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.
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.
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!
Very intro (like, Stats 101 intro): Purves, Pisani, and Freedman's is the best book I've seen. I'd combine that with something like Tufte's or Wainer's statistical graphics books to try to get some sophistication (for lack of a better word).
The e-Handbook won't make you an expert statistician, but as an engineer needing to understand and apply statistical methods, I've found it to be a good starting point.
