Applied Multivariate Statistical Analysis in Medicine 1st Edition

Applied Multivariate Statistical Analysis in Medicine (1st Edition) by Jingmei Jiang is an essential resource for clinicians, researchers, and public‑health professionals who need clear, practical guidance on advanced statistical techniques used in medical research. This authoritative text draws attention with accessible explanations of multivariate methods tailored specifically to the complexities of healthcare data.

Building on real clinical scenarios and case studies, the book guides readers through core topics—principal component and factor analysis, multivariate regression, discriminant analysis, MANOVA, cluster analysis, and approaches for correlated outcomes common in longitudinal and epidemiologic studies. Each chapter connects statistical theory to interpretation and decision‑making, helping you translate results into meaningful clinical insights and publishable findings.

Designed for busy practitioners and graduate students alike, the tone is concise yet thorough, emphasizing applied examples, model selection, diagnostics, and best practices for reproducible analysis. Whether you work in academic hospitals, pharmaceutical research, or public‑health agencies in North America, Europe, or Asia, this volume equips you with tools to handle high‑dimensional datasets, improve study design, and strengthen evidence-based conclusions.

If you want a dependable, practitioner-focused guide to multivariate methods in medicine, Jingmei Jiang’s book is an investment in stronger analysis and clearer communication of complex results. Add this title to your library to advance your research skills and confidently tackle multivariate challenges in clinical and epidemiologic work.

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