9780521762939 - Data Analysis and Graphics Using R: An Example-Based Approach (Cambridge Series in Statistical and Probabilistic Mathematics) (Cambridge Series in ... and Probabili... (8 Ergebnisse)

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Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 564 | Sprache: Englisch | Produktart: Bücher | 3rd edition. Hands-on guide for scientists, students and practising statisticians.

Hardcover. Zustand: Brand New. 3rd edition. 560 pages. 10.08x6.93x1.26 inches. In Stock.

Zustand: New. Hands-on guide to the R system for data analysis for scientists, students and practising statisticians. Series: Cambridge Series in Statistical and Probabilistic Mathematics. Num Pages: 549 pages, 150 b/w illus. 12 colour illus. 40 tables. BIC Classification: UFM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 257 x 180 x 29. Weight in Grams: 1316. An Example-Based Approach. Series: Cambridge Series in Statistical and Probabilistic Mathematics. 552 pages, 150 b/w illus. 12 colour illus. 40 tables. Hands-on guide to the R system for data analysis for scientists, students and practising statisticians. Cateogry: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. BIC Classification: UFM. Dimension: 257 x 180 x 29. Weight: 1318. . 2010. 3rd Edition. Hardcover. . . . . Books ship from the US and Ireland.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.