Nonparametric Statistical Methods 2nd Edition by Myles Hollander (PDF)

Description

The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis. Today, these highly efficient techniques are being applied to an ever-widening variety of experimental designs in the social, behavioral, biological, and physical sciences. This long-awaited Second Edition of Myles Hollander and Douglas A. Wolfe’s successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its highly acclaimed predecessor, the revised edition, along with its companion ftp site, aims to equip students with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. Rewritten and updated, this Second Edition now includes new or expanded coverage of: * Nonparametric regression methods. * The bootstrap. * Contingency tables and the odds ratio. * Life distributions and survival analysis. * Nonparametric methods for experimental designs. * More procedures, real-world data sets, and problems. * Illustrated examples using Minitab and StatXact. An ideal text for an upper-level undergraduate or first-year graduate course, this text is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics. An Instructor’s Manual presenting detailed solutions to all the problems in the book is available upon request from the Wiley editorial department.

User’s Reviews

Editorial Reviews: Review The second edition of Nonparametric Statistical Methods provides a complete, thorough, and in-depth presentation of various techniques in nonparametric statistics.-Journal of Quality Technology Vol. 33, No. 2, April 2001 “Hollander and Wolfe may have waited 25 years to update their book, but it would hardly have been possible for them to have done a new edition that is any more impressive than this second edition. … This book should be an essential part of the personal library of every practicing statistician.” (Technometrics, Vol. 41, No. 4, Nov. 1999) From the Back Cover The new edition of Hollander and Wolfe’s classic text on nonparametric statistical methods. The importance of nonparametric methods in modern statistics has grown dramatically since their inception in the mid-1930s. Requiring few or no assumptions about the populations from which data are obtained, they have emerged as the preferred methodology among statisticians and researchers performing data analysis. Today, these highly efficient techniques are being applied to an ever-widening variety of experimental designs in the social, behavioral, biological, and physical sciences. This long-awaited Second Edition of Myles Hollander and Douglas A. Wolfe’s successful Nonparametric Statistical Methods meets the needs of a new generation of users, with completely up-to-date coverage of this important statistical area. Like its highly acclaimed predecessor, the revised edition, along with its companion ftp site, aims to equip readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for a given situation. An extensive array of examples drawn from actual experiments illustrates clearly how to use nonparametric approaches to handle one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. Rewritten and updated, this Second Edition now includes new or expanded coverage of: * Nonparametric regression methods * The bootstrap * Contingency tables and the odds ratio * Life distributions and survival analysis * Nonparametric methods for experimental designs. Plus: * More procedures, real-world data sets, and problems * Illustrated examples using Minitab and StatXact An ideal text for an upper-level undergraduate or first-year graduate course, Nonparametric Statistical Methods, Second Edition is also an invaluable source for professionals who want to keep abreast of the latest developments within this dynamic branch of modern statistics. About the Author MYLES HOLLANDER is Robert O. Lawton Distinguished Professor of Statistics at Florida State University in Tallahassee. He served as editor of the Theory and Methods Section of the Journal of the American Statistical Association from 1993-96. DOUGLAS A. WOLFE is a Professor of Statistics at Ohio State University in Columbus. He is a two-time recipient of the Ohio State University Alumni Distinguished Teaching Award, in 1973-74 and 1988-89. Read more

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐It will take about 3 weeks to get used to the convoluted organization of this book. The explanations are actually fairly good, as are the examples, but the set up of text, comments, examples and problems is often difficult to follow. Tables are not where they are expected, references are circular, and so on. I will say that at least the system, as it is, is ‘applied’ consistently, so once you get used to it, it only slows understanding slightly. Of course, the editors were statiticians, not editors…

⭐Precise and informative text

⭐The textbook is an excellent reference. The introductory statistics courses today tend not to cover non-parametric statistics, which leaves many wandering what to do when the standard assumptions they take for granted truly do not apply. This textbook provides a catalog of methods for a large variety of situations which are valid under much weaker assumptions.

⭐I fully recommend this book as part of one’s nonparametric statistics library. It has wonderful detailing of the assumptions for the methods, and the book is tied together by a strong practical applications focus.

⭐Nice book

⭐I highly recommend this book. Has very useful statistical tables in it as well as applied statistical concepts. Do not let the equations inside the book frighten you, but use them to guide you.

⭐just needed for school. it is ok. just needed for school. it is ok. just needed for school. it is ok. just needed for school. it is ok. just needed for school. it is ok.

⭐This book is great for what it is – an encyclopedia for non-parametric methods. This isn’t the text to read for an exposition about the development and all theory and proof behind the method (Lehmann is great for that) this is the place to go where you say my data has such and such a structure, and I have such and such hypothesis about it. This doesn’t mean that this is a cookie cutter book – it provides the assumptions about each tests, the formulas for hand calculation (great for checking if the R function was coded correctly!), the relative efficiencies, info about ties and the asymptotics. as well as the notes at the end. In this sense it has something for must levels. I agree though the tables are a bit silly and the price is a bit highA couple of notes:1) Some people have critiqued the structure – I actually like it but can see how it can be a bit confusing2) The book is definitely about classic non-parametrics based on rank tests. There is much more to non-parametrics but this book fits a good nicheIf you are a practitioner who realizes ANOVAs are silly you will get something out of this book. And if you have a good understanding of theory it won’t feel too dumbed down.

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