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


Using R for Data Management, Statistical Analysis, and Graphics

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Nicholas J. Horton 作者
CRC Press
譯者
2010 出版日期
297 頁數
$ 74.52 價格
叢書系列
9781439827550 圖書編碼

Using R for Data Management, Statistical Analysis, and Graphics 在線電子書 圖書標籤: 統計  編程  數據分析  數學  英文版  計算機  放下  圖書館   


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

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

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



Using R for Data Management, Statistical Analysis, and Graphics 在線電子書 用戶評價

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Using R for Data Management, Statistical Analysis, and Graphics 在線電子書 著者簡介


Using R for Data Management, Statistical Analysis, and Graphics 在線電子書 著者簡介


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

Using R for Data Management, Statistical Analysis, and Graphics 在線電子書 圖書描述

Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphics Using R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation and vast number of add-on packages. Organized by short, clear descriptive entries, the book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, multivariate methods, and the creation of graphics. Through the extensive indexing, cross-referencing, and worked examples in this text, users can directly find and implement the material they need. The text includes convenient indices organized by topic and R syntax. Demonstrating the R code in action and facilitating exploration, the authors present example analyses that employ a single data set from the HELP study. They also provide several case studies of more complex applications. Data sets and code are available for download on the book's website. Helping to improve your analytical skills, this book lucidly summarizes the aspects of R most often used by statistical analysts. New users of R will find the simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.

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