A Practical Guide to Data Analysis Using R (Hardcover)

John H. Maindonald

ISBN 10: 1009282271 / ISBN 13: 9781009282277

Published by Cambridge University Press, Cambridge, 2024

About this Item

Hardcover. Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R. Using diverse real-world examples, this book explores the use of R for data analysis, with extensive use of graphical presentation. It assists scientists in the analysis of their own data, demonstrating how to check the underlying assumptions, and gives students in statistical theory exposure to practical data analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781009282277

Bibliographic Details

• Title: A Practical Guide to Data Analysis Using R (Hardcover)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2024
• Language: English
• ISBN 10: 1009282271
• ISBN 13: 9781009282277
• Binding: Hardcover
• Condition: new

About this title

Synopsis

Using diverse real-world examples, this text examines what models used for data analysis mean in a specific research context. What assumptions underlie analyses, and how can you check them? Building on the successful 'Data Analysis and Graphics Using R,' 3rd edition (Cambridge, 2010), it expands upon topics including cluster analysis, exponential time series, matching, seasonality, and resampling approaches. An extended look at p-values leads to an exploration of replicability issues and of contexts where numerous p-values exist, including gene expression. Developing practical intuition, this book assists scientists in the analysis of their own data, and familiarizes students in statistical theory with practical data analysis. The worked examples and accompanying commentary teach readers to recognize when a method works and, more importantly, when it doesn't. Each chapter contains copious exercises. Selected solutions, notes, slides, and R code are available online, with extensive references pointing to detailed guides to R.

About the Authors

John H. Maindonald is Contract Associate at Statistics Research Associates and was previously Visiting Fellow at the Australian National University. He has had wide experience both as a university lecturer and as a quantitative problem solver, working with researchers in diverse areas. He is the author of 'Statistical Computation' (1984), and the senior author of 'Data Analysis and Graphics Using R' (third edition, 2010).

W. John Braun is Professor at the University of British Columbia, where he is Director of the UBCO campus of the Banff International Research Station for Mathematical Innovation and Discovery. In 2020, he received the Statistical Society of Canada Award for Impact of Applied and Collaborative Work.

Jeffrey Andrews is Associate Professor at the University of British Columbia. He currently serves as Principal Co-director of the Master of Data Science program and President-elect of The Classification Society (TCS). He is the 2013 Distinguished Dissertation Award winner from TCS and a recipient of the 2017 Chikio Hayashi Award for Young Researchers from the International Federation of Classification Societies.