It covers such areas as biotechnology and biomedical engineering.

    There's a wonderful mix of science, history and wit, all in bite-sized chapters on a broad range of topics.Urgent and essential, Numbers Don't Lie inspires readers to interrogate what they take to be true in these significant times. Smil is on a mission to make facts matter, because after all, numbers may not lie, but which truth do they convey?'The best book to read to better understand our world. Once in a while a book comes along that helps us see our planet more clearly. By showing us numbers about science, health, green technology and more, Smil's book does just that. It should be on every bookshelf!' Linda Yueh, author of The Great Economists'He is rigorously numeric, using data to illuminate every topic he writes about. The word "polymath" was invented to describe people like him' Bill Gates 'Important' Mark Zuckerberg, on Energy 'One of the world's foremost thinkers on development history and a master of statistical analysis . . . The nerd's nerd' Guardian 'There is perhaps no other academic who paints pictures with numbers like Smil' Guardian 'In a world of specialized intellectuals, Smil is an ambitious and astonishing polymath who swings for fences . . . They're among the most data-heavy books you'll find, with a remarkable way of framing basic facts' Wired 'He's a slayer of bullshit' David Keith, Gordon McKay Professor of Applied Physics & Professor of Public Policy, Harvard UniversityVaclav Smil is Distinguished Professor Emeritus at the University of Manitoba. He is the author of over forty books on topics including energy, environmental and population change, food production and nutrition, technical innovation, risk assessment and public policy. No other living scientist has had more books (on a wide variety of topics) reviewed in Nature. A Fellow of the Royal Society of Canada, in 2010 he was named by Foreign Policy as one of the Top 100 Global Thinkers. This is his first book for a more general readership.

    This book charts its growth and development by sampling from the work of some of its foremost practitioners, beginning with Isaac Newton and Gottfried Wilhelm Leibniz in the late seventeenth century and continuing to Henri Lebesgue at the dawn of the twentieth--mathematicians whose achievements are comparable to those of Bach in music or Shakespeare in literature. William Dunham lucidly presents the definitions, theorems, and proofs. Students of literature read Shakespeare; students of music listen to Bach, he writes. But this tradition of studying the major works of the masters is, if not wholly absent, certainly uncommon in mathematics. This book seeks to redress that situation.Like a great museum, The Calculus Gallery is filled with masterpieces, among which are Bernoulli's early attack upon the harmonic series (1689), Euler's brilliant approximation of pi (1779), Cauchy's classic proof of the fundamental theorem of calculus (1823), Weierstrass's mind-boggling counterexample (1872), and Baire's original category theorem (1899). Collectively, these selections document the evolution of calculus from a powerful but logically chaotic subject into one whose foundations are thorough, rigorous, and unflinching--a story of genius triumphing over some of the toughest, most subtle problems imaginable.Anyone who has studied and enjoyed calculus will discover in these pages the sheer excitement each mathematician must have felt when pushing into the unknown. In touring The Calculus Gallery, we can see how it all came to be.

    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 codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Therefore, this book provides the fundamental concepts and techniques of real analysis for readers in all of these areas. It helps one develop the ability to think deductively, analyze mathematical situations and extend ideas to a new context. Like the first two editions, this edition maintains the same spirit and user-friendly approach with some streamlined arguments, a few new examples, rearranged topics, and a new chapter on the Generalized Riemann Integral.

    We need a brand new way to take it all in. 'Information is Beautiful' transforms the ideas surrounding and swamping us into graphs and maps that anyone can follow at a single glance.

    Peter Bernstein has written a comprehensive history of man's efforts to understand risk and probability, beginning with early gamblers in ancient Greece, continuing through the 17th-century French mathematicians Pascal and Fermat and up to modern chaos theory. Along the way he demonstrates that understanding risk underlies everything from game theory to bridge-building to winemaking.

    Its big-picture, calculus-based approach makes it an especially authoriatative reference for engineering and science majors. Now thoroughly update, this second edition includes vital new coverage of order statistics, best critical regions, likelihood ratio tests, and other key topics.

    Trading Bases explains how he did it. After the fall of Lehman Brothers, Joe Peta was out of a job. He found a new one but lost that, too, when an ambulance mowed him down. In search of a way to cheer himself up while he recuperated in a wheelchair, Peta started watching baseball again, as he had growing up. That’s when inspiration hit: Why not apply his outstanding risk-analysis skills to improve on sabermetrics, the method made famous by Moneyball—and beat the only market in town, the Vegas betting line? Why not treat MLB like the S&P 500? In Trading Bases, Peta shows how to subtract luck—in particular “cluster luck,” as he puts it—from a team’s statistics to best predict how it will perform in the next game and over the whole season. His baseball “hedge fund” returned an astounding 41 percent in 2011—and has never been down more than 5 percent. Peta takes readers to the ballpark in San Francisco, trading floors and baseball bars in New York, and sports books in Vegas, all while tracing the progress of his wagers. Often humorous, occasionally touching, and with a wink toward the sheer implausibility of the whole project, Trading Bases is all about the love of critical reasoning, trading cultures, risk management, and baseball. And not necessarily in that order.

    The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo MCMC. Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

    Why is it better to buy a lottery ticket on a Friday? Why are showers always too hot or too cold? And what's the connection between a rugby player taking a conversion and a tourist trying to get the best photograph of Nelson's Column?These and many other fascinating questions are answered in this entertaining and highly informative book, which is ideal for anyone wanting to remind themselves – or discover for the first time – that maths is relevant to almost everything we do.Dating, cooking, travelling by car, gambling and even life-saving techniques have links with intriguing mathematical problems, as you will find explained here. Whether you have a PhD in astrophysics or haven't touched a maths problem since your school days, this book will give you a fresh understanding of the world around you.

    Along the way we’ll learn how selection bias can explain why your boss doesn’t know he sucks (even when everyone else does); how to use Bayes’ Theorem to decide if your partner is cheating on you; and why Mark Zuckerberg should never be used as an example for anything. See the world in a whole new light, and make better decisions and judgements without ever going near a t-test. Think. Think Statistically.

    More and more leading businesses today use estimation questions in interviews to test applicants' abilities to think on their feet. Guesstimation enables anyone with basic math and science skills to estimate virtually anything--quickly--using plausible assumptions and elementary arithmetic.Lawrence Weinstein and John Adam present an eclectic array of estimation problems that range from devilishly simple to quite sophisticated and from serious real-world concerns to downright silly ones. How long would it take a running faucet to fill the inverted dome of the Capitol? What is the total length of all the pickles consumed in the US in one year? What are the relative merits of internal-combustion and electric cars, of coal and nuclear energy? The problems are marvelously diverse, yet the skills to solve them are the same. The authors show how easy it is to derive useful ballpark estimates by breaking complex problems into simpler, more manageable ones--and how there can be many paths to the right answer. The book is written in a question-and-answer format with lots of hints along the way. It includes a handy appendix summarizing the few formulas and basic science concepts needed, and its small size and French-fold design make it conveniently portable. Illustrated with humorous pen-and-ink sketches, Guesstimation will delight popular-math enthusiasts and is ideal for the classroom.

    Beginning at the intermediate level and ending at a level appropriate for the graduate student, this is a core text for upper level undergraduate and taught graduate microeconomics courses.

    Beginning with the background and very nature of probability theory, the book then proceeds through sample spaces, combinatorial analysis, fluctuations in coin tossing and random walks, the combination of events, types of distributions, Markov chains, stochastic processes, and more. The book's comprehensive approach provides a complete view of theory along with enlightening examples along the way.