored logistic regression methods that enhance model building precision and interpretation clarity, matching your skill level and areas of interest. By delivering content that aligns closely with your goals, it reveals nuanced techniques and practical considerations often overlooked in general texts. This tailored approach enables you to navigate complex logistic regression concepts with confidence, supporting your learning journey with targeted explanations and examples. The book delves into essential topics such as variable selection, model diagnostics, and interpretation nuances, providing a learning experience that adapts to your specific needs and accelerates mastery. It bridges foundational theory with application, making logistic regression accessible and relevant to your professional or academic pursuits.

Paul D. Allison brings his deep expertise in statistical analysis and SAS programming to this edition, guiding you through logistic regression with clarity and practical insight. He not only explains the foundational theory but also walks you through implementing various logistic regression models using SAS, including binary, multinomial, and ordered logit analyses. You gain hands-on understanding of advanced SAS procedures like PROC LOGISTIC and PROC SURVEYLOGISTIC, with detailed examples illustrating real-world applications such as longitudinal data analysis and discrete-choice modeling. This book suits researchers and students familiar with multiple linear regression who want to expand their toolkit into logistic regression nuances.

Xing Liu Ph.D.'s extensive background in educational research and advanced statistical methods led him to craft this focused guide on ordinal logistic regression. The book demystifies both single-level and multilevel modeling of ordinal categorical data, using Stata software to bridge theory and application. You'll find detailed explanations on conducting analyses, interpreting Stata output, and effectively presenting results in scholarly writing, with practical examples that clarify complex statistical concepts. This work suits students, researchers, and practitioners aiming to deepen their understanding of ordinal logistic regression within applied contexts, particularly in educational and social science research.

After completing extensive research in medical statistics, Thara Tunthanathip developed this guide to simplify the use of Jamovi for binary logistic regression in medical research. You learn how to navigate Jamovi from importing data to performing descriptive and inferential statistics, all illustrated with clear screenshots and practical examples using real medical datasets. The book is especially suited for medical researchers and clinicians who want to apply logistic regression without getting lost in complex coding. For example, chapter 4 walks you through setting up binary logistic models step-by-step, making statistical analysis accessible even if you’re new to Jamovi or statistical software in general.

This tailored book offers a hands-on exploration of logistic regression, guiding you through focused exercises and projects designed to accelerate your skills over 30 days. It covers fundamental concepts and progressively builds to more complex applications, emphasizing practical understanding through active problem-solving. By matching your background and specific goals, this personalized guide allows you to concentrate on areas that matter most, fostering deeper comprehension and confidence in logistic regression techniques. Each chapter reveals tailored challenges and insights that make learning both engaging and effective, providing a pathway that bridges core theories with your unique learning needs.

What happens when detailed SAS expertise meets the practical needs of logistic regression? This volume by the SAS Institute provides a focused exploration of creating and handling SAS data sets tailored for logistic regression analysis. You gain a hands-on understanding of how raw data is structured and printed within the SAS environment, which is crucial for anyone looking to apply logistic regression models using this system. While not an in-depth statistical theory text, it offers clear examples that support users in navigating SAS's capabilities effectively. This book suits data analysts and statisticians who work specifically with SAS and want to improve their data preparation and management skills for logistic regression tasks.

Drawing from decades of expertise in analytics software, SAS Institute crafted this course note to demystify foundational statistical methods including ANOVA, regression, and logistic regression. You’ll gain a clear understanding of basic regression techniques and their applications, with practical examples suited for beginners and those preparing for statistical analyst certifications. The book breaks down concepts like variable relationships and model interpretation, making it accessible for analysts transitioning to more advanced data work. If you’re looking to solidify your grasp on introductory statistical modeling with an emphasis on logistic regression, this guide offers straightforward explanations without unnecessary jargon.