'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.

    No investment book in history had either the immediate impact, or the long-term relevance and value, of its first edition in 1934. By 1951, seventeen years past its original publication and more than a decade beyond its revised and acclaimed 1940 second edition, authors Benjamin Graham and David Dodd had seen business and investment markets travel from the depths of Depression to the heights of recovery, and had observed investor behavior during both the calm of peacetime and the chaos of World War II.The prescient thinking and insight displayed by Graham and Dodd in the first two editions of Security Analysis reached new heights in the third edition. In words that could just as easily have been written today as fifty years ago, they detail techniques and strategies for attaining success as individual investors, as well as the responsibilities of corporate decision makers to build shareholder value and transparency for those investors.The focus of the book, however, remains its timeless guidance and advice--that careful analysis of balance sheets is the primary road to investment success, with all other considerations little more than distractions. The authors had seen and survived the Great Depression as well as the political and financial instabilities of World War II and were now better able to outline a program for sensible and profitable investing in the latter half of the century.Security Analysis: The Classic 1951 Edition marks the return of this long-out-of-print work to the investment canon. It will reacquaint you with the foundations of value investing--more relevant than ever in tumultuous twenty-first century markets--and allow you to own the third installment in what has come to be regarded as the most accessible and usable title in the history of investment publishing.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.

    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.

    Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

    

    Dozens of examples in innumeracy show us how it affects not only personal economics and travel plans, but explains mis-chosen mates, inappropriate drug-testing, and the allure of pseudo-science.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    This text enables students to conduct serious analysis of applied problems in contrast to merely running simple“canned” applications to help students become stronger analysts and future managers. It is also at a mathematically higher level than most business statistics texts.

    Written in clear and incisive prose, The Wealth of Nations articulates the concepts indispensable to an understanding of contemporary society; and Robert Reich's Introduction both clarifies Smith's analyses and illuminates his overall relevance to the world in which we live. As Reich writes, "Smith's mind ranged over issues as fresh and topical today as they were in the late eighteenth century--jobs, wages, politics, government, trade, education, business, and ethics."Introduction by Robert Reich - Commentary by R. H. Campbell and A. S. Skinner - Includes a Modern Library Reading Group Guide

    Only billionaire J.P. Morgan was able to save the stock market.Black Tuesday (1929): As the newly created Federal Reserve System repeatedly adjusted interest rates in all the wrong ways, investment trusts, the darlings of that decade, became the catalyst that caused the bubble to burst, and the Dow fell dramatically, leading swiftly to the Great Depression.Black Monday (1987): When "portfolio insurance," a new tool meant to protect investments, instead led to increased losses, and corporate raiders drove stock prices above their real values, the Dow dropped an astonishing 22.6 percent in one day.The Great Recession (2008): As homeowners began defaulting on mortgages, investment portfolios that contained them collapsed, bringing the nation's largest banks, much of the economy, and the stock market down with them.The Flash Crash (2010): When one investment manager, using a runaway computer algorithm that was dangerously unstable and poorly understood, reacted to the economic turmoil in Greece, the stock market took an unprecedentedly sudden plunge, with the Dow shedding 998.5 points (roughly a trillion dollars in valuation) in just minutes.The stories behind the great crashes are filled with drama, human foibles, and heroic rescues. Taken together they tell the larger story of a nation reaching enormous heights of financial power while experiencing precipitous dips that alter and reset a market where millions of Americans invest their savings, and on which they depend for their futures. Scott Nations vividly shows how each of these major crashes played a role in America's political and cultural fabric, each providing painful lessons that have strengthened us and helped us to build the nation we know today.A History of the United States in Five Crashes clearly and compellingly illustrates the connections between these major financial collapses and examines the solid, clear-cut lessons they offer for preventing the next one.

    

    The authors provide precise definitions and full proofs of results, sacrificing generalities and limiting the scope of the material in order to do so. The text is organized in four parts: strategic games, extensive games with perfect information, extensive games with imperfect information, and coalitional games. It includes over 100 exercises. Solution ManualTable of Contents, Errata, and more...

    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.