Data analysis using regression and multilevel/hierarchical models

What Hacker News says

1. maydOct 2023
   A good introductory book is this one: "The Statistical Sleuth. A Course in Methods of Data Analysis" (3rd Edition) by Fred Ramsey and Daniel Schafer <http://www.statisticalsleuth.com/>.

   Another good book at a more 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 way of thinking.

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2. epguiDec 2021
   I spend a lot of time sniffing them out.

   One thing I'll do once in a while is visit a university bookstore to get a sense of what's being used in classrooms. I ended up with a lot of my law textbooks like that. In law, I've really loved:

   - Canadian Administrative Law (Guy Régimbald): https://store.lexisnexis.ca/en/categories/shop-by-jurisdicti...

   - Tort Law (Horsey & Rackley): https://www.amazon.ca/Tort-Law-Kirsty-Horsey-dp-019886776X/d...

   - Handbook of Canadian Higher Education Law (Shanahan, Nilso & Broshko): https://www.amazon.ca/Handbook-Canadian-Higher-Education-Law...

   I've been on a maths, stats and ML/AI binge in the last year, and so far my top picks are:

   - Calculus (Stewart): https://www.amazon.ca/Calculus-James-Stewart/dp/1337624187/

   - Linear Algebra Done Right (Sheldon Axler): https://www.amazon.ca/Linear-Algebra-Right-Undergraduate-Mat...

   - An Illustrative Guide to Multivariable and Vector Calculus (Miklavcic): https://www.amazon.ca/gp/product/3030334589/

   - Probability: A Graduate Course (Allan Gut): https://www.amazon.ca/gp/product/1461447070/

   - Statistical Rethinking (McElreath): https://www.amazon.ca/gp/product/036713991X/

   - An Introduction to Statistical Learning (James, 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 Learning (Sutton & Barto): https://www.amazon.ca/Reinforcement-Learning-Introduction-Ri...

   - Deep Learning (Godfellow, Bengio & Courville): https://www.amazon.ca/Deep-Learning-Ian-Goodfellow/dp/026203...

   - Networks (Mark Newman): https://www.amazon.ca/Networks-Mark-Newman/dp/0198805098/

   I have a ton of textbooks in my wishlist that seem noteworthy and interesting, but that I haven't gotten the chance to read yet: no promises, but you might find something in there https://www.amazon.ca/hz/wishlist/ls/1FWW046Q3RWWM

   My area of expertise is biochemistry, life science, immunology, cell bio... So if anyone wants good recommendations in these topics I have plenty! :)

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3. fractionalhareSep 2020
   Lots of tech and finance companies (particularly those with standardized interview processes) will blacklist questions if they're found online. Those companies will constantly check GitHub, GeeksForGeeks and Leetcode to see if their questions are listed there with solutions.

   This probably won't be the case for a question as basic as, "what is regression?" But for any intermediate to advanced interview question involving 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.

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4. martingoodsonMay 2020
   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.

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5. mlthoughts2018Oct 2019
   The problem with discussions like this is that they never provide systematic examples of how a portfolio of metrics or qualitative checking can be integrated into a modeling problem. There’s a lot of finger pointing at metrics and complacency about problems, but the solutions are super vague, like the sanctimonious passage in this article about hiring from under-indexed groups in tech companies and just listening to first-person accounts (which is probably a bad idea if you actually want to help).

   Ultimately I agree with the underlying idea, but I think to be helpful you have to present case 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 vision and deep neural networks.

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6. halhenJul 2018
   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...

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