This introductory mathematics course for students on science and engineering degrees and pre-degree courses is suitable for both classroom use and self study. The CD-ROM included with the book provides stepped hints, worked solutions and immediate feedback on exercises and problems.

本书论述了自17世纪迄今数理统计学发展的简要历史。内容包括:概率基本概念的起源和发展,伯努利大数定律和狄莫旨二项概率正态逼近,贝叶斯关于统计推断的思想,最小二乘法与误差分布--高其正态分布的发现过程,社会统计学家对数理统计方法的主要贡献等。

本书是天才数学家陶哲轩的第一本书，论述解决数学问题时会涉及的各种策略、方法，旨在激发青少年对数学的兴趣。书中涵盖的内容包括：数论、代数、分析、欧几里得几何、解析几何。
本书启发性强，既能激发学生的数学兴趣、培养思维逻辑，又能充分展现数学的魅力，适合对数学感兴趣的青少年阅读。

数学科普作家顾森（Matrix67）精心审校·倾情推荐
2016《经济学人》年度荐书、《科学美国人》、欧洲数学协会重点推荐
听会说脱口秀的数学家讲一场克服数学恐惧症的数学栋笃笑
◎ 编辑推荐
☆计算机如何思考？
如何用函数制作不一样的情人节礼物？
如何构建四维立方体？
☆四维空间 没有你想象的那么抽象，
这本书可以切割、剪裁、折叠，
将带你探索四维空间！
☆自助式的游戏，
与学校课堂所学不一样的数学，
治愈你和孩子的数学恐惧症！
◎ 内容简介
不少人常常觉得数学有时会违背我们的直觉，但本书的作者认为，数学的非凡之处在于，通过数学逻辑推理工具，我们能够处理超过大脑认知能力的事物，掌握越来越多的抽象概念。在本书中，作者用幽默风趣的语言以学校教授的数学基础（数字、几何）为起点，逐章介绍二维图形、三维图形，最后构建四维图形，带领读者理解四维空间中的奇特图形和数学理论。此外，本书还介绍了素数的奥秘、纽结论、图论、优化算法、条形码和苹果手机屏幕背后涉及的数学原理以及大小不同的无穷，这些理论最终又巧妙地与四维空间联系到一起，超乎想象。本书通过各种数字游戏、谜题、魔术和图形操作，介绍蕴藏其中的趣味数学原理，使原本看起来令人望而生畏的理论变得简单易懂，让读者在阅读中享受数学的乐趣。
◎ 媒体推荐
《图书馆期刊》、《新科学家》、英国《观察者》报、加拿大广播公司重点推荐。
这是自马丁·加德纳（Martin Gardner）的《最佳数学和逻辑难题》（My Best Mathematical and Logic Questions）之后关于趣味数学的最佳书籍。
——《图书馆期刊》
◎ 名人推荐
该书展示了数学的趣味性和多样性，内容宽泛，从经典的纽结论、尺规作图到一些比较离奇的主题，如啤酒商标的拓扑结构和纠错围巾。
——乔丹·埃伦贝格（Jordan Ellenberg）
古根海姆自然科学奖获得者，《如何不犯错》（How to Not Be Wrong）的作者
马特·帕克集恶作剧者、魔术师和天才于一身——聪明、幽默，又有些淘气。
——亚当·拉瑟福德（Adam Rutherford）
英国遗传学家、《自然》杂志编辑，《创造》（Creation）的作者

What makes a bridge wobble when it's not meant to? Billions of dollars mysteriously vanish into thin air? A building rock when its resonant frequency matches a gym class leaping to Snap's 1990 hit I've Got The Power? The answer is maths. Or, to be precise, what happens when maths goes wrong in the real world.
As Matt Parker shows us, our modern lives are built on maths: computer programmes, finance, engineering. And most of the time this maths works quietly behind the scenes, until ... it doesn't. Exploring and explaining a litany of glitches, near-misses and mishaps involving the internet, big data, elections, street signs, lotteries, the Roman empire and a hapless Olympic shooting team, Matt Parker shows us the bizarre ways maths trips us up, and what this reveals about its essential place in our world.
Mathematics doesn't have good 'people skills', but we would all be better off, he argues, if we saw it as a practical ally. This book shows how, by making maths our friend, we can learn from its pitfalls. It also contains puzzles, challenges, geometric socks, jokes about binary code and three deliberate mistakes. Getting it wrong has never been more fun.

“A dizzying, dazzling debut.” —Nature
“A welcome addition to the math-for-people-who-hate-math genre...All but the stubbornly innumerate will enjoy this amusing mathematical miscellany.” —Kirkus Reviews
“Kit Yates is a brilliant explainer and storyteller. Perhaps most charming of all, his stories are a bit like Sherlock Holmes tales: mysteries whose solutions seem surprising and then elementary, once the clever reasoning behind them is revealed. I loved this book and learned something on every page.”
—Steven Strogatz, professor of mathematics, Cornell University, and author of Infinite Powers and The Joy of X
“Kit Yates shows how our private and social lives are suffused by mathematics. Ignorance may bring tragedy or farce. This is an exquisitely interesting book. It’s a deeply serious one too and, for those like me who have little math, it’s delightfully readable.”
—Ian McEwan, author of Atonement
“Kit Yates is a natural storyteller. Through fascinating stories and examples, he shows how math is the beating heart of so much of modern life. An exciting new voice in the world of science communication.”
—Marcus du Sautoy, author of The Music of the Primes
“Used wisely, mathematics can save your life. Used unwisely, it can ruin it. A lucid and enthralling account of why math matters in everyone’s life. A real eye-opener.”
—Ian Stewart, author of In Pursuit of the Unknown: 17 Equations That Changed the World

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.

乐观主义者认为当今世界是较佳可能的世界，悲观主义者却认为未必尽然。但什么是较佳可能的世界呢？我们怎样定义它呢？是那个以最有效的方式运转的世界吗？还是那个生活于其中的大多数人感到舒适和满足的世界？在17世纪和18世纪之间的某个时间，科学家们感到他们可以回答这个问题了。
这本书就是关于他们的故事。伊瓦尔·埃克朗带领读者踏上了一个用科学方法展望很好可能世界的旅程。他从法国数学家莫培督开始，莫培督的最小作用量原理断言自然界中的万物以需要最小作用量的方式发生。埃克朗说明这一思想是科学上的一个关键突破，因为这是对优化概念或最有效和最起作用系统的设计的第1次表述，尽管后来最小作用量原理被细化并作了很大修改，但是从中产生的优化概念几乎触及到今天的每一门科学学科。
沿着优化的深刻影响以及它影响数学、生物学、经济学甚至政治学研究的出入意料的方式，埃克朗从头到尾展示了优化思想是如何推动我们较大的智力突破的。其结果是一个迷人的故事——一个科普爱好者和科学史学家必不可少的读物。

This book describes, simply and in general terms, the process of analyzing data. The authors have extensive experience both managing data analysts and conducting their own data analyses, and have carefully observed what produces coherent results and what fails to produce useful insights into data. This book is a distillation of their experience in a format that is applicable to both practitioners and managers in data science.

The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. Our inaugural effort is OpenIntro Statistics. Probability is optional, inference is key, and we feature real data whenever possible. Files for the entire book are freely available at openintro.org, and anybody can purchase a paperback copy from amazon.com for under $10.
The future for OpenIntro depends on the involvement and enthusiasm of our community. Visit our website, openintro.org. We provide free course management tools, including an online question bank, utilities for creating course quizzes, and many other helpful resources.
http://www.openintro.org/stat/

The standard rules of probability can be interpreted as uniquely valid principles in logic. In this book, E. T. Jaynes dispels the imaginary distinction between 'probability theory' and 'statistical inference', leaving a logical unity and simplicity, which provides greater technical power and flexibility in applications. This book goes beyond the conventional mathematics of probability theory, viewing the subject in a wider context. New results are discussed, along with applications of probability theory to a wide variety of problems in physics, mathematics, economics, chemistry and biology. It contains many exercises and problems, and is suitable for use as a textbook on graduate level courses involving data analysis. The material is aimed at readers who are already familiar with applied mathematics at an advanced undergraduate level or higher. The book will be of interest to scientists working in any area where inference from incomplete information is necessary.

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.
The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.
By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.

This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms.

Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a higher level of math competency, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating but inescapable field. Engineering professor Barbara Oakley knows firsthand how it feels to struggle with math. She flunked her way through high school math and science courses, before enlisting in the army immediately after graduation. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life.
In A Mind for Numbers, Dr. Oakley lets us in on the secrets to effectively learning math and science—secrets that even dedicated and successful students wish they’d known earlier. Contrary to popular belief, math requires creative, as well as analytical, thinking. Most people think that there’s only one way to do a problem, when in actuality, there are often a number of different solutions—you just need the creativity to see them. For example, there are more than three hundred different known proofs of the Pythagorean Theorem. In short, studying a problem in a laser-focused way until you reach a solution is not an effective way to learn math. Rather, it involves taking the time to step away from a problem and allow the more relaxed and creative part of the brain to take over. A Mind for Numbers shows us that we all have what it takes to excel in math, and learning it is not as painful as some might think!

A Programmer's Introduction to Mathematics uses your familiarity with ideas from programming and software to teach mathematics.
You'll learn about the central objects and theorems of mathematics, covering graphs, calculus, linear algebra, eigenvalues, optimization, and more. You'll also be immersed in the often unspoken cultural attitudes of mathematics, learning both how to read and write proofs while understanding why mathematics is the way it is. Between each technical chapter is an essay describing a different aspect of mathematical culture, and discussions of the insights and meta-insights that constitute mathematical intuition.
As you learn, we'll use new mathematical ideas to create wondrous programs, from cryptographic schemes to neural networks to hyperbolic tessellations. Each chapter also contains a set of exercises that have you actively explore mathematical topics on your own. By the end of the book, you will be able to learn mathematics on your own. In short, this book will teach you to engage with mathematics.

本书是世界著名的数学科普读物，它搜集了许多经典的数学珍品，对整个数学领域中的基本概念与方法，做了精深而生动的阐述。无论是数学专业人士，或是愿意作数学思考者都可以阅读本书。特别对中学数学教师、大学生和高中生，本书是一本极好的参考书。

《数学家的眼光》被中外专家誉为是一部具有世界先进水平的科普佳作。适合阅读对象：中小学生及其中、小学教育工作者。《数学家的眼光》讲的不是解某一类数学题的技巧，它告诉读者的是思考数学问题的思路和方法，重在帮助读者全面提高解决数学问题的能力。

《费马大定理》是关于一个困惑了世间智者358年的谜题的传奇。本书既有振奋人心的故事讲述方式，也有引人入胜的科学发现的历史。西蒙•辛格讲述了一个英国人，经过数年秘密辛苦的工作，终于解决了最具挑战性的数学问题的艰辛旅程。

《古今数学思想》论述了从古代一直到20世纪头几十年，这数千年中数学大部分分支的历史发展，阐述了一些重要的数学思想的来源、数学之间与数学和其他自然科学，尤其是力学、物理学的关系。
第一册的内容有美索不达米亚的数学、埃及的数学、古典希腊数学的产生等。