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Practical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. Using real data and including dozens of interesting data sets, this bestselling text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.
This book provides an updated account of the regression techniques employed in comparing analytical methods and to test the biases of one method relative to others – a problem commonly found in fields like analytical chemistry, biology, engineering, and medicine. Methods comparison involves a non-standard regression problem; when a method is to be tested in a laboratory, it may be used on samples of suitable reference material, but frequently it is used with other methods on a range of suitable materials whose concentration levels are not known precisely. By presenting a sound statistical background not found in other books for the type of problem addressed, this book complements and extends topics discussed in the current literature. It highlights the applications of the presented techniques with the support of computer routines implemented using the R language, with examples worked out step-by-step. This book is a valuable resource for applied statisticians, practitioners, laboratory scientists, geostatisticians, process engineers, geologists and graduate students.
When interpreting environmental data, scientists and engineers first must select the correct statistical tool to use for their analysis. By doing this they will be able to make sound decisions in their efforts to solve environmental problems. They need a detailed reference that points out the subtle differences between statistical procedures, making clear what procedure to use when trying to find the answer to a specific problem. Statistical Tools for Environmental Quality Measurement provides a detailed review of statistical tools used in analyzing and addressing environmental issues. This book examines commonly-used techniques found in USEPA guidelines and discusses their potential impact on decision-making. The authors are not constrained by statistical formalism; they advise when to go outside of standard statistical models when making difficult decisions. The content is presented in a practical style that prioritizes methods that work, based upon the authors' extensive experience. The text points out that simplicity facilitates effective communication of an analysis and decision to a "consumer" of statistics. The book emphasizes the exact question that each procedure addresses, so that environmental scientists and engineers can clearly identify precisely the question they want to ask, and correctly interpret the results. Features
This volume contains original, refereed worldwide contributions. They were prompted by presentations made at the ninth AMCTM Conference held in G teborg (Sweden) in June 2011 on the theme of advanced mathematical and computational tools in metrology and also, in the title of this book series, in testing. The themes in this volume reflect the importance of the mathematical, statistical and numerical tools and techniques in metrology and testing and, also in keeping the challenge promoted by the Metre Convention, to access a mutual recognition for the measurement standards.
This annotated bibliography documents literature addressing the design and implementation of vegetation monitoring. It provides resources managers, ecologists, and scientists access to the great volume of literature addressing many aspects of vegetation monitoring: planning and objective setting, choosing vegetation attributes to measure, sampling design, sampling methods, statistical and graphical analysis, and communication of results. Over half of the 1400 references have been annotated. Keywords pertaining to the type of monitoring or method are included with each bibliographic entry. Keyword index.
Originally published in 1959, this classic volume has had a major impact on generations of statisticians. Newly issued in the Wiley Classics Series, the book examines the basic theory of analysis of variance by considering several different mathematical models. Part I looks at the theory of fixed-effects models with independent observations of equal variance, while Part II begins to explore the analysis of variance in the case of other models.