**Название**: A Practical Guide to Data Analysis Using R: An Example-Based Approach

**Автор**: John H. Maindonald, W. John Braun, Jeffrey L. Andrews

**Издательство**: Cambridge University Press

**Год**: 2024

**Страниц**: 551

**Язык**: английский

**Формат**: pdf

**Размер**: 15.9 MB

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, 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. This text is designed as an aid, for learning and for reference, in the navigation of a world in which unprecedented new data sources, and tools for data analysis, are pervasive. It aims to teach, using real-world examples, a style of analysis and critique that, given meaningful data, can generate defensible analysis results. The text is suitable for a style of learning where readers work through the text with a computer at their side, running the R code as and when this seems helpful. It complements more mathematically oriented accounts of statistical methodology. The appendix provides a brief account of R, primarily as a starting point for learning. We encourage readers with limited R experience to avail themselves of the wealth of instructional material on the web.