Applied Nonparametric Statistical Methods
5th Edition
Published on 31. March 2025
476 pages
978-1-040-26775-2 (ISBN)
Description
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Numerous historical references throughout the text, from which to explore the origins of nonparametric methods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.
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.
More details
Series
Edition
5th edition
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
College/higher education
Illustrations
91 Tables, black and white; 31 Line drawings, black and white; 31 Illustrations, black and white
File size
6,40 MB
ISBN-13
978-1-040-26775-2 (9781040267752)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Nigel Smeeton | Neil H. Spencer | Peter Sprent
Applied Nonparametric Statistical Methods
Book
03/2025
5th Edition
Chapman & Hall/CRC
€128.60
Shipment within 15-20 days
Persons
Nigel Smeeton commenced his career as the Statistical Assistant at Leeds University in the Department of Statistics, teaching statistics to undergraduate and postgraduate students, and running the statistical advisory service. He then joined the General Practice Research Unit at the Institute of Psychiatry, London, focusing on the modelling of episodes of mental illness, classification of mental illness, and repetition of attempted suicide. Moving to the United Medical and Dental Schools (now part of King's College London), he specialised in stroke and asthma epidemiology. Having established the undergraduate (BDS) course in dental statistics, he wrote the introductory text Dental Statistics Made Easy, which has run to three editions. With Peter Sprent, he co-authored the third and fourth editions of Applied Nonparametric Statistical Methods. He has been Editor of the applied statistics journal The Statistician.
He joined the Centre for Research in Public Health and Community Care, University of Hertfordshire in 2013. Having worked in the areas of adolescent behaviour and epilepsy risk, he is currently part of a UK National Institute for Health and Care Research (NIHR) funded team evaluating public health interventions. His statistical expertise and interests include capture-recapture methods, proportional hazards regression, kappa statistics and the historical development of statistical methods.
Neil Spencer is Professor of Applied Statistics and Head of the Business Research Unit and Statistical Services and Consultancy Unit in Hertfordshire Business School at the University of Hertfordshire. He completed a BSc in Applied Statistics at the University of Reading before moving to the University of Southampton for an MSc in Social Statistics and then to Lancaster University for a PhD in Applied Statistics. He then became a lecturer at Staffordshire University, where he first became involved in consultancy, and moved on to the University of Hertfordshire. He is a Chartered Statistician.
He has undertaken research in a variety of subjects, from Victorian censuses to value-added school league tables, paramedics treating patients whilst wearing protective equipment, health behaviour surveys of school children, compassion in education, creation of family memories, and surveys of gig economy workers across 13 European countries. Consultancy work has included testing the randomness of National Lottery machines (for over two decades), parish plan surveys, appearing as an expert witness, methods for in-service testing of utility meters, and others, for a range of clients large and small. He is author of the books SAS Programming: The One-Day Course and Essentials of Multivariate Data Analysis.
Peter Sprent started out as Tutor, then Lecturer in Mathematics at the University of Tasmania, where he taught undergraduate students and provided statistical support within the University and to government departments and agencies. A sabbatical at the renown British agricultural institution Rothhamsted Experimental Station resulted in an appointment at East Malling Research Station, where he focused on the application of statistical methods to agricultural science. Moving to the University of Dundee, he developed the provision of teaching in statistics and headed up the statistical consultancy service within the University. His research areas included statistical regression, the analysis of small experiments, and the mathematics of size and shape. He was appointed to a personal Chair and continued working at the University of Dundee until his retirement, when he was awarded an Emeritus Professorship.
He devoted his later life to writing texts, mostly on nonparametric statistics. In addition, determined to raise the understanding of statistics in society as a whole he published the books Taking Risks: The Science of Uncertainty and Understanding Data. With his wife Janet, a botanist, he co-produced the text Nitrogen Fixing Organisms: Pure and Applied Aspects and a guide to the mountains of north-west Scotland.
He joined the Centre for Research in Public Health and Community Care, University of Hertfordshire in 2013. Having worked in the areas of adolescent behaviour and epilepsy risk, he is currently part of a UK National Institute for Health and Care Research (NIHR) funded team evaluating public health interventions. His statistical expertise and interests include capture-recapture methods, proportional hazards regression, kappa statistics and the historical development of statistical methods.
Neil Spencer is Professor of Applied Statistics and Head of the Business Research Unit and Statistical Services and Consultancy Unit in Hertfordshire Business School at the University of Hertfordshire. He completed a BSc in Applied Statistics at the University of Reading before moving to the University of Southampton for an MSc in Social Statistics and then to Lancaster University for a PhD in Applied Statistics. He then became a lecturer at Staffordshire University, where he first became involved in consultancy, and moved on to the University of Hertfordshire. He is a Chartered Statistician.
He has undertaken research in a variety of subjects, from Victorian censuses to value-added school league tables, paramedics treating patients whilst wearing protective equipment, health behaviour surveys of school children, compassion in education, creation of family memories, and surveys of gig economy workers across 13 European countries. Consultancy work has included testing the randomness of National Lottery machines (for over two decades), parish plan surveys, appearing as an expert witness, methods for in-service testing of utility meters, and others, for a range of clients large and small. He is author of the books SAS Programming: The One-Day Course and Essentials of Multivariate Data Analysis.
Peter Sprent started out as Tutor, then Lecturer in Mathematics at the University of Tasmania, where he taught undergraduate students and provided statistical support within the University and to government departments and agencies. A sabbatical at the renown British agricultural institution Rothhamsted Experimental Station resulted in an appointment at East Malling Research Station, where he focused on the application of statistical methods to agricultural science. Moving to the University of Dundee, he developed the provision of teaching in statistics and headed up the statistical consultancy service within the University. His research areas included statistical regression, the analysis of small experiments, and the mathematics of size and shape. He was appointed to a personal Chair and continued working at the University of Dundee until his retirement, when he was awarded an Emeritus Professorship.
He devoted his later life to writing texts, mostly on nonparametric statistics. In addition, determined to raise the understanding of statistics in society as a whole he published the books Taking Risks: The Science of Uncertainty and Understanding Data. With his wife Janet, a botanist, he co-produced the text Nitrogen Fixing Organisms: Pure and Applied Aspects and a guide to the mountains of north-west Scotland.
Content
1. Basic concepts of statistical inference. 2. Fundamentals of nonparametric methods. 3. Exploring averages for single samples. 4. Other single-sample inferences. 5. Methods for paired samples. 6. Methods for two independent samples. 7. Basic tests for three or more samples. 8. Analysis of structured data. 9. Analysis of survival data. 10. Correlation and concordance. 11. Bivariate linear regression. 12. Categorical data. 13. Association in categorical data. 14. Robust estimation. 15. Nonparametric methods in action.
