Handbook of Statistical Analyses using R

AvTorsten Hothorn,Brian S. Everitt

Häftad, Engelska, 2014

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Beskrivning

Like the best-selling first two editions, A Handbook of Statistical Analyses using R, Third Edition provides an up-to-date guide to data analysis using the R system for statistical computing. The book explains how to conduct a range of statistical analyses, from simple inference to recursive partitioning to cluster analysis.New to the Third Edition Three new chapters on quantile regression, missing values, and Bayesian inferenceExtra material in the logistic regression chapter that describes a regression model for ordered categorical response variablesAdditional exercisesMore detailed explanations of R codeNew section in each chapter summarizing the results of the analyses Updated version of the HSAUR package (HSAUR3), which includes some slides that can be used in introductory statistics coursesWhether you’re a data analyst, scientist, or student, this handbook shows you how to easily use R to effectively evaluate your data. With numerous real-world examples, it emphasizes the practical application and interpretation of results.

Produktinformation

Utgivningsdatum:2014-06-25
Mått:156 x 234 x 21 mm
Vikt:642 g
Format:Häftad
Språk:Engelska
Antal sidor:304
Upplaga:3
Förlag:Taylor & Francis Inc
ISBN:9781482204582

Utforska kategorier

Matematisk statistik inom Naturvetenskap och teknik
Programspråk inom Data och IT

Mer om författaren

Torsten Hothorn, Brian S. Everitt

Recensioner i media

“I truly appreciate how grounded in practicality this book is—and the way its chapters are structured really underlines this. Furthermore, all the datasets are interesting and vary widely in subject matter. If nothing else, this book is an excellent source of examples one might use to illustrate a variety of statistical techniques. … it offers a lot of good places to start if one wants to analyze data. … The book comes hand-in-hand with an R package, HSAUR3, with all the data and the code used in the text. The book is thus fully reproducible. Overall, it provides a great way for a statistician to get started doing a wide variety of things in the R environment. It would be particularly useful, then, for working statisticians looking to change their software. The book cites all the relevant packages one might need, which is quite nice for those attempting to navigate the vast array of packages freely available, and is quite clear in its presentation of the code. Between this and the datasets, it makes for quite a valuable and enjoyable reference.”—The American Statistician, August 2015"… a handy primer for using R to perform standard statistical data analysis. … students, analysts, professors, and scientists: if you are looking to add R to your toolkit for analyzing data statistically, then this book will get you there."—Kendall Giles on his blog, September 2014Praise for the Second Edition:"I find the book by Everitt and Hothorn quite pleasant and bound to fit its purpose. The layout and presentation [are] nice. It should appeal to all readers as it contains a wealth of information about the use of R for statistical analysis. Included seasoned R users: When reading the first chapters, I found myself scribbling small lightbulbs in the margin to point out features of R I was not aware of. In addition, the book is quite handy for a crash introduction to statistics for (well-enough motivated) nonstatisticians."—International Statistical Review (2011), 79"… an extensive selection of real data analyzed with [R] … Viewed as a collection of worked examples, this book has much to recommend it. Each chapter addresses a specific technique. … the examples provide a wide variety of partial analyses and the datasets cover a diversity of fields of study. … This handbook is unusually free of the sort of errors spell checkers do not find."—MAA Reviews, April 2011

Innehållsförteckning

An Introduction to R. Data Analysis Using Graphical Displays. Simple Inference. Conditional Inference. Analysis of Variance. Simple and Multiple Linear Regression. Logistic Regression and Generalized Linear Models. Density Estimation. Recursive Partitioning. Scatterplot Smoothers and Additive Models. Survival Analysis. Quantile Regression. Analyzing Longitudinal Data I. Analyzing Longitudinal Data II. Simultaneous Inference and Multiple Comparisons. Missing Values. Meta-Analysis. Bayesian Inference. Principal Component Analysis. Multidimensional Scaling. Cluster Analysis. Bibliography. Index.