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readers to identify up-to-date literature in their field for research, publications and teaching materialNumerous historical references throughout the text, from which to explore the origins of nonparametric methodsApplied Nonparametric Statistical Methods, Fifth Edition, is a comprehensive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

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research. Unlike m

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.

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.

Aimed at researchers and students , SAS Programming: The One-Day Course provides an introduction to the SAS programming language. It gives the reader a start in SAS programming and the basic data manipulations and statistical summaries that are available through SAS. The book has its origins in material prepared by the author for a one-day course i

Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research. Unlike most books on multivariate methods, this one makes straightforward analyses easy to perform for those who are unfamiliar with advanced mathematical formulae. An easily understood dataset is used throughout to illustrate the techniques. The accompanying add-in for Microsoft Excel can be used to carry out the analyses in the text. The dataset and Excel add-in are available for download on the book‘s CRC Press web page.Providing a firm foundation in the most commonly used multivariate techniques, this text helps readers choose the appropriate method, learn how to apply it, and understand how to interpret the results. It prepares them for more complex analyses using software such as Minitab, R, SAS, SPSS, and Stata.

Aimed at researchers and students , SAS Programming: The One-Day Course provides an introduction to the SAS programming language. It gives the reader a start in SAS programming and the basic data manipulations and statistical summaries that are available through SAS. The book has its origins in material prepared by the author for a one-day course in SAS programming, and the fact that it has been developed from a training course is reflected in the concise nature of the presentation.Unlike other introductory competitors on the market, this is a pocket-sized reference that does not clutter the programming techniques presented by trying to teach statistical techniques at the same time. Strong on explanations of how to carry out data manipulations that real-life data often call for, each programming technique is supported by tasks to develop skills and confidence. It also contains "tasks" for the reader, complete with solutions. Datasets and the programming code are available to download from www.crcpress.com/e_products/downloads.Once readers have mastered the topics covered in the book, they will be well placed to learn further aspects of SAS programming.