What's new in the Second Edition? 1. Fully compliant with the latest version of SPSS version 12 2. More coverage of advanced statistics including completely new coverage of non-parametric statistics. The book is 50 per cent longer than the First Edition. 3. Each section of each chapter now has a notation - 1,2 or 3 - referring to the intended level of study. This helps students navigate their way through the book and makes it user-friendly for students of ALL levels. 4. Has a 'how to use this book' section at the start of the text. 5. Characters in each chapter have defined roles - summarizing key points, to pose questions etc 6. Each chapter now has several examples for students to work through. Answers provided on the enclosed CD-ROM

    What began more than sixty years ago as a modest proposal that a mathematician and an economist write a short paper together blossomed, in 1944, when Princeton University Press published Theory of Games and Economic Behavior. In it, John von Neumann and Oskar Morgenstern conceived a groundbreaking mathematical theory of economic and social organization, based on a theory of games of strategy. Not only would this revolutionize economics, but the entirely new field of scientific inquiry it yielded--game theory--has since been widely used to analyze a host of real-world phenomena from arms races to optimal policy choices of presidential candidates, from vaccination policy to major league baseball salary negotiations. And it is today established throughout both the social sciences and a wide range of other sciences.This sixtieth anniversary edition includes not only the original text but also an introduction by Harold Kuhn, an afterword by Ariel Rubinstein, and reviews and articles on the book that appeared at the time of its original publication in the New York Times, tthe American Economic Review, and a variety of other publications. Together, these writings provide readers a matchless opportunity to more fully appreciate a work whose influence will yet resound for generations to come.

    Addressing readers with both a general and scholarly interest in mathematics, Nahin weaves into this narrative entertaining historical facts, mathematical discussions, and the application of complex numbers and functions to important problems.

    Hardy, in the years before World War I. Through their eyes the reader is taken on a journey through numbers theory. Ramanujan would regularly telescope 12 steps of logic into two - the effect is said to be like Dr Watson in the train of some argument by Sherlock Holmes. The language of symbols and infinitely large (and small) regions of mathematics should be rendered with clarity for the general reader.

    With boundless erudition and in delightfully clear prose, Surowiecki ranges across fields as diverse as popular culture, psychology, ant biology, behavioral economics, artificial intelligence, military history, and politics to show how this simple idea offers important lessons for how we live our lives, select our leaders, run our companies, and think about our world.

    Its world-class authors provide guidance on all aspects of Bayesian data analysis and include examples of real statistical analyses, based on their own research, that demonstrate how to solve complicated problems. Changes in the new edition include:Stronger focus on MCMC Revision of the computational advice in Part III New chapters on nonlinear models and decision analysis Several additional applied examples from the authors' recent research Additional chapters on current models for Bayesian data analysis such as nonlinear models, generalized linear mixed models, and more Reorganization of chapters 6 and 7 on model checking and data collectionBayesian computation is currently at a stage where there are many reasonable ways to compute any given posterior distribution. However, the best approach is not always clear ahead of time. Reflecting this, the new edition offers a more pluralistic presentation, giving advice on performing computations from many perspectives while making clear the importance of being aware that there are different ways to implement any given iterative simulation computation. The new approach, additional examples, and updated information make Bayesian Data Analysis an excellent introductory text and a reference that working scientists will use throughout their professional life.

    Just about everyone has at least heard of Albert Einstein's formulation of 1905, which came into the world as something of an afterthought. But far fewer can explain his insightful linkage of energy to mass. David Bodanis offers an easily grasped gloss on the equation. Mass, he writes, "is simply the ultimate type of condensed or concentrated energy," whereas energy "is what billows out as an alternate form of mass under the right circumstances." Just what those circumstances are occupies much of Bodanis's book, which pays homage to Einstein and, just as important, to predecessors such as Maxwell, Faraday, and Lavoisier, who are not as well known as Einstein today. Balancing writerly energy and scholarly weight, Bodanis offers a primer in modern physics and cosmology, explaining that the universe today is an expression of mass that will, in some vastly distant future, one day slide back to the energy side of the equation, replacing the "dominion of matter" with "a great stillness"--a vision that is at once lovely and profoundly frightening. Without sliding into easy psychobiography, Bodanis explores other circumstances as well; namely, Einstein's background and character, which combined with a sterling intelligence to afford him an idiosyncratic view of the way things work--a view that would change the world. --Gregory McNamee

    Now he brings his considerable talents to the history of one of math's most enduring puzzles: the seemingly paradoxical nature of infinity.Is infinity a valid mathematical property or a meaningless abstraction? The nineteenth-century mathematical genius Georg Cantor's answer to this question not only surprised him but also shook the very foundations upon which math had been built. Cantor's counterintuitive discovery of a progression of larger and larger infinities created controversy in his time and may have hastened his mental breakdown, but it also helped lead to the development of set theory, analytic philosophy, and even computer technology.Smart, challenging, and thoroughly rewarding, Wallace's tour de force brings immediate and high-profile recognition to the bizarre and fascinating world of higher mathematics.

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    Gordon strips engineering of its confusing technical terms, communicating its founding principles in accessible, witty prose.For anyone who has ever wondered why suspension bridges don't collapse under eight lanes of traffic, how dams hold back--or give way under--thousands of gallons of water, or what principles guide the design of a skyscraper, a bias-cut dress, or a kangaroo, this book will ease your anxiety and answer your questions.Structures: Or Why Things Don't Fall Down is an informal explanation of the basic forces that hold together the ordinary and essential things of this world--from buildings and bodies to flying aircraft and eggshells. In a style that combines wit, a masterful command of his subject, and an encyclopedic range of reference, Gordon includes such chapters as "How to Design a Worm" and "The Advantage of Being a Beam," offering humorous insights in human and natural creation.Architects and engineers will appreciate the clear and cogent explanations of the concepts of stress, shear, torsion, fracture, and compression. If you're building a house, a sailboat, or a catapult, here is a handy tool for understanding the mechanics of joinery, floors, ceilings, hulls, masts--or flying buttresses.Without jargon or oversimplification, Structures opens up the marvels of technology to anyone interested in the foundations of our everyday lives.

    It is a ‘ must’ book for those in every branch of science . . . in addition, economists, politicians, statesmen, and businessmen cannot afford to overlook cybernetics and its tremendous, even terrifying implications. "It is a beautifully written book, lucid, direct, and despite its complexity, as readable by the layman as the trained scientist." -- John B. Thurston, "The Saturday Review of Literature" Acclaimed one of the "seminal books . . . comparable in ultimate importance to . . . Galileo or Malthus or Rousseau or Mill," "Cybernetics" was judged by twenty-seven historians, economists, educators, and philosophers to be one of those books published during the "past four decades", which may have a substantial impact on public thought and action in the years ahead." -- Saturday Review

    presidential elections have been won by the second most popular candidate. The reason was a "spoiler"--a minor candidate who takes enough votes away from the most popular candidate to tip the election to someone else. The spoiler effect is more than a glitch. It is a consequence of one of the most surprising intellectual discoveries of the twentieth century: the "impossibility theorem" of Nobel laureate economist Kenneth Arrow. The impossibility theorem asserts that voting is fundamentally unfair--a finding that has not been lost on today's political consultants. Armed with polls, focus groups, and smear campaigns, political strategists are exploiting the mathematical faults of the simple majority vote. In recent election cycles, this has led to such unlikely tactics as Republicans funding ballot drives for Green spoilers and Democrats paying for right-wing candidates' radio ads. Gaming the Vote shows that there is a solution to the spoiler problem that will satisfy both right and left. A systemcalled range voting, already widely used on the Internet, is the fairest voting method of all, according to computer studies. Despite these findings, range voting remains controversial, and Gaming the Vote assesses the obstacles confronting any attempt to change the American electoral system. The latest of several books by William Poundstone on the theme of how important scientific ideas have affected the real world, Gaming the Vote is a wry exposé of how the political system really works, and a call to action.

    Wolfram lets the world see his work in A New Kind of Science, a gorgeous, 1,280-page tome more than a decade in the making. With patience, insight, and self-confidence to spare, Wolfram outlines a fundamental new way of modeling complex systems. On the frontier of complexity science since he was a boy, Wolfram is a champion of cellular automata--256 "programs" governed by simple nonmathematical rules. He points out that even the most complex equations fail to accurately model biological systems, but the simplest cellular automata can produce results straight out of nature--tree branches, stream eddies, and leopard spots, for instance. The graphics in A New Kind of Science show striking resemblance to the patterns we see in nature every day. Wolfram wrote the book in a distinct style meant to make it easy to read, even for nontechies; a basic familiarity with logic is helpful but not essential. Readers will find themselves swept away by the elegant simplicity of Wolfram's ideas and the accidental artistry of the cellular automaton models. Whether or not Wolfram's revolution ultimately gives us the keys to the universe, his new science is absolutely awe-inspiring. --Therese Littleton

    The Sociology Book takes on some of humankind's biggest questions: What is society? What makes it tick? Why do we interact in the way that we do with our friends, coworkers, and rivals? The Sociology Book profiles the world's most renowned sociologists and more than 100 of their biggest ideas, including issues of equality, diversity, identity, and human rights; the effects of globalization; the role of institutions; and the rise of urban living in modern societyEasy to navigate and chock-full of key concepts, profiles of major sociological thinkers, and conversation starters galore, this is a must-have, in-a-nutshell guide to some of the most fascinating questions on earth.The Sociology Book is part of the award-winning Big Ideas Simply Explained series, designed to distill big ideas and elusive theories into graspable, memorable concepts, using an approachable graphic treatment and creative typography.

    Suppose that different food inspectors give different ratings to indistinguishable restaurants — or that when a company is handling customer complaints, the resolution depends on who happens to be handling the particular complaint. Now imagine that the same doctor, the same judge, the same inspector, or the same company official makes different decisions, depending on whether it is morning or afternoon, or Monday rather than Wednesday. These are examples of noise: variability in judgments that should be identical.   In Noise, Daniel Kahneman, Cass R. Sunstein, and Olivier Sibony show how noise contributes significantly to errors in all fields, including medicine, law, economic forecasting, police behavior, food safety, bail, security checks at airports, strategy, and personnel selection. And although noise can be found wherever people make judgments and decisions, individuals and organizations alike are commonly oblivious to the role of chance in their judgments and in their actions.   Drawing on the latest findings in psychology and behavioral economics, and the same kind of diligent, insightful research that made Thinking, Fast and Slow and Nudge groundbreaking New York Times bestsellers, Noise explains how and why humans are so susceptible to noise in judgment — and what we can do about it.