Leo Kadanoff is a theoretical physicist who has done research on chaos theory, superconductivity, phase transitions, fluid flow, the sociology of urban areas, heat transfer in missiles and elementary particle physics, During the last twenty-five years, he has devoted considerable effort to the development of teaching programs for underaduates based upon the use of small computers. Much of this teaching program has been an outgrowth of his interest in the description of chaos in physical systems.
此书为英文版。

This book is an introduction to the field of asymptotic statistics. The treatment is both practical and mathematically rigorous. In addition to most of the standard topics of an asymptotics course, including likelihood inference, M-estimation, the theory of asymptotic efficiency, U-statistics, and rank procedures, the book also presents recent research topics such as semiparametric models, the bootstrap, and empirical processes and their applications. The topics are organized from the central idea of approximation by limit experiments, which gives the book one of its unifying themes. This entails mainly the local approximation of the classical i.i.d. set up with smooth parameters by location experiments involving a single, normally distributed observation. Thus, even the standard subjects of asymptotic statistics are presented in a novel way. Suitable as a graduate or Master's level statistics text, this book will also give researchers an overview of research in asymptotic statistics.

《面向生态学数据的贝叶斯统计:层次模型、算法和R编程》内容简介：作为统计学的两大分支，频率论和贝叶斯统计创立的时间相差无几，但贝叶斯统计直到近10年才被逐步引进到生态学数据分析。《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》涵盖方法引论与实验分析应用两部分，针对多个时空尺度，介绍了适合于生态学数据的统计推断方法和层次模型，涉及经典频率论和贝叶斯统计的模型、算法和具体编程。首先阐述了生态学数据的层次结构和时空变异性，以及频率论和贝叶斯统计。然后介绍贝叶斯推断的基础概念、分析框架和算法原理；并进一步针对生态学层次模型、时间序列及时空复合格局数据依次展开分析模拟。在应用操作部分，配合方法部分的各章内容介绍基于R的算法与编程实践。最后《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》还附录了与生态学数据密切相关的频率论与贝叶斯统计的基础知识。
《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》适用于生态学和环境科学专业的研究生和科研人员，可作为实验和观测数据分析的教材或参考书。具有一定概率论和贝叶斯统计基础及统计软件R应用编程技术的人员，对于理解和应用《面向生态学数据的贝叶斯统计：层次模型、算法和R编程》所涉及的相关方法是必要的。

Causality is central to the understanding and use of data. Without an understanding of cause effect relationships, we cannot use data to answer questions as basic as, “Does this treatment harm or help patients?” But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data.
Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest.
This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
Clarifies both traditional methods and current, popular algorithms (e.g. neural nets, random forests)
Written by two world-leading researchers
Addressed to all fields that work with data

An essential guide to the trouble spots and oddities of R. In spite of the quirks exposed here, R is the best computing environment for most data analysis tasks. R is free, open-source, and has thousands of contributed packages. It is used in such diverse fields as ecology, finance, genomics and music. If you are using spreadsheets to understand data, switch to R. You will have safer -- and ultimately, more convenient -- computations.

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.

随着大数据的概念变得越来越流行，对数据的探索、分析和预测成为大数据分析领域的基本技能之一。作为探索和分析数据的基本理论和工具，机器学习和数据挖掘成为时下炙手可热的技术。R作为功能强大并且免费的数据分析工具，在数据分析领域获得了越来越多用户的青睐。
本书通过丰富的实际案例来探索如何应用R来进行现实世界问题的机器学习，如何从数据中获取可以付诸行动的洞察力。本书案例清晰而实用，讲解循序渐进，是一本用R进行机器学习的实用指南，既适用于机器学习的初学者，也适用于具有一定经验的老手，本书将帮助他们回答有关R的所有问题。

本书收录StephenM.Stigler的22篇论文，主要关于统计的发展历史，同时涉及统计思维、统计标准与统计检测等诸多方面。本书特别关注统计学史的两个方面。首要的方面包括了概念性的问题，这些问题出现在社会科学和自然科学领域对数据的解读上，非常艰深难懂。另一方面是数学方法问题，具有相当的技术复杂性，出现在对概率模型的建立和运用中。我们需要用这些模型来明确对不确定性的描述，否则无法判断结论及预测的可靠性。
全书共分为五个部分。第一部分讲述皮尔逊所受到的挑战，接着探讨凯特勒在1831年为赋予个体集合的平均值意义而做出的努力。第二部分主要介绍高尔顿的观点以及一些相关观点的进展。第三部分回顾的是更久远的17世纪，以及什么是数理概率在科学问题分析中的最早应用。第四部分关注的是“发现”在两个层面上的问题：“发现”作为科学社会学中的一个主旨，以及公众是如何看待一个新发现的。第五部分探讨的是统计对于标准的作用，以及标准在统计中的角色。

This book demonstrates the contribution that statistics can and should make to linguistic studies.The range of work to which statistical analysis is applicable is vast:including,for example,language acquisition,language variation and many aspects of applied linguistics,The aubhors give a wide variety of linguixtic examples to demonstrate the use of statistics in summarising data in the most appropriate way,and then making helpful inferences form the processed information.
Students and resesarchers in many fields of linguistics will find this book an invaluable introduction to the use of statistics,and a practical text tor the development of skils in the application of statistics.

本书是当代国际最著名的统计学家之一C.R.劳的一部统计学哲理论著，也是他毕生统计学术思想的总结，同时还是一本通俗的关于统计学原理的普及教科书。在本书中，作者从哲学的角度论述了统计学原理，通过实例，不仅证明了统计学是一门最严格、最合理的认识论和方法学 ，还深刻地揭示了现代统计学发展的过程，特别是那些很深刻的理论是如何从一些非常简单实际的问题中发展起来的。
本书前5章讲述了统计学从最初收集、汇编数据为行政管理服务，发展成为有一整套原理和研究方法的独立学科的历史，第6章谈及了普通公众对统计学的理解，强调了从数字中学习有助于成为有效率的公民。
最引人注目的特点是，书中提到的所有科学的学科调查与决策和统计之间的关联，是由一系列实例来说明的。本书使用非专业语言通俗地阐述了统计学的基本概念和方法，适合大众读者。

《测度论讲义(第2版)》系统介绍一般可测空间上的测度与积分，Hausdorff空间上的测度与积分以及测度的弱收敛等，此外还介绍了和测度论有关的概率统计等有关知识，如条件数学期望、正则条件概率、随机变量族的一致可积性、解析集及经典鞅论。第二版增加了Hilbert空间和Banach空间上的测度内容，部分章节也增加了一些新内容和作者的研究成果。

丹尼尔 A.鲍威斯和谢宇教授合著的《分类数据分析的统计方法》一书对分类数据分析的方法和模型，以及在社会科学研究中的应用作了全面的介绍。本书的一个明确目标是整合变换方法和潜在变量方法，它们是两类不同但又相互补充的处理分类数据分析的传统方法。这也是第一次在一单册书中严密地介绍针对离散因变量、交叉分类和跟踪数据的模型和方法。目前还没有看到有类似的著作。
本书的第二版增加了应用于分类数据的多水平模型。许多章节的内容经过了进一步的修订，并扩充了新的应用和实例。第二版中显著的特点是详细讨论了针对分层或多水平模型的经典贝叶斯估计技术，拓展了离散时间生存分析模型和Cox回归模型的内容，以及针对背离模型假设的评估和调适方法。辅助网址列举了使用各种统计软件包重复书中每一个例子的程序，实践证明是教师、学生和研究者学习的重要资源。
本书介绍了基本的方法和模型，它们构成了当代社会统计学的核心。本书介绍的模型跨度非同寻常，它们被广泛应用在社会学、人口学、心理测验学、计量经济学、政治学、生物统计学及其他领域。作为学生学习高级社会统计课程的研究生教材和应用研究者的参考书，是非常有用的。

统计学的思想和各种统计数据对政府、社会乃至我们的工作和日常生活都产生着直接的影响，这种影响可能远远超乎你的想像。通过阅读本书，你将对我们这个世界有一个更完整、更清晰的认识。
本书一点儿也不枯燥乏味，恰恰相反，它是那样生动有趣，深入浅出地把统计学的概念和分析方法呈现在你面前。通过一个个真实的小故事，本书能让你在会心的微笑中不知不觉地增长专业知识，提高分析水平。这是一本能给你带来乐趣的书，也是一本能让你更加睿智的书。

《回归分析》源于作者多年在密歇根大学教授回归分析的课程讲义，从基本的统计概念讲起，对线性回归分析的基本假定、回归中的统计推论和回归诊断做了详尽的介绍，同时还涵盖了很多在社会科学中对实际研究非常有用的内容，包括虚拟变量、交互作用、辅助回归、多项式回归、样条函数回归和阶跃函数回归等。此外，《回归分析》还涉及通径分析、纵贯数据模型、多层线性模型和Iogit模型等方面的内容。

这是一本由学术界公认的大师和睿智的教师介绍现代社会科学研究方法的一流教材。Treiman使复杂的问题变得简单，并提供了许多实用的建议和最优的方法。本书没有复杂的数学推导，通过大量的实例领会社会科学研究的基本逻辑和设计思想，图文并茂，浅显易懂，把握前沿最新社会科学成果。《量化数据分析：通过社会研究检验想法》不只是讲授统计学——它讲授如何用统计学回答社会问题,它教会学生如何运用统计学开展一流的量化研究。