A Practical Guide to Data Analysis Using R: An Example-Based Approach [Hardcover]

Examples from diverse areas of statistical application demonstrate the use of R for data analysis and associated graphics.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.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.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.1. Learning from data, and tools for the task; 2. Generalizing from models; 3. Multiple linear regression; 4. Exploiting the linear model framework; 5. Generalized linear models and survival analyl£Y