A Second Course in Statistics: Regression Analysis, Seventh Edition , focuses on building linear statistical models and developing skills for implementing regression analysis in real situations. This text offers applications for engineering, sociology, psychology, science, and business. The authors use real data and scenarios extracted from news articles, journals, and actual consulting problems to show how to apply the concepts. In addition, seven case studies, now located throughout the text after applicable chapters, invite readers to focus on specific problems.
This book is not really your general “math” textbook. The authors clearly aim to make the material accessible even to readers without a strong mathematical background, which mathematicians or statisticians may find insufficiently rigorous. While the exposition and overall structure are not my favorite, the book does cover a wide and comprehensive range of topics. Importantly, it goes beyond model construction and assumptions to emphasize what regression models actually mean, both conceptually and in practice, and how to exercise sound judgment not only about the model itself but also about the context in which it is applied, the consequences of its underlying assumptions, and the caveats and potential pitfalls that may arise. This interpretive and context-aware emphasis is sometimes forgotten in practice, despite being crucial to the discipline and one of the features that distinguishes statistics from areas where interpretability is often secondary, such as our neighbouring discipline of machine learning.