Statistics in a nutshell

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

此记录保留了作者、时间和来源链接，请打开 HN 阅读完整上下文。

…ally well. The best person to teach someone is a beginner , because they understand the beginner's perspective. So you are in a unique position to create this. O'Reillys statistics in a nutshell is a good reference* book, but not quite a textbook. Here you go. Including my refid. http://www.amazon.com/gp/product/0596510497?ie=UTF8&tag=... *True masters have beginner perspectives, so they are good teachers as well.

… a fun way to motivate yourself to learn some algorithms, number theory, and a few other math topics, by solving programming puzzles: http://projecteuler.net/ O'Reilly's Statistics in a Nutshell is a good stats textbook and reference, has some good exercises to be done in R (or S or Matlab), and is generally more programming-oriented than most statistics treatments. http://oreilly.com/catalog/9780596510497/ Two important classics are Knuth's The Art of Computer Programming (more algorithms/programming-focused; very steep learning curve) and Concrete Mathematics (with Ron Graham and Oren Patashnik; more math-focused; may be easier for self-study). Both have exercises you can work through, though many of the exercises are proofs (not easily translatable to programming).

O'Reilly's Statistics In a Nutshell is quite good as both a textbook and a reference. I'm (slowly) working through it on my own.