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.

"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

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).

This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach. Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 . Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

This best-selling introductory statistics text is designed for courses in psychological and educational statistics and is written in an intuitive, explanatory style. New to this edition: In the Literature sections help students learn how to report statistics and results; as well as learn how to read and interpret scientific journals. The Minitab chapter has been removed; it is now a separate supplement for those who wish to cover it.

在经过不断地摸索以及少量成功大量失败的尝试之后，我已经学会了以某种方式教授统计学，我和我的许多学生认为这种方式不会让人感到害怕，同时能够传递大量的信息。 通过这本书可以了解基础统计学的范围并学习所有应该掌握的信息，也可以了解整理和分析数据的基本思路和最常用的技术。本书理论部分有一些，但是很少，数学证明或特定数学程式的合理性讨论也很少。 为什么《爱上统计学》这本书不增加更多理论内容?很简单，初学者不需要。这并不是我认为理论不重要，而是在学习的这个阶段，我想提供的是我认为通过一定程度的努力可以理解和掌握的资料，同时又不会让你感到害怕而放弃将来选修更多的课程。我和其他老师都希望你能成功。 因此，如果你想详细了解方差分析中F值的含义，可以从Sage出版社查找其他的好书(我愿意向你推荐书目)。但是如果你想了解统计学为什么以及如何为你所用，这本书很合适。这本书能帮助你理解在专业文章中看到的资料，解释许多统计分析结果的意义，并且能教你运用基本的统计过程。 --- 第I部分　耶!我喜欢统计学 1　统计学还是虐待学?由你决定 为什么学习统计学 统计学简史 统计学：是什么(或不是什么) 我在统计学课堂上做什么 使用这本书的十种方式(同时也在学统计学!) 关于那些符号 难度指数 第Ⅱ部分　西格玛·弗洛伊德和描述统计 2　必须完成的功课——计算和理解平均数 计算均值 需要记忆的内容 计算中位数 需要记忆的内容 计算众数 何时用什么 应用计算机并计算描述统计值 3　性别差异——理解变异性 为什么理解变异性很重要 计算极差 计算标准差 需要记忆的内容 计算方差 使用计算机计算变异性量数 4　一幅图真的相当于千言万语 为什么要用图表说明数据 好图表的十个方面(少贪新，多练习) 首先是建立频数分布 图形密度：建立直方图 扁平和细长的频数分布 其他的图表数据的绝妙方法 使用计算机图示数据 5　冰淇淋和犯罪——计算相关系数 相关系数到底是什么 需要记忆的内容 计算简单相关系数 理解相关系数的含义 决定性的努力：相关系数平方 其他重要的相关 使用计算机计算相关系数 第Ⅲ部分　抓住那些有趣又有利的机会 6　你和假设：检验你的问题 也许你想成为一个科学家 零假设 研究假设 好假设的标准是什么 7　你的曲线是正态的吗——概率和概率的重要性 为什么学习概率 正态曲线(或钟型曲线) 我们最中意的标准值：z值 使用计算机计算z值 第Ⅳ部分　显著性差异——使用推论统计 8　显著性的显著——对你我来说意味着什么 显著性的概念 显著性与意义 推论统计介绍 显著性检验介绍 9　两个群体的t检验——不同群体的均值检验 独立样本t检验介绍 计算检验统计量 特殊效果：差异是真实的吗 使用计算机进行t检验 10　两个群体的t检验——两个相关群体的均值检验 …… 第V部分　你得了解和记忆的内容 附录A　30分钟SPSS教学 附录B　数据表 附录C　数据集

In this irreverent and illuminating book, acclaimed writer and scientist Leonard Mlodinow shows us how randomness, change, and probability reveal a tremendous amount about our daily lives, and how we misunderstand the significance of everything from a casual conversation to a major financial setback. As a result, successes and failures in life are often attributed to clear and obvious cases, when in actuality they are more profoundly influenced by chance. The rise and fall of your favorite movie star of the most reviled CEO--in fact, of all our destinies--reflects as much as planning and innate abilities. Even the legendary Roger Maris, who beat Babe Ruth's single-season home run record, was in all likelihood not great but just lucky. And it might be shocking to realize that you are twice as likely to be killed in a car accident on your way to buying a lottery ticket than you are to win the lottery. How could it have happened that a wine was given five out of five stars, the highest rating, in one journal and in another it was called the worst wine of the decade? Mlodinow vividly demonstrates how wine ratings, school grades, political polls, and many other things in daily life are less reliable than we believe. By showing us the true nature of change and revealing the psychological illusions that cause us to misjudge the world around us, Mlodinow gives fresh insight into what is really meaningful and how we can make decisions based on a deeper truth. From the classroom to the courtroom, from financial markets to supermarkets, from the doctor's office to the Oval Office, Mlodinow's insights will intrigue, awe, and inspire. Offering readers not only a tour of randomness, chance, and probability but also a new way of looking at the world, this original, unexpected journey reminds us that much in our lives is about as predictable as the steps of a stumbling man fresh from a night at the bar.

Barry Cohen教授所著《心理统计学》（第三版）是一本广受欢迎的心理统计学教材。它是一本非常全面的心理统计学教材，既包括入门性的统计学知识（如零假设检验的基本概念和局限性），也包括心理统计的高级内容（如复杂设计方差分析和多元回归分析）。 虽然是一本统计教材，但作者无时不考虑结合研究设计来解释有关概念，因此该书的重点是讲授各个统计公式或手段的适用条件以及如何解释统计结果的意义。作者实验心理学博士出身，在纽约大学教授心理学统计学超过30年，通过阅读本书，你会深刻体会“将实验设计与数据处理相结合”的妙处。 这本书的另一个优点是，它既适合于那些对统计不甚了解的人，也适合于专业研究人员（包括硕士生和博士生）。它每章基本上包括ABC三个部分，每个部分是相对独立的，初学者可以只看A和B部分的内容；而在C部分（虽不能只说更高级），作者介绍了很多最近才出现的分析手段，也反映了数据处理的一些发展趋势，对心理学研究者非常有帮助。

Review Journal of Modern History : The book is a pleasure to read: the prose sparkles; the protagonists are vividly drawn; the illustrations are handsome and illuminating; the insights plentiful and sharp. This will remain the definitive work on the early development of mathematical statistics for some time to come. --Lorraine J. Daston Science : An exceptionally searching, almost loving, study of the relevant inspirations and aberrations of its principal characters James Bernoulli, de Moivre, Bayes, Laplace, Gauss, Quetelet, Lexis, Galton, Edgeworth, and Pearson, not neglecting a grand supporting cast...The definitive record of an intellectual Golden Age, an overoptimistic climb to a height not to be maintained. --M. Stone New York Times Book Review : One is tempted to say that the history of statistics in the nineteenth century will be associated with the name Stigler. --Morris Kline Contemporary Psychology : In this tour de force of careful scholarship, Stephen Stigler has laid bare the people, ideas, and events underlying the development of statistics...He has written an important and wonderful book...Sometimes Stigler's prose is so evocative it is almost poetic. --Howard Wainer Review Stigler's book exhibits a rare combination of mastery of technical materials, sensitivity to conceptual milieu, and near exhaustive familiarity with primary sources. An exemplary study --Lorraine Daston

Praise for the First Edition "This is a superb text from which to teach categorical data analysis, at a variety of levels. . . [t]his book can be very highly recommended." — Short Book Reviews "Of great interest to potential readers is the variety of fields that are represented in the examples: health care, financial, government, product marketing, and sports, to name a few." — Journal of Quality Technology "Alan Agresti has written another brilliant account of the analysis of categorical data." —The Statistician The use of statistical methods for categorical data is ever increasing in today's world. An Introduction to Categorical Data Analysis, Second Edition provides an applied introduction to the most important methods for analyzing categorical data. This new edition summarizes methods that have long played a prominent role in data analysis, such as chi-squared tests, and also places special emphasis on logistic regression and other modeling techniques for univariate and correlated multivariate categorical responses. This Second Edition features: Two new chapters on the methods for clustered data, with an emphasis on generalized estimating equations (GEE) and random effects models A unified perspective based on generalized linear models An emphasis on logistic regression modeling An appendix that demonstrates the use of SAS(r) for all methods An entertaining historical perspective on the development of the methods Specialized methods for ordinal data, small samples, multicategory data, and matched pairs More than 100 analyses of real data sets and nearly 300 exercises Written in an applied, nontechnical style, the book illustrates methods using a wide variety of real data, including medical clinical trials, drug use by teenagers, basketball shooting, horseshoe crab mating, environmental opinions, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Second Edition is an invaluable tool for social, behavioral, and biomedical scientists, as well as researchers in public health, marketing, education, biological and agricultural sciences, and industrial quality control.

In this new edition the author has added substantial material on Bayesian analysis, including lengthy new sections on such important topics as empirical and hierarchical Bayes analysis, Bayesian calculation, Bayesian communication, and group decision making. With these changes, the book can be used as a self-contained introduction to Bayesian analysis. In addition, much of the decision-theoretic portion of the text was updated, including new sections covering such modern topics as minimax multivariate (Stein) estimation.

WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level.

雷奥奇·卡塞拉、罗杰L.贝耶编著的《统计推断（英文版原书第2版）》从概率论的基础开始，通过例子与习题的旁征博引，引进了大量近代统计处理的新技术和一些国内同类教材中不能见而广为使用的分布。其内容包括工科概率论入门、经典统计和现代统计的基础，又加进了不少近代统计中数据处理的实用方法和思想，例如：Bootstrap再抽样法、刀切（Jackknife）估计、EM算法、Logistic回归、稳健（Robust）回归、Markov链、Monte Carlo方法等。它的统计内容与国内流行的教材相比，理论较深，模型较多，案例的涉及面要广，理论的应用面要丰富，统计思想的阐述与算法更为具体。《统计推断（英文版原书第2版）》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。

雷奥奇·卡塞拉、罗杰L.贝耶编著的《统计推断（英文版原书第2版）》从概率论的基础开始，通过例子与习题的旁征博引，引进了大量近代统计处理的新技术和一些国内同类教材中不能见而广为使用的分布。其内容包括工科概率论入门、经典统计和现代统计的基础，又加进了不少近代统计中数据处理的实用方法和思想，例如：Bootstrap再抽样法、刀切（Jackknife）估计、EM算法、Logistic回归、稳健（Robust）回归、Markov链、Monte Carlo方法等。它的统计内容与国内流行的教材相比，理论较深，模型较多，案例的涉及面要广，理论的应用面要丰富，统计思想的阐述与算法更为具体。《统计推断（英文版原书第2版）》可作为工科、管理类学科专业本科生、研究生的教材或参考书，也可供教师、工程技术人员自学之用。

Major changes in this edition include the substitution of probabilistic arguments for combinatorial artifices, and the addition of new sections on branching processes, Markov chains, and the De Moivre-Laplace theorem.

"Applied Linear Statistical Models, 5e" is the long established leading authoritative text and reference on statistical modeling. The text includes brief introductory and review material, and then proceeds through regression and modeling for the first half, and through ANOVA and Experimental Design in the second half. All topics are presented in a precise and clear style supported with solved examples, numbered formulae, graphic illustrations, and "Notes" to provide depth and statistical accuracy and precision. The Fifth edition provides an increased use of computing and graphical analysis throughout, without sacrificing concepts or rigor. In general, the 5e uses larger data sets in examples and exercises, and where methods can be automated within software without loss of understanding, it is so done.

无论中外，统计学一直是各相关专业学生的梦魇。Frederick J.Gravetter和Larry B.Wallnau两位教授正是考虑到这一点，从而以深入浅出、通俗易懂的方式，将统计知识清晰地整合到实际的行为科学研究中，以直接、易学、详尽的方法向学生讲授统计学的应用。 本书自出版以来一直是美国心理学、社会学等专业领域中使用最广的统计学教材，是一本非常适用于数学基础薄弱学生的统计入门书。