Data Analysis and Graphics Using R 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024


Data Analysis and Graphics Using R

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John Maindonald 作者
Cambridge University Press
译者
2010-5-6 出版日期
549 页数
GBP 70.00 价格
Hardcover
Cambridge Series in Statistical and Probabilistic Mathematics 丛书系列
9780521762939 图书编码

Data Analysis and Graphics Using R 在线电子书 图书标签: R  统计  数据分析  statistics  Meta-analysis  科普  数据处理   


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发表于2024-11-05


Data Analysis and Graphics Using R 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Data Analysis and Graphics Using R 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Data Analysis and Graphics Using R 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024



Data Analysis and Graphics Using R 在线电子书 用户评价

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648旁听教材 // 应用性较强,需要自己查很多方法和概念

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10.7.4 Chapter 10: Multi-level models and repeated measures

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10.7.4 Chapter 10: Multi-level models and repeated measures

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10.7.4 Chapter 10: Multi-level models and repeated measures

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10.7.4 Chapter 10: Multi-level models and repeated measures

Data Analysis and Graphics Using R 在线电子书 著者简介

John Maindonald is Visiting Fellow at the Mathematical Sciences Institute at the Australian National University. He has collaborated extensively with scientists in a wide range of application areas, from medicine and public health to population genetics, machine learning, economic history, and forensic linguistics.

W. John Braun is Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. He has collaborated with biostatisticians, biologists, psychologists, and most recently has become involved with a network of forestry researchers.


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Data Analysis and Graphics Using R 在线电子书 图书描述

Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this book guides users through the practical, powerful tools that the R system provides. The emphasis is on hands-on analysis, graphical display, and interpretation of data. The many worked examples, from real-world research, are accompanied by commentary on what is done and why. The companion website has code and datasets, allowing readers to reproduce all analyses, along with solutions to selected exercises and updates. Assuming basic statistical knowledge and some experience with data analysis (but not R), the book is ideal for research scientists, final-year undergraduate or graduate-level students of applied statistics, and practising statisticians. It is both for learning and for reference. This third edition expands upon topics such as Bayesian inference for regression, errors in variables, generalized linear mixed models, and random forests.

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