Peter Sprent – författare

Nonparametric statistical methods minimize the number of assumptions that need to be made about the distribution of data being analysed, unlike classical parametric methods. As such, they are an essential part of a statistician’s armoury, and this book is an essential resource in their application. Starting from the basics of statistics, it takes the reader through the main nonparametric approaches with an emphasis on carefully explained examples backed up by use of the R programming language.Key features of this fully revised and extended fifth edition include the following:An introductory chapter that provides a gentle introduction to the basics of statistics, including types of data, hypothesis testing, confidence intervals and ethical issuesAn R package containing functions that have been written for the examples in the text and the exercisesSummary bullet points at the end of each section to enable the reader to locate important principles quicklyA case study from medical research to demonstrate nonparametric approaches to the data analysisExamples fully integrated into the text, drawn from published research on contemporary issues, with more detail given in their explanationExtensive exercises along with complete solutions that allow the reader to test their understanding of the materialArticles used in the examples and exercises carefully chosen to enable readers to identify up-to-date literature in their field for research, publications and teaching materialNumerous historical references throughout the text, from which to explore the origins of nonparametric methodsApplied Nonparametric Statistical Methods, Fifth Edition, is a comprehensive course text in nonparametric techniques suitable for undergraduate students of mathematics and statistics. It assumes only basic previous experience of statistics, and with algebra kept to a minimum, it is also ideal for quantitative methods modules delivered to undergraduate or postgraduate students in science, business and health service training. It is an invaluable resource for researchers, medical practitioners, business managers, research and development staff, and others needing to interpret quantitative information. Suitable for self-directed learning in continuing professional development, it also acts as a handy accessible reference manual.

Data Driven Statistical Methods is designed for use either as a text book at the undergraduate level, as a source book providing material and suggestions for teachers wishing to incorporate some of its features into more general courses, and also as a self-instruction manual for applied statisticians seeking a simple introduction to many important practical concepts that use the 'data driven' rather than the 'model driven' approach.

While preserving the clear, accessible style of previous editions, Applied Nonparametric Statistical Methods, Fourth Edition reflects the latest developments in computer-intensive methods that deal with intractable analytical problems and unwieldy data sets. Reorganized and with additional material, this edition begins with a brief summary of some relevant general statistical concepts and an introduction to basic ideas of nonparametric or distribution-free methods. Designed experiments, including those with factorial treatment structures, are now the focus of an entire chapter. The text also expands coverage on the analysis of survival data and the bootstrap method. The new final chapter focuses on important modern developments, such as large sample methods and computer-intensive applications.Keeping mathematics to a minimum, this text introduces nonparametric methods to undergraduate students who are taking either mainstream statistics courses or statistics courses within other disciplines. By giving the proper attention to data collection and the interpretation of analyses, it provides a full introduction to nonparametric methods.