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.
Understanding and investigating the impacts of market risk onthe financial landscape is crucial in preventing crises. Written bya hedge fund specialist, the Handbook of Market Risk is thecomprehensive guide to the subject of market risk.
Featuring a format that is accessible and convenient, thehandbook employs numerous examples to underscore the application ofthe material in a real-world setting. The book starts byintroducing the various methods to measure market risk whilecontinuing to emphasize stress testing, liquidity, and interestrate implications. Covering topics intrinsic to understanding andapplying market risk, the handbook features:An introduction to financial marketsThe historical perspective from marketevents and diverse mathematics to thevalue-at-riskReturn and volatility estimatesDiversification, portfolio risk, andefficient frontierThe Capital Asset Pricing Modeland the Arbitrage Pricing TheoryThe use of a fundamentalmulti-factors modelFinancial derivatives instrumentsFixed income and interest rate riskLiquidity riskAlternative investmentsStress testing and back testingBanks and Basel II/III
The Handbook of Market Risk is a must-have resource forfinancial engineers, quantitative analysts, regulators, riskmanagers in investments banks, and large-scale consultancy groupsadvising banks on internal systems. The handbook is also anexcellent text for academics teaching postgraduate courses onfinancial methodology.
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.
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 protocol
Packed 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.
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 indifficulty
1,001 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.
Risk Management and Financial Institutions, Fifth Edition explains all aspects of financial risk and financial institution regulation, helping you better understand the financial markets—and their potential dangers. Inside, you’ll learn the different types of risk, how and where they appear in different types of institutions, and how the regulatory structure of each institution affects risk management practices. Comprehensive ancillary materials include software, practice questions, and all necessary teaching supplements, facilitating more complete understanding and providing an ultimate learning resource.
All financial professionals need to understand and quantify the risks associated with their decisions. This book provides a complete guide to risk management with the most up to date information.
• Understand how risk affects different types of financial institutions
• Learn the different types of risk and how they are managed
• Study the most current regulatory issues that deal with risk
• Get the help you need, whether you’re a student or a professional
Risk management has become increasingly important in recent years and a deep understanding is essential for anyone working in the finance industry; today, risk management is part of everyone's job. For complete information and comprehensive coverage of the latest industry issues and practices, Risk Management and Financial Institutions, Fifth Edition is an informative, authoritative guide.
Increase your chances of acing that probability exam -- or winningat the casino!
Whether you're hitting the books for a probability or statisticscourse or hitting the tables at a casino, working out probabilitiescan be problematic. This book helps you even the odds. Usingeasy-to-understand explanations and examples, it demystifiesprobability -- and even offers savvy tips to boost your chances ofgambling success!
Discover how to
* Conquer combinations and permutations
* Understand probability models from binomial to exponential
* Make good decisions using probability
* Play the odds in poker, roulette, and other games
". . . [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 power
Each 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.
“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.
“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.
Most guides to R, whether books or online, focus on R functions and procedures. But now, thanks to Statistical Analysis with R For Dummies, you have access to a trusted, easy-to-follow guide that focuses on the foundational statistical concepts that R addresses—as well as step-by-step guidance that shows you exactly how to implement them using R programming.
People are becoming more aware of R every day as major institutions are adopting it as a standard. Part of its appeal is that it's a free tool that's taking the place of costly statistical software packages that sometimes take an inordinate amount of time to learn. Plus, R enables a user to carry out complex statistical analyses by simply entering a few commands, making sophisticated analyses available and understandable to a wide audience. Statistical Analysis with R For Dummies enables you to perform these analyses and to fully understand their implications and results.Gets you up to speed on the #1 analytics/data science software toolDemonstrates how to easily find, download, and use cutting-edge community-reviewed methods in statistics and predictive modelingShows you how R offers intel from leading researchers in data science, free of chargeProvides information on using R Studio to work with R
Get ready to use R to crunch and analyze your data—the fast and easy way!
For undergraduate courses in Risk Management and Insurance.
Principles and Practices: Managing Risk with Consumer Considerations
Redja’s Principles of Risk Management and Insurance provides an in-depth examination of major risk themes. Using rich and up-to-date content on the basic concepts of risk and insurance, and introductory and advanced topics in traditional and enterprise risk management, the text is relevant to a wide number of disciplines in the business realm.
Fully updated and revised, the Thirteenth Edition now covers global topics ranging from natural disasters and terrorism, to domestic issues like the ever-evolving Affordable Care Act and Healthcare Reform. Principles of Risk Management and Insurance sets itself apart by placing primary emphasis on insurance consumers and blends basic risk management and insurance principles with consumer considerations, allowing students to apply basic concepts to their own personal risk management and insurance programs.
Tableau For Dummies brings order to the chaotic world of data. Understanding your data and organizing it into formats and visualizations that make sense to you are crucial to making a real impact on your business with the information that's already at your fingertips. This easy-to-use reference explores the user interface, and guides you through the process of connecting your data sources to the software. Additionally, this approachable, yet comprehensive text shows you how to use graphs, charts, and other images to bring visual interest to your data, how to create dashboards from multiple data sources, and how to export the visualizations that you have developed into multiple formats that translate into positive change for your business.
The mission of Tableau Software is to grant you access to data that, when put into action, will help you build your company. Learning to use the data available to you helps you make informed, grounded business decisions that can spell success for your company.Navigate the user interface to efficiently access the features you needConnect to various spreadsheets, databases, and other data sources to create a multi-dimensional snapshot of your businessDevelop visualizations with easy to use drag and drop featuresStart building your data with templates and sample workbooks to spark your creativity and help you organize your information
Tableau For Dummies is a step-by-step resource that helps you make sense of the data landscape—and put your data to work in support of your business.
Packed with fresh and practical examples appropriate for a range of degree-seeking students, Statistics II For Dummies helps any reader succeed in an upper-level statistics course. It picks up with data analysis where Statistics For Dummies left off, featuring new and updated examples, real-world applications, and test-taking strategies for success. This easy-to-understand guide covers such key topics as sorting and testing models, using regression to make predictions, performing variance analysis (ANOVA), drawing test conclusions with chi-squares, and making comparisons with the Rank Sum Test.
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 methods
Key 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.
Featuring hands-on applications with JMP Pro®, a statistical package from the SAS Institute, the book
uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting.
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® also includes:Detailed summaries that supply an outline of key topics at the beginning of each chapterEnd-of-chapter examples and exercises that allow readers to expand their comprehension of the presented materialData-rich case studies to illustrate various applications of data mining techniquesA companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors www.dataminingbook.com
Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro® is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field.
Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner ®, Third Edition, both published by Wiley.
Mia Stephens is Academic Ambassador at JMP®, a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley.
Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition, also published by Wiley.
"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.
You don't need a time machine to predict the future. All it takes is a little knowledge and know-how, and Predictive Analytics For Dummies gets you there fast. With the help of this friendly guide, you'll discover the core of predictive analytics and get started putting it to use with readily available tools to collect and analyze data. In no time, you'll learn how to incorporate algorithms through data models, identify similarities and relationships in your data, and predict the future through data classification. Along the way, you'll develop a roadmap by preparing your data, creating goals, processing your data, and building a predictive model that will get you stakeholder buy-in.
Big Data has taken the marketplace by storm, and companies are seeking qualified talent to quickly fill positions to analyze the massive amount of data that are being collected each day. If you want to get in on the action and either learn or deepen your understanding of how to use predictive analytics to find real relationships between what you know and what you want to know, everything you need is a page away!Offers common use cases to help you get startedCovers details on modeling, k-means clustering, and moreIncludes information on structuring your dataProvides tips on outlining business goals and approaches
The future starts today with the help of Predictive Analytics For Dummies.
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 learning
By 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.
"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 experiments
Methods 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.
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.
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 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:
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.
"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."
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.
Chance and randomness are encountered on a daily basis. Authoredby a highly qualified professor in the field, Probability: WithApplications and R delves into the theories and applicationsessential to obtaining a thorough understanding of probability.
With real-life examples and thoughtful exercises from fields asdiverse as biology, computer science, cryptology, ecology, publichealth, and sports, the book is accessible for a variety ofreaders. The book’s emphasis on simulation through the use ofthe popular R software language clarifies and illustrates keycomputational and theoretical results.
Probability: With Applications and R helps readersdevelop problem-solving skills and delivers an appropriate mix oftheory and application. The book includes:Chapters covering first principles, conditional probability,independent trials, random variables, discrete distributions,continuous probability, continuous distributions, conditionaldistribution, and limitsAn early introduction to random variables and Monte Carlosimulation and an emphasis on conditional probability,conditioning, and developing probabilistic intuitionAn R tutorial with example script filesMany classic and historical problems of probability as well asnontraditional material, such as Benford’s law, power-lawdistributions, and Bayesian statisticsA topics section with suitable material for projects andexplorations, such as random walk on graphs, Markov chains, andMarkov chain Monte CarloChapter-by-chapter summaries and hundreds of practicalexercises
Probability: With Applications and R is an ideal text fora beginning course in probability at the undergraduate level.
"As with previous editions, the authors have produced a leadingtextbook on regression."
—Journal of the American Statistical Association
A comprehensive and up-to-date introduction to thefundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Editioncontinues to present both the conventional and less common uses oflinear regression in today’s cutting-edge scientificresearch. The authors blend both theory and application to equipreaders with an understanding of the basic principles needed toapply regression model-building techniques in various fields ofstudy, including engineering, management, and the healthsciences.
Following a general introduction to regression modeling,including typical applications, a host of technical tools areoutlined such as basic inference procedures, introductory aspectsof model adequacy checking, and polynomial regression models andtheir variations. The book then discusses how transformations andweighted least squares can be used to resolve problems of modelinadequacy and also how to deal with influential observations. TheFifth Edition features numerous newly added topics,including:A chapter on regression analysis of time series data thatpresents the Durbin-Watson test and other techniques for detectingautocorrelation as well as parameter estimation in time seriesregression modelsRegression models with random effects in addition to adiscussion on subsampling and the importance of the mixedmodelTests on individual regression coefficients and subsets ofcoefficientsExamples of current uses of simple linear regression models andthe use of multiple regression models for understanding patientsatisfaction data.
In addition to Minitab, SAS, and S-PLUS, the authors haveincorporated JMP and the freely available R software to illustratethe discussed techniques and procedures in this new edition.Numerous exercises have been added throughout, allowing readers totest their understanding of the material.
Introduction to Linear Regression Analysis, Fifth Editionis an excellent book for statistics and engineering courses onregression at the upper-undergraduate and graduate levels. The bookalso serves as a valuable, robust resource for professionals in thefields of engineering, life and biological sciences, and the socialsciences.
Written in a highly accessible style, Introduction to Statisticsthrough Resampling Methods and R, Second Edition guides students inthe understanding of descriptive statistics, estimation, hypothesistesting, and model building. The book emphasizes the discoverymethod, enabling readers to ascertain solutions on their own ratherthan simply copy answers or apply a formula by rote. TheSecond Edition utilizes the R programming language to simplifytedious computations, illustrate new concepts, and assist readersin completing exercises. The text facilitates quick learningthrough the use of:
More than 250 exercises—with selected "hints"—scatteredthroughout to stimulate readers' thinking and to actively engagethem in applying their newfound skills
An increased focus on why a method is introduced
Multiple explanations of basic concepts
Real-life applications in a variety of disciplines
Dozens of thought-provoking, problem-solving questions in the finalchapter to assist readers in applying statistics to real-lifeapplications
Introduction to Statistics through Resampling Methods and R, SecondEdition is an excellent resource for students and practitioners inthe fields of agriculture, astrophysics, bacteriology, biology,botany, business, climatology, clinical trials, economics,education, epidemiology, genetics, geology, growth processes,hospital administration, law, manufacturing, marketing, medicine,mycology, physics, political science, psychology, social welfare,sports, and toxicology who want to master and learn to applystatistical methods.
The R language is recognized as one of the most powerful andflexible statistical software packages, enabling users to applymany statistical techniques that would be impossible without suchsoftware to help implement such large data sets. R has become anessential tool for understanding and carrying out research.
This edition:Features full colour text and extensive graphicsthroughout.Introduces a clear structure with numbered section headings tohelp readers locate information more efficiently.Looks at the evolution of R over the past five years.Features a new chapter on Bayesian Analysis andMeta-Analysis.Presents a fully revised and updated bibliography and referencesection.Is supported by an accompanying website allowing examples fromthe text to be run by the user.
Praise for the first edition:
‘…if you are an R user or wannabe R user, this textis the one that should be on your shelf. The breadth oftopics covered is unsurpassed when it comes to texts on dataanalysis in R.’ (The American Statistician, August2008)‘The High-level software language of R is setting standardsin quantitative analysis. And now anybody can get to grips with itthanks to The R Book…’ (Professional Pensions,July 2007)
Multivariate Analysis for the Biobehavioral and Social Sciences:A Graphical Approach outlines the essential multivariate methodsfor understanding data in the social and biobehavioral sciences.Using real-world data and the latest software applications, thebook addresses the topic in a comprehensible and hands-on manner,making complex mathematical concepts accessible to readers.
The authors promote the importance of clear, well-designedgraphics in the scientific process, with visual representationsaccompanying the presented classical multivariate statisticalmethods . The book begins with a preparatory review of univariatestatistical methods recast in matrix notation, followed by anaccessible introduction to matrix algebra. Subsequent chaptersexplore fundamental multivariate methods and related key concepts,including:
Factor analysis and related methods
Multivariate analysis of variance (MANOVA)
Multiple regression and the general linear model (GLM)
Each topic is introduced with a research-publication case studythat demonstrates its real-world value. Next, the question "how doyou do that?" is addressed with a complete, yet simplified,demonstration of the mathematics and concepts of the method.Finally, the authors show how the analysis of the data is performedusing Stata®, SAS®, and SPSS®. The discussedapproaches are also applicable to a wide variety of modernextensions of multivariate methods as well as modern univariateregression methods. Chapters conclude with conceptual questionsabout the meaning of each method; computational questions that testthe reader's ability to carry out the procedures on simpledatasets; and data analysis questions for the use of the discussedsoftware packages.
Multivariate Analysis for the Biobehavioral and Social Sciencesis an excellent book for behavioral, health, and social sciencecourses on multivariate statistics at the graduate level. The bookalso serves as a valuable reference for professionals andresearchers in the social, behavioral, and health sciences whowould like to learn more about multivariate analysis and itsrelevant applications.
The text presents the fundamentals of chemical engineering operations and processes in a simple style that helps the students to gain a thorough understanding of chemical process calculations. The book deals with the principles of stoichiometry to formulate and solve material and energy balance problems in processes with and without chemical reactions. With the help of examples, the book explains the construction and use of reference-substance plots, equilibrium diagrams, psychrometric charts, steam tables and enthalpy composition diagrams. It also elaborates on thermophysics and thermochemistry to acquaint the students with the thermodynamic principles of energy balance calculations.
The book is supplemented with Solutions Manual for instructors containing detailed solutions of all chapter-end unsolved problems.NEW TO THE SECOND EDITION
• Incorporates a new chapter on Bypass, Recycle and Purge Operations
• Comprises updations in some sections and presents new sections on Future Avenues and Opportunities in Chemical Engineering, Processes in Biological and Energy Systems
• Contains several new worked-out examples in the chapter on Material Balance with Chemical Reaction
• Includes GATE questions with answers up to the year 2016 in Objective-type questions
• SI units are used throughout the book.
• All basic chemical engineering operations and processes are introduced, and different types of problems are illustrated with worked-out examples.
• Stoichiometric principles are extended to solve problems related to bioprocessing, environmental engineering, etc.
• Exercise problems (more than 810) are organised according to the difficulty level and all are provided with answers.
Ono describes his rocky path through college and graduate school, interweaving Ramanujan’s story with his own and telling how at key moments, he was inspired by Ramanujan and guided by mentors who encouraged him to pursue his interest in exploring Ramanujan’s mathematical legacy.
Picking up where others left off, beginning with the great English mathematician G.H. Hardy, who brought Ramanujan to Cambridge in 1914, Ono has devoted his mathematical career to understanding how in his short life, Ramanujan was able to discover so many deep mathematical truths, which Ramanujan believed had been sent to him as visions from a Hindu goddess. And it was Ramanujan who was ultimately the source of reconciliation between Ono and his parents.
Ono’s search for Ramanujan ranges over three continents and crosses paths with mathematicians whose lives span the globe and the entire twentieth century and beyond. Along the way, Ken made many fascinating discoveries. The most important and surprising one of all was his own humanity."
Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes:Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented materialData-rich case studies to illustrate various applications of data mining techniquesCompletely new chapters on social network analysis and text miningA companion site with additional data sets, instructors material that include solutions to exercises and case studies, and Microsoft PowerPoint® slides https://www.dataminingbook.comFree 140-day license to use XLMiner for Education software
Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner®, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses as well as professional programs on data mining, predictive modeling, and Big Data analytics. The new edition is also a unique reference for analysts, researchers, and practitioners working with predictive analytics in the fields of business, finance, marketing, computer science, and information technology.
Praise for the Second Edition
"…full of vivid and thought-provoking anecdotes... needs to be read by anyone with a serious interest in research and marketing."– Research Magazine
"Shmueli et al. have done a wonderful job in presenting the field of data mining - a welcome addition to the literature." – ComputingReviews.com
"Excellent choice for business analysts...The book is a perfect fit for its intended audience." – Keith McCormick, Consultant and Author of SPSS Statistics For Dummies, Third Edition and SPSS Statistics for Data Analysis and Visualization
Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, The Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks and book chapters.
Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com. He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective, also published by Wiley.
Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad for 15 years.
Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine conditional probability and conditional expectation, normal processes and covariance stationary processes, and counting processes and Poisson processes. The text concludes with explorations of renewal counting processes, Markov chains, random walks, and birth and death processes, including examples of the wide variety of phenomena to which these stochastic processes may be applied. Numerous examples and exercises complement every section.
A superb combination of lucid prose and informative graphics,written by four of the world’s leading experts on allaspects of meta-analysis. Borenstein, Hedges, Higgins, andRothstein provide a refreshing departure from cookbookapproaches with their clear explanations of the what and whyof meta-analysis. The book is ideal as a course textbook or forself-study. My students, who used pre-publication versionsof some of the chapters, raved about the clarity of theexplanations and examples. David Rindskopf, DistinguishedProfessor of Educational Psychology, City University of New York,Graduate School and University Center, & Editor of the Journalof Educational and Behavioral Statistics.
The approach taken by Introduction to Meta-analysis isintended to be primarily conceptual, and it is amazinglysuccessful at achieving that goal. The reader can comfortably skipthe formulas and still understand their application andunderlying motivation. For the more statisticallysophisticated reader, the relevant formulas and worked examplesprovide a superb practical guide to performing ameta-analysis. The book provides an eclectic mix of examplesfrom education, social science, biomedical studies, and evenecology. For anyone considering leading a course inmeta-analysis, or pursuing self-directed study, Introduction toMeta-analysis would be a clear first choice. Jesse A.Berlin, ScD
Introduction to Meta-Analysis is an excellent resource fornovices and experts alike. The book provides a clear andcomprehensive presentation of all basic and most advancedapproaches to meta-analysis. This book will be referencedfor decades. Michael A. McDaniel, Professor of Human Resourcesand Organizational Behavior, Virginia Commonwealth University
This new fourth edition looks at recent techniques such asvariational methods, Bayesian importance sampling, approximateBayesian computation and Reversible Jump Markov Chain Monte Carlo(RJMCMC), providing a concise account of the way in which theBayesian approach to statistics develops as well as how itcontrasts with the conventional approach. The theory is built upstep by step, and important notions such as sufficiency are broughtout of a discussion of the salient features of specificexamples.
This edition:Includes expanded coverage of Gibbs sampling, including morenumerical examples and treatments of OpenBUGS, R2WinBUGS andR2OpenBUGS.Presents significant new material on recent techniques such asBayesian importance sampling, variational Bayes, ApproximateBayesian Computation (ABC) and Reversible Jump Markov Chain MonteCarlo (RJMCMC).Provides extensive examples throughout the book to complementthe theory presented.Accompanied by a supporting website featuring new material andsolutions.
More and more students are realizing that they need to learnBayesian statistics to meet their academic and professional goals.This book is best suited for use as a main text in courses onBayesian statistics for third and fourth year undergraduates andpostgraduate students.
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
Exponential curve models
Maturity curve 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 will be useful to everyone who has struggled with displaying data in an informative and attractive way. Some basic knowledge of R is necessary (e.g., importing data into R). ggplot2 is a mini-language specifically tailored for producing graphics, and you'll learn everything you need in the book. After reading this book you'll be able to produce graphics customized precisely for your problems, and you'll find it easy to get graphics out of your head and on to the screen or page.
"The book makes a valuable contribution by synthesizing currentresearch and identifying areas for future investigation for eachaspect of the survey process."
—Journal of the American Statistical Association
"Overall, the high quality of the text material is matched bythe quality of writing . . ."
—Public Opinion Quarterly
". . . it should find an audience everywhere surveys are beingconducted."
This new edition of Survey Methodology continues toprovide a state-of-the-science presentation of essential surveymethodology topics and techniques. The volume's six world-renownedauthors have updated this Second Edition to present newly emergingapproaches to survey research and provide more comprehensivecoverage of the major considerations in designing and conducting asample survey.
Key topics in survey methodology are clearly explained in thebook's chapters, with coverage including sampling frame evaluation,sample design, development of questionnaires, evaluation ofquestions, alternative modes of data collection, interviewing,nonresponse, post-collection processing of survey data, andpractices for maintaining scientific integrity. Acknowledging thegrowing advances in research and technology, the Second Editionfeatures:Updated explanations of sampling frame issues for mobiletelephone and web surveys
New scientific insight on the relationship between nonresponserates and nonresponse errors
Restructured discussion of ethical issues in survey research,emphasizing the growing research results on privacy, informedconsent, and confidentiality issues
The latest research findings on effective questionnairedevelopment techniques
The addition of 50% more exercises at the end of each chapter,illustrating basic principles of survey design
An expanded FAQ chapter that addresses the concerns thataccompany newly established methods
Providing valuable and informative perspectives on the mostmodern methods in the field, Survey Methodology, SecondEdition is an ideal book for survey research courses at theupper-undergraduate and graduate levels. It is also anindispensable reference for practicing survey methodologists andany professional who employs survey research methods.
Does the subject of data analysis make you dizzy? You've come tothe right place! Statistics For Big Data For Dummies breaksthis often-overwhelming subject down into easily digestible parts,offering new and aspiring data analysts the foundation they need tobe successful in the field. Inside, you'll find an easy-to-followintroduction to exploratory data analysis, the lowdown oncollecting, cleaning, and organizing data, everything you need toknow about interpreting data using common software and programminglanguages, plain-English explanations of how to make sense of datain the real world, and much more.
Data has never been easier to come by, and the tools studentsand professionals need to enter the world of big data are based onapplied statistics. While the word "statistics" alone can evokefeelings of anxiety in even the most confident student orprofessional, it doesn't have to. Written in the familiar andfriendly tone that has defined the For Dummies brand for more thantwenty years, Statistics For Big Data For Dummies takes theintimidation out of the subject, offering clear explanations andtons of step-by-step instruction to help you make sense of datamining—without losing your cool.Helps you to identify valid, useful, and understandablepatterns in dataProvides guidance on extracting previously unknown informationfrom large databasesShows you how to discover patterns available in big dataGives you access to the latest tools and techniques for workingin big data
If you're a student enrolled in a related Applied Statisticscourse or a professional looking to expand your skillset,Statistics For Big Data For Dummies gives you access toeverything you need to succeed.
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.
"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."
"If you do any analysis of categorical data, this is anessential desktop reference."
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 solutions
Categorical 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.
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.
A. Y. Khinchin made significant contributions to probability theory, statistical physics, and several other fields. His elegant, groundbreaking work will prove of substantial interest to advanced undergraduates, graduate students, and professionals in the fields of statistics, probability, and operations research.
The hallmark features of this text have been retained in this eleventh edition: superior writing style; excellent exercises and examples covering the wide breadth of coverage of probability topic; and real-world applications in engineering, science, business and economics. The 65% new chapter material includes coverage of finite capacity queues, insurance risk models, and Markov chains, as well as updated data. The book contains compulsory material for new Exam 3 of the Society of Actuaries including several sections in the new exams. It also presents new applications of probability models in biology and new material on Point Processes, including the Hawkes process. There is a list of commonly used notations and equations, along with an instructor's solutions manual.
This text will be a helpful resource for professionals and students in actuarial science, engineering, operations research, and other fields in applied probability.Updated data, and a list of commonly used notations and equations, instructor's solutions manualOffers new applications of probability models in biology and new material on Point Processes, including the Hawkes processIntroduces elementary probability theory and stochastic processes, and shows how probability theory can be applied in fields such as engineering, computer science, management science, the physical and social sciences, and operations researchCovers finite capacity queues, insurance risk models, and Markov chains Contains compulsory material for new Exam 3 of the Society of Actuaries including several sections in the new examsAppropriate for a full year course, this book is written under the assumption that students are familiar with calculus
An understanding of statistical tools is essential for engineersand scientists who often need to deal with data analysis over thecourse of their work. Statistics and Probability withApplications for Engineers and Scientists walks readers througha wide range of popular statistical techniques, explainingstep-by-step how to generate, analyze, and interpret data fordiverse applications in engineering and the natural sciences.
Unique among books of this kind, Statistics and Probabilitywith Applications for Engineers and Scientists coversdescriptive statistics first, then goes on to discuss thefundamentals of probability theory. Along with case studies,examples, and real-world data sets, the book incorporates clearinstructions on how to use the statistical packages Minitab®and Microsoft® Office Excel® to analyze various datasets. The book also features:
• Detailed discussions on sampling distributions,statistical estimation of population parameters, hypothesistesting, reliability theory, statistical quality control includingPhase I and Phase II control charts, and process capabilityindices
• A clear presentation of nonparametric methods andsimple and multiple linear regression methods, as well as a briefdiscussion on logistic regression method
• Comprehensive guidance on the design of experiments,including randomized block designs, one- and two-way layoutdesigns, Latin square designs, random effects and mixed effectsmodels, factorial and fractional factorial designs, and responsesurface methodology
• A companion website containing data sets for Minitaband Microsoft Office Excel, as well as JMP ® routines andresults
Assuming no background in probability and statistics,Statistics and Probability with Applications for Engineers andScientists features a unique, yet tried-and-true, approach thatis ideal for all undergraduate students as well as statisticalpractitioners who analyze and illustrate real-world data inengineering and the natural sciences.
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.