Handbook of Regression Analysis With Applications in R

This comprehensive guide covers regression analysis techniques in R, including classical and advanced methods. The Handbook of Regression Analysis With Applications in R is essential for students and professionals alike.

The Handbook of Regression Analysis With Applications in R, Second Edition serves as an essential resource for both students and practitioners involved in statistical regression-based analyses using R. This comprehensive guide not only revisits the classical regression analysis introduced in the first edition but also delves into more complex topics such as time-to-event survival data, as well as longitudinal and clustered data. The authors have ensured that the content is up-to-date, reflecting the latest advancements in the field.

In this edition, the book emphasizes methods that have gained prominence due to the advent of large data sets. Techniques such as regularization methods, smoothing methods, and tree-based methods are discussed in detail, providing readers with a robust toolkit for tackling modern statistical challenges. The integration of these advanced methods makes this work particularly relevant for those looking to enhance their analytical capabilities in real-world scenarios.

The Handbook of Regression Analysis With Applications in R is designed with accessibility in mind. It includes numerous examples drawn from a variety of practical applications and data sets, ensuring that readers can relate the concepts to real-life situations. Additionally, all R code and data used in the examples are available through an author-maintained website, making it easier for users to apply what they learn. This book is an invaluable asset for undergraduate and graduate students, as well as for practicing data scientists and statisticians seeking to refine their skills.