Statistical Inference cover
Statistical Inference
by George Casella, Roger L. Berger
Description: Statistical Inference by George Casella and Roger L. Berger presents the foundational principles of probability theory and develops the key concepts and techniques used in statistical inference. The text covers theory from basic probability through advanced statistical methods
ISBN: 0534243126
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noelwelsh · 2023-10-12 · Original thread
Depends where you are starting from and what you want to learn. The linked book is a first year introduction, and does a good job of that. If you want to go further there are many other options:

* Statistical Inference by Casella and Berger. This book has a very good reputation for building statistics from first principles. I won't link to them, but you can find full PDF scans online with a simple search. Amazon reviews: https://www.amazon.com/Statistical-Inference-Roger-Berger/dp...

* Statistics by Freedman, Pisani, and Purves has similarly very good reviews and can be easily found online. Amazon reviews: https://www.amazon.com/Statistics-Fourth-David-Freedman-eboo...

* The majority of the Berkeley data science core curriculum books are online. This is not purely statistics but 1) is taught in a modern style that makes use of computation and randomization and 2) uses tools that may be useful to learn about.

1. https://inferentialthinking.com/chapters/intro.html (Data 8)

2. https://learningds.org/intro.html (Data 100)

3. http://prob140.org/textbook/content/README.html (Data 140)

4. https://data102.org/fa23/resources/#textbooks-from-previous-... (Data 102; this gets into machine learning and pure statistics)

The Berkeley curriculum is not the only one; there are tens, possibly hundreds, of online courses. The Berkeley curriculum is just 1) quite extensive and 2) the one I happened to read the most about when I was recently researching how data science is currently taught.

RA_Fisher · 2018-09-02 · Original thread
I highly recommend this econometrics text for getting started with statistics: https://www.amazon.com/Principles-Econometrics-5th-Carter-Hi...

For modeling I found Wooldridge's panel and cross-section data book very useful: https://www.amazon.com/Econometric-Analysis-Cross-Section-Pa...

Greene is a really useful reference text: https://www.amazon.com/Econometric-Analysis-8th-William-Gree...

For advanced stats theory, I recommend Casella and Berger https://www.amazon.com/Statistical-Inference-George-Casella/...

Hope that helps!

The more specific a model can be made to the problem at hand, the better it'll perform. Supervised ML models are great starting / baseline models.