此书主要介绍统计思维能力的培养，书中例子都是通过R进行演算，适合初学统计和R的对象。此书是free的，大小不到6m

链接如下:

www.stat.duke.edu/~michael/book.html

以下为此书的介绍，

Introduction to Statistical Thought grew out of my teaching graduate and undergraduate statistics courses for many years, and from my experience as a statistical consultant and collaborator. I wanted to write a text that

• explains how statisticians think about data,
• introduces modern statistical computing, and
• has lots of real examples.

The book is intended as an upper level undergraduate or introductory graduate textbook in statistical thinking with a likelihood emphasis for students with a good knowledge of calculus and the ability to think abstractly. By "statistical thinking" is meant a focus on ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. The book does contain technical details, but they are not the focus. By "likelihood emphasis" is meant that the likelihood function and likelihood principle are unifying ideas throughout the text.

Another unusual aspect is the use of statistical software as a pedagogical tool. That is, instead of viewing the computer merely as a convenient and accurate calculating device, the book uses computer calculation and simulation as another way of explaining and helping readers understand the underlying concepts. The book is written with the statistical language R embedded throughout. R and accompanying manuals are available for free download from http://www.r-project.org.

Introduction to Statistical Thought is not finished yet, but is sufficiently complete to be used as a course text by knowledgable instructors. Material will be added. Corrections will be made. Let me know your suggestions and any errors you find. And please let me hear about your experience reading the book or using it as a text.

Introduction to Statistical Thought is available for free download. Because it is not handled by a commercial publisher, it will not be advertised as most commercial texts are. Therefore it must rely on word of mouth. If you like it, please let others know. If you don't like it, please let me know, especially if you can say why. I don't promise to adopt your suggestions but I do promise to take them seriously.