本书作为实验设计与分析领域的名著, 是作者在亚利桑那州立大学、华盛顿大学和佐治亚理工学院三所大学近40年实验设计教学经验的基础上编写的. 全书内容广泛, 实例丰富,包括简单比较试验、析因设计、分式析因第1章设计、拟合回归模型、响应曲面方法和设计、稳健参数设计和过程稳健性研究、含随机因子的实验、嵌套设计和裂区设计等. 本书可作为自然科学研究人员、工程技术人员、管理人员进行科学实验设计与分析的参考书, 也可作为农林类、医学类、生物类、统计类的教师和高年级本科生和研究生的教学参考用书.

Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

统计学：基本概念和方法，ISBN：9787040078916，作者：（美）[G.R.埃维森]Gudmund R.Iversen，（美）[M.格根]Mary Gergen著；吴喜之等译

这《R语言统计入门(第2版)》以最恰当的方式向初学者介绍了R的全貌，内容涵盖基本的R语言编程方法、基本数据处理和一些高级数据操作的技巧，有助于读者理解R向量化编程的特点。此外，作者在本书中还详细描述了包含回归分析、假设检验、广义线性模型、非线性拟合等常用统计方法的原理。虽然本书以实际案例解析居多，但是并非不重视理论，作者恰当而到位地描述了理论方面的内容，既不晦涩，也非浅白，而是向读者打开了一扇窗。作者希望这本书可以作为一道“开胃菜”引导更多的人投入到对统计和R的研究之中。

近20多年来，Meta分析作为一种整合系列独立研究结果的方法，已经成为很多领域极其重要的研究工具，在诸如医学、药理学、流行病学、教育学、心理学、商业和生态学等许多学科中得到应用。由于Meta分析方法涉及较深奥的统计学知识，往往较难学习和理解，《Meta分析导论》由浅入深、简明扼要地就有关Meta分析的相关主题进行了系统、广泛而深入的讨论，能使读者较快地理解和掌握该方法。《Meta分析导论》内容包括：Meta分析在研究过程中的地位和作用；效应量和干预效应的计算方法；固定和随机效应模型整合数据的方法；研究间的变异评估分析方法和正确解释；相关概念的案例及图文解释；Meta分析中共性错误的避免；Meta分析相关争议的讨论及进一步学习的相关资源链接。 《Meta分析导论》为读者理解Meta分析的基本思想及如何正确地应用、解释Meta分析结果提供了一个基本框架，可供研究者、临床工作者和统计工作者阅读，也可以作为医学、药理学、流行病学、教育学、心理学、商业和生态学等学科从事Meta分析人员的参考书。

With more than 200 practical recipes, this book helps you perform data analysis with R quickly and efficiently. The R language provides everything you need to do statistical work, but its structure can be difficult to master. This collection of concise, task-oriented recipes makes you productive with R immediately, with solutions ranging from basic tasks to input and output, general statistics, graphics, and linear regression. Each recipe addresses a specific problem, with a discussion that explains the solution and offers insight into how it works. If you're a beginner, R Cookbook will help get you started. If you're an experienced data programmer, it will jog your memory and expand your horizons. You'll get the job done faster and learn more about R in the process. * Create vectors, handle variables, and perform other basic functions * Input and output data * Tackle data structures such as matrices, lists, factors, and data frames * Work with probability, probability distributions, and random variables * Calculate statistics and confidence intervals, and perform statistical tests * Create a variety of graphic displays * Build statistical models with linear regressions and analysis of variance (ANOVA) * Explore advanced statistical techniques, such as finding clusters in your data "Wonderfully readable, R Cookbook serves not only as a solutions manual of sorts, but as a truly enjoyable way to explore the R language-one practical example at a time." -Jeffrey Ryan, software consultant and R package author

《统计模型:理论和实践(原书第2版)》是一本优秀的统计模型教材，着重讲解线性模型的应用问题，包括广义最小二乘和两步最小二乘模型，以及二分变量的probit及logit模型的应用。《统计模型:理论和实践(原书第2版)》还包括关于研究设计、二分变量回归及矩阵代数的背景知识。此外,《统计模型:理论和实践(原书第2版)》附有大量的练习，并且其中多数练习题在书后都有答案，便于读者学习、巩固和提高。 温馨提示：本书2012年11月第一版第二次印刷与2010年9月第一版第一次印刷，是一个版本，只是不同的印次，书的内容没有任何变化，请客户知悉。

《漫画玩转统计学》内容简介：这是一套用漫画来诠释科学新知的系列，被数十所大学用作参考书，其中就包括：哥伦比亚大学、康奈尔大学、哈佛大学、约翰霍普金斯大学、伦敦经济学院、麻省理工学院、纽约大学、斯坦福大学、华盛顿大学、耶鲁大学等。这个系列还被译成葡萄牙语、希腊语、捷克语、波兰语等多国文字，深受读者喜爱。

This manual provides an introduction to "R", a software package for statistical computing and graphics. R is free software, distributed under the GNU General Public License. It can be used with GNU/Linux, Unix and Microsoft Windows.

'In this brilliant new edition, Andy Field has introduced important new introductory material on statistics that the student will need and was missing at least in the first edition. This book is the best blend that I know of a textbook in statistics and a manual on SPSS. It is a balanced composite of both topics, using SPSS to illustrate important statistical material and, through graphics, to make visible important approaches to data analysis. There are many places in the book where I had to laugh, and that's saying a lot for a book on statistics. His excellent style engages the reader and makes reading about statistics fun' - David C Howell, Professor Emeritus, University of Vermont, USA. This award-winning text, now fully updated with SPSS Statistics, is the only book on statistics that you will need! Fully revised and restructured, this new edition is even more accessible as it now takes students through from introductory to advanced level concepts, all the while grounding knowledge through the use of SPSS Statistics. Andy Field's humorous and self-deprecating style and the book's host of characters make the journey entertaining as well as educational. While still providing a very comprehensive collection of statistical methods, tests and procedures, and packed with examples and self-assessment tests to reinforce knowledge, the new edition now also offers: a more gentle introduction to basic-level concepts and methods for beginners; new textbook features to make the book more user-friendly for those learning about more advanced concepts, encouraging 'critical thinking'; a brand new, full-colour design, making it easy for students to navigate between topics, and to understand how to use the latest version of SPSS Statistics; both 'real world' (the bizarre and the wonderful) and invented examples that illustrate the concepts and make the techniques come alive for students; an additional chapter on multilevel modelling for advanced-level students; and, reinforced binding to make the book easier to handle at a computer workstation. The book also includes access to a brand new and improved companion Website, bursting with features including: animated 'SPSS walk-through' videos clearly demonstrating how to use the latest SPSS Statistics modules; self-marking multiple choice questions; data sets for psychology, business and management and health sciences; a flash-card glossary for testing knowledge of key concepts; and, access to support material from SAGE study skills books. Statistics lecturers are also provided with a whole range of resources and teaching aids, including: the test bank; over 300 multiple-choice questions ready to upload to WebCT, Blackboard or other virtual learning environments; charts and diagrams in electronic format for inclusion in lecture slides; and, PowerPoint slides written by the author to accompany chapters of the text.

The field of statistics is rapidly transforming into a discipline that Hal Varian at Google has called "sexy". And with good reason - from batting averages and political polls to game shows and medical research - the real-world application of statistics is growing by leaps and bounds. In Naked Statistics, Charles Wheelan strips away the arcane and technical details to get at the underlying intuition that is key to understanding the power of statistical concepts. Tackling a wide-ranging set of problems, he demonstrates how statistics can be used to look at questions that are important and relevant to us today. With the trademark wit, accessibility and fun that made Naked Economics a bestseller, Wheelan brings another essential discipline to life with a one-in-a-million statistics book that you will read for pleasure.

"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century." —Rachel Maddow, author of Drift Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. The New York Times now publishes FiveThirtyEight.com, where Silver is one of the nation’s most influential political forecasters. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future. In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science. Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise. With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

《SPSS宝典》(宝典丛书100万)共分24章，主要介绍SPSS for Windows的基础知识、统计数据的创建和管理、SPSS统计分析功能、SPSS的图形绘制功能以及SPSS编程功能。其中包括SPSS的窗口及其设置、统计数据的创建与编辑、SPSS数据的管理、数据转换与SPSS函数、SPSS基本统计分析、多重反应分析、SPSS的自定义表格、均值的比较与检验、方差分析、非参数检验、相关分析、回归分析、对数线性模型、聚类分析、判别分析、因子分析、对应分析、信度分析、统计图形的创建和编辑、交互图形的创建和编辑、SPSS的命令语句程序设计、利用SPSS语句读取数据文件、宏等内容。 　　本书内容全面，论述翔实，深入浅出。全书以SPSS统计功能为主线，涵盖数据管理和SPSS高级编程等内容，可供高等院校相关专业本科生、研究生，以及从事统计分析和决策的各领域相关的读者学习参考，亦可作SPSS培训和自学教材。

《多变量分析：SPSS的操作与应用》所介绍的多变量分析技术，除了SPSS/Base功能外，也针对Advanced等模块的功能加以说明，如平均数检定、一般线性模式、因素分析、集群分析、区别分析、回归分析等，并探讨一般书上少见的多元尺度法、TREE、Logistic、规则相关分析、联合分析、时间数列分析等进阶的多变量分析。此外，有关SPSS的外挂程序，包括结构方程模型AMOS与数据探勘Clementine等也多有着墨。

这本书为机器学习技术提供了一些非常棒的案例研究。它并不想成为一本关于机器学习的工具书或者理论书籍，它注重的是一个学习的过程，因而对于任何有一些编程背景和定量思维的人来说，它都是不错的选择。 ——Max Shron OkCupid 机器学习是计算机科学和人工智能中非常重要的一个研究领域，近年来，机器学习不但在计算机科学的众多领域中大显身手，而且成为一些交叉学科的重要支撑技术。本书比较全面系统地介绍了机器学习的方法和技术，不仅详细阐述了许多经典的学习方法，还讨论了一些有生命力的新理论、新方法。 全书案例既有分类问题，也有回归问题；既包含监督学习，也涵盖无监督学习。本书讨论的案例从分类讲到回归，然后讨论了聚类、降维、最优化问题等。这些案例包括分类：垃圾邮件识别，排序：智能收件箱，回归模型：预测网页访问量，正则化：文本回归，最优化：密码破解，无监督学习：构建股票市场指数，空间相似度：用投票记录对美国参议员聚类，推荐系统：给用户推荐R语言包，社交网络分析：在Twitter上感兴趣的人，模型比较：给你的问题找到最佳算法。各章对原理的叙述力求概念清晰、表达准确，突出理论联系实际，富有启发性，易于理解。在探索这些案例的过程中用到的基本工具就是R统计编程语言。R语言非常适合用于机器学习的案例研究，因为它是一种用于数据分析的高水平、功能性脚本语言。 本书主要内容： ·开发一个朴素贝叶斯分类器，仅仅根据邮件的文本信息来判断这封邮件是否是垃圾邮件； ·使用线性回归来预测互联网排名前1000网站的PV； ·利用文本回归理解图书中词与词之间的关系； ·通过尝试破译一个简单的密码来学习优化技术； ·利用无监督学习构建股票市场指数，用于衡量整体市场行情的好坏； ·根据美国参议院的投票情况，从统计学的角度对美国参议员聚类； ·通过K近邻算法构建向用户推荐R语言包； ·利用Twitter数据来构建一个“你可能感兴趣的人”的推荐系统； ·模型比较：给你的问题找到最佳算法。

"There is terror in numbers," writes Darrell Huff in How to Lie with Statistics. And nowhere does this terror translate to blind acceptance of authority more than in the slippery world of averages, correlations, graphs, and trends. Huff sought to break through "the daze that follows the collision of statistics with the human mind" with this slim volume, first published in 1954. The book remains relevant as a wake-up call for people unaccustomed to examining the endless flow of numbers pouring from Wall Street, Madison Avenue, and everywhere else someone has an axe to grind, a point to prove, or a product to sell. "The secret language of statistics, so appealing in a fact-minded culture, is employed to sensationalize, inflate, confuse, and oversimplify," warns Huff. Although many of the examples used in the book are charmingly dated, the cautions are timeless. Statistics are rife with opportunities for misuse, from "gee-whiz graphs" that add nonexistent drama to trends, to "results" detached from their method and meaning, to statistics' ultimate bugaboo--faulty cause-and-effect reasoning. Huff's tone is tolerant and amused, but no-nonsense. Like a lecturing father, he expects you to learn something useful from the book, and start applying it every day. Never be a sucker again, he cries! Even if you can't find a source of demonstrable bias, allow yourself some degree of skepticism about the results as long as there is a possibility of bias somewhere. There always is. Read How to Lie with Statistics. Whether you encounter statistics at work, at school, or in advertising, you'll remember its simple lessons. Don't be terrorized by numbers, Huff implores. "The fact is that, despite its mathematical base, statistics is as much an art as it is a science." --Therese Littleton

《SPSS其实很简单》力图打破这种局面：从实际问题入手，剥离出需要研究的问题，帮助读者理解如何选择恰当的统计方法。软件的发展，使统计从专业方法变成大众的游戏。只要输入格式无误的数据，就能得到漂亮的结果，然而最重要的问题一一方法的选择以及结果的解读却被忽略。从使用SPSS生成变量开始，到最终实现撰写APA（美国心理协会）格式的结果，提供SPSS每一操作步骤的截图，并对输出结果进行解读，帮助读者在面对大量输出结果时，快速有效地找到所需部分，并做出合理分析。总结统计方法使用的前提假设和利用SPSS进行各种统计分析的程序步骤，带领读者理解统计方法的实质。

《结构方程模型:方法与应用》以通俗易懂的方式系统地阐述结构方程模型的基本概念和统计原理，侧重各种结构方程模型的实际运用。《结构方程模型:方法与应用》采用国际著名SEM软件Mplus，使用真实数据来演示各种常见的以及某些新近发展起来的较高级的结构方程模型，提供相应的Mplus程序，并详细解读程序输出结果。参照《结构方程模型:方法与应用》提供的例题和相应的计算机程序，读者便能自己实践各种SEM模型。《结构方程模型:方法与应用》可作为大学社会科学及公共卫生学院研究生以及统计和生物统计专业本科生教材，也可作为相关学科的研究人员从事统计分析的工具书。

This new edition of Field's bestselling textbook provides students of statistical methods with everything they need to understand, use and report statistics - at every level. Written in Andy Field's vivid and entertaining style, and furnished with playful examples from everyday student life (among other places), the book forms an accessible gateway into the often intimidating world of statistics and a unique opportunity for students to ground their knowledge of statistics through the use of SPSS. The text is fully compliant with the latest release of SPSS (version 13).