Each technique is introduced with a simple, jargon-free explanation, practical examples, and hands-on guidance for solving real problems with Excel or a TI-83/84 series calculator, including Plus models. Hate math? No sweat. You'll be amazed how little you need! For those who do have an interest in mathematics, optional "Equation Blackboard" sections review the equations that provide the foundations for important concepts. David M. Levine is a much-honored innovator in statistics education. He is Professor Emeritus of Statistics and Computer Information Systems at Bernard M. Baruch College (CUNY), and co-author of several best-selling books, including Statistics for Managers using Microsoft Excel, Basic Business Statistics, Quality Management, and Six Sigma for Green Belts and Champions. Instructional designer David F. Stephan pioneered the classroom use of personal computers, and is a leader in making Excel more accessible to statistics students. He has co-authored several textbooks with David M. Levine. Here's just some of what you'll learn how to do... Use statistics in your everyday work or study Perform common statistical tasks using a Texas Instruments statistical calculator or Microsoft Excel Build and interpret statistical charts and tables "Test Yourself" at the end of each chapter to review the concepts and methods that you learned in the chapter Work with mean, median, mode, standard deviation, Z scores, skewness, and other descriptive statistics Use probability and probability distributions Work with sampling distributions and confidence intervals Test hypotheses and decision-making risks with Z, t, Chi-Square, ANOVA, and other techniques Perform regression analysis and modeling The easy, practical introduction to statistics--for everyone! Thought you couldn't learn statistics? Think again. You can--and you will!

    Bread, cash, dosh, dough, loot, lucre, moolah, readies, the wherewithal: Call it what you like, it matters. To Christians, love of it is the root of all evil. To generals, it’s the sinews of war. To revolutionaries, it’s the chains of labor. But in The Ascent of Money, Niall Ferguson shows that finance is in fact the foundation of human progress. What’s more, he reveals financial history as the essential backstory behind all history. With the clarity and verve for which he is known, Ferguson elucidates key financial institutions and concepts by showing where they came from. What is money? What do banks do? What’s the difference between a stock and a bond? Why buy insurance or real estate? And what exactly does a hedge fund do? This is history for the present. Ferguson travels to post-Katrina New Orleans to ask why the free market can’t provide adequate protection against catastrophe. He delves into the origins of the subprime mortgage crisis.

    These themes include mathematical reasoning, combinatorial analysis, discrete structures, algorithmic thinking, and enhanced problem-solving skills through modeling. Its intent is to demonstrate the relevance and practicality of discrete mathematics to all students. The Fifth Edition includes a more thorough and linear presentation of logic, proof types and proof writing, and mathematical reasoning. This enhanced coverage will provide students with a solid understanding of the material as it relates to their immediate field of study and other relevant subjects. The inclusion of applications and examples to key topics has been significantly addressed to add clarity to every subject. True to the Fourth Edition, the text-specific web site supplements the subject matter in meaningful ways, offering additional material for students and instructors. Discrete math is an active subject with new discoveries made every year. The continual growth and updates to the web site reflect the active nature of the topics being discussed. The book is appropriate for a one- or two-term introductory discrete mathematics course to be taken by students in a wide variety of majors, including computer science, mathematics, and engineering. College Algebra is the only explicit prerequisite.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    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.

    Readers profit from its verbal-conceptual exposition and frequent use of examples.The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

    John Maynard Keynes, the mercurial Cambridge economist, believed that government had a duty to spend when others would not. He met his opposite in a little-known Austrian economics professor, Freidrich Hayek, who considered attempts to intervene both pointless and potentially dangerous. The battle lines thus drawn, Keynesian economics would dominate for decades and coincide with an era of unprecedented prosperity, but conservative economists and political leaders would eventually embrace and execute Hayek's contrary vision.From their first face-to-face encounter to the heated arguments between their ardent disciples, Nicholas Wapshott here unearths the contemporary relevance of Keynes and Hayek, as present-day arguments over the virtues of the free market and government intervention rage with the same ferocity as they did in the 1930s.

    This internationally recognized standard provides the essential tools to practice project management and deliver organizational results.

    This connectedness is found in many incarnations: in the rapid growth of the Internet, in the ease with which global communication takes place, and in the ability of news and information as well as epidemics and financial crises to spread with surprising speed and intensity. These are phenomena that involve networks, incentives, and the aggregate behavior of groups of people; they are based on the links that connect us and the ways in which our decisions can have subtle consequences for others. This introductory undergraduate textbook takes an interdisciplinary look at economics, sociology, computing and information science, and applied mathematics to understand networks and behavior. It describes the emerging field of study that is growing at the interface of these areas, addressing fundamental questions about how the social, economic, and technological worlds are connected.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Veblen's The Theory of the Leisure Class is in the tradition of Adam Smith's The Wealth of Nations and Thomas Hobbes's Leviathan, yet it provides a surprisingly contemporary look at American economics and society.Establishing such terms as "conspicuous consumption" and "pecuniary emulation," Veblen's most famous work has become an archetype not only of economic theory, but of historical and sociological thought as well. As sociologist Alan Wolfe writes in his Introduction, Veblen "skillfully . . . wrote a book that will be read so long as the rich are different from the rest of us; which, if the future is anything like the past, they always will be."

    An unprecedented redistribution of jobs, population, and wealth is under way in America, and it is likely to accelerate in the years to come. America’s new economic map shows growing differences, not just between people but especially between communities. In this important and persuasive book, U.C. Berkeley economist Enrico Moretti provides a fresh perspective on the tectonic shifts that are reshaping America’s labor market—from globalization and income inequality to immigration and technological progress—and how these shifts are affecting our communities. Drawing on a wealth of stimulating new studies, Moretti uncovers what smart policies may be appropriate to address the social challenges that are arising. We’re used to thinking of the United States in dichotomous terms: red versus blue, black versus white, haves versus have-nots. But today there are three Americas. At one extreme are the brain hubs—cities like San Francisco, Boston, Austin, and Durham—with a well-educated labor force and a strong innovation sector. Their workers are among the most productive, creative, and best paid on the planet. At the other extreme are cities once dominated by traditional manufacturing, which are declining rapidly, losing jobs and residents. In the middle are a number of cities that could go either way. For the past thirty years, the three Americas have been growing apart at an accelerating rate. This divergence is one the most important recent developments in the United States and is causing growing geographic disparities is all other aspects of our lives, from health and longevity to family stability and political engagement. But the winners and losers aren’t necessarily who you’d expect. Moretti’s groundbreaking research shows that you don’t have to be a scientist or an engineer to thrive in one of these brain hubs. Among the beneficiaries are the workers who support the "idea-creators"—the carpenters, hair stylists, personal trainers, lawyers, doctors, teachers and the like. In fact, Moretti has shown that for every new innovation job in a city, five additional non-innovation jobs are created, and those workers earn higher salaries than their counterparts in other cities. It wasn’t supposed to be this way. As the global economy shifted from manufacturing to innovation, geography was supposed to matter less. But the pundits were wrong. A new map is being drawn—the inevitable result of deep-seated but rarely discussed economic forces. These trends are reshaping the very fabric of our society. Dealing with this split—supporting growth in the hubs while arresting the decline elsewhere—will be the challenge of the century, and　The New Geography of Jobs　lights the way.

    Joan Bolker, midwife to more than one hundred dissertations and co-founder of the Harvard Writing Center, offers invaluable suggestions for the graduate-student writer. Using positive reinforcement, she begins by reminding thesis writers that being able to devote themselves to a project that truly interests them can be a pleasurable adventure. She encourages them to pay close attention to their writing method in order to discover their individual work strategies that promote productivity; to stop feeling fearful that they may disappoint their advisors or family members; and to tailor their theses to their own writing style and personality needs. Using field-tested strategies she assists the student through the entire thesis-writing process, offering advice on choosing a topic and an advisor, on disciplining one's self to work at least fifteen minutes each day; setting short-term deadlines, on revising and defing the thesis, and on life and publication after the dissertation. Bolker makes writing the dissertation an enjoyable challenge.

    Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day. Describing data; Data distributions; Variation of study results: confidence intervals; Hypothesis testing; Regression and decision making; Some common statistical errors, and what they teach us For all readers interested in statistics.

    An Introduction to Game Theory, by Martin J. Osborne, presents the main principles of game theory and shows how they can be used to understand economic, social, political, and biological phenomena. The book introduces in an accessible manner the main ideas behind the theory rather than their mathematical expression. All concepts are defined precisely, and logical reasoning is used throughout. The book requires an understanding of basic mathematics but assumes no specific knowledge of economics, political science, or other social or behavioral sciences. Coverage includes the fundamental concepts of strategic games, extensive games with perfect information, and coalitional games; the more advanced subjects of Bayesian games and extensive games with imperfect information; and the topics of repeated games, bargaining theory, evolutionary equilibrium, rationalizability, and maxminimization. The book offers a wide variety of illustrations from the social and behavioral sciences and more than 280 exercises. Each topic features examples that highlight theoretical points and illustrations that demonstrate how the theory may be used. Explaining the key concepts of game theory as simply as possible while maintaining complete precision, An Introduction to Game Theory is ideal for undergraduate and introductory graduate courses in game theory.