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Statistical Rethinking: A Bayesian Course with Examples in R and STAN Hardcover – March 16 2020

4.8 out of 5 stars 295 ratings

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Review

"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 McElreath’s 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 statistician’s 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 McElreath’s 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 statistician’s 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

About the Author

Richard McElreath studies human evolutionary ecology and is a Director at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany. He has published extensively on the mathematical theory and statistical analysis of social behavior, including his first book (with Robert Boyd), Mathematical Models of Social Evolution.

Product details

  • Publisher ‏ : ‎ Chapman and Hall/CRC; 2 edition (March 16 2020)
  • Language ‏ : ‎ English
  • Hardcover ‏ : ‎ 594 pages
  • ISBN-10 ‏ : ‎ 036713991X
  • ISBN-13 ‏ : ‎ 978-0367139919
  • Item weight ‏ : ‎ 1.42 kg
  • Dimensions ‏ : ‎ 18.8 x 2.29 x 25.65 cm
  • Customer Reviews:
    4.8 out of 5 stars 295 ratings

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Customer reviews

4.8 out of 5 stars
4.8 out of 5
295 global ratings

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Clive Fox
5.0 out of 5 stars Fantastic introduction to Bayesian approaches
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G. F. M. P.
5.0 out of 5 stars Pitched at just the right level
Reviewed in the United Kingdom 🇬🇧 on June 24, 2020
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Flavio M. M. Barros
5.0 out of 5 stars Uma das melhores introduções a inferência bayesiana
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5.0 out of 5 stars Great way to learn the fundamentals of applied Bayesian stats
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Alessandro
5.0 out of 5 stars consigliato come primo libro per avvicinarsi alla statistica Bayesiana.
Reviewed in Italy 🇮🇹 on March 3, 2021
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