We use cookies to provide essential features and services. By using our website you agree to our use of cookies .

×

COVID-19 Response at Fishpond

Read what we're doing...

Statistical Rethinking
By

Rating

Product Description
Product Details

Table of Contents

Preface to the Second Edition
Preface
Audience
Teaching strategy
How to use this book
Installing the rethinking R package
Acknowledgments

Chapter 1. The Golem of Prague
Statistical golems
Statistical rethinking
Tools for golem engineering
Summary

Chapter 2. Small Worlds and Large Worlds
The garden of forking data
Building a model
Components of the model
Making the model go
Summary
Practice

Chapter 3. Sampling the Imaginary
Sampling from a grid-appromate posterior
Sampling to summarize
Sampling to simulate prediction
Summary
Practice

Chapter 4. Geocentric Models
Why normal distributions are normal
A language for describing models
Gaussian model of height
Linear prediction
Curves from lines
Summary
Practice

Chapter 5. The Many Variables & The Spurious Waffles
Spurious association
Masked relationship
Categorical variables
Summary
Practice

Chapter 6. The Haunted DAG & The Causal Terror
Multicollinearity
Post-treatment bias
Collider bias
Confronting confounding
Summary
Practice

Chapter 7. Ulysses' Compass
The problem with parameters
Entropy and accuracy
Golem Taming: Regularization
Predicting predictive accuracy
Model comparison
Summary
Practice

Chapter 8. Conditional Manatees
Building an interaction
Symmetry of interactions
Continuous interactions
Summary
Practice

Chapter 9. Markov Chain Monte Carlo
Good King Markov and His island kingdom
Metropolis Algorithms
Hamiltonian Monte Carlo
Easy HMC: ulam
Care and feeding of your Markov chain
Summary
Practice

Chapter 10. Big Entropy and the Generalized Linear Model
Mamum entropy
Generalized linear models
Mamum entropy priors
Summary

Chapter 11. God Spiked the Integers
Binomial regression
Poisson regression
Multinomial and categorical models
Summary
Practice

Chapter 12. Monsters and Mixtures
Over-dispersed counts
Zero-inflated outcomes
Ordered categorical outcomes
Ordered categorical predictors
Summary
Practice

Chapter 13. Models With Memory
Example: Multilevel tadpoles
Varying effects and the underfitting/overfitting trade-off
More than one type of cluster
Divergent transitions and non-centered priors
Multilevel posterior predictions
Summary
Practice

Chapter 14. Adventures in Covariance
Varying slopes by construction
Advanced varying slopes
Instruments and causal designs
Social relations as correlated varying effects
Continuous categories and the Gaussian process
Summary
Practice

Chapter 15. Missing Data and Other Opportunities
Measurement error
Missing data
Categorical errors and discrete absences
Summary
Practice

Chapter 16. Generalized Linear Madness
Geometric people
Hidden minds and observed behavior
Ordinary differential nut cracking
Population dynamics
Summary
Practice

Chapter 17. Horoscopes
Endnotes

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.

Reviews

"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

Ask a Question About this Product More...
Write your question below:
Look for similar items by category
Home » Books » Science » Mathematics » Statistics » General
Item ships from and is sold by Fishpond World Ltd.
Back to top