: Written at an intermediate mathematical level, the text is accessible to students and practitioners with basic knowledge of calculus and probability.

: Selection of methods is heavily influenced by the availability of user-friendly statistical software, making it highly practical for researchers.

: Includes mathematical appendices covering matrix algebra, probability calculus, and statistical inference to assist readers with the necessary background.

: Theoretical concepts are reinforced with numerous real-world data examples and case studies from social, economic, and life sciences.

The second edition of this textbook was released in 2021 by Springer , expanding its coverage of contemporary regression techniques. Regression: Models, Methods and Applications - Amazon.com

: A flexible framework for modeling complex data structures.