The coverage of the book includes 13 topics relevant to classical mechanics, such as integration of one-dimensional equations of motion; the Hamiltonian equations of motion; and adiabatic invariants.
The book will be of great use to physics students studying classical mechanics.
This book gives a survey of astrophysics at the advanced undergraduate level, providing a physics-centred analysis of a broad range of astronomical systems. It originates from a two-semester course sequence at Rutgers University that is meant to appeal not only to astrophysics students but also more broadly to physics and engineering students. The organisation is driven more by physics than by astronomy; in other words, topics are first developed in physics and then applied to astronomical systems that can be investigated, rather than the other way around.
The first half of the book focuses on gravity. The theme in this part of the book, as well as throughout astrophysics, is using motion to investigate mass. The goal of Chapters 2-11 is to develop a progressively richer understanding of gravity as it applies to objects ranging from planets and moons to galaxies and the universe as a whole. The second half uses other aspects of physics to address one of the big questions. While “Why are we here?” lies beyond the realm of physics, a closely related question is within our reach: “How did we get here?” The goal of Chapters 12-20 is to understand the physics behind the remarkable story of how the Universe, Earth and life were formed. This book assumes familiarity with vector calculus and introductory physics (mechanics, electromagnetism, gas physics and atomic physics); however, all of the physics topics are reviewed as they come up (and vital aspects of vector calculus are reviewed in the Appendix).
A key objective of these volumes, which present a vector treatment of the principles of mechanics, is to help the student gain confidence in transforming problems into appropriate mathematical language that may be manipulated to give useful physical conclusions or specific numerical results. In the first volume, the elements of vector calculus and the matrix algebra are reviewed in appendices. Unusual mathematical topics, such as singularity functions and some elements of tensor analysis, are introduced within the text. A logical and systematic building of well-known kinematic concepts, theorems, and formulas, illustrated by examples and problems, is presented offering insights into both fundamentals and applications. Problems amplify the material and pave the way for advanced study of topics in mechanical design analysis, advanced kinematics of mechanisms and analytical dynamics, mechanical vibrations and controls, and continuum mechanics of solids and fluids.
Volume I of Principles of Engineering Mechanics provides the basis for a stimulating and rewarding one-term course for advanced undergraduate and first-year graduate students specializing in mechanics, engineering science, engineering physics, applied mathematics, materials science, and mechanical, aerospace, and civil engineering. Professionals working in related fields of applied mathematics will find it a practical review and a quick reference for questions involving basic kinematics.
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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.
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.
Vector methods are developed in the first two chapters and are used throughout the book. Other chapters cover the fundamentals of Newtonian mechanics, the special theory of relativity, gravitational attraction and potentials, oscillatory motion, Lagrangian and Hamiltonian dynamics, central-force motion, two-particle collisions, and the wave equation.
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.
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
Superior writing styleExcellent exercises and examples covering the wide breadth of coverage of probability topics Real-world applications in engineering, science, business and economics
This volume contains twenty-two contributions written by distinguished invited speakers from all part of the world to the iutam symposium on mechanical properties of cellular materials. It provides a survey on recent advances in the characterisation and modeling of the mechanical properties of solid cellular materials under static and dynamic loading as well as their applications in lightweight structures analysis and design. This volume will be of interest to structural, mechanical and material scientists and engineers working on different aspects of this new class of materials (for example in microstructure observation, micromechanical and multiscale modeling, phenomenological models, structural impact behaviour and numerical validation).
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.
". . . [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.
“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.
Researchers have recently identified eight critical research tasks facing the field of computational mechanics. These tasks have come about because it appears possible to reach a new level of mathematical modelling and numerical solution that will lead to a much deeper understanding of nature and to great improvements in engineering design.
The eight tasks are:
The automatic solution of mathematical models
Effective numerical schemes for fluid flows
The development of an effective mesh-free numerical solution method
The development of numerical procedures for multiphysics problems
The development of numerical procedures for multiscale problems
The modelling of uncertainties
The analysis of complete life cycles of systems
Education - teaching sound engineering and scientific judgement
Readers of Computational Fluid and Solid Mechanics 2003 will be able to apply the combined experience of many of the world's leading researchers to their own research needs. Those in academic environments will gain a better insight into the needs and constraints of the industries they are involved with; those in industry will gain a competitive advantage by gaining insight into the cutting edge research being carried out by colleagues in academia.
Bridges the gap between academic researchers and practitioners in industry
Outlines the eight main challenges facing Research and Design in Computational mechanics and offers new insights into the shifting the research agenda
Provides a vision of how strong, basic and exciting education at university can be harmonized with life-long learning to obtain maximum value from the new powerful tools of analysis
The workshop brought together researchers and students who work in disciplines associated with mechanisms science and offered a great opportunity for scientists from all over the world to present their achievements, exchange innovative ideas and create solid international links, setting the trend for future developments in this important and creative field.
The topics treated in this volume are mechanisms and machine design, mechanical transmissions, mechatronic and biomechanic applications, computational and experimental methods, history of mechanism and machine science and teaching methods.
This book provides a comprehensive review of the above issues through the selection of representative papers presented at the unique series of international workshops and conferences on ship stability held between 2000 and 2009. The editorial committee has selected papers for this book from the following events: STAB 2000 Conference (Launceston, Tasmania), 5th Stability Workshop (Trieste, 2001), 6th Stability Workshop (Long Island, 2002), STAB 2003 Conference (Madrid), 7th Stability Workshop (Shanghai, 2004), 8th Stability Workshop (Istanbul, 2005), STAB 2006 Conference (Rio de Janeiro), 9th Stability Workshop (Hamburg, 2007), 10th Stability Workshop (Daejeon, 2008), and STAB 2009 Conference (St. Petersburg).
The papers have been clustered around the following themes: Stability Criteria, Stability of the Intact Ship, Parametric Rolling, Broaching, Nonlinear Dynamics, Roll Damping, Probabilistic Assessment of Ship Capsize, Environmental Modelling, Damaged Ship Stability, CFD Applications, Design for Safety, Naval Vessels, and Accident Investigations.
The main part of the book deals with X-ray stress analysis (XSA), focussing on measurement and evaluation methods which can help to solve the problems of today, the numerous applications of metallic, polymeric and ceramic materials as well as of thin-film-substrate composites and of advanced microcomponents. Furthermore it contains data, results, hints and recommendations that are valuable to laboratories for the certification and accreditation of their stress analysis.
Stress analysis is an active field in which many questions remain unsettled. Accordingly, unsolved problems and conflicting results are discussed as well. The assessment of the experimentally determined residual and structural stress states on the static and dynamic behavior of materials and components is handled in a separate chapter.
Students and engineers of materials science and scientists working in laboratories and industries will find this book invaluable.
The book, although slim, is fairly well self-contained; the only prerequisite is a reasonable familiarity with linear algebra (in particular, manipulation of vectors and tensors) and with the usual differential operators of mathematical physics (gradient, divergence, curl, and Laplacian); the few nonstandard notions are introduced with care. Support material for all parts of the book is found in the final Appendix.
"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 commentaries
Patrick 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.
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.
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.
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 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.
This volume offers an international selection of the most important new results and developments, in 73 papers, grouped in seven different parts, representing a well-balanced overview, and spanning the general theory of machines and mechanisms, through analysis and synthesis of planar and spatial mechanisms, dynamics of machines and mechanisms, linkages and cams, computational mechanics, rotor dynamics, biomechanics, mechatronics, vibration and noise in machines, optimization of mechanisms and machines, control and monitoring systems of machines, accuracy and reliability of machines and mechanisms, robots and manipulators to the mechanisms of textile machines.
A discussion on FEM, formulations and techniques currently in use is followed up by machining case studies. Orthogonal cutting, oblique cutting, 3D simulations for turning and milling, grinding, and state-of-the-art topics such as high speed machining and micromachining are explained with relevant examples. This is all supported by a literature review and a reference list for further study.
As FEM is a key method for researchers in the manufacturing and especially in the machining sector, Finite Element Method in Machining Processes is a key reference for students studying manufacturing processes but also for industry professionals.
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.
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 resources
U 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.
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 Hadoop
Doing 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.
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
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.
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.
Mechanical engineering the book provides good essential reference that can give you an approach on a wide range of aspects related to this engineering subject.
The book written in simple language to describe each topic in a brief manner that offers optimum support to the learners. The book of Mechanical Engineering covers engineering material, material testing, heat engines, IC engines, control, mechanical measurement, machine tools, design, and manufacturing to understand mechanical systems.
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This is the first book on robotics that presents solutions for coupled, decoupled, uncoupled, fully-isotropic and maximally regular robotic manipulators with Schönflies motions systematically generated by using the structural synthesis approach proposed in Part 1. Overconstrained non-redundant/overactuated/redundantly actuated solutions with simple/complex limbs are proposed. Many solutions are presented here for the first time in the literature. The author had to make a difficult and challenging choice between protecting these solutions through patents and releasing them directly into the public domain. The second option was adopted by publishing them in various recent scientific publications and above all in this book. In this way, the author hopes to contribute to a rapid and widespread implementation of these solutions in future industrial products.