Bryan F. J. Manly – författare

This book provides students and research workers in the biological, medical and social sciences with the statistical background needed to collect and analyse data in an intelligent and critical manner. Key examples and case studies are used to illustrate commonly encountered research problems and to explain how they may be solved or even avoided altogether. Professor Manly also presents a clear understanding of the opportunities and limitations of different research designs, as well as an introduction to some new methods of analysis that are proving increasingly popular. Topics covered include the differences between observational and experimental studies; the design of sample surveys; multiple regression and its generalizations to log-linear and logistic models; experimental and quasi-experimental designs; interrupted time series; computer intensive methods of statistical inference; and the ethical considerations of research. In the final chapter, there is a useful discussion of how the various components of a research study (including deciding on the objectives, planning, designing, and the collection and analysis of data) come together. This practical and well-structured book will be essential reading for graduate students and researchers in a wide range of disciplines, including biology, anthropology, medicine and the social sciences.

Every day, biologists in parkas, raincoats, and rubber boots go into the field to capture and mark a variety of animal species. Back in the office, statisticians create analytical models for the field biologists' data. But many times, representatives of the two professions do not fully understand one another's roles. This book bridges this gap by helping biologists understand state-of-the-art statistical methods for analyzing capture-recapture data. In so doing, statisticians will also become more familiar with the design of field studies and with the real-life issues facing biologists. Reliable outcomes of capture-recapture studies are vital to answering key ecological questions. Is the population increasing or decreasing? Do more or fewer animals have a particular characteristic? In answering these questions, biologists cannot hope to capture and mark entire populations. And frequently, the populations change unpredictably during a study. Thus, increasingly sophisticated models have been employed to convert data into answers to ecological questions.This book, by experts in capture-recapture analysis, introduces the most up-to-date methods for data analysis while explaining the theory behind those methods. Thorough, concise, and portable, it will be immensely useful to biologists, biometricians, and statisticians, students in both fields, and anyone else engaged in the capture-recapture process.

Multivariate Statistical Methods: A Primeroffers an introduction to multivariate statistical methods in a rigorous yet intuitive way, without an excess of mathematical details. In this fifth edition, all chapters have been revised and updated, with clearer and more direct language than in previous editions, and with more up-to-date examples, exercises, and references, in areas as diverse as biology, environmental sciences, economics, social medicine, and politics.Features• A concise and accessible conceptual approach that requires minimal mathematical background.• Suitable for a wide range of applied statisticians and professionals from the natural and social sciences.• Presents all the key topics for a multivariate statistics course.• The R code in the appendices has been updated, and there is a new appendix introducing programming basics for R.• The data from examples and exercises are available on a companion website.This book continues to be a great starting point for readers looking to become proficient in multivariate statistical methods, but who might not be deeply versed in the language of mathematics. In this edition, we provide readers with conceptual introductions to methods, practical suggestions, new references, and a more extensive collection of R functions and code that will help them to deepen their toolkit of multivariate statistical methods.

Multivariate Statistical Methods: A Primeroffers an introduction to multivariate statistical methods in a rigorous yet intuitive way, without an excess of mathematical details. In this fifth edition, all chapters have been revised and updated, with clearer and more direct language than in previous editions, and with more up-to-date examples, exercises, and references, in areas as diverse as biology, environmental sciences, economics, social medicine, and politics.Features• A concise and accessible conceptual approach that requires minimal mathematical background.• Suitable for a wide range of applied statisticians and professionals from the natural and social sciences.• Presents all the key topics for a multivariate statistics course.• The R code in the appendices has been updated, and there is a new appendix introducing programming basics for R.• The data from examples and exercises are available on a companion website.This book continues to be a great starting point for readers looking to become proficient in multivariate statistical methods, but who might not be deeply versed in the language of mathematics. In this edition, we provide readers with conceptual introductions to methods, practical suggestions, new references, and a more extensive collection of R functions and code that will help them to deepen their toolkit of multivariate statistical methods.