Many textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is a book about how to use regression to solve real problems of comparison, estimation, prediction, and causal inference. It focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use fresh out of the box.
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This book introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression and machine learning and helps you develop skills such as R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux shell, version control with GitHub, and reproducible document preparation with R markdown.
This book provides a helpful resource for anyone looking to use R for their research projects, regardless of their level of programming or statistical analysis experience. Each chapter presents essential concepts in data analysis in a concise, thorough, and user-friendly manner. R4NP will guide you through your first steps on a possibly endless journey full of ‘awe and wonder’.
This book is designed to be accessible to beginners while also providing valuable insights for experienced analysts looking to transition to R from other computational software. You don’t need any prior knowledge or programming skills to get started. R4NP provides a comprehensive entry into R programming, regardless of your background.
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