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Statistics by Freedman et al. All exercises use real data as cases, I learned a lot more from them!

https://www.amazon.com/Statistics-4th-David-Freedman/dp/0393...

⬐ krnsll

The sequel to this book -- Statistical Models: Theory and Practice -- and it's accompanying (well, this is how I was encouraged to read them) text -- Statistical Models and Causal Inference: A Dialogue with Social Sciences -- are excellent for intuition building and accumulating a number of examples for personal reference.

⬐ Boulth

Wow, I didn't know there was a sequel, oh what a pleasant surprise!

Statistics already had a lot of useful random trivia and left me with an impression that this is how a good student's resource should look like (my university had a really bad stats course so I had to learn stats myself).

But now you say there is more? Thank you random internet stranger!

I agree with this suggestion. It took me a year to slowly absorb the entire book of Statistics [0] including solving all exercises. It's just like walking to school but there is no external supervision. I made a rule to complete one chapter every evening including exercises and sticked to it.

[0]: https://www.amazon.com/Statistics-4th-David-Freedman/dp/0393...

⬐ Noumenon72

Your story implies there are 365 chapters.

⬐ krageon

It implies he completed 365 chapters but says nothing about repetition of the chapters.

Yankoff, you might want to be more specific. Intro statistics in general, or for computer scientists, or scientists, or looking to learn R at the same time? I liked Freedman, Pisani, and Purves [1], and have TA'ed using McClave, Sincich, and Mendenhall [2]. You may want something a little more advanced than these, but they are pretty good for intro level.

[1]: http://www.amazon.com/Statistics-4th-David-Freedman/dp/03939... [2]: http://www.amazon.com/Statistics-11th-Edition-Book-CD/dp/013... [3]: http://stats.stackexchange.com/questions/421/what-book-would...

⬐ None

None

⬐ yankoff

Yeah, I meant something for computer scientists. I'm going over coursera ML course currently and wanted to learn at least basics of statistics in parallel.

Thanks, I'll check out your links.

Btw, what do you think of OpenIntro statistics?http://www.openintro.org/stat/down/OpenIntroStatSecond.pdf

⬐ mattrepl

You may like Wasserman's All of Statistics:

http://www.stat.cmu.edu/~larry/all-of-statistics/

It was written as an introduction to statistics for people in CS and related fields.

⬐ vdm

That OpenIntro pdf looks great! Thanks for the link.

⬐ christopheraden

You will find the intro books don't talk much about parallel computing. Most of the general data sets in intro books will be no more than 30 observations. They are trying to teach classical methods moreso than useful computational techniques. As for parallel statistics, I don't have a good book recommendation. Most of my knowledge on the topic comes from papers and vignettes from the R community and not books. Maybe check out one of those O'Reilly books about big data techniques?

I haven't seen this OpenIntro statistics before. I'll check it out!

- "Statistics" by Freedman, Pisani, and Purves: http://www.amazon.com/Statistics-4th-David-Freedman/dp/03939... (but an old edition would be fine)

- Any of Wainer's books (the author of the original pdf)

(Added later): books that explain the history of statistical thought are surprisingly good, because they explain the context and the problems the statistics were originally meant to solve. I really enjoyed "the lady tasting tea" and I think I learned from it.

You're talking college stats and calc. And even then Chicago and Stanford. It's like saying that Harvard's Math 55 should be the model for intro math courses in college.

I think the original poster was thinking high school and a target much more like Freedman's text (http://www.amazon.com/Statistics-4th-David-Freedman/dp/03939...).

And what Freedman's book does probably better than any other text in the field is teach how to think about statistics. It doesn't have a lot of formalisms, but if can come to an understanding of what he teaches in that book you'll have a rich understanding of stats.

With that said, if Calc was taught in the context of functions and probability, as in the Gemignani text then I think we'd be better off than how Calc today is focused around engineering.