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## Details

Genre/Form: | Handbooks and manuals Handbooks, manuals, etc |
---|---|

Additional Physical Format: | Print version: Hothorn, Torsten. Handbook of statistical analyses using R. Boca Raton, FL : CRC Press/Taylor & Francis Group, [2014] (DLC) 2014498897 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
Torsten Hothorn; Brian Everitt |

ISBN: | 9781482204599 1482204592 |

OCLC Number: | 951725488 |

Notes: | "A Chapman & Hall book"--Cover. Previous editions cataloged under main entry for Brian S. Everitt. |

Description: | 1 online resource |

Contents: | Introduction Density Estimation Analysis Using R Summary of Findings Final CommentsRecursive Partitioning Introduction Recursive Partitioning Analysis Using R Summary of FindingsFinal Comments ã ã ã Scatterplot Smoothers and Additive Models Introduction ã ã ã Scatterplot Smoothers and Generalised Additive Models ã ã ã Analysis Using R ã ã ã Summary of FindingsFinal CommentsSurvival Analysis Introduction ã ã ã Survival Analysis ã ã ã Analysis Using R ã ã ã Summary of FindingsFinal Comments ã ã ã Quantile RegressionIntroduction ã ã ã Quantile Regression ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final Comments ã ã ã Analysing Longitudinal Data I Introduction ã ã ã Analysing Longitudinal Data ã ã ã Linear Mixed Effects Models ã ã ã Analysis Using R ã ã ã Prediction of Random Effects ã ã ã The Problem of Dropouts ã ã ã Summary of Findings ã ã ã Final CommentsAnalysing Longitudinal Data II Introduction ã ã ã Methods for Non-Normal Distributions ã ã ã Analysis Using R: GEE ã ã ã Analysis Using R: Random Effects Summary of Findings ã ã ã Final Comments ã ã ã Simultaneous Inference and Multiple Comparisons Introduction ã ã ã Simultaneous Inference and Multiple Comparisons ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final CommentsMissing ValuesIntroduction ã ã ã The Problems of Missing Data ã ã ã Dealing with Missing Values ã ã ã Imputing Missing Values ã ã ã Analyzing Multiply Imputed Data ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final CommentsMeta-Analysis Introduction ã ã ã Systematic Reviews and Meta-Analysis ã ã ã Statistics of Meta-Analysis ã ã ã Analysis Using R ã ã ã Meta-Regression ã ã ã Publication Bias ã ã ã Summary of Findings ã ã ã Final Comments ã ã ã Bayesian Inference Introduction ã ã ã Bayesian Inference ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final Comments ã ã ã Principal Component Analysis Introduction ã ã ã Principal Component Analysis ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final CommentsMultidimensional Scaling Introduction ã ã ã Multidimensional Scaling ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final Comments ã ã ã Cluster Analysis Introduction ã ã ã Cluster Analysis ã ã ã Analysis Using R ã ã ã Summary of Findings ã ã ã Final Comments Bibliography Index |

Responsibility: | Torsten Hothorn (Universität Zürich, Zürich, Switzerland), Brian S. Everitt (Professor Emeritus, King's College, London, UK). |

## Reviews

*Editorial reviews*

Publisher Synopsis

"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 Read more...

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