"The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreaths engaging writing style and humor, and personally found the infusion of humor quite refreshing." - Adam Loy, Carleton College
"(The chapter) Generalized Linear Madness represents another great chapter of an even better edition of an already awesome textbook." - Benjamin K. Goodrich, Columbia University
"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory." - Josep Fortiana Gregori, University of Barcelona
"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." - Nguyet Nguyen, Youngstown State University
"As a textbook it successfully brings the statisticians toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new."
- Nathan Green, Journal of the Royal Statistical Society, 2021, https://doi.org/10.1111/rssa.12755
"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques."
- Abhirup Mallik in Technometrics, August 2021 "The first edition (and this second edition) of *Statistical Rethinking* beautifully outlines the key steps in the statistical analysis cycle, starting from formulating the research question. I find that many statistics textbooks omit the issue of problem formulation and either jump into data acquisition or further into analysis after the fact. McElreath has created a fantastic text for students of applied statistics to not only learn about the Bayesian paradigm, but also to gain a deep appreciation for the statistical thought process. I also found that many students appreciated McElreaths engaging writing style and humor, and personally found the infusion of humor quite refreshing." ~Adam Loy, Carleton College
"(The chapter) Generalized Linear Madness represents another great chapter of an even better edition of an already awesome textbook." ~Benjamin K. Goodrich, Columbia University
"(Chapter 16) is a worthy concluding chapter to a masterful book. Eminently readable and enjoyable. Brimful of small thought-provoking bits which may inspire deeper studies, but first and foremost a window on the trial and error process involved in building a statistical model or rather, indeed, any scientific theory." ~Josep Fortiana Gregori, University of Barcelona
"I do regard the manuscript as technically correct, clearly written, and at an appropriate level of difficulty. The technical approaches and the R codes of the book are perfect for our students. They can learn concepts of Bayesian models, data analysis, and model validation methods through using the R codes. The codes help students to have better understanding of the models and data analysis process." ~Nguyet Nguyen, Youngstown State University
"In conclusion, Statistical Rethinking frames usual methods and tools taught in graduate statistical courses into a different way to encourage the reader to understand the details and appreciate the underlying assumptions. The accompanying R package offers example codes for some interesting problems that are not available in standard library or other popular packages. This book can be used as a supplement to a graduate course or it can be used by practitioners wanting to brush up their knowledge with better understanding of statistical techniques." ~Abhirup Mallik in Technometrics, August 2021
"As a textbook it successfully brings the statisticians toolbox to a wider audience with an accessible style and good humour. It should be recommended to statistics students, both old and new." ~ Nathan Green, Journal of the Royal Statistical Society, 2021
