Regression and Other Stories
Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.
Stats for nerds
11recommend
20mentions
14people
2020–2025years
75% recommend25% neutral
Mentions per year
What Hacker News says
- My go to for teaching statistics is Statistical Rethinking. It’s basically a course in how to actually thing about modeling: what you’re really looking for is analyzing a hypothesis, and a model may be consistent with a number of hypotheses, figuring out what hypotheses any given model implies is the hard/fun part, and this book teaches you that. The only drawback is that it’s not free. (Although there are excellent lectures by the author available for free on YouTube. These are worth watching even if you don’t get the book.)
I also recommend Gelman’s (one of the authors of the linked book) Regression and Other Stories as a more approachable text for this content.
Think Bayes and Bayesian Methods for Hackers are introductory books from a beginner coming from a programming background.
If you want something more from the ML world that heavily emphasizes the benefits of probabilistic (Bayesian) methods, I highly recommend Kevin Murphy’s Probabilistic Machine Learning. I have only read the first edition before he split it into two volumes and expanded it, but I’ve only heard good things about the new volumes too.
- Regression and Other Stories. It’s also co-authored by Gelman and it reads like an updated version of his previous book Data Analysis Using Hierarchical/Multilevel Models.
Statistical Rethinking is a good option too.
- Can second Regression and Other Stories, it's freely available here: https://users.aalto.fi/~ave/ROS.pdf, and you can access additional information such as data and code (including Python and Julia ports) here: https://avehtari.github.io/ROS-Examples/index.html
- BDA is THE book to learn Bayesian Modeling in depth rigorously. For different approaches there are a number shared here like Statistical Rethinking from Richard McElreath or Regression and other stories which Gelman and Aki wrote as well.
I also write a book on the topic which is focused a code and example approach. It's available for open access here. https://bayesiancomputationbook.com
- He co-wrote the reference textbook on the topic and made interesting methodological contributions, but Gelman acknowledges other people as creators of the theoretical underpinnings of multilevel/hierarchical modeling, including Stein or Donoho [1]. The field is quite old, one can find hierarchical models in articles that were published many decades ago.
Also, IMHO, his best work has been done describing how to do statistics. He has written somewhere I cannot find now that he sees himself as a user of mathematics, not as a creator of new theories. His book Regression and Other Stories is elementary but exceptionally well written. He describes how great Bayesian statisticians think and work, and this is invaluable.
He is updating Data Analysis Using Regression and Multilevel/Hierarchical Models to the same standard, and I guess BDA will eventually come next. As part of the refresh, I imagine everything will be ported to Stan. Interestingly, Bob Carpenter and others working on Stan are now pursuing ideas on variational inference to scale things further.
[1] https://sites.stat.columbia.edu/gelman/research/unpublished/...
- Here is one path to learn Bayesian starting from basics, assuming modern R path with tidyverse (recommended):
First learn some basic probability theory: Peter K. Dunn (2024). The theory of distributions. https://bookdown.org/pkaldunn/DistTheory
Then frequentist statistics: Chester Ismay, Albert Y. Kim, and Arturo Valdivia - https://moderndive.com/v2/ Mine Çetinkaya-Rundel and Johanna Hardin - https://openintrostat.github.io/ims/
Finally Bayesian: Johnson, Ott, Dogucu - https://www.bayesrulesbook.com/ This is a great book, it will teach you everything from very basics to advanced hierachical bayesian modeling and all that by using reproducible code and stan/rstanarm
Once you master this, next level may be using brms and Solomon Kurz has done full Regression and Other Stories Book using tidyerse/brms. His knowledge of tidyverse and brms is impressive and demonstrated in his code. https://github.com/ASKurz/Working-through-Regression-and-oth...
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