A biostatistics text which is also motivated by clinical problems. It is written in reponse to the increasing number of medical schools moving over to a problem-based curriculum in which clinical skills and basic science are learnt in an integrated manner.
This book presents a variety of statistical methods for analyzing medical data, ranging from basic to advanced techniques, utilizing the latest versions of S-PLUS. Each chapter features multiple sets of medical data analyzed through graphical and model-fitting approaches. At the conclusion of each chapter, S-PLUS script files are provided, allowing readers to replicate the analyses and graphics, with downloadable files available online. The book aims to demonstrate S-PLUS as a robust tool for conducting diverse statistical analyses, from simple inference to complex model fitting, while also generating informative graphics. These methods are increasingly vital for managing data from various medical investigations, including epidemiological studies and clinical trials. The integration of real data examples with theoretical background makes this resource valuable for both students and researchers. Exercises at the end of each chapter enhance students' proficiency in using S-PLUS's command line language. Professor Brian Everitt, Head of the Department of Biostatistics and Computing at the Institute of Psychiatry in London, and Sophia Rabe-Hesketh, a senior lecturer in the same department, bring their expertise to this comprehensive guide, with Professor Everitt having authored over 30 statistics books, including two previously co-authored with Dr. Rabe-Hesketh.
Focused on multivariate analysis techniques, this book offers a thorough exploration of various methods applicable across research disciplines. It emphasizes practical application by providing extensive examples of R code, enabling readers to implement these techniques effectively. The content is designed to enhance understanding and skills in handling complex data sets using R, making it a valuable resource for researchers and data analysts alike.
A Proven Guide for Easily Using R to Effectively Analyze Data Like its bestselling predecessor, A Handbook of Statistical Analyses Using R, Second Edition provides a guide to data analysis using the R system for statistical computing. Each chapter includes a brief account of the relevant statistical background, along with appropriate references. New to the Second Edition New chapters on graphical displays, generalized additive models, and simultaneous inference A new section on generalized linear mixed models that completes the discussion on the analysis of longitudinal data where the response variable does not have a normal distribution New examples and additional exercises in several chapters A new version of the HSAUR package (HSAUR2), which is available from CRAN This edition continues to offer straightforward descriptions of how to conduct a range of statistical analyses using R, from simple inference to recursive partitioning to cluster analysis. Focusing on how to use R and interpret the results, it provides students and researchers in many disciplines with a self-contained means of using R to analyze their data.
Exploring the concept of chance throughout history, this edition delves into the beliefs of early gamblers who thought divine forces influenced dice rolls, alongside contemporary insights from geneticists and quantum theorists who incorporate probability into their research. The narrative bridges ancient superstitions and modern scientific understanding, highlighting the evolution of our perception of chance.
This book provides an entertaining and popular introduction to basic concepts of probability theory. No previous knowledge of probability is assumed.