《多元统计分析及R语言建模（第2版）》共分14章，主要内容有：多元数据的收集和整理、多元数据的直观显示、线性与非线性模型及广义线性模型、判别分析、聚类分析、主成分分析、因子分析、对应分析、典型相关分析等常见的主流方法。《多元统计分析及R语言建模（第2版）》还参考国内外大量文献，系统地介绍了这些年在经济管理等领域应用颇广的一些较新方法，可作为统计学专业本科生和研究生的多元分析课程教材。

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

本书是一本面向应用的经典多元数据分析教材，自1979年出版第1版至今，深受读者好评。本书循序渐进地介绍了各种多元统计分析方法，并通过丰富的实例演示了这些方法的应用。书中不仅涵盖多元数据分析的基本方法，而且还介绍了一些新方法，如结构方程建模和偏最小二乘法等。 本书特色  以循序渐进方式（流水线方式）组织内容：在内容组织上，各章集中概述一个论题，每章均从基础开始并讨论应用，后面各章逐步深入。  扩展各种方法应用：对“经验法则”给出解释，包括像样本容量这类重要问题。  重新组织结构方程建模这一重要内容，包括结构方程建模概述、验证性因素分析、估计和检验结构模型的相关问题，以及验证性因素分析和结构方程建模的一些高级主题，如检验更高阶因子模型、群组模型、调节变量与中间变量。

《商务统计轻松学》系统地阐述了统计学的基本知识，通俗易懂，深入浅出，理论与实践相结合。所有的统计学基本概念都以简单明了的语言进行定义，并配以实例和说明进行阐释，使读者对概念一目了然，书中编排了丰富的实例和习题，其小“问题详解”不仅是统计理论的简单应用，还为读者指明了相应的解题思路和方法，并突出了如何使用Microsoft Excel工作表与统计计算器来解决统计问题，此外，《商务统计轻松学》还配有大量的网上资源和习题供读者下载练习使用。《商务统计轻松学》是那些畏惧统计学的人和在日常生活中将会用到统计学的人的最佳读本。 读者对象：大众。

数据分析方法，ISBN：9787040186840，作者：梅长林、范金城

Machine learning allows computational systems to adaptively improve their performance with experience accumulated from the observed data. Its techniques are widely applied in engineering, science, finance, and commerce. This book is designed for a short course on machine learning. It is a short course, not a hurried course. From over a decade of teaching this material, we have distilled what we believe to be the core topics that every student of the subject should know. We chose the title `learning from data' that faithfully describes what the subject is about, and made it a point to cover the topics in a story-like fashion. Our hope is that the reader can learn all the fundamentals of the subject by reading the book cover to cover. ---- Learning from data has distinct theoretical and practical tracks. In this book, we balance the theoretical and the practical, the mathematical and the heuristic. Our criterion for inclusion is relevance. Theory that establishes the conceptual framework for learning is included, and so are heuristics that impact the performance of real learning systems. ---- Learning from data is a very dynamic field. Some of the hot techniques and theories at times become just fads, and others gain traction and become part of the field. What we have emphasized in this book are the necessary fundamentals that give any student of learning from data a solid foundation, and enable him or her to venture out and explore further techniques and theories, or perhaps to contribute their own. ---- The authors are professors at California Institute of Technology (Caltech), Rensselaer Polytechnic Institute (RPI), and National Taiwan University (NTU), where this book is the main text for their popular courses on machine learning. The authors also consult extensively with financial and commercial companies on machine learning applications, and have led winning teams in machine learning competitions.

NumPy是一个优秀的科学计算库，提供了很多实用的数学函数、强大的多维数组对象和优异的计算性能，不仅可以取代Matlab和Mathematica的许多功能，而且业已成为Python科学计算生态系统的重要组成部分。但与这些商业产品不同，它是免费的开源软件。 本书从NumPy安装讲起，逐渐过渡到数组对象、常用函数、矩阵运算、线性代数、金融函数、窗函数、质量控制等内容，致力于向初中级Python编程人员全面讲述NumPy及其使用。另外，通过书中丰富的示例，你还将学会Matplotlib绘图，并结合使用其他Python科学计算库（如SciPy和Scikits），让工作更有成效，让代码更加简洁而高效。 主要内容： 在不同平台安装NumPy； 用简洁高效的NumPy代码实现高性能计算； 使用功能强大的通用函数； 使用NumPy数组和矩阵； 用NumPy模块轻松执行复杂的数值计算； Matplotlib绘图； NumPy代码测试。

《实用多元统计分析(第6版)》多元统计分析是统计学中内容十分丰富、应用范围极为广泛的一个分支。在自然科学和社会科学的许多学科中，研究者都有可能需要分析处理有多个变量的数据的问题。能否从表面上看起来杂乱无章的数据中发现和提炼出规律性的结论，不仅需要对所研究的专业领域有很好的训练，而且要掌握必要的统计分析工具。对研究者来说，《实用多元统计分析》是学习掌握多元统计分析的各种模型和方法的一本有价值的参考书：首先，它做到了“浅入深出”，既可供初学者入门，又能使有较深基础的人受益；其次，它既侧重于应用，又兼顾必要的推理论证，使学习者既能学到“如何”做，又能在一定程度上了解“为什么”这样做；最后，它内涵丰富、全面，不仅基本包括各种在实际中常用的多元统计分析方法，而且对现代统计学的最新思想和进展有所介绍。

内容简介： 随着信息技术和互联网的发展，人们逐渐从信息匮乏的时代走入了信息过载（information overload）的时代 。在这个时代，无论是信息消费者还是信息生产者都遇到了很大的挑战：对于信息消费者，从大量信息中找到自己感兴趣的信息是一件非常困难的事情；对于信息生产者，让自己生产的信息脱颖而出，受到广大用户的关注，也是一件非常困难的事情。推荐系统就是解决这一矛盾的重要工具。推荐系统的任务就是联系用户和信息，一方面帮助用户发现对自己有价值的信息，另一方面让信息能够展现在对它感兴趣的用户面前，从而实现信息消费者和信息生产者的双赢。

本书介绍的实用技巧运用了统计学原理，还借鉴了教育学和心理学上的测量和实验研究方法。这些技巧可以帮你解决商业、游戏以及日常生活中的各类问题。 本书主要内容： 在德州扑克、二十一点、轮盘赌、骰子游戏甚至买彩票时如何聪明地下注 设计能稳操胜券的酒吧赌注，赢到钱，在朋友面前赚足面子 预测体育比赛结果，知道足球比赛中何时应该“得两分” 揭秘惊人巧合，辨别真正的随机行为和表面上的随机行为 识别伪造数据，揭穿欺骗行为，破解密码 如何将观察结果与被观察对象分离 你是一位睡梦中都在做统计的统计迷，还是能够从趣味解题中找到乐趣的普通人？无论如何，只要善用本书介绍的知识，都能大大提高自己做事的成功几率。

数据之美（影印版），ISBN：9787564122720，作者：（美）西格兰，（美）哈梅巴赫 著

Now updated with new research and even more intuitive explanations, a demystifying explanation of how managers can inform themselves to make less risky, more profitable business decisions This insightful and eloquent book will show you how to measure those things in your own business that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI. Adds even more intuitive explanations of powerful measurement methods and shows how they can be applied to areas such as risk management and customer satisfaction Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods Shows the common reasoning for calling something immeasurable, and sets out to correct those ideas Offers practical methods for measuring a variety of "intangibles" Adds recent research, especially in regards to methods that seem like measurement, but are in fact a kind of "placebo effect" for management - and explains how to tell effective methods from management mythology Written by recognized expert Douglas Hubbard-creator of Applied Information Economics-"How to Measure Anything, Second Edition" illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.

《数据模型与决策》(管理科学篇)(原书第11版)的目的是帮助学生更好地理解与应用管理科学当中的数学与技术方面的概念。因此，作者从描述和解决问题这个角度来介绍管理科学方法与模型，其中包括如何对问题求解的技术。这种方法不仅可以使学生了解管理科学的应用程序，而且还可以了解到管理科学是如何辅助决策的。本书还引用了很多被广泛认可的理论，使水平较高的学生可以很容易读懂一些高水平的材料。在第11版中，作者对决策分析、实践中的管理科学、案例和问题等内容进行了大量修订和更新，内容更加贴近管理实际，可读性更强。

Wouldn't it be great if there were a statistics book that made histograms, probability distributions, and chi square analysis more enjoyable than going to the dentist? "Head First Statistics" brings this typically dry subject to life, teaching you everything you want and need to know about statistics through engaging, interactive, and thought-provoking material, full of puzzles, stories, quizzes, visual aids, and real-world examples. Whether you're a student, a professional, or just curious about statistical analysis, "Head First's" brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it's important; learn how to calculate probability and expectation; and much more."Head First Statistics" is ideal for high school and college students taking statistics and satisfies the requirements for passing the College Board's Advanced Placement (AP) Statistics Exam. With this book, you'll: study the full range of topics covered in first-year statistics; tackle tough statistical concepts using Head First's dynamic, visually rich format proven to stimulate learning and help you retain knowledge; explore real-world scenarios, ranging from casino gambling to prescription drug testing, to bring statistical principles to life; discover how to measure spread, calculate odds through probability, and understand the normal, binomial, geometric, and Poisson distributions; and conduct sampling, use correlation and regression, do hypothesis testing, perform chi square analysis, and more.Before you know it, you'll not only have mastered statistics, you'll also see how they work in the real world. "Head First Statistics" will help you pass your statistics course, and give you a firm understanding of the subject so you can apply the knowledge throughout your life.

If you know how to program with Python and also know a little about probability, you’re ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the way, the Bayesian fundamentals will become clearer, and you’ll begin to apply these techniques to real-world problems. Bayesian statistical methods are becoming more common and more important, but not many resources are available to help beginners. Based on undergraduate classes taught by author Allen Downey, this book’s computational approach helps you get a solid start. Use your existing programming skills to learn and understand Bayesian statistics Work with problems involving estimation, prediction, decision analysis, evidence, and hypothesis testing Get started with simple examples, using coins, M&Ms, Dungeons & Dragons dice, paintball, and hockey Learn computational methods for solving real-world problems, such as interpreting SAT scores, simulating kidney tumors, and modeling the human microbiome.

统计决策论及贝叶斯分析：第二版，ISBN：9787503725333，作者：（美）[J.O.伯杰]James O.Berger著；贾乃光译

《统计学习基础:数据挖掘、推理与预测》介绍了这些领域的一些重要概念。尽管应用的是统计学方法，但强调的是概念，而不是数学。许多例子附以彩图。《统计学习基础:数据挖掘、推理与预测》内容广泛，从有指导的学习（预测）到无指导的学习，应有尽有。包括神经网络、支持向量机、分类树和提升等主题，是同类书籍中介绍得最全面的。计算和信息技术的飞速发展带来了医学、生物学、财经和营销等诸多领域的海量数据。理解这些数据是一种挑战，这导致了统计学领域新工具的发展，并延伸到诸如数据挖掘、机器学习和生物信息学等新领域。许多工具都具有共同的基础，但常常用不同的术语来表达。