Data Analysis and Graphics Using R 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024


Data Analysis and Graphics Using R

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John Maindonald 作者
Cambridge University Press
譯者
2010-5-6 出版日期
549 頁數
GBP 70.00 價格
Hardcover
Cambridge Series in Statistical and Probabilistic Mathematics 叢書系列
9780521762939 圖書編碼

Data Analysis and Graphics Using R 在線電子書 圖書標籤: R  統計  數據分析  statistics  Meta-analysis  科普  數據處理   


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Data Analysis and Graphics Using R 在線電子書 epub 下載 mobi 下載 pdf 下載 txt 下載 2024

Data Analysis and Graphics Using R 在線電子書 epub 下載 pdf 下載 mobi 下載 txt 下載 2024

Data Analysis and Graphics Using R 在線電子書 pdf 下載 txt下載 epub 下載 mobi 下載 2024



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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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648旁聽教材 // 應用性較強,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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648旁聽教材 // 應用性較強,需要自己查很多方法和概念

Data Analysis and Graphics Using R 在線電子書 著者簡介

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.


Data Analysis and Graphics Using R 在線電子書 著者簡介


Data Analysis and Graphics Using R 在線電子書 pdf 下載 txt下載 epub 下載 mobi 在線電子書下載

Data Analysis and Graphics Using R 在線電子書 圖書描述

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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