Applied Categorical and Count Data Analysis 2nd Edition

Capture sharper insights from complex discrete data with Applied Categorical and Count Data Analysis, 2nd Edition by Wan Tang, Hua He, and Xin M. Tu. This authoritative, practice-focused guide brings clarity to the modeling of binary, multinomial, ordinal, and count outcomes—essential for biostatisticians, epidemiologists, social scientists, and data analysts worldwide.

Step into a book that translates advanced statistical theory into usable techniques. Clear explanations of logistic and multinomial regression, ordinal models, Poisson and negative binomial approaches, overdispersion, and generalized estimating equations are paired with model-checking strategies and real-world case studies. Readers benefit from step-by-step worked examples and annotated code snippets that illustrate implementation in commonly used software environments, helping you move from concepts to conclusions quickly.

Whether you’re conducting clinical research in North America, designing public-health studies in Europe, or analyzing population data in Asia, this edition equips you with robust tools for accurate inference and effective reporting. The authors’ combined expertise ensures rigorous yet accessible coverage, balancing mathematical detail with practical guidance for study design, interpretation, and communication of results.

Perfect for graduate courses, professional reference, or as a desk companion for applied researchers, this edition sharpens your analytical toolkit and boosts confidence in handling discrete data. Upgrade your methodology and deliver clearer, more credible findings in any applied research setting.

Order your copy today and transform how you analyze categorical and count data—become the go-to expert in your team or institution.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.