Developing a model-based approach that enables any cross-over trial, of any degree of imbalance, to be analyzed both for direct effects and for residual effects, using consistent procedures that employ commercially available statistical software, this text offers a guide to the analysis of cross-over designs.;Illustrating practical applications throughout with examples, this book: emphasizes the importance of choosing highly efficient designs that separate treatment and carryover effects; demonstrates the exact methodology needed to handle the analysis of data; presents a new methodology for the analysis of binary and categorical data; and considers the effects of blocking. The appendices facilitate the choosing of an appropriate design for every experimental need.
Developing a model-based approach that enables any cross-over trial, of any degree of imbalance, to be analyzed both for direct effects and for residual effects, using consistent procedures that employ commercially available statistical software, this text offers a guide to the analysis of cross-over designs.;Illustrating practical applications throughout with examples, this book: emphasizes the importance of choosing highly efficient designs that separate treatment and carryover effects; demonstrates the exact methodology needed to handle the analysis of data; presents a new methodology for the analysis of binary and categorical data; and considers the effects of blocking. The appendices facilitate the choosing of an appropriate design for every experimental need.
With thirty revised and updated chapters the new edition of this classic text brings benefits to professors and students alike who will find new sections on many topics concerning modern food microbiology. This authoritative book builds on the trusted and established sections on food preservation by modified atmosphere, high pressure and pulsed electric field processing. It further covers food-borne pathogens, food regulations, fresh-cut produce, new food products, and risk assessment and analysis. In-depth references, appendixes, illustrations, index and thorough updating of taxonomies make this an essential for every food scientist.
This monograph provides a careful review of the major statistical techniques used to analyze regression data with nonconstant variability and skewness. The authors have developed statistical techniques--such as formal fitting methods and less formal graphical techniques-- that can be applied to many problems across a range of disciplines, including pharmacokinetics, econometrics, biochemical assays, and fisheries research. While the main focus of the book in on data transformation and weighting, it also draws upon ideas from diverse fields such as influence diagnostics, robustness, bootstrapping, nonparametric data smoothing, quasi-likelihood methods, errors-in-variables, and random coefficients. The authors discuss the computation of estimates and give numerous examples using real data. The book also includes an extensive treatment of estimating variance functions in regression.
Twenty years ago, researchers wishing to identify contaminated areas in aquatic environments generally took water samples, and analysed them badly (as we have since discovered) for a few "pollutants" which were of topical note at the time (and which could be quantified by the methods then available). Today, the use of aquatic organisms as biomonitors in preference to water analysis has become commonplace, and many national and interna tional programmes exist around the world involving such studies. We believe that this trend will continue, and have complete faith in the methodology (when it is employed correctly). We hope that the following text assists in some part in attaining this goal, such that the quality of our most basic global resource -water - is adequately protected in the future. DAVE PHILLIPS, PHIL RAINBOW England, March 1992 vii Acknowledgements Our thanks for contributions to this book are due to several individuals and groups, for varying reasons. Firstly, a co-authored book is always a triumph, and we trust that the following text is an acceptable compromise of the views of two individual authors, on a complex and developing topic. Secondly, many of the ideas herein have crystallised over the last two decades as the field has grown, and we are individually and collectively grateful to a number of researchers for their insight and assistance.
Statistics provides tools and strategies for the analysis of data. While much has been written about the methodology, sometimes without reference to data, little has been said about the data. In this volume we present sets of data obtained from many situations without any direct reference to a particular type of analysis. Our view of the usefulness of bringing together a broad collection of sets of data has been shared by many friends and contributors. Students of statistics need to gain facility with their art by applying their knowledge to many sets of data. Textbook examples tend to be small and selected primarily to illustrate a particular technique, thus failing to demonstrate the questioning, iterative nature of statistical analysis. The situations which gave rise to the more extensive sets of data given in this volume are colourful and interesting, and can be readily understood by laymen, students and research workers with diverse interests. These sets were often chosen for their perverse reluctance to yield under the naive application of standard procedures. They do not have correct solutions. They describe situations where the statisti cian can develop skills and learn the limitations of statistical methods.
This book brings together the scattered literature associated with the seemingly unrelated regression equations (SURE) model used by econometricians and others. It focuses on the theoretical statistical results associated with the SURE model.
Nonlinear measurement data arise in a wide variety of biological and biomedical applications, such as longitudinal clinical trials, studies of drug kinetics and growth, and the analysis of assay and laboratory data. Nonlinear Models for Repeated Measurement Data provides the first unified development of methods and models for data of this type, with a detailed treatment of inference for the nonlinear mixed effects and its extensions. A particular strength of the book is the inclusion of several detailed case studies from the areas of population pharmacokinetics and pharmacodynamics, immunoassay and bioassay development and the analysis of growth curves.
More than 300 species of Australian native animals — mammals, birds, reptiles and amphibians — use tree hollows, but there has never been a complete inventory of them. Many of these species are threatened, or are in decline, because of land-use practices such as grazing, timber production and firewood collection. All forest management agencies in Australia attempt to reduce the impact of logging on hollow-dependent fauna, but the nature of our eucalypt forests presents a considerable challenge. In some cases, tree hollows suitable for vertebrate fauna may take up to 250 years to develop, which makes recruiting and perpetuating this resource very difficult within the typical cycle of human-induced disturbance regimes. Tree Hollows and Wildlife Conservation in Australia is the first comprehensive account of the hollow-dependent fauna of Australia and introduces a considerable amount of new data on this subject. It not only presents a review and analysis of the literature, but also provides practical approaches for land management.
Risk Analysis concerns itself with the quantification of risk, the modeling of identified risks and how to make decisions from those models. Quantitative risk analysis (QRA) using Monte Carlo simulation offers a powerful and precise method for dealing with the uncertainty and variability of a problem. By providing the building blocks the author guides the reader through the necessary steps to produce an accurate risk analysis model and offers general and specific techniques to cope with most modeling problems. A wide range of solved problems is used to illustrate these techniques and how they can be used together to solve otherwise complex problems.
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