An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.

劳伦斯·汉米尔顿编著的《应用STATA做统计分析(更新至STATA10.0版)》从STATA软件与STATA的资源，数据管理，制图，概要统计及交互表，方差分析和其他比较方法，线性回归分析，回归诊断，拟合曲线，稳健回归，LOGISTlC回归，生存模型与事件计数模型，主成分、因子和聚类分析，时间序列分析，编程入门，等等，完整而精练地介绍了STATA软件或软件包的各项基本功能和在统计分析中的应用。《应用STATA做统计分析》以列举实例的方式编写，并穿插了上百幅图片，广泛引证各种相关资料中的数据，简明地介绍了常用的各种命令的分析运行情况，便于学习掌握。此外，在最后一章拓展性地介绍了常用的编程知识和技能，以便于能更加灵活地运用STATA软件做更多的统计分析。
《应用STATA做统计分析(更新至STATA10.0版)》突出了程序性、实用性、完整性，本书兼具教材和使用手册的特点，适宜作为致力于统计学研究和数据分析应用的专家和学者自学参考。