eGrade Plus offers an integrated suite of teaching and learning resources, including an online version of Black's Business Statistics for Contemporary Decision Making, Fourth Edition Update, in one easy-to-use Web site. Organized around the essential activities you perform in class, eGrade Plus helps you: Create class presentation using a wealth of Wiley-provided resources. you may easily adapt, customize, and add to his content to meet the needs of your course. Automate the assigning and grading of homework or quizzes by using Wiley-provided question banks, or by writing your won. Student results will be automatically graded and recorded in your gradebook. Track your students' progress. An instructor's gradebook allows you to an analyze individual and overall class results to determine each student's progress and level of understanding. Administer your course. eGrade Plus can easily be integrated with another course management system, gradebook, or other resources you are using in your class. Provide students with problem-solving support. eGrade Plus can link homework problems to the relevant section of the online text, providing context-sensitive help. Best of all, instructors can arrange to have eGrade Plus packaged FREE with new copies of Business Statistics for Contemporary Decision Making, Fourth Edition Update, All instructors have to do is adopt the eGrade Plus version of this book and activate their eGrade Plus course.

    'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    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.

    The new edition includes the most up-to-date research developments and many more examples and problems.

    Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.

    Since then, Sylvia Nasar's celebrated biography A Beautiful Mind, the basis of a new major motion picture, has revealed the man. The Essential John Nash reveals his work--in his own words. This book presents, for the first time, the full range of Nash's diverse contributions not only to game theory, for which he received the Nobel, but to pure mathematics--from Riemannian geometry and partial differential equations--in which he commands even greater acclaim among academics. Included are nine of Nash's most influential papers, most of them written over the decade beginning in 1949.From 1959 until his astonishing remission three decades later, the man behind the concepts "Nash equilibrium" and "Nash bargaining"--concepts that today pervade not only economics but nuclear strategy and contract talks in major league sports--had lived in the shadow of a condition diagnosed as paranoid schizophrenia. In the introduction to this book, Nasar recounts how Nash had, by the age of thirty, gone from being a wunderkind at Princeton and a rising mathematical star at MIT to the depths of mental illness.In his preface, Harold Kuhn offers personal insights on his longtime friend and colleague; and in introductions to several of Nash's papers, he provides scholarly context. In an afterword, Nash describes his current work, and he discusses an error in one of his papers. A photo essay chronicles Nash's career from his student days in Princeton to the present. Also included are Nash's Nobel citation and autobiography.The Essential John Nash makes it plain why one of Nash's colleagues termed his style of intellectual inquiry as "like lightning striking." All those inspired by Nash's dazzling ideas will welcome this unprecedented opportunity to trace these ideas back to the exceptional mind they came from.

    Men like Andrew Carnegie built empires like Carnegie Steel, and financiers like J.P. Morgan merged and consolidated them. The era also made names like Astor, Cooke, and Vanderbilt instantly recognizable across the globe. Over time, the unfathomable wealth generated by the businesses made the individuals on top incredibly rich, and that in turn led to immense criticism and an infamous epithet used to rail against them: robber barons. Dozens of men were called “robber barons”, but few of them were as notorious as Cornelius Vanderbilt, who also happened to be one of the nation’s first business titans. Vanderbilt was a railroad and shipping magnate at a time that the industry was almost brand new, but he rode his success to become one of the richest and most powerful men in American history. When historians are asked to name the richest man in history, a name that often pops up is that of John D. Rockefeller, who co-founded Standard Oil and turned it into the first real trust in the United States. Rockefeller had been groomed ambitiously by a huckster father nicknamed “Devil Bill”, who was just as willing to cheat his son as an unsuspecting public, and John certainly chased his dreams of living long and large. Rockefeller forged his empire in the first few decades of his life and nearly worked himself to death by the time he was 50, which helped compel him to retire for the last several decades of his life. At one point, Rockefeller’s wealth was worth more than 1.5% of the entire country’s gross domestic product, and by adjusting for inflation, he is arguably the richest man in American history if not world history. When robber barons across America took the reins of vast industries, they needed financing, and many of them turned to the most famous banker of all: John Pierpont Morgan. It was J.P. Morgan who bankrolled the consolidation of behemoth corporations across various industries, including the merging of Edison General Electric and Thomson-Houston Electric Company, which subsequently became General Electric, still known simply as GE across the world today. Similarly, he financed Federal Steel Company and consolidated various other steel businesses to help form the United States Steel Corporation. While critics complained about the outsized influence that these gigantic businesses had, Morgan’s massive wealth also gave him unprecedented power in the financial sector and the ability to deal with politicians. In fact, Morgan played an important part in the Panic of 1907 and the subsequent decision to create the Federal Reserve as a monetary oversight. Ironically, one of America’s most famous robber barons, Andrew Carnegie, epitomized the American Dream, migrating with his poor family to America in the mid-19th century and rising to the top of the business world in his adopted country. A prodigious writer in addition to his keen sense of business, Carnegie was one of the most outspoken champions of capitalism at a time when there was pushback among lower social classes who witnessed the great disparities in wealth; as he once put it, “Upon the sacredness of property civilization itself depends—the right of the laborer to his hundred dollars in the savings bank, and equally the legal right of the millionaire to his millions.

    Suitable for a two semester course in complex analysis, or as a supplementary text for an advanced course in function theory, this book aims to give students a good foundation of complex analysis and provides a basis for solving problems in mathematics, physics, engineering and many other sciences.

    The Twelfth Canadian Edition benefits from a superior research and development process, and sets the standard in using technology to enhance teaching and learning - in and beyond the classroom. The Twelfth Canadian Edition continues to build on the core strengths that have made it a market leader - presenting accounting principles in a clear, comprehensive, and technically accurate text that has been developed in accordance with evolving market needs. New to the Twelfth Canadian Edition is the introduction of the Student Success Cycle to reinforce the learning process. Learning involves a cycle, and financial accounting is no exception. Following the steps in the Student Success Cycle will help students learn, rather than simply memorize, the basics of financial accounting. Critical Thinking Exercises have also been introduced to encourage the application of this knowledge. This text also continues to boast a superior supplements and technology package, which supports the widest range of teaching and learning, whether in a traditional or technology-enhanced classroom. Like the text, the supporting resources and technology solutions have been developed in accordance with market needs and set a new standard in Financial Accounting.

    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.

    I also hope that it will serve as a useful reference too for the many workers engaged in one type of solid state research activity or another, who may be without formal training in the subject.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    We chase ‘winning streaks’ that are often just illusions, and we are all too predictable exactly when we try hardest not to be.In the 1970s, Daniel Kahneman and Amos Tversky coined the phrase ‘representativeness’ to describe the psychology of this behaviour. Since then representativeness has been used by auditors to catch people fiddling their tax returns and by hedge fund managers to reap billions from the emotions of small investors. Now Poundstone for the first time makes these techniques fun, easy, and profitable for everyone, in the everyday situations that matter. You’ll learn how to tackle multiple choice tests, what internet passwords to avoid, how to up your odds of winning the office Premier League sweepstakes, and the best ways to invest your money.