Offers a humanistic and cultural view of modern physics.

    Premiere authors Dennis Wackerly, William Mendenhall, and Richard L. Scheaffer present a solid foundation in statistical theory while conveying the relevance and importance of the theory in solving practical problems in the real world. The authors' use of practical applications and excellent exercises helps readers discover the nature of statistics and understand its essential role in scientific research.

    The new edition provides invitations - not requirements - to use technology, as well as new conceptual problems, and new projects that focus on writing and working in teams.

    

    Part I introduces concepts of thermodynamics and statistical mechanics from a unified view. Parts II and III explore further applications of classical thermodynamics and statistical mechanics. Throughout, the emphasis is on real-world applications.

    There are deep connections between stars and atoms, between the cosmos and the microworld. Just six numbers, imprinted in the "big bang," determine the essential features of our entire physical world. Moreover, cosmic evolution is astonishingly sensitive to the values of these numbers. If any one of them were "untuned," there could be no stars and no life. This realization offers a radically new perspective on our universe, our place in it, and the nature of physical laws.

    Over the years sports debates have become muddled by many myths that do not match the numbers generated by those playing the games. In The Wages of Wins, the authors use layman's language and easy to follow examples based on their own academic research to debunk many of the most commonly held beliefs about sports.In this updated version of their book, these authors explain why Allen Iverson leaving Philadelphia made the 76ers a better team, why the Yankees find it so hard to repeat their success from the late 1990s, and why even great quarterbacks like Brett Favre are consistently inconsistent. The book names names, and makes it abundantly clear that much of the decision making of coaches and general managers does not hold up to an analysis of the numbers. Whether you are a fantasy league fanatic or a casual weekend fan, much of what you believe about sports will change after reading this book.

    This is the currently used textbook for "Probabilistic Systems Analysis," an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains, a number of more advanced topics, from which an instructor can choose to match the goals of a particular course. These topics include transforms, sums of random variables, least squares estimation, the bivariate normal distribution, and a fairly detailed introduction to Bernoulli, Poisson, and Markov processes. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis has been just intuitively explained in the text, but is developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems. The book has been widely adopted for classroom use in introductory probability courses within the USA and abroad.

    Smoking has gone from doctor recommended to deadly. We used to think the Earth was the center of the universe and that Pluto was a planet. For decades, we were convinced that the brontosaurus was a real dinosaur. In short, what we know about the world is constantly changing.   But it turns out there’s an order to the state of knowledge, an explanation for how we know what we know. Samuel Arbesman is an expert in the field of scientometrics—literally the science of science. Knowl­edge in most fields evolves systematically and predict­ably, and this evolution unfolds in a fascinating way that can have a powerful impact on our lives.   Doctors with a rough idea of when their knowl­edge is likely to expire can be better equipped to keep up with the latest research. Companies and govern­ments that understand how long new discoveries take to develop can improve decisions about allocating resources. And by tracing how and when language changes, each of us can better bridge gen­erational gaps in slang and dialect.   Just as we know that a chunk of uranium can break down in a measurable amount of time—a radioactive half-life—so too any given field’s change in knowledge can be measured concretely. We can know when facts in aggregate are obsolete, the rate at which new facts are created, and even how facts spread.   Arbesman takes us through a wide variety of fields, including those that change quickly, over the course of a few years, or over the span of centuries. He shows that much of what we know consists of “mesofacts”—facts that change at a middle timescale, often over a single human lifetime. Throughout, he of­fers intriguing examples about the face of knowledge: what English majors can learn from a statistical analysis of The Canterbury Tales, why it’s so hard to measure a mountain, and why so many parents still tell kids to eat their spinach because it’s rich in iron.   The Half-life of Facts is a riveting journey into the counterintuitive fabric of knowledge. It can help us find new ways to measure the world while accepting the limits of how much we can know with certainty.

    It can be used in a variety of disciplines and is ideal for an introductory comprehensive undergraduate or graduate level course. Through its conversational, informal style it makes material that is often challenging for students both accessible and understandable. The Research Methods Knowledge Base, 3e covers everything from the development of a research question to the writing of a final report, describing both practical and technical issues of sampling, measurement, design and analysis.

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    Through the allegory of Alice's adventures and encounters, Gilmore makes the essential features of the quantum world clear and accessible. It is a thrilling introduction to some essential, often difficult-to-grasp concepts about the world we inhabit.

    He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of FiveThirtyEight.com. Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the "prediction paradox": The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good-or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary-and dangerous-science.Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

    Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.

    When Stephen's boss asks him to work on a project with the American Coalition for Civil Liberties, Stephen innocently obliges, believing he is mining Ubatoo's vast databases to protect people unfairly targeted in the name of national security. But nothing is as it seems. Suspicious individuals surface, doing all they can to access Ubatoo's wealth of confidential information. This need not require technical wizardry--simply knowing how to manipulate a well-intentioned intern may be enough." The Silicon Jungle" is a cautionary fictional tale of data mining's promise and peril. Baluja raises ethical questions about contemporary technological innovations, and how minute details can be routinely pieced together into rich profiles that reveal our habits, goals, and secret desires--all ready to be exploited.