Nigel Smeeton – 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readers to identify up-to-date literature in their field for research, publications and teaching materialNumerous historical references throughout the text, from which to explore the origins of nonparametric methodsApplied Nonparametric Statistical Methods, Fifth Edition, is a comprehensive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.

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 issues An R package containing functions that have been written for the examples in the text and the exercises Summary bullet points at the end of each section to enable the reader to locate important principles quickly A case study from medical research to demonstrate nonparametric approaches to the data analysis Examples fully integrated into the text, drawn from published research on contemporary issues, with more detail given in their explanation Extensive exercises along with complete solutions that allow the reader to test their understanding of the material Articles 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 material Numerous historical references throughout the text, from which to explore the origins of nonparametric methods

Applied 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.