Data analysis using regression and multilevel/hierarchical models
by Andrew Gelman · first published 2006
83% recommend17% neutral
Why HN recommends it
Practical introduction to regression and Bayesian multilevel modeling that takes readers from basics to complex models; recommended for beginners in statistics and R, and for working scientists who want depth.
Summarised by Claude from recommending comments
"Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, inc
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…of pure Bayesian reasoning[1] (and how to address them). When people ask me where to get started with statistics, both of the books I recommend are Gelman's: Teaching Statistics: A Bag of Tricks and Data Analysis Using Regression and Multilevel/Hierarchical Models. Both have tremendously off-putting titles, but they're actually incredibly accessible. Gelman is great at many things, but picking sexy titles is not one. If you're interested in understanding the…
on “Philosophy and the practice of Bayesian statistics [pdf]” ↗…studies of reproducing research but with metric optimization swapped out for a holistic variety of metrics plus qualitative checking. I recommend the books Bayesian Data Analysis by Gelman et al and Data Analysis Using Regression and Multilevel/Hierarchical Models by Gelman and Hill if you want to read good accounts of doing this in practice with real data sets. There’s definitely room for a book like this that focuses on more domain specific models in NLP, computer…
on “The problem with metrics is a big problem for AI” ↗…Witten, Hastie & Tibshirani): https://www.amazon.ca/Introduction-Statistical-Learning-Appl... - Regression and Other Stories (Gelman, Hill & Vehtari): https://www.amazon.ca/gp/product/1107676517/ - Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman & Hill): https://www.amazon.ca/gp/product/052168689X/ - Statistical and Data Analysis for Financial Engineering (Ruppert & Matteson): https://www.amazon.ca/gp/product/1493926136/ - Reinforcement…
on “Ask HN: What do you read for general knowledge?” ↗The best first book on R I've read is Gelman and Hill's "Data Analysis Using Regression and Multilevel/Hierarchical Models" (http://www.stat.columbia.edu/~gelman/arm/). It's more focused on regression and Bayesian methods than basic probability, but it's a great book that's priced very competitively (only $39.99).
on “Ask HN: Suggestions for learning statistics (& prob) really fast?” ↗Gelman and Hill is a wonderful and under-rated book. My guess is that its clumsy title (Data Analysis Using Regression and Multilevel/Hierarchical Models) hides the fact that it's an introductory textbook that takes the reader from knowing nothing to eventually constructing complex Bayesian models. Plus, it's a pretty good tutorial on R and BUGS.
on “Try R — A new online course, for free” ↗…probably want to tackle first. Many of the concerns addressed in the Hernan/Robins book probably wouldn't even make sense to you if you didn't have a firm statistical background already. Gelman/Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models or Angrist/Pischke's Mastering 'Metrics embed ideas and techniques from causal inference into the broader context of regression modeling which makes these books more immediately useful. Those would probably…
on “Causal Inference in Statistics: A Primer” ↗It's a great book if you want to understand bayesian modeling in detail. Its not 'dry' as in boring - it's an interesting read. If you want something less technical then read Gelman and Hill 'Data Analysis Using Regression and Multilevel/Hierarchical Models', which is also great. More for scientists than statisticians, I'd say.
on “Bayesian Data Analysis, Third Edition [pdf]” ↗…advanced level is this one: "Regression and Other Stories" by Andrew Gelman and Jennifer Hill <http://www.stat.columbia.edu/~gelman/arm/>. Another good book at an even more advanced level is this one: "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman and Jennifer Hill <http://www.stat.columbia.edu/~gelman/arm/>. These books are all very polished productions. What make makes them special is that they emphasis teaching the statistical…
on “Introduction to Modern Statistics” ↗I'm not well rounded enough to draw a clear path from where you are. For me Gelmans Data Analysis Using Regression and Multilevel/Hierarchical Models [0] drove home many, many points. More recently, I have a sense/hope that Pearl's The Book of Why [1] might take this to yet another level. [0] http://www.stat.columbia.edu/~gelman/arm/ [1] https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/0465...
on “Ask HN: What are your favorite statistics and probability textbooks?” ↗…regression, I would expect companies to jealously guard it. If you're earnestly interested in building and testing your knowledge, I would recommend you read The Elements of Statistical Learning and Data Analysis Using Regression and Multilevel/Hierarchical Models. Also a good upper undergrad textbook in probability, like A First Course in Probability.
on “Data science interview questions with answers” ↗Fuller title on "Regression Analysis by Gilman"? I cannot find such a title author combination online. Perhaps?: "Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman & Jennifer Hill
on “Why is machine learning ‘hard’?” ↗…cursory data analysis and data exploration to gain insight into the data, corner cases and which modeling approaches are plausible. Just to give some examples, Bayesian Data Analysis (Gelman et al), Data Analysis Using Regression and Multilevel/Hierarchical Models (Gelman and Hill), Doing Bayesian Data Analysis (Kruschke), Deep Learning (Goodfellow, Bengio, Courville), Pattern Recognition and Machine Learning (Bishop) and the excellent practical blog post [0] by…
on “A dumb reason computer vision apps aren’t working: Exif Orientation” ↗…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…
on “Bayesian Data Analysis, Third edition (2013) [pdf]” ↗