## Similar

"The effort of Professor Fuller is commendable . . . [the book]provides a complete treatment of an important and frequentlyignored topic. Those who work with measurement error models willfind it valuable. It is the fundamental book on the subject, andstatisticians will benefit from adding this book to theircollection or to university or departmental libraries."

-Biometrics

"Given the large and diverse literature on measurementerror/errors-in-variables problems, Fuller's book is most welcome.Anyone with an interest in the subject should certainly have thisbook."

-Journal of the American Statistical Association

"The author is to be commended for providing a completepresentation of a very important topic. Statisticians working withmeasurement error problems will benefit from adding this book totheir collection."

-Technometrics

" . . . this book is a remarkable achievement and the product ofimpressive top-grade scholarly work."

-Journal of Applied Econometrics

Measurement Error Models offers coverage of estimation forsituations where the model variables are observed subject tomeasurement error. Regression models are included with errors inthe variables, latent variable models, and factor models. Resultsfrom several areas of application are discussed, including recentresults for nonlinear models and for models with unequal variances.The estimation of true values for the fixed model, prediction oftrue values under the random model, model checks, and the analysisof residuals are addressed, and in addition, procedures areillustrated with data drawn from nearly twenty real data sets.

Major topics include:

* Moving average and autoregressive processes

* Introduction to Fourier analysis

* Spectral theory and filtering

* Large sample theory

* Estimation of the mean and autocorrelations

* Estimation of the spectrum

* Parameter estimation

* Regression, trend, and seasonality

* Unit root and explosive time series

To accommodate a wide variety of readers, review material,especially on elementary results in Fourier analysis, large samplestatistics, and difference equations, has been included.

New York Times Bestseller

“Not so different in spirit from the way public intellectuals like John Kenneth Galbraith once shaped discussions of economic policy and public figures like Walter Cronkite helped sway opinion on the Vietnam War…could turn out to be one of the more momentous books of the decade.”

—New York Times Book Review

"Nate Silver's The Signal and the Noise is The Soul of a New Machine for the 21st century."

—Rachel Maddow, author of Drift

"A serious treatise about the craft of prediction—without academic mathematics—cheerily aimed at lay readers. Silver's coverage is polymathic, ranging from poker and earthquakes to climate change and terrorism."

—New York Review of Books

Nate Silver built an innovative system for predicting baseball performance, predicted the 2008 election within a hair’s breadth, and became a national sensation as a blogger—all by the time he was thirty. He solidified his standing as the nation's foremost political forecaster with his near perfect prediction of the 2012 election. Silver is the founder and editor in chief of the website FiveThirtyEight.

Drawing on his own groundbreaking work, Silver examines the world of prediction, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society, because most of us have a poor understanding of probability and uncertainty. Both experts and laypeople mistake more confident predictions for more accurate ones. But overconfidence is often the reason for failure. If our appreciation of uncertainty improves, our predictions can get better too. This is the “prediction paradox”: The more humility we have about our ability to make predictions, the more successful we can be in planning for the future.

In keeping with his own aim to seek truth from data, Silver visits the most successful forecasters in a range of areas, from hurricanes to baseball, from the poker table to the stock market, from Capitol Hill to the NBA. He explains and evaluates how these forecasters think and what bonds they share. What lies behind their success? Are they good—or just lucky? What patterns have they unraveled? And are their forecasts really right? He explores unanticipated commonalities and exposes unexpected juxtapositions. And sometimes, it is not so much how good a prediction is in an absolute sense that matters but how good it is relative to the competition. In other cases, prediction is still a very rudimentary—and dangerous—science.

Silver observes that the most accurate forecasters tend to have a superior command of probability, and they tend to be both humble and hardworking. They distinguish the predictable from the unpredictable, and they notice a thousand little details that lead them closer to the truth. Because of their appreciation of probability, they can distinguish the signal from the noise.

With everything from the health of the global economy to our ability to fight terrorism dependent on the quality of our predictions, Nate Silver’s insights are an essential read.

The Essentials For Dummies Series

Dummies is proud to present our new series, The Essentials ForDummies. Now students who are prepping for exams, preparing tostudy new material, or who just need a refresher can have aconcise, easy-to-understand review guide that covers an entirecourse by concentrating solely on the most important concepts. Fromalgebra and chemistry to grammar and Spanish, our expert authorsfocus on the skills students most need to succeed in a subject.

The fun and easy way to get down to business with statistics

Stymied by statistics? No fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life.

Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more.

Tracks to a typical first semester statistics courseUpdated examples resonate with today's studentsExplanations mirror teaching methods and classroom protocolPacked with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance.

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.

For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.

And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

These may not sound like typical questions for an economist to ask. But Steven D. Levitt is not a typical economist. He is a much-heralded scholar who studies the riddles of everyday life—from cheating and crime to sports and child-rearing—and whose conclusions turn conventional wisdom on its head.

Freakonomics is a groundbreaking collaboration between Levitt and Stephen J. Dubner, an award-winning author and journalist. They usually begin with a mountain of data and a simple question. Some of these questions concern life-and-death issues; others have an admittedly freakish quality. Thus the new field of study contained in this book: Freakonomics.

Through forceful storytelling and wry insight, Levitt and Dubner show that economics is, at root, the study of incentives—how people get what they want, or need, especially when other people want or need the same thing. In Freakonomics, they explore the hidden side of . . . well, everything. The inner workings of a crack gang. The truth about real-estate agents. The myths of campaign finance. The telltale marks of a cheating schoolteacher. The secrets of the Ku Klux Klan.

What unites all these stories is a belief that the modern world, despite a great deal of complexity and downright deceit, is not impenetrable, is not unknowable, and—if the right questions are asked—is even more intriguing than we think. All it takes is a new way of looking.

Freakonomics establishes this unconventional premise: If morality represents how we would like the world to work, then economics represents how it actually does work. It is true that readers of this book will be armed with enough riddles and stories to last a thousand cocktail parties. But Freakonomics can provide more than that. It will literally redefine the way we view the modern world.

Bonus material added to the revised and expanded 2006 edition

The original New York Times Magazine article about Steven D. Levitt by Stephen J. Dubner, which led to the creation of this book.Seven “Freakonomics” columns written for the New York Times Magazine, published between August 2005 and April 2006.Selected entries from the Freakonomics blog, posted between April 2005 and May 2006 at http://www.freakonomics.com/blog/.One is heuristic and nonrigorous, and attempts to develop in students an intuitive feel for the subject that enables him or her to think probabilistically. The other approach attempts a rigorous development of probability by using the tools of measure theory. The first approach is employed in this text.

The book begins by introducing basic concepts of probability theory, such as the random variable, conditional probability, and conditional expectation. This is followed by discussions of stochastic processes, including Markov chains and Poison processes. The remaining chapters cover queuing, reliability theory, Brownian motion, and simulation. Many examples are worked out throughout the text, along with exercises to be solved by students.

This book will be particularly useful to those interested in learning how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. Ideally, this text would be used in a one-year course in probability models, or a one-semester course in introductory probability theory or a course in elementary stochastic processes.

New to this Edition:

65% new chapter material including coverage of finite capacity queues, insurance risk models and Markov chainsContains compulsory material for new Exam 3 of the Society of Actuaries containing several sections in the new examsUpdated data, and a list of commonly used notations and equations, a robust ancillary package, including a ISM, SSM, and test bankIncludes SPSS PASW Modeler and SAS JMP software packages which are widely used in the field

Hallmark features:

Superior writing styleExcellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics

The author begins with basic characteristics of financial timeseries data before covering three main topics:

Analysis and application of univariate financial timeseriesThe return series of multiple assetsBayesian inference in finance methodsKey features of the new edition include additional coverage ofmodern day topics such as arbitrage, pair trading, realizedvolatility, and credit risk modeling; a smooth transition fromS-Plus to R; and expanded empirical financial data sets.

The overall objective of the book is to provide some knowledgeof financial time series, introduce some statistical tools usefulfor analyzing these series and gain experience in financialapplications of various econometric methods.

". . . [this book] should be on the shelf of everyone interestedin . . . longitudinal data analysis."

—Journal of the American Statistical Association

Features newly developed topics and applications of theanalysis of longitudinal data

Applied Longitudinal Analysis, Second Edition presentsmodern methods for analyzing data from longitudinal studies and nowfeatures the latest state-of-the-art techniques. The bookemphasizes practical, rather than theoretical, aspects of methodsfor the analysis of diverse types of longitudinal data that can beapplied across various fields of study, from the health and medicalsciences to the social and behavioral sciences.

The authors incorporate their extensive academic and researchexperience along with various updates that have been made inresponse to reader feedback. The Second Edition features six newlyadded chapters that explore topics currently evolving in the field,including:

Fixed effects and mixed effects modelsMarginal models and generalized estimating equationsApproximate methods for generalized linear mixed effectsmodelsMultiple imputation and inverse probability weightedmethodsSmoothing methods for longitudinal dataSample size and powerEach chapter presents methods in the setting of applications todata sets drawn from the health sciences. New problem sets havebeen added to many chapters, and a related website features sampleprograms and computer output using SAS, Stata, and R, as well asdata sets and supplemental slides to facilitate a completeunderstanding of the material.

With its strong emphasis on multidisciplinary applications andthe interpretation of results, Applied LongitudinalAnalysis, Second Edition is an excellent book for courses onstatistics in the health and medical sciences at theupper-undergraduate and graduate levels. The book also serves as avaluable reference for researchers and professionals in themedical, public health, and pharmaceutical fields as well as thosein social and behavioral sciences who would like to learn moreabout analyzing longitudinal data.

1,001 Statistics Practice Problems For Dummies takes youbeyond the instruction and guidance offered in Statistics ForDummies to give you a more hands-on understanding of statistics.The practice problems offered range in difficulty, includingdetailed explanations and walk-throughs.

In this series, every step of every solution is shown withexplanations and detailed narratives to help you solve eachproblem. With the book purchase, you’ll also get access topractice statistics problems online. This content features 1,001practice problems presented in multiple choice format; on-the-goaccess from smart phones, computers, and tablets; customizablepractice sets for self-directed study; practice problemscategorized as easy, medium, or hard; and a one-year subscriptionwith book purchase.

Offers on-the-go access to practice statistics problemsGives you friendly, hands-on instruction1,001 statistics practice problems that range indifficulty1,001 Statistics Practice Problems For Dummies providesample practice opportunities for students who may have takenstatistics in high school and want to review the most importantconcepts as they gear up for a faster-paced college class.

This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables.

Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include:

A chapter on the analysis of correlated outcome dataA wealth of additional material for topics ranging from Bayesian methods to assessing model fitRich data sets from real-world studies that demonstrate each method under discussionDetailed examples and interpretation of the presented results as well as exercises throughoutApplied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines.

“This book should be an essential part of the personallibrary of every practicingstatistician.”—Technometrics

Thoroughly revised and updated, the new edition of NonparametricStatistical Methods includes additional modern topics andprocedures, more practical data sets, and new problems fromreal-life situations. The book continues to emphasize theimportance of nonparametric methods as a significant branch ofmodern statistics and equips readers with the conceptual andtechnical skills necessary to select and apply the appropriateprocedures for any given situation.

Written by leading statisticians, Nonparametric StatisticalMethods, Third Edition provides readers with crucialnonparametric techniques in a variety of settings, emphasizing theassumptions underlying the methods. The book provides an extensivearray of examples that clearly illustrate how to use nonparametricapproaches for handling one- or two-sample location and dispersionproblems, dichotomous data, and one-way and two-way layoutproblems. In addition, the Third Edition features:

The use of the freely available R software to aid incomputation and simulation, including many new R programs writtenexplicitly for this new editionNew chapters that address density estimation, wavelets,smoothing, ranked set sampling, and Bayesian nonparametricsProblems that illustrate examples from agricultural science,astronomy, biology, criminology, education, engineering,environmental science, geology, home economics, medicine,oceanography, physics, psychology, sociology, and spacescienceNonparametric Statistical Methods, Third Edition is anexcellent reference for applied statisticians and practitioners whoseek a review of nonparametric methods and their relevantapplications. The book is also an ideal textbook forupper-undergraduate and first-year graduate courses in appliednonparametric statistics."Seamless R and C++ integration with Rcpp" is simply a wonderful book. For anyone who uses C/C++ and R, it is an indispensable resource. The writing is outstanding. A huge bonus is the section on applications. This section covers the matrix packages Armadillo and Eigen and the GNU Scientific Library as well as RInside which enables you to use R inside C++. These applications are what most of us need to know to really do scientific programming with R and C++. I love this book. -- Robert McCulloch, University of Chicago Booth School of Business

Rcpp is now considered an essential package for anybody doing serious computational research using R. Dirk's book is an excellent companion and takes the reader from a gentle introduction to more advanced applications via numerous examples and efficiency enhancing gems. The book is packed with all you might have ever wanted to know about Rcpp, its cousins (RcppArmadillo, RcppEigen .etc.), modules, package development and sugar. Overall, this book is a must-have on your shelf. -- Sanjog Misra, UCLA Anderson School of Management

The Rcpp package represents a major leap forward for scientific computations with R. With very few lines of C++ code, one has R's data structures readily at hand for further computations in C++. Hence, high-level numerical programming can be made in C++ almost as easily as in R, but often with a substantial speed gain. Dirk is a crucial person in these developments, and his book takes the reader from the first fragile steps on to using the full Rcpp machinery. A very recommended book! -- Søren Højsgaard, Department of Mathematical Sciences, Aalborg University, Denmark

"Seamless R and C ++ Integration with Rcpp" provides the first comprehensive introduction to Rcpp. Rcpp has become the most widely-used language extension for R, and is deployed by over one-hundred different CRAN and BioConductor packages. Rcpp permits users to pass scalars, vectors, matrices, list or entire R objects back and forth between R and C++ with ease. This brings the depth of the R analysis framework together with the power, speed, and efficiency of C++.

Dirk Eddelbuettel has been a contributor to CRAN for over a decade and maintains around twenty packages. He is the Debian/Ubuntu maintainer for R and other quantitative software, edits the CRAN Task Views for Finance and High-Performance Computing, is a co-founder of the annual R/Finance conference, and an editor of the Journal of Statistical Software. He holds a Ph.D. in Mathematical Economics from EHESS (Paris), and works in Chicago as a Senior Quantitative Analyst.

"It is, as far as I'm concerned, among the best books in math ever written....if you are a mathematician and want to have the top reference in probability, this is it." (Amazon.com, January 2006)

A complete and comprehensive classic in probability and measure theory

Probability and Measure, Anniversary Edition by Patrick Billingsley celebrates the achievements and advancements that have made this book a classic in its field for the past 35 years. Now re-issued in a new style and format, but with the reliable content that the third edition was revered for, this Anniversary Edition builds on its strong foundation of measure theory and probability with Billingsley's unique writing style. In recognition of 35 years of publication, impacting tens of thousands of readers, this Anniversary Edition has been completely redesigned in a new, open and user-friendly way in order to appeal to university-level students.

This book adds a new foreward by Steve Lally of the Statistics Department at The University of Chicago in order to underscore the many years of successful publication and world-wide popularity and emphasize the educational value of this book. The Anniversary Edition contains features including:

An improved treatment of Brownian motionReplacement of queuing theory with ergodic theoryTheory and applications used to illustrate real-life situationsOver 300 problems with corresponding, intensive notes and solutionsUpdated bibliographyAn extensive supplement of additional notes on the problems and chapter commentariesPatrick Billingsley was a first-class, world-renowned authority in probability and measure theory at a leading U.S. institution of higher education. He continued to be an influential probability theorist until his unfortunate death in 2011. Billingsley earned his Bachelor's Degree in Engineering from the U.S. Naval Academy where he served as an officer. he went on to receive his Master's Degree and doctorate in Mathematics from Princeton University.Among his many professional awards was the Mathematical Association of America's Lester R. Ford Award for mathematical exposition. His achievements through his long and esteemed career have solidified Patrick Billingsley's place as a leading authority in the field and been a large reason for his books being regarded as classics.

This Anniversary Edition of Probability and Measure offers advanced students, scientists, and engineers an integrated introduction to measure theory and probability. Like the previous editions, this Anniversary Edition is a key resource for students of mathematics, statistics, economics, and a wide variety of disciplines that require a solid understanding of probability theory.

Machine Learning: Hands-On for Developers and TechnicalProfessionals provides hands-on instruction and fully-codedworking examples for the most common machine learning techniquesused by developers and technical professionals. The book contains abreakdown of each ML variant, explaining how it works and how it isused within certain industries, allowing readers to incorporate thepresented techniques into their own work as they follow along. Acore tenant of machine learning is a strong focus on datapreparation, and a full exploration of the various types oflearning algorithms illustrates how the proper tools can help anydeveloper extract information and insights from existing data. Thebook includes a full complement of Instructor's Materials tofacilitate use in the classroom, making this resource useful forstudents and as a professional reference.

At its core, machine learning is a mathematical, algorithm-basedtechnology that forms the basis of historical data mining andmodern big data science. Scientific analysis of big data requires aworking knowledge of machine learning, which forms predictionsbased on known properties learned from training data. MachineLearning is an accessible, comprehensive guide for thenon-mathematician, providing clear guidance that allows readersto:

Learn the languages of machine learning including Hadoop,Mahout, and WekaUnderstand decision trees, Bayesian networks, and artificialneural networksImplement Association Rule, Real Time, and Batch learningDevelop a strategic plan for safe, effective, and efficientmachine learningBy learning to construct a system that can learn from data,readers can increase their utility across industries. Machinelearning sits at the core of deep dive data analysis andvisualization, which is increasingly in demand as companiesdiscover the goldmine hiding in their existing data. For the techprofessional involved in data science, Machine Learning:Hands-On for Developers and Technical Professionals providesthe skills and techniques required to dig deeper.

The Qlik platform was designed to provide a fast and easy data analytics tool, and QlikView Your Business is your detailed, full-color, step-by-step guide to understanding Qlikview's powerful features and techniques so you can quickly start unlocking your data’s potential. This expert author team brings real-world insight together with practical business analytics, so you can approach, explore, and solve business intelligence problems using the robust Qlik toolset and clearly communicate your results to stakeholders using powerful visualization features in QlikView and Qlik Sense.

This book starts at the basic level and dives deep into the most advanced QlikView techniques, delivering tangible value and knowledge to new users and experienced developers alike. As an added benefit, every topic presented in the book is enhanced with tips, tricks, and insightful recommendations that the authors accumulated through years of developing QlikView analytics.

This is the book for you:

The book covers three common business scenarios - Sales, Profitability, and Inventory Analysis. Each scenario contains four chapters, covering the four main disciplines of business analytics: Business Case, Data Modeling, Scripting, and Visualizations.

The material is organized by increasing levels of complexity. Following our comprehensive tutorial, you will learn simple and advanced QlikView and Qlik Sense concepts, including the following:

Data Modeling:

How to use the Data Load Script language for implementing data modeling techniquesHow to build and use the QVD data layerBuilding a multi-tier data architecturesUsing variables, loops, subroutines, and other script control statementsAdvanced scripting techniques for a variety of ETL solutionsBuilding Insightful Visualizations in QlikView:

Introduction into QlikView sheet objects — List Boxes, Text Objects, Charts, and moreDesigning insightful Dashboards in QlikViewUsing advanced calculation techniques, such as Set Analysis and Advanced AggregationUsing variables for What-If Analysis, as well as using variables for storing calculations, colors, and selection filtersAdvanced visualization techniques - normalized and non-normalized Mekko charts, Waterfall charts, Whale Tail charts, and more

Building Insightful Visualizations in Qlik Sense:

Whether you are just starting out with QlikView or are ready to dive deeper, QlikView Your Business is your comprehensive guide to sharpening your QlikView skills and unleashing the power of QlikView in your organization.

Across various industries, compensation professionals work toorganize and analyze aspects of employment that deal with elementsof pay, such as deciding base salary, bonus, and commissionprovided by an employer to its employees for work performed.Acknowledging the numerous quantitative analyses of data that are apart of this everyday work, Statistics for Compensation provides acomprehensive guide to the key statistical tools and techniquesneeded to perform those analyses and to help organizations makefully informed compensation decisions.

This self-contained book is the first of its kind to explore theuse of various quantitative methods—from basic notions aboutpercents to multiple linear regression—that are used in themanagement, design, and implementation of powerful compensationstrategies. Drawing upon his extensive experience as a consultant,practitioner, and teacher of both statistics and compensation, theauthor focuses on the usefulness of the techniques and theirimmediate application to everyday compensation work, thoroughlyexplaining major areas such as:

Frequency distributions and histograms

Measures of location and variability

Model building

Linear models

Exponential curve models

Maturity curve models

Power models

Market models and salary survey analysis

Linear and exponential integrated market models

Job pricing market models

Throughout the book, rigorous definitions and step-by-stepprocedures clearly explain and demonstrate how to apply thepresented statistical techniques. Each chapter concludes with a setof exercises, and various case studies showcase the topic'sreal-world relevance. The book also features an extensive glossaryof key statistical terms and an appendix with technical details.Data for the examples and practice problems are available in thebook and on a related FTP site.

Statistics for Compensation is an excellent reference forcompensation professionals, human resources professionals, andother practitioners responsible for any aspect of base pay,incentive pay, sales compensation, and executive compensation intheir organizations. It can also serve as a supplement forcompensation courses at the upper-undergraduate and graduatelevels.

"This book is . . . an excellent source of examples forregression analysis. It has been and still is readily readable andunderstandable."

—Journal of the American StatisticalAssociation Regression analysis is a conceptually simplemethod for investigating relationships among variables. Carryingout a successful application of regression analysis, however,requires a balance of theoretical results, empirical rules, andsubjective judgment. Regression Analysis by Example, FifthEdition has been expanded and thoroughly updated to reflectrecent advances in the field. The emphasis continues to be onexploratory data analysis rather than statistical theory. The bookoffers in-depth treatment of regression diagnostics,transformation, multicollinearity, logistic regression, and robustregression.

The book now includes a new chapter on the detection andcorrection of multicollinearity, while also showcasing the use ofthe discussed methods on newly added data sets from the fields ofengineering, medicine, and business. The Fifth Edition alsoexplores additional topics, including:

Surrogate ridge regressionFitting nonlinear modelsErrors in variablesANOVA for designed experimentsMethods of regression analysis are clearly demonstrated, andexamples containing the types of irregularities commonlyencountered in the real world are provided. Each example isolatesone or two techniques and features detailed discussions, therequired assumptions, and the evaluated success of each technique.Additionally, methods described throughout the book can be carriedout with most of the currently available statistical softwarepackages, such as the software package R.

Regression Analysis by Example, Fifth Edition is suitablefor anyone with an understanding of elementary statistics.

This book is aimed at business analysts with basic programming skills for using R for Business Analytics. Note the scope of the book is neither statistical theory nor graduate level research for statistics, but rather it is for business analytics practitioners. Business analytics (BA) refers to the field of exploration and investigation of data generated by businesses. Business Intelligence (BI) is the seamless dissemination of information through the organization, which primarily involves business metrics both past and current for the use of decision support in businesses. Data Mining (DM) is the process of discovering new patterns from large data using algorithms and statistical methods. To differentiate between the three, BI is mostly current reports, BA is models to predict and strategize and DM matches patterns in big data. The R statistical software is the fastest growing analytics platform in the world, and is established in both academia and corporations for robustness, reliability and accuracy.

The book utilizes Albert Einstein’s famous remarks on making things as simple as possible, but no simpler. This book will blow the last remaining doubts in your mind about using R in your business environment. Even non-technical users will enjoy the easy-to-use examples. The interviews with creators and corporate users of R make the book very readable. The author firmly believes Isaac Asimov was a better writer in spreading science than any textbook or journal author.

Wouldn't it be wonderful if studying statistics were easier?With U Can: Statistics I For Dummies, it is! This one-stopresource combines lessons, practical examples, study questions, andonline practice problems to provide you with the ultimate guide tohelp you score higher in your statistics course. Foundationalstatistics skills are a must for students of many disciplines, andleveraging study materials such as this one to supplement yourstatistics course can be a life-saver. Because U Can: StatisticsI For Dummies contains both the lessons you need to learn andthe practice problems you need to put the concepts into action,you'll breeze through your scheduled study time.

Statistics is all about collecting and interpreting data, and isapplicable in a wide range of subject areas—which translatesinto its popularity among students studying in diverse programs.So, if you feel a bit unsure in class, rest assured that there isan easy way to help you grasp the nuances of statistics!

Understand statistical ideas, techniques, formulas, andcalculationsInterpret and critique graphs and charts, determineprobability, and work with confidence intervalsCritique and analyze data from polls and experimentsCombine learning and applying your new knowledge with practicalexamples, practice problems, and expanded online resourcesU Can: Statistics I For Dummies contains everything youneed to score higher in your fundamental statistics course!

The ever-growing use of derivative products makes it essentialfor financial industry practitioners to have a solid understandingof derivative pricing. To cope with the growing complexity,narrowing margins, and shortening life-cycle of the individualderivative product, an efficient, yet modular, implementation ofthe pricing algorithms is necessary. Mathematical Finance isthe first book to harmonize the theory, modeling, andimplementation of today's most prevalent pricing models under oneconvenient cover. Building a bridge from academia to practice, thisself-contained text applies theoretical concepts to real-worldexamples and introduces state-of-the-art, object-orientedprogramming techniques that equip the reader with the conceptualand illustrative tools needed to understand and develop successfulderivative pricing models.

Utilizing almost twenty years of academic and industryexperience, the author discusses the mathematical concepts that arethe foundation of commonly used derivative pricing models, andinsightful Motivation and Interpretation sections for each conceptare presented to further illustrate the relationship between theoryand practice. In-depth coverage of the common characteristics foundamongst successful pricing models are provided in addition to keytechniques and tips for the construction of these models. Theopportunity to interactively explore the book's principal ideas andmethodologies is made possible via a related Web site that featuresinteractive Java experiments and exercises.

While a high standard of mathematical precision is retained,Mathematical Finance emphasizes practical motivations,interpretations, and results and is an excellent textbook forstudents in mathematical finance, computational finance, andderivative pricing courses at the upper undergraduate or beginninggraduate level. It also serves as a valuable reference forprofessionals in the banking, insurance, and asset managementindustries.

Key features of Number Theory: Structures, Examples, and Problems:

* A rigorous exposition starts with the natural numbers and the basics.

* Important concepts are presented with an example, which may also emphasize an application. The exposition moves systematically and intuitively to uncover deeper properties.

* Topics include divisibility, unique factorization, modular arithmetic and the Chinese Remainder Theorem, Diophantine equations, quadratic residues, binomial coefficients, Fermat and Mersenne primes and other special numbers, and special sequences. Sections on mathematical induction and the pigeonhole principle, as well as a discussion of other number systems are covered.

* Unique exercises reinforce and motivate the reader, with selected solutions to some of the problems.

* Glossary, bibliography, and comprehensive index round out the text.

Written by distinguished research mathematicians and renowned teachers, this text is a clear, accessible introduction to the subject and a source of fascinating problems and puzzles, from advanced high school students to undergraduates, their instructors, and general readers at all levels.

This book can be used as a text for a year long graduate course in statistics, computer science, or mathematics, for self-study, and as an invaluable research reference on probabiliity and its applications. Particularly worth mentioning are the treatments of distribution theory, asymptotics, simulation and Markov Chain Monte Carlo, Markov chains and martingales, Gaussian processes, VC theory, probability metrics, large deviations, bootstrap, the EM algorithm, confidence intervals, maximum likelihood and Bayes estimates, exponential families, kernels, and Hilbert spaces, and a self contained complete review of univariate probability.

“The book follows faithfully the style of the original edition. The approach is heavily motivated by real-world time series, and by developing a complete approach to model building, estimation, forecasting and control."

- Mathematical Reviews

Bridging classical models and modern topics, the Fifth Edition of Time Series Analysis: Forecasting and Control maintains a balanced presentation of the tools for modeling and analyzing time series. Also describing the latest developments that have occurred in the field over the past decade through applications from areas such as business, finance, and engineering, the Fifth Edition continues to serve as one of the most influential and prominent works on the subject.

Time Series Analysis: Forecasting and Control, Fifth Edition provides a clearly written exploration of the key methods for building, classifying, testing, and analyzing stochastic models for time series and describes their use in five important areas of application: forecasting; determining the transfer function of a system; modeling the effects of intervention events; developing multivariate dynamic models; and designing simple control schemes. Along with these classical uses, the new edition covers modern topics with new features that include:

A redesigned chapter on multivariate time series analysis with an expanded treatment of Vector Autoregressive, or VAR models, along with a discussion of the analytical tools needed for modeling vector time seriesAn expanded chapter on special topics covering unit root testing, time-varying volatility models such as ARCH and GARCH, nonlinear time series models, and long memory modelsNumerous examples drawn from finance, economics, engineering, and other related fieldsThe use of the publicly available R software for graphical illustrations and numerical calculations along with scripts that demonstrate the use of R for model building and forecastingUpdates to literature references throughout and new end-of-chapter exercisesStreamlined chapter introductions and revisions that update and enhance the expositionTime Series Analysis: Forecasting and Control, Fifth Edition is a valuable real-world reference for researchers and practitioners in time series analysis, econometrics, finance, and related fields. The book is also an excellent textbook for beginning graduate-level courses in advanced statistics, mathematics, economics, finance, engineering, and physics.Written by a highly-experienced author, Foundations of Linear and Generalized Linear Models is a clear and comprehensive guide to the key concepts and results of linearstatistical models. The book presents a broad, in-depth overview of the most commonly usedstatistical models by discussing the theory underlying the models, R software applications,and examples with crafted models to elucidate key ideas and promote practical modelbuilding.

The book begins by illustrating the fundamentals of linear models, such as how the model-fitting projects the data onto a model vector subspace and how orthogonal decompositions of the data yield information about the effects of explanatory variables. Subsequently, the book covers the most popular generalized linear models, which include binomial and multinomial logistic regression for categorical data, and Poisson and negative binomial loglinear models for count data. Focusing on the theoretical underpinnings of these models, Foundations ofLinear and Generalized Linear Models also features:

An introduction to quasi-likelihood methods that require weaker distributional assumptions, such as generalized estimating equation methodsAn overview of linear mixed models and generalized linear mixed models with random effects for clustered correlated data, Bayesian modeling, and extensions to handle problematic cases such as high dimensional problemsNumerous examples that use R software for all text data analysesMore than 400 exercises for readers to practice and extend the theory, methods, and data analysisA supplementary website with datasets for the examples and exercisesAn invaluable textbook for upper-undergraduate and graduate-level students in statistics and biostatistics courses, Foundations of Linear and Generalized Linear Models is also an excellent reference for practicing statisticians and biostatisticians, as well as anyone who is interested in learning about the most important statistical models for analyzing data.

Addressing the highly competitive and risky environments ofcurrent-day financial and sports gambling markets, Forecasting inFinancial and Sports Gambling Markets details the dynamic processof constructing effective forecasting rules based on both graphicalpatterns and adaptive drift modeling (ADM) of cointegrated timeseries. The book uniquely identifies periods of inefficiency thatthese markets oscillate through and develops profitable forecastingmodels that capitalize on irrational behavior exhibited duringthese periods.

Providing valuable insights based on the author's firsthandexperience, this book utilizes simple, yet unique, candlestickcharts to identify optimal time periods in financial markets andoptimal games in sports gambling markets for which forecastingmodels are likely to provide profitable trading and wageringoutcomes. Featuring detailed examples that utilize actual data, thebook addresses various topics that promote financial andmathematical literacy, including:

Higher order ARMA processes in financial markets

The effects of gambling shocks in sports gambling markets

Cointegrated time series with model drift

Modeling volatility

Throughout the book, interesting real-world applications arepresented, and numerous graphical procedures illustrate favorabletrading and betting opportunities, which are accompanied bymathematical developments in adaptive model forecasting and riskassessment. A related web site features updated reviews in sportsand financial forecasting and various links on the topic.

Forecasting in Financial and Sports Gambling Markets is anexcellent book for courses on financial economics and time seriesanalysis at the upper-undergraduate and graduate levels. The bookis also a valuable reference for researchers and practitionersworking in the areas of retail markets, quant funds, hedge funds,and time series. Also, anyone with a general interest in learningabout how to profit from the financial and sports gambling marketswill find this book to be a valuable resource.

Key Features:

Provides a clear introduction and a comprehensive account ofmultilevel models.New methodological developments and applications areexplored.Written by a leading expert in the field of multilevelmethodology.Illustrated throughout with real-life examples, explainingtheoretical concepts.This book is suitable as a comprehensive text for postgraduatecourses, as well as a general reference guide. Appliedstatisticians in the social sciences, economics, biological andmedical disciplines will find this book beneficial.

This volume includes information on the underlying mechanisms ofmicrobial emergence, the technology used to detect them, and thestrategies available to contain them. The author describes thediseases and their causative agents that are major factors in thehealth of populations the world over.

The book contains up-to-date selections from infectious diseasejournals as well as information from the Centers for DiseaseControl and Prevention, the World Health Organization, MedLinePlus, and the American Society for Microbiology.

Perfect for students or those new to the field, the bookcontains Summary Overviews (thumbnail sketches of the basicinformation about the microbe and the associated disease underexamination), Review Questions (testing students' knowledge of thematerial), and Topics for Further Discussion (encouraging a widerconversation on the implications of the disease and challengingstudents to think creatively to develop new solutions).

This important volume provides broad coverage of a variety ofemerging infectious diseases, of which most are directly importantto health practitioners in the United States.

In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.

Topics include:

Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

—American Journal of Psychiatry

In the two decades since the second edition of StatisticalMethods for Rates and Proportions was published, evolvingtechnologies and new methodologies have significantly changed theway today’s statistics are viewed and handled. The explosivedevelopment of personal computing and statistical software hasfacilitated the sophisticated analysis of data, puttingcapabilities that were once the domain of specialists into thehands of every researcher.

The Third Edition of this important text addresses thesechanges and brings the literature up to date. While the previousedition focused on the use of desktop and handheld calculators, thenew edition takes full advantage of modern computing power withoutlosing the elegant simplicity that made the text so popular withstudents and practitioners alike. In authoritative yet clearterminology, the authors have brought the science of data analysisup to date without compromising its accessibility.

Features of the Third Edition include:

New material on sample size calculations and issues in clinicaltrials, and entirely new chapters on single-sample data, logisticregression, Poisson regression, regression models for matchedsamples, the analysis of correlated binary data, and methods foranalyzing fourfold tables with missing dataThe addition of many new problems, both numerical andtheoreticalAnswer sections for numerical problems and hints for tacklingthe theoretical onesA frequentist approach enhanced by the inclusion of empiricalBayesian methodology where appropriateCombining the latest research with the original studies thatestablished the previous editions as leaders in the field,Statistical Methods for Rates and Proportions, Third Editionwill continue to be an invaluable resource for students,statisticians, biostatisticians, and epidemiologists.

First published in 1971, Random Data served as anauthoritative book on the analysis of experimental physical datafor engineering and scientific applications. This FourthEdition features coverage of new developments in random datamanagement and analysis procedures that are applicable to a broadrange of applied fields, from the aerospace and automotiveindustries to oceanographic and biomedical research.

This new edition continues to maintain a balance of classictheory and novel techniques. The authors expand on the treatment ofrandom data analysis theory, including derivations of keyrelationships in probability and random process theory. The bookremains unique in its practical treatment of nonstationary dataanalysis and nonlinear system analysis, presenting the latesttechniques on modern data acquisition, storage, conversion, andqualification of random data prior to its digital analysis. TheFourth Edition also includes:

A new chapter on frequency domain techniques to model andidentify nonlinear systems from measured input/output randomdataNew material on the analysis of multiple-input/single-outputlinear modelsThe latest recommended methods for data acquisition andprocessing of random dataImportant mathematical formulas to design experiments andevaluate results of random data analysis and measurementproceduresAnswers to the problem in each chapterComprehensive and self-contained, Random Data, FourthEdition is an indispensible book for courses on random dataanalysis theory and applications at the upper-undergraduate andgraduate level. It is also an insightful reference for engineersand scientists who use statistical methods to investigate and solveproblems with dynamic data.

"The obvious enthusiasm of Myers, Montgomery, and Vining andtheir reliance on their many examples as a major focus of theirpedagogy make Generalized Linear Models a joy to read. Everystatistician working in any area of applied science should buy itand experience the excitement of these new approaches to familiaractivities."

—Technometrics

Generalized Linear Models: With Applications in Engineeringand the Sciences, Second Edition continues to provide a clearintroduction to the theoretical foundations and key applications ofgeneralized linear models (GLMs). Maintaining the same nontechnicalapproach as its predecessor, this update has been thoroughlyextended to include the latest developments, relevant computationalapproaches, and modern examples from the fields of engineering andphysical sciences.

This new edition maintains its accessible approach to the topicby reviewing the various types of problems that support the use ofGLMs and providing an overview of the basic, related concepts suchas multiple linear regression, nonlinear regression, least squares,and the maximum likelihood estimation procedure. Incorporating thelatest developments, new features of this Second Editioninclude:

A new chapter on random effects and designs for GLMs

A thoroughly revised chapter on logistic and Poisson regression,now with additional results on goodness of fit testing, nominal andordinal responses, and overdispersion

A new emphasis on GLM design, with added sections on designs forregression models and optimal designs for nonlinear regressionmodels

Expanded discussion of weighted least squares, includingexamples that illustrate how to estimate the weights

Illustrations of R code to perform GLM analysis

The authors demonstrate the diverse applications of GLMs throughnumerous examples, from classical applications in the fields ofbiology and biopharmaceuticals to more modern examples related toengineering and quality assurance. The Second Edition hasbeen designed to demonstrate the growing computational nature ofGLMs, as SAS®, Minitab®, JMP®, and R softwarepackages are used throughout the book to demonstrate fitting andanalysis of generalized linear models, perform inference, andconduct diagnostic checking. Numerous figures and screen shotsillustrating computer output are provided, and a related FTP sitehouses supplementary material, including computer commands andadditional data sets.

Generalized Linear Models, Second Edition is an excellentbook for courses on regression analysis and regression modeling atthe upper-undergraduate and graduate level. It also serves as avaluable reference for engineers, scientists, and statisticians whomust understand and apply GLMs in their work.

Multivariate Time Series Analysis: With R and FinancialApplications is the much anticipated sequel coming from one ofthe most influential and prominent experts on the topic of timeseries. Through a fundamental balance of theory and methodology,the book supplies readers with a comprehensible approach tofinancial econometric models and their applications to real-worldempirical research.

Differing from the traditional approach to multivariate timeseries, the book focuses on reader comprehension by emphasizingstructural specification, which results in simplified parsimoniousVAR MA modeling. Multivariate Time Series Analysis: With R andFinancial Applications utilizes the freely available Rsoftware package to explore complex data and illustrate relatedcomputation and analyses. Featuring the techniques and methodologyof multivariate linear time series, stationary VAR models, VAR MAtime series and models, unitroot process, factor models, andfactor-augmented VAR models, the book includes:

• Over 300 examples and exercises to reinforce thepresented content

• User-friendly R subroutines and research presentedthroughout to demonstrate modern applications

• Numerous datasets and subroutines to provide readerswith a deeper understanding of the material

Multivariate Time Series Analysis is an ideal textbookfor graduate-level courses on time series and quantitative financeand upper-undergraduate level statistics courses in time series.The book is also an indispensable reference for researchers andpractitioners in business, finance, and econometrics.

“This book will serve to greatly complement the growingnumber of texts dealing with mixed models, and I highly recommendincluding it in one’s personal library.”

—Journal of the American StatisticalAssociation

Mixed modeling is a crucial area of statistics, enablingthe analysis of clustered and longitudinal data. Mixed Models:Theory and Applications with R, Second Edition fills a gap inexisting literature between mathematical and applied statisticalbooks by presenting a powerful examination of mixed model theoryand application with special attention given to the implementationin R.

The new edition provides in-depth mathematical coverage of mixedmodels’ statistical properties and numerical algorithms, aswell as nontraditional applications, such as regrowth curves,shapes, and images. The book features the latest topics instatistics including modeling of complex clustered or longitudinaldata, modeling data with multiple sources of variation, modelingbiological variety and heterogeneity, Healthy Akaike InformationCriterion (HAIC), parameter multidimensionality, and statistics ofimage processing.

Mixed Models: Theory and Applications with R, SecondEdition features unique applications of mixed modelmethodology, as well as:

Comprehensive theoretical discussions illustrated by examplesand figuresOver 300 exercises, end-of-section problems, updated data sets,and R subroutinesProblems and extended projects requiring simulations in Rintended to reinforce materialSummaries of major results and general points of discussion atthe end of each chapterOpen problems in mixed modeling methodology, which can be usedas the basis for research or PhD dissertationsIdeal for graduate-level courses in mixed statistical modeling,the book is also an excellent reference for professionals in arange of fields, including cancer research, computer science, andengineering.

The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The next six chapters develop the probability material that bridges the gap between descriptive and inferential statistics. Point estimation, inferences based on statistical intervals, and hypothesis testing are then introduced in the next three chapters. The remainder of the book explores the use of this methodology in a variety of more complex settings.

This edition includes a plethora of new exercises, a number of which are similar to what would be encountered on the actuarial exams that cover probability and statistics. Representative applications include investigating whether the average tip percentage in a particular restaurant exceeds the standard 15%, considering whether the flavor and aroma of Champagne are affected by bottle temperature or type of pour, modeling the relationship between college graduation rate and average SAT score, and assessing the likelihood of O-ring failure in space shuttle launches as related to launch temperature.

Other key features:

Provides an accessible overview of the current most widely usedmethods for sensitivity analysis.Opens with a detailed worked example to explain the motivationbehind the book.Includes a range of examples to help illustrate the conceptsdiscussed.Focuses on implementation of the methods in the software SIMLAB- a freely-available sensitivity analysis software packagedeveloped by the authors.Contains a large number of references to sources for furtherreading.Authored by the leading authorities on sensitivityanalysis.Featuring contributions from leading researchers andacademicians in the field of survey research, QuestionEvaluation Methods: Contributing to the Science of Data Qualitysheds light on question response error and introduces aninterdisciplinary, cross-method approach that is essential foradvancing knowledge about data quality and ensuring the credibilityof conclusions drawn from surveys and censuses. Offering a varietyof expert analyses of question evaluation methods, the bookprovides recommendations and best practices for researchers workingwith data in the health and social sciences.

Based on a workshop held at the National Center for HealthStatistics (NCHS), this book presents and compares various questionevaluation methods that are used in modern-day data collection andanalysis. Each section includes an introduction to a method by aleading authority in the field, followed by responses from otherexperts that outline related strengths, weaknesses, and underlyingassumptions. Topics covered include:

Behavior codingCognitive interviewingItem response theoryLatent class analysisSplit-sample experimentsMultitrait-multimethod experimentsField-based data methodsA concluding discussion identifies common themes across thepresented material and their relevance to the future of surveymethods, data analysis, and the production of Federal statistics.Together, the methods presented in this book offer researchersvarious scientific approaches to evaluating survey quality toensure that the responses to these questions result in reliable,high-quality data.

Question Evaluation Methods is a valuable supplement for courseson questionnaire design, survey methods, and evaluation methods atthe upper-undergraduate and graduate levels. it also serves as areference for government statisticians, survey methodologists, andresearchers and practitioners who carry out survey research in theareas of the social and health sciences.

An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

"A must-have book for anyone expecting to do research and/orapplications in categorical data analysis."

—Statistics in Medicine

"It is a total delight reading this book."

—Pharmaceutical Research

"If you do any analysis of categorical data, this is anessential desktop reference."

—Technometrics

The use of statistical methods for analyzing categorical datahas increased dramatically, particularly in the biomedical, socialsciences, and financial industries. Responding to new developments,this book offers a comprehensive treatment of the most importantmethods for categorical data analysis.

Categorical Data Analysis, Third Edition summarizes thelatest methods for univariate and correlated multivariatecategorical responses. Readers will find a unified generalizedlinear models approach that connects logistic regression andPoisson and negative binomial loglinear models for discrete datawith normal regression for continuous data. This edition alsofeatures:

An emphasis on logistic and probit regression methods forbinary, ordinal, and nominal responses for independent observationsand for clustered data with marginal models and random effectsmodelsTwo new chapters on alternative methods for binary responsedata, including smoothing and regularization methods,classification methods such as linear discriminant analysis andclassification trees, and cluster analysisNew sections introducing the Bayesian approach for methods inthat chapterMore than 100 analyses of data sets and over 600 exercisesNotes at the end of each chapter that provide references torecent research and topics not covered in the text, linked to abibliography of more than 1,200 sourcesA supplementary website showing how to use R and SAS; for allexamples in the text, with information also about SPSS and Stataand with exercise solutionsCategorical Data Analysis, Third Edition is an invaluabletool for statisticians and methodologists, such as biostatisticiansand researchers in the social and behavioral sciences, medicine andpublic health, marketing, education, finance, biological andagricultural sciences, and industrial quality control.

By showing us the true nature of chance and revealing the psychological illusions that cause us to misjudge the world around us, Mlodinow gives us the tools we need to make more informed decisions. From the classroom to the courtroom and from financial markets to supermarkets, Mlodinow's intriguing and illuminating look at how randomness, chance, and probability affect our daily lives will intrigue, awe, and inspire.

From the Trade Paperback edition.

Providing a complete overview of operational risk modeling andrelevant insurance analytics, Fundamental Aspects of OperationalRisk and Insurance Analytics: A Handbook of Operational Riskoffers a systematic approach that covers the wide range of topicsin this area. Written by a team of leading experts in the field,the handbook presents detailed coverage of the theories,applications, and models inherent in any discussion of thefundamentals of operational risk, with a primary focus on BaselII/III regulation, modeling dependence, estimation of risk models,and modeling the data elements.

Fundamental Aspects of Operational Risk and Insurance Analytics:A Handbook of Operational Risk begins with coverage on the fourdata elements used in operational risk framework as well asprocessing risk taxonomy. The book then goes further in-depth intothe key topics in operational risk measurement and insurance, forexample diverse methods to estimate frequency and severity models.Finally, the book ends with sections on specific topics, such asscenario analysis; multifactor modeling; and dependence modeling. Aunique companion with Advances in Heavy Tailed Risk Modeling: AHandbook of Operational Risk, the handbook also features:

Discussions on internal loss data and key risk indicators,which are both fundamental for developing a risk-sensitiveframeworkGuidelines for how operational risk can be inserted into afirm’s strategic decisionsA model for stress tests of operational risk under the UnitedStates Comprehensive Capital Analysis and Review (CCAR)program

A valuable reference for financial engineers, quantitativeanalysts, risk managers, and large-scale consultancy groupsadvising banks on their internal systems, the handbook is alsouseful for academics teaching postgraduate courses on themethodology of operational risk.

Operational Risk: Modeling Analytics is organized around theprinciple that the analysis of operational risk consists, in part,of the collection of data and the building of mathematical modelsto describe risk. This book is designed to provide risk analystswith a framework of the mathematical models and methods used in themeasurement and modeling of operational risk in both the bankingand insurance sectors.

Beginning with a foundation for operational risk modeling and afocus on the modeling process, the book flows logically todiscussion of probabilistic tools for operational risk modeling andstatistical methods for calibrating models of operational risk.Exercises are included in chapters involving numerical computationsfor students' practice and reinforcement of concepts.

Written by Harry Panjer, one of the foremost authorities in theworld on risk modeling and its effects in business management, thisis the first comprehensive book dedicated to the quantitativeassessment of operational risk using the tools of probability,statistics, and actuarial science.

In addition to providing great detail of the many probabilistic andstatistical methods used in operational risk, this bookfeatures:

* Ample exercises to further elucidate the concepts in thetext

* Definitive coverage of distribution functions and relatedconcepts

* Models for the size of losses

* Models for frequency of loss

* Aggregate loss modeling

* Extreme value modeling

* Dependency modeling using copulas

* Statistical methods in model selection and calibration

Assuming no previous expertise in either operational riskterminology or in mathematical statistics, the text is designed forbeginning graduate-level courses on risk and operational managementor enterprise risk management. This book is also useful as areference for practitioners in both enterprise risk management andrisk and operational management.

"This book is a reflection of the vast experience andknowledge of the author. It is a useful reference for students andpractitioners dealing with cross sectional data analysis ... Thestrength of the book lies in its wealth of material and wellstructured guidelines ..." Prof. Yohanes EkoRiyanto, Nanyang Technological University, Singapore

"This is superb and brilliant. Prof. Agung has skilfullytransformed his best experiences into new knowledge ... creating anew way of understanding data analysis." Dr. I Putu Gede ArySuta, The Ary Suta Center, Jakarta

Basic theoretical concepts of statistics as well as samplingmethods are often misinterpreted by students and less experiencedresearchers. This book addresses this issue by providing a hands-onpractical guide to conducting data analysis using EViews combinedwith a variety of illustrative models (and their extensions).Models having numerically dependent variables based on across-section data set (such as univariate, multivariate andnonlinear models as well as non-parametric regressions) areconcentrated on. It is shown that a wide variety of hypotheses caneasily be tested using EViews.

Cross Section and Experimental Data Analysis UsingEViews:

Provides step-by-step directions on how to apply EViews tocross section data analysis - from multivariate analysis andnonlinear models to non-parametric regressionPresents a method to test for all possible hypotheses based oneach modelProposes a new method for data analysis based on amultifactorial design modelDemonstrates that statistical summaries in the form oftabulations are invaluable inputs for strategic decisionmakingContains 200 examples with special notes and comments based onthe author’s own empirical findings as well as over 400illustrative outputs of regressions from EViewsTechniques are illustrated through practical examples from realsituationsComes with supplementary material, including work-filescontaining selected equation and system specifications that havebeen applied in the bookThis user-friendly introduction to EViews is ideal for Advancedundergraduate and graduate students taking finance, econometrics,population, or public policy courses, as well as applied policyresearchers.

"For a beginner [this book] is a treasure trove; for anexperienced person it can provide new ideas on how better to pursuethe subject of applied statistics."

—Journal of Quality Technology

Sensibly organized for quick reference, Statistical Rules ofThumb, Second Edition compiles simple rules that arewidely applicable, robust, and elegant, and each captures keystatistical concepts. This unique guide to the use of statisticsfor designing, conducting, and analyzing research studiesillustrates real-world statistical applications through examplesfrom fields such as public health and environmental studies. Alongwith an insightful discussion of the reasoning behind everytechnique, this easy-to-use handbook also conveys the variouspossibilities statisticians must think of when designing andconducting a study or analyzing its data.

Each chapter presents clearly defined rules related toinference, covariation, experimental design, consultation, and datarepresentation, and each rule is organized and discussed under fivesuccinct headings: introduction; statement and illustration of therule; the derivation of the rule; a concluding discussion; andexploration of the concept's extensions. The author also introducesnew rules of thumb for topics such as sample size for ratioanalysis, absolute and relative risk, ANCOVA cautions, anddichotomization of continuous variables. Additional features of theSecond Edition include:

Additional rules on Bayesian topicsNew chapters on observational studies and Evidence-BasedMedicine (EBM)

Additional emphasis on variation and causation

Updated material with new references, examples, and sources

A related Web site provides a rich learning environment andcontains additional rules, presentations by the author, and amessage board where readers can share their own strategies anddiscoveries. Statistical Rules of Thumb, SecondEdition is an ideal supplementary book for courses inexperimental design and survey research methods at theupper-undergraduate and graduate levels. It also serves as anindispensable reference for statisticians, researchers,consultants, and scientists who would like to develop anunderstanding of the statistical foundations of their researchefforts. A related website www.vanbelle.org provides additionalrules, author presentations and more.