Isbn: 9780367651350 - nonparametric statistical methods using r (23 risultati)

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Hardback. Condizione: New. Praise for the first edition:"This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R."-The American StatisticianThis thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.Key Features:Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.Numerous examples illustrate the methods and their computation.R packages are available for computation and datasets.Contains two completely new chapters on big data and multivariate analysis.The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.…

Hardcover. Condizione: New Books. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.…

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Hardback. Condizione: New. Praise for the first edition:"This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R."-The American StatisticianThis thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.Key Features:Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.Numerous examples illustrate the methods and their computation.R packages are available for computation and datasets.Contains two completely new chapters on big data and multivariate analysis.The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.…

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Buch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Praise for the first edition:'This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.'-The American StatisticianThis thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.Key Features:Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.Numerous examples illustrate the methods and their computation.R packages are available for computation and datasets.Contains two completely new chapters on big data and multivariate analysis.The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice. 466 pp. Englisch.…

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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. John D. Kloke is a bit of a jack-of-all-trades as he has worked as a clinical trial statistician supporting industry as well as academic studies and he also served as a teacher-scholar at several academic institutions. He has held faculty position.…

Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Praise for the first edition:'This book would be especially good for the shelf of anyone who already knows nonparametrics, but wants a reference for how to apply those techniques in R.'-The American StatisticianThis thoroughly updated and expanded second edition of Nonparametric Statistical Methods Using R covers traditional nonparametric methods and rank-based analyses. Two new chapters covering multivariate analyses and big data have been added. Core classical nonparametrics chapters on one- and two-sample problems have been expanded to include discussions on ties as well as power and sample size determination. Common machine learning topics --- including k-nearest neighbors and trees --- have also been included in this new edition.Key Features:Covers a wide range of models including location, linear regression, ANOVA-type, mixed models for cluster correlated data, nonlinear, and GEE-type.Includes robust methods for linear model analyses, big data, time-to-event analyses, timeseries, and multivariate.Numerous examples illustrate the methods and their computation.R packages are available for computation and datasets.Contains two completely new chapters on big data and multivariate analysis.The book is suitable for advanced undergraduate and graduate students in statistics and data science, and students of other majors with a solid background in statistical methods including regression and ANOVA. It will also be of use to researchers working with nonparametric and rank-based methods in practice.…