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Statistical Rethinking
Book • 2015
Statistical Rethinking is a comprehensive guide to statistical modeling and causal inference, emphasizing Bayesian methods.
It introduces modern computational tools for data analysis, focusing on understanding the underlying principles rather than rote memorization.
The book covers a wide range of topics, including linear models, multilevel models, and causal inference techniques.
It encourages readers to think critically about model assumptions and to use simulation to validate their analyses.
Statistical Rethinking is designed to equip students and researchers with the skills to tackle complex real-world problems.
The book promotes a hands-on approach to learning, emphasizing the importance of understanding both the mathematical foundations and the practical applications of statistical modeling.
It introduces modern computational tools for data analysis, focusing on understanding the underlying principles rather than rote memorization.
The book covers a wide range of topics, including linear models, multilevel models, and causal inference techniques.
It encourages readers to think critically about model assumptions and to use simulation to validate their analyses.
Statistical Rethinking is designed to equip students and researchers with the skills to tackle complex real-world problems.
The book promotes a hands-on approach to learning, emphasizing the importance of understanding both the mathematical foundations and the practical applications of statistical modeling.
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Alex Andorra
#34 Multilevel Regression, Post-stratification & Missing Data, with Lauren Kennedy
