Regression and other stories

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. Key features : - Real examples, real stories from the authors' real-world experience demonstrate what can be achieved by regression and what the limitations are ; - Computation with the popular open-source programs R and Stan instead of deriving formulas, with all code available online ; - Emphasis on using graphics and presentation to understand and check models that have been fit to data ; - Practical advice for understanding assumptions and implementing methods for experiments and observational studies ; - Smooth transition to logistic regression and generalized linear model ; - Clear presentation of key ideas in data collection, sampling, generalization, and causal inference.