Search results for ""author myles hollander""

The importance of nonparametric methods in modern statistics hasgrown dramatically since their inception in the mid-1930s.Requiring few or no assumptions about the populations from whichdata are obtained, they have emerged as the preferred methodologyamong statisticians and researchers performing data analysis.Today, these highly efficient techniques are being applied to anever-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 theneeds of a new generation of users, with completely up-to-datecoverage of this important statistical area. Like its highlyacclaimed predecessor, the revised edition, along with itscompanion ftp site, aims to equip students with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for a given situation. An extensive array of examplesdrawn from actual experiments illustrates clearly how to usenonparametric approaches to handle one- or two-sample location anddispersion problems, dichotomous data, and one-way and two-waylayout problems. Rewritten and updated, this Second Edition nowincludes 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-yeargraduate course, this text is also an invaluable source forprofessionals who want to keep abreast of the latest developmentswithin this dynamic branch of modern statistics. An Instructor's Manual presenting detailed solutions to all theproblems in the book is available upon request from the Wileyeditorial department.

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The Wiley Classics Library consists of selected books that have become recognized classics in their respective fields. With these new unabridged and inexpensive editions, Wiley hopes to extend the life of these important works by making them available to future generations of mathematicians and scientists. Currently available in the Series: T. W. Anderson The Statistical Analysis of Time Series T. S. Arthanari & Yadolah Dodge Mathematical Programming in Statistics Emil Artin Geometric Algebra Norman T. J. Bailey The Elements of Stochastic Processeswith Applications to the Natural Sciences Robert G. Bartle The Elements of Integration and Lebesgue Measure George E. P. Box & Norman R. Draper Evolutionary Operation: A Statistical Method for Process Improvement George E. P. Box & George C. Tiao Bayesian Inference in Statistical Analysis R. W. Carter Finite Groups of Lie Type: Conjugacy Classes and Complex Characters R. W. Carter Simple Groups of Lie Type William G. Cochran & Gertrude M. Cox Experimental Designs, Second Edition Richard Courant Differential and Integral Calculus, Volume I Richard Courant Differential and Integral Calculus, Volume II Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume I Richard Courant & D. Hilbert Methods of Mathematical Physics, Volume II D. R. Cox Planning of Experiments Harold S. M. Coxeter Introduction to Geometry, Second Edition Charles W. Curtis & Irving Reiner Representation Theory of Finite Groups andAssociative Algebras Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume I Charles W. Curtis & Irving Reiner Methods of Representation Theory with Applications to Finite Groups and Orders, Volume II Cuthbert Daniel & Fred S. Wood Fitting Equations to Data: Computer Analysis of Multifactor Data, Second Edition Bruno de Finetti Theory of Probability, Volume I Bruno de Finetti Theory of Probability, Volume II Morris H. DeGroot Optimal Statistical Decisions W. Edwards Deming Sample Design in Business Research Amos de Shalit & Herman Feshbach Theoretical Nuclear Physics, Volume 1—Nuclear Structure Harold F. Dodge & Harry G. Romig Sampling Inspection Tables: Single and Double Sampling J. L. Doob Stochastic Processes

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Praise for the Second Edition “This book should be an essential part of the personal library of every practicing statistician.”—Technometrics Thoroughly revised and updated, the new edition of Nonparametric Statistical Methods includes additional modern topics and procedures, more practical data sets, and new problems from real-life situations. The book continues to emphasize the importance of nonparametric methods as a significant branch of modern statistics and equips readers with the conceptual and technical skills necessary to select and apply the appropriate procedures for any given situation. Written by leading statisticians, Nonparametric Statistical Methods, Third Edition provides readers with crucial nonparametric techniques in a variety of settings, emphasizing the assumptions underlying the methods. The book provides an extensive array of examples that clearly illustrate how to use nonparametric approaches for handling one- or two-sample location and dispersion problems, dichotomous data, and one-way and two-way layout problems. In addition, the Third Edition features: The use of the freely available R software to aid in computation and simulation, including many new R programs written explicitly for this new edition New chapters that address density estimation, wavelets, smoothing, ranked set sampling, and Bayesian nonparametrics Problems that illustrate examples from agricultural science, astronomy, biology, criminology, education, engineering, environmental science, geology, home economics, medicine, oceanography, physics, psychology, sociology, and space science Nonparametric Statistical Methods, Third Edition is an excellent reference for applied statisticians and practitioners who seek a review of nonparametric methods and their relevant applications. The book is also an ideal textbook for upper-undergraduate and first-year graduate courses in applied nonparametric statistics.

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