Douglas Hubbard helps us create a path to know the answer to almost any question in business, in science, or in life . . . Hubbard helps us by showing us that when we seek metrics to solve problems, we are really trying to know something better than we know it now. How to Measure Anything provides just the tools most of us need to measure anything better, to gain that insight, to make progress, and to succeed." -Peter Tippett, PhD, M.D. Chief Technology Officer at CyberTrust and inventor of the first antivirus software "Doug Hubbard has provided an easy-to-read, demystifying explanation of how managers can inform themselves to make less risky, more profitable business decisions. We encourage our clients to try his powerful, practical techniques." -Peter Schay EVP and COO of The Advisory Council "As a reader you soon realize that actually everything can be measured while learning how to measure only what matters. This book cuts through conventional cliches and business rhetoric and offers practical steps to using measurements as a tool for better decision making. Hubbard bridges the gaps to make college statistics relevant and valuable for business decisions." -Ray Gilbert EVP Lucent "This book is remarkable in its range of measurement applications and its clarity of style. A must-read for every professional who has ever exclaimed, 'Sure, that concept is important, but can we measure it?'" -Dr. Jack Stenner Cofounder and CEO of MetraMetrics, Inc.

    But are you using it to “out-think” your rivals? If not, you may be missing out on a potent competitive tool.In Competing on Analytics: The New Science of Winning, Thomas H. Davenport and Jeanne G. Harris argue that the frontier for using data to make decisions has shifted dramatically. Certain high-performing enterprises are now building their competitive strategies around data-driven insights that in turn generate impressive business results. Their secret weapon? Analytics: sophisticated quantitative and statistical analysis and predictive modeling.Exemplars of analytics are using new tools to identify their most profitable customers and offer them the right price, to accelerate product innovation, to optimize supply chains, and to identify the true drivers of financial performance. A wealth of examples—from organizations as diverse as Amazon, Barclay’s, Capital One, Harrah’s, Procter & Gamble, Wachovia, and the Boston Red Sox—illuminate how to leverage the power of analytics.

    Mandelbrot, one of the century's most influential mathematicians, is world-famous for making mathematical sense of a fact everybody knows but that geometers from Euclid on down had never assimilated: Clouds are not round, mountains are not cones, coastlines are not smooth. To these classic lines we can now add another example: Markets are not the safe bet your broker may claim. In his first book for a general audience, Mandelbrot, with co-author Richard L. Hudson, shows how the dominant way of thinking about the behavior of markets-a set of mathematical assumptions a century old and still learned by every MBA and financier in the world-simply does not work. As he did for the physical world in his classic The Fractal Geometry of Nature, Mandelbrot here uses fractal geometry to propose a new, more accurate way of describing market behavior. The complex gyrations of IBM's stock price and the dollar-euro exchange rate can now be reduced to straightforward formulae that yield a far better model of how risky they are. With his fractal tools, Mandelbrot has gotten to the bottom of how financial markets really work, and in doing so, he describes the volatile, dangerous (and strangely beautiful) properties that financial experts have never before accounted for. The result is no less than the foundation for a new science of finance.

    

    The other books in the series are The Black Swan, Antifragile,and The Bed of Procrustes.

    Sullivan Elin M. Wicks C. Patrick Koelling   A succinct job description for an engineer consists of just two words: problem solver. Broadly speaking, engineers use knowledge to find new ways of doing things economically. Engineering design solutions do not exist in a vacuum, but within the context of a business opportunity. Truly, every problem has multiple solutions, so the question is, “How does one rationally select the design solution with the most favorable economic result?” The answer to this question can also be put forth in two words: engineering economy. This field of engineering provides a systematic framework for evaluating the economic aspects of competing design solutions. Just as engineers model the stress on a support column or the thermodynamic properties of a steam turbine, they must also model the economic impact of their engineering recommendations. Engineering economy is the subject of this textbook.   Highlights of Engineering Economy, Fourteenth Edition: ×           Fifty percent of end-of-chapter problems are new or revised. ×           A bank of algorithmically generated test questions is available to adopting instructors. ×           Fundamentals of Engineering (FE) exam-style questions are included among the end-of-chapter problem sets. ×           Spreadsheet models are integratedthroughout. ×           An appendix on the basics of accounting is included in Chapter 2. ×           Chapter 3 on Cost Estimation appears early in the book. ×           An appendix on techniques for using Excel in engineering economy is available for reference. ×           Numerous comprehensive examples and case studies appear throughout the book. ×           Extended learning exercises appear in most chapters. ×           Personal finance problems are featured in most chapters. ×           Many pointers to relevant Web sites are provided.   ISBN-13: 978-0-13-614297-3 ISBN-10: 0-13-614297-4

    Each chapter utilizes case studies and numerous examples. Mathematical and technical sections can be skipped without loss of continuity. Most textbooks do not include models and decision support systems robust enough for industry, but that is not true of this new edition.The accompanying CD-ROM also features the return of two simulations, the Computerized Beer Game and the Risk Pool Game and a computerized tool. These simulations help users develop and execute supply chain contracts while also illustrating many of the concepts discussed in the text.

    Drawing on his experience as a professional trader, author Sheldon Natenberg examines both the theory and reality of option trading. He presents the foundations of option theory explaining how this theory can be used to identify and exploit trading opportunities. "Option Volatility & Pricing" teaches you to use a wide variety of trading strategies and shows you how to select the strategy that best fits your view of market conditions and individual risk tolerance.New sections include: Expanded coverage of stock option Strategies for stock index futures and options A broader, more in-depth discussion volatility Analysis of volatility skews Intermarket spreading with options

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    Until recently, the venerable firm had been regarded as the accounting profession's conscience. In Final Accounting, Barbara Ley Toffler, former Andersen partner-in-charge of Andersen's Ethics & Responsible Business Practices consulting services, reveals that the symptoms of Andersen's fatal disease were evident long before Enron. Drawing on her expertise as a social scientist and her experience as an Andersen insider, Toffler chronicles how a culture of arrogance and greed infected her company and led to enormous lapses in judgment among her peers. Final Accounting exposes the slow deterioration of values that led not only to Enron but also to the earlier financial scandals of other Andersen clients, including Sunbeam and Waste Management, and illustrates the practices that paved the way for the accounting fiascos at WorldCom and other major companies. Chronicling the inner workings of Andersen at the height of its success, Toffler reveals "the making of an Android," the peculiar process of employee indoctrination into the Andersen culture; how Androids—both accountants and consultants--lived the mantra "keep the client happy"; and how internal infighting and "billing your brains out" rather than quality work became the all-important goals. Toffler was in a position to know when something was wrong. In her earlier role as ethics consultant, she worked with over 60 major companies and was an internationally renowned expert at spotting and correcting ethical lapses. Toffler traces the roots of Andersen's ethical missteps, and shows the gradual decay of a once-proud culture.Uniquely qualified to discuss the personalities and principles behind one of the greatest shake-ups in United States history, Toffler delivers a chilling report with important ramifications for CEOs and individual investors alike.From the Hardcover edition.

    Page by page, Derman details his adventures in this field--analyzing the incompatible personas of traders and quants, and discussing the dissimilar nature of knowledge in physics and finance. Throughout this tale, he also reflects on the appropriate way to apply the refined methods of physics to the hurly-burly world of markets.

    For the introductory Finance course, given during the junior year and required at all undergraduate business schools.Keown enables students to see the big picture by letting them understand the logic that drives finance rather than having them memorize formulas.

    Combine this with the fact that the discipline of distress investing doesn't always follow what conventional wisdom says, and you can see why it is one of the most challenging areas in finance.Nobody understands this better than Martin Whitman--the legendary founder of Third Avenue Management LLC and a pioneer in the field of distressed markets--and leading academic Dr. Fernando Diz of Syracuse University. That's why they decided to write Distress Investing. As an outgrowth of annual distress and value investing seminars the two have taught together at Syracuse University's Martin J. Whitman School of Management, this reliable resource will help you gain a better understanding of the essential principles and techniques associated with distress investing and show you how to effectively apply them in the real world.Divided into four comprehensive parts--the General Landscape of Distress Investing, Restructuring Troubled Issuers, the Investment Process, and Cases and Implications for Public Policy--this book comprehensively covers the practice of buy-and-hold investing in distressed credits, whether it be performing loans or the reinstated issues of a reorganized issuer.From the recent changes to U.S. bankruptcy code and creditor rights to cash bailouts, you'll quickly learn how to analyze distressed situations such as pricing issues, arbitrage opportunities, tax disadvantages, and the reorganization of funding plans. Along the way, case studies of both large and small distress investing deals--from Kmart to Home Products International--will give you a better perspective of the business.Critical topics addressed throughout these pages include: Chapter 11 bankruptcy and why it's not considered an ending, but rather a beginning when it comes to distress investing The "Five Basic Truths" of distress investing The difficulty of due diligence for distressed issues Distress investing risks--from reorganization risk to risk associated with the alteration of priority of payments in bankruptcy Valuing companies by both going concern as well as their resource conversion attributes In today's turbulent economic environment, distress investing presents some enticing opportunities. Put yourself in a better position to excel at this endeavor with Distress Investing as your guide.

    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

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data