Hi,
I’m a biologist who really sucks in statistics… I would like to resolve this problem definitively, but I don’t know where to start. I previously learned things like t-test, r2, ANOVA, PCA, and stuff like that, but I wouldn’t be able to use those notions now…
Basically, I’m looking for some kind of well-structured books or online course which present the theoretical aspect, followed by exercise and solution. I know how to use R and python, it would be great if the course is using one of those languages (I did my PhD using R, so I’m more confident with it, but I learn Python recently, and I would like to enhance my skill with this language).
Thank you for your answer !
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Casella/Berger is the go-to book for a reason. You can find solutions online, which is super helpful. What I really like about this book is that it takes you step-by-step through math stats, starting from the basics. It’s not just a bunch of random techniques to memorize. The topics connect nicely, making it easy to follow, and it’s pretty theoretical. It’s well-written but can be a bit heavy, and some of the exercises are pretty tough. If you’re looking for something a bit easier, Wackerly is a solid alternative.
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Second Wackerly. It aims to cover lot of topics (probability,.param est, hypothesis testing, regression and experimental design) is less rigorous. Don’t worry,.you will still see plenty of math,.proofs and theorems and problems. It can appear hard if don’t have 1st year uni math. A fabulous book nonetheless. Hogg’s book is another one but it only covers probility, not t test or param estimation. Ghahramani (tougher) and Sheldon Ross-s books are good too, but they only cover probability theory.
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Statistical Rethinking is the book for this in my opinion
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Oooh. Another one for the list. I’m also a biologist trying to “get statistics”. I can do some of it, but I dont think i have a good enough foundation/intuition.
I was recommended “practical statistics” and “Intro to statistical learning”
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I recommend reading Statistics by Freedman, Pisani, & Purves. Quite early one, I read Introductory Statistics with R by Dalgaard and I thought it was very good too.
They are some of the best books around for beginners; clear, solid explanations, plenty of exercises and useful as references as you progress forward. They are tried and tested. I read FPP quite late in my Stats education/journey, and if anything I thought I was unlucky not to have used it earlier.
If you feel more adventurous and trustful of your Maths/Stats: Statistical Models by Davison and Information Theory, Inference and Learning Algorithms by MacKay are absolutely beautiful books and classics on their own terms to solidify ones Statistics knowledge. (from an applied perspective)
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I really liked Freedman’s Statistics. Absolutely brilliant statistician.
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