Applied Nonparametric Statistical Methods, Fourth Edition / Edition 4

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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.

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Editorial Reviews

From the Publisher
… The greatest strength of this book is that it is written at a level that is perfectly understandable by readers with only a course or two of introductory-level statistics. As such, it is appropriate for use as either a textbook for a first course in nonparametric methods for undergraduate statistics majors or as a reference for practitioners in other fields. It is also quite suitable as a supplementary statistics textbook for graduate students … . Key concepts are taught using worked-out examples from a variety of fields. … a worthwhile choice for either an introductory-level textbook or a self-study reference for nonspecialists. The writing is very accessible and not weighted down by any mathematics beyond the grasp of the intended audience. …
Psychometrika, Vol. 75, No. 3, September 2010

… this book has an effective organization and covers a wider scope of non-parametric methods than former editions. Therefore, I believe that this book can serve its intended audience.
Journal of the Royal Statistical Society, Series A, Vol. 173, Issue 1, January 2010

Most fourth editions look surprisingly similar to the third editions. Applied Nonparametric Statistical Methods is an exception. Sprent and Smeeton have taken an accessible and well-regarded work and expanded, reorganized, and improved on it. … Sprent and Smeeton offer a strong connection with respect to the how and why of the techniques. … The book’s major strength is its prioritization of coverage. The authors take painstaking care to inculcate an understanding of the appropriate use of nonparametric methods, as well as an appreciation for their application over a wide range of fields. The examples are well chosen, and the variety should ensure that every reader finds at least some of the problems interesting. … As a competitor to the texts by Conover (1999), Gibbons and Chakraborti (2004), Higgins (2004), and Wasserman (2006), Applied Nonparametric Statistical Methods more than holds its own. The combination of clear writing and comprehensive coverage make it an excellent introductory text. …
Technometrics, Vol. 51, No. 2, May 2009

…The chapters have been substantially reorganized, and new material is provided on methods related to factorial designs and time-to-event data. An entirely new chapter, ‘Modern Nonparametrics,’ closes the text with a variety of topics … the worked examples are thoroughly and meticulously done … constant mention is made of the available software (e.g., StatXact, R, Minitab, SPSS) to conduct specific procedures. … solutions to selected end-of-chapter exercises are annotated and quite helpful. Overall, this is a solid choice for a first course in nonparametric statistics for undergraduates.
Journal of the American Statistical Association, Vol. 104, No. 487, September 2009

… expands coverage on the analysis of survival data and the bootstrap method. … the new edition also focuses on some modern developments. The formal testing procedures are illustrated in a nice way with realistic examples leading to final conclusions, comments, and a discussion… The book has a clear style with well-organized material. The book works well as a reference book for users of nonparametric methods in different research areas. It is also a good textbook for undergraduate courses in statistics as well as courses for students majoring in other disciplines.
—Hannu Oja, International Statistical Review, Vol. 27, No. 1, 2008

Praise for the Third Edition
Strengths of this text certainly include its organization and writing style. Applied Nonparametric Statistical Methods provides a very clear exposition of modern nonparametric methods. Many students and practitioners will find it an excellent resource and reference for nonparametric statistics.
—Technometrics, 2003

… extremely valuable for statisticians as well as for researchers in applied fields. … This well-written book is highly recommended for those readers who want to get a feeling for the nonparametric methods which they apply when analysing their data.
Statistics in Medicine, 2004

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Product Details

Table of Contents


Basic Statistics
Populations and Samples
Hypothesis Testing
Ethical Issues

A Permutation Test
Binomial Tests
Order Statistics and Ranks
Exploring Data
Efficiency of Nonparametric Procedures
Computers and Nonparametric Methods
Further Reading

Layout of Examples
Continuous Data Samples
Inferences about Medians Based on Ranks
The Sign Test
Use of Alternative Scores
Comparing Tests and Robustness
Fields of Application

Other Data Characteristics
Matching Samples to Distributions
Inferences for Dichotomous Data
Tests Related to the Sign Test
A Runs Test for Randomness
Angular Data
Fields of Application

Comparisons in Pairs
A Less Obvious Use of the Sign Test
Power and Sample Size
Fields of Application

Centrality Tests and Estimates
The Median Test
Normal Scores
Tests for Equality of Variance
Tests for a Common Distribution
Power and Sample Size
Fields of Application

Comparisons with Parametric Methods
Centrality Tests for Independent Samples
The Friedman Quade and Page Tests
Binary Responses
Tests for Heterogeneity of Variance
Some Miscellaneous Considerations
Fields of Application

Factorial Treatment Structures
Balanced 2 × 2 Factorial Structures
The Nature of Interactions
Alternative Approaches to Interactions
Cross-Over Experiments
Specific and Multiple Comparison Tests
Fields of Application

Special Features of Survival Data
Modified Wilcoxon Tests
Savage Scores and the Log-Rank Transformation
Median Tests for Sequential Data
Choice of Tests
Fields of Application

Correlation in Bivariate Data
Ranked Data for Several Variables
Fields of Application

Fitting Straight Lines
Fields of Application

Categories and Counts
Nominal Attribute Categories
Ordered Categorical Data
Goodness-of-fit Tests for Discrete Data
Extension of McNemar's Test
Fields of application

The Analysis of Association
Some Models for Contingency Tables
Combining and Partitioning of Tables
A Legal Dilemma
Fields of Application

When Assumptions Break Down
Outliers and Influence
The Bootstrap
M-estimators and Other Robust Estimators
Fields of Application

A Change in Emphasis
Density Estimation
Logistic Regression
Multivariate Data
New Methods for Large Data Sets
Correlations within Clusters

Exercises appear in each chapter.


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