The 2nd edition of R for Marketing Research and Analytics continues to be the best place to learn R for marketing research. This book is a complete introduction to the power of R for marketing research practitioners. The text describes statistical models from a conceptual point of view with a minimal amount of mathematics, presuming only an introductory knowledge of statistics. Hands-on chapters accelerate the learning curve by asking readers to interact with R from the beginning. Core topics include the R language, basic statistics, linear modeling, and data visualization, which is presented throughout as an integral part of analysis. Later chapters cover more advanced topics yet are intended to be approachable for all analysts. These sections examine logistic regression, customer segmentation, hierarchical linear modeling, market basket analysis, structural equation modeling, and conjoint analysis in R. The text uniquely presents Bayesian models with a minimally complex approach, demonstrating and explaining Bayesian methods alongside traditional analyses for analysis of variance, linear models, and metric and choice-based conjoint analysis. With its emphasis on data visualization, model assessment, and development of statistical intuition, this book provides guidance for any analyst looking to develop or improve skills in R for marketing applications. The 2nd edition increases the book's utility for students and instructors with the inclusion of exercises and classroom slides. At the same time, it retains all of the features that make it a vital resource for practitioners: non-mathematical exposition, examples modeled on real world marketing problems, intuitive guidance on research methods, and immediately applicable code.

This book discusses the latest advances in algorithms for symbolic summation, factorization, symbolic-numeric linear algebra and linear functional equations. It presents a collection of papers on original research topics from the Waterloo Workshop on Computer Algebra (WWCA-2016), a satellite workshop of the International Symposium on Symbolic and Algebraic Computation (ISSAC'2016), which was held at Wilfrid Laurier University (Waterloo, Ontario, Canada) on July 23-24, 2016. This workshop and the resulting book celebrate the 70th birthday of Sergei Abramov (Dorodnicyn Computing Centre of the Russian Academy of Sciences, Moscow), whose highly regarded and inspirational contributions to symbolic methods have become a crucial benchmark of computer algebra and have been broadly adopted by many Computer Algebra systems.

This textbook is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning. With careful treatment of topics that fill the curricular needs for the course, Probability and Statistics for Computer Science features: * A treatment of random variables and expectations dealing primarily with the discrete case. * A practical treatment of simulation, showing how many interesting probabilities and expectations can be extracted, with particular emphasis on Markov chains. * A clear but crisp account of simple point inference strategies (maximum likelihood; Bayesian inference) in simple contexts. This is extended to cover some confidence intervals, samples and populations for random sampling with replacement, and the simplest hypothesis testing. * A chapter dealing with classification, explaining why it's useful; how to train SVM classifiers with stochastic gradient descent; and how to use implementations of more advanced methods such as random forests and nearest neighbors. * A chapter dealing with regression, explaining how to set up, use and understand linear regression and nearest neighbors regression in practical problems. * A chapter dealing with principal components analysis, developing intuition carefully, and including numerous practical examples. There is a brief description of multivariate scaling via principal coordinate analysis. * A chapter dealing with clustering via agglomerative methods and k-means, showing how to build vector quantized features for complex signals. Illustrated throughout, each main chapter includes many worked examples and other pedagogical elements such as boxed Procedures, Definitions, Useful Facts, and Remember This (short tips). Problems and Programming Exercises are at the end of each chapter, with a summary of what the reader should know. Instructor resources include a full set of model solutions for all problems, and an Instructor's Manual with accompanying presentation slides.

Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with numerous high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in detail, and their application is carefully demonstrated in a chapter dedicated to concrete use cases. With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis for lecturers, students and researchers.

The introduction of digital applications into businesses has revolutionized the way employees and managers carry out their jobs while also benefiting them socially. Smartphone and App Implementations that Improve Productivity looks at the benefits of apps in the workplace and introduces academic perspectives that link prospective advantages with practical commercial examples. The analysis is structured into chapters that include real world application while at the same time critically assess implied benefits of the new app technology and draw out the main findings and conclusions. Tahir M. Nisar brings into focus the emerging role of digital applications and big data in enterprise decision making. Readers will learn how companies can achieve more efficiency and effectiveness in their business operations through new types of organizational design strategies and mechanisms of employee mobility and work-life balance that draw on digital apps.

This book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.

This book provides practical applications of doubly classified models by using R syntax to generate the models. It also presents these models in symbolic tables so as to cater to those who are not mathematically inclined, while numerous examples throughout the book illustrate the concepts and their applications. For those who are not aware of this modeling approach, it serves as a good starting point to acquire a basic understanding of doubly classified models. It is also a valuable resource for academics, postgraduate students, undergraduates, data analysts and researchers who are interested in examining square contingency tables.

This book systematically addresses the design and analysis of efficient techniques for independent random sampling. Both general-purpose approaches, which can be used to generate samples from arbitrary probability distributions, and tailored techniques, designed to efficiently address common real-world practical problems, are introduced and discussed in detail. In turn, the monograph presents fundamental results and methodologies in the field, elaborating and developing them into the latest techniques. The theory and methods are illustrated with a varied collection of examples, which are discussed in detail in the text and supplemented with ready-to-run computer code. The main problem addressed in the book is how to generate independent random samples from an arbitrary probability distribution with the weakest possible constraints or assumptions in a form suitable for practical implementation. The authors review the fundamental results and methods in the field, address the latest methods, and emphasize the links and interplay between ostensibly diverse techniques.

Die Grenzen zwischen Sach- und Dienstleistung verschwimmen zunehmend - Produkte sind in der Regel hybrid. Sie sind als kundenorientierte Problemloesungen zu interpretieren, die durch das Schnuren von Leistungsbundeln aus Sach- und Dienstleistungskomponenten entstehen. UEber die Wettbewerbsvorteile und den oekonomischen Nutzen von hybriden Produkten herrscht weitgehend Einigkeit. Dennoch existieren kaum Ansatze zur integrierten Entwicklung von Sach- und Dienstleistungen. Die Tatsache, dass der wirtschaftliche Erfolg eines Leistungsbundels massgeblich von dessen Konzeption und kundenindividueller Gestaltung abhangt, wird damit vernachlassigt. In diesem Band werden neue Methoden zur Gestaltung hybrider Produkte vorgestellt. Dabei werden sowohl aktuelle Problemstellungen und Loesungsansatze als auch zukunftige Entwicklungsperspektiven betrachtet. Die einzelnen Kapitel fokussieren einerseits die Produktion und den Absatz hybrider Produkte und andererseits Informationssysteme, welche die Produktion und den Absatz hybrider Produkte unterstutzen. Es wird dem Grundgedanken gefolgt, dass die Erfullung einer Kundenanforderung nicht von vorneherein entweder an eine Sach- oder eine Dienstleistungskomponente geknupft ist - diese Zuordnung soll sich erst wahrend des Entwicklungsprozesses eines hybriden Produkts ergeben. Als "roter Faden" dient ein durchgangiges Fallbeispiel aus der technischen Gebrauchsguterbranche, bei dem die Nutzung mobiler Endgerate zu Verbesserungen in der Erbringung technischer Kundendienstleistungen fuhrt.

This second edition is an intensively revised and updated version of the book MATLAB (R) and Design Recipes for Earth Sciences. It aims to introduce students to the typical course followed by a data analysis project in earth sciences. A project usually involves searching relevant literature, reviewing and ranking published books and journal articles, extracting relevant information from the literature in the form of text, data, or graphs, searching and processing the relevant original data using MATLAB, and compiling and presenting the results as posters, abstracts, and oral presentations using graphics design software. The text of this book includes numerous examples on the use of internet resources, on the visualization of data with MATLAB, and on preparing scientific presentations. As with the book MATLAB Recipes for Earth Sciences-4rd Edition (2015), which demonstrates the use of statistical and numerical methods on earth science data, this book uses state-of-the art software packages, including MATLAB and the Adobe Creative Suite, to process and present geoscientific information collected during the course of an earth science project. The book's supplementary electronic material (available online through the publisher's website) includes color versions of all figures, recipes with all the MATLAB commands featured in the book, the example data, exported MATLAB graphics, and screenshots of the most important steps involved in processing the graphics.

This book presents various recently developed and traditional statistical techniques, which are increasingly being applied in social science research. The social sciences cover diverse phenomena arising in society, the economy and the environment, some of which are too complex to allow concrete statements; some cannot be defined by direct observations or measurements; some are culture- (or region-) specific, while others are generic and common. Statistics, being a scientific method - as distinct from a 'science' related to any one type of phenomena - is used to make inductive inferences regarding various phenomena. The book addresses both qualitative and quantitative research (a combination of which is essential in social science research) and offers valuable supplementary reading at an advanced level for researchers.

The quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues. "Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald "WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book." - Leighton Cardwell, Technical Director, WSP. "From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to "talk" to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds." - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd "Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources". - Yaron Hollander, author of "Transport Modelling for a Complete Beginner"

This book includes a wide selection of the papers presented at the 48th Scientific Meeting of the Italian Statistical Society (SIS2016), held in Salerno on 8-10 June 2016. Covering a wide variety of topics ranging from modern data sources and survey design issues to measuring sustainable development, it provides a comprehensive overview of the current Italian scientific research in the fields of open data and big data in public administration and official statistics, survey sampling, ordinal and symbolic data, statistical models and methods for network data, time series forecasting, spatial analysis, environmental statistics, economic and financial data analysis, statistics in the education system, and sustainable development. Intended for researchers interested in theoretical and empirical issues, this volume provides interesting starting points for further research.

It's finally here: your all-in-one guide to Employee Central Payroll! From step-by-step instructions for configuring wages, personal data, pay statements (and more!) to guidance on running your payroll and reporting on system operations, this book has it all. You'll walk through master data integration with Employee Central and master every aspect of the Payroll Control Center. Whether you're new to SAP SuccessFactors or adding to your existing cloud HR landscape, this is the book for you!Highlights include:1) Payroll Control Center2) Wage types3) Compensation data4) Pay statements5) System provisioning6) Master data integration7) Master data replication8) Single sign-on9) Monitoring and troubleshooting10) Integration11) Project planning

This textbook addresses postgraduate students in applied mathematics, probability, and statistics, as well as computer scientists, biologists, physicists and economists, who are seeking a rigorous introduction to applied stochastic processes. Pursuing a pedagogic approach, the content follows a path of increasing complexity, from the simplest random sequences to the advanced stochastic processes. Illustrations are provided from many applied fields, together with connections to ergodic theory, information theory, reliability and insurance. The main content is also complemented by a wealth of examples and exercises with solutions.

This book explores inductive inference using the minimum message length (MML) principle, a Bayesian method which is a realisation of Ockham's Razor based on information theory. Accompanied by a library of software, the book can assist an applications programmer, student or researcher in the fields of data analysis and machine learning to write computer programs based upon this principle. MML inference has been around for 50 years and yet only one highly technical book has been written about the subject. The majority of research in the field has been backed by specialised one-off programs but this book includes a library of general MML-based software, in Java. The Java source code is available under the GNU GPL open-source license. The software library is documented using Javadoc which produces extensive cross referenced HTML manual pages. Every probability distribution and statistical model that is described in the book is implemented and documented in the software library. The library may contain a component that directly solves a reader's inference problem, or contain components that can be put together to solve the problem, or provide a standard interface under which a new component can be written to solve the problem. This book will be of interest to application developers in the fields of machine learning and statistics as well as academics, postdocs, programmers and data scientists. It could also be used by third year or fourth year undergraduate or postgraduate students.

This book expands the foundations of general systems theory to enable progress beyond the rich heuristic practices available today. It establishes a foundational framework for the development of scientific transdisciplinary systems principles and shows how these can amplify the potential of individuals and teams working in multi-, inter- and transdisciplinary contexts or striving to translate their progress across disciplinary boundaries. Three general scientific systems principles are presented, and their relevance to the design, analysis, management and transformation of systems is explored. Applying lessons from the history and philosophy science, this book disambiguates key concepts of general systemology, clarifies the role of general systemology within the field of systemology, and explains how general systemology supports other forms of transdisciplinarity. These insights are used to develop new perspectives, strategies and tools for addressing long-standing challenges to the advancement and transdisciplinary application of general insights into the nature of complex systems.The material presented in this book includes comprehensive models of the structure of systemology as a disciplinary field, the structure and significance of the general systems worldview, and the role of general systemology as the heart of systems science, systems engineering and systems practice. It explains what a fully-fledged general theory of systems would look like, what its potential is, what routes are available to us to develop it further, and how to leverage the knowledge we have attained so far. Many examples and analogies show how general systemology has the potential to enable scientific discovery, insightful theory building, and practical innovation in all the disciplines as they study, design, nurture or transform complex systems. This book is essential reading for anyone wishing to master the concepts, terminology, models and strategies needed to make effective use of current general systems knowledge and to engage in the further development of the philosophy, science, and practice of general systemology.

Innovationen entziehen sich immer deutlicher einer technologisch verkurzten Sichtweise. Angesichts der gestiegenen Dynamik und Komplexitat der Umfeldbedingungen fur Unternehmen kommen der Fruherkennung und dem Monitoring technologischer, wirtschaftlicher, politischer und gesellschaftlicher Entwicklungen immer grossere Bedeutung fur den Innovationserfolg zu. Dies gilt besonders fur dynamische Technologiefelder, wie dem Pervasive Computing. Die fortschreitende Nutzung des Internets, die Einbettung von Elektronikkomponenten in Alltagsgegenstande und der Trend zur standigen Vernetzung ( Always on - Anywhere & Anytime") sind unubersehbar. Die vorliegende Arbeit ermittelt okologische Veranderungspotenziale und geht der Frage nach, wie Nachhaltigkeitsanforderungen bereits fruhzeitig in Innovationsprozesse einbezogen werden konnen. Mit Blick auf diese Aufgabe wurde das Konzept der Integrierten Technologie-Roadmap" entwickelt. Mit der Integrierten Technologie-Roadmap wird es moglich, mehrere Dimensionen zukunftsfahigen Wirtschaftens in dynamischen Technologiefeldern simultan zu betrachten. Sie fragt nach technologischen Beitragen zur Bewaltigung von gesellschaftlichen, okonomischen, politischen und okologischen Herausforderungen und ruckt die Sicht der Anwender in den Mittelpunkt. Beides hilft Unsicherheiten bei Technologieentwicklung, Markteinfuhrung und Geschaftsmodellen zu minimieren und die Richtungssicherheit zu erhohen."

Mit der Globalisierung ruckt die Logistik als Wettbewerbsfaktor fur Produktionsunternehmen zunehmend in den Vordergrund. In dem Band wird erstmals ein ganzheitlicher, sozio-technischer Ansatz fur das Auftragsmanagement in der industriellen Produktion vorgestellt, der die Wechselwirkungen zwischen Systemfunktionen, Nutzern und der Organisation berucksichtigt. Unter Einbeziehung dieser Aspekte wird die Gestaltung und Einfuhrung eines Auftragsmanagement-Systems nachvollziehbar beschrieben und durch zahlreiche Bilder und Praxisbeispiele veranschaulicht."

The quick way to learn Microsoft Project 2019! This is learning made easy. Get more done quickly with Microsoft Project 2019. Jump in wherever you need answers-brisk lessons and informative screenshots show you exactly what to do, step by step. Other Project users will want to grab this book as well. Quickly start new plans, build task lists, and assign resources View resource capacity and track progress Capture and fine-tune work and cost details Visualize schedules with Gantt charts and other views and reports Consolidate projects, and share resources across plans Manage modern Agile projects (James Mills, Jr., contributor) Customize Project to maximize your efficiency Leverage improvements to task linking, timelines, and accessibility Master PM best practices while you learn Project Look up just the tasks and lessons you need

This volume constitutes the proceedings of the 11th IFIP WG 8.1 Conference on the Practice of Enterprise Modeling held in October/November 12018 in Vienna, Austria. The conference was created by the International Federation for Information Processing (IFIP) Working Group 8.1 to offer a forum for knowledge transfer and experience sharing between the academic and practitioner communities. The 21 full papers and 5 short papers accepted were carefully reviewed and selected from 64 submissions. They are grouped by the following topics: business process modeling, model derivation; collaboration modeling; reviews and analyses of modeling methods; semantics and reasoning, experience reports; and teaching challenges.

This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years; computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method. Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology. Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.

This book focuses on the methodological treatment of UML/P and addresses three core topics of model-based software development: code generation, the systematic testing of programs using a model-based definition of test cases, and the evolutionary refactoring and transformation of models. For each of these topics, it first details the foundational concepts and techniques, and then presents their application with UML/P. This separation between basic principles and applications makes the content more accessible and allows the reader to transfer this knowledge directly to other model-based approaches and languages. After an introduction to the book and its primary goals in Chapter 1, Chapter 2 outlines an agile UML-based approach using UML/P as the primary development language for creating executable models, generating code from the models, designing test cases, and planning iterative evolution through refactoring. In the interest of completeness, Chapter 3 provides a brief summary of UML/P, which is used throughout the book. Next, Chapters 4 and 5 discuss core techniques for code generation, addressing the architecture of a code generator and methods for controlling it, as well as the suitability of UML/P notations for test or product code. Chapters 6 and 7 then discuss general concepts for testing software as well as the special features which arise due to the use of UML/P. Chapter 8 details test patterns to show how to use UML/P diagrams to define test cases and emphasizes in particular the use of functional tests for distributed and concurrent software systems. In closing, Chapters 9 and 10 examine techniques for transforming models and code and thus provide a solid foundation for refactoring as a type of transformation that preserves semantics. Overall, this book will be of great benefit for practical software development, for academic training in the field of Software Engineering, and for research in the area of model-based software development. Practitioners will learn how to use modern model-based techniques to improve the production of code and thus significantly increase quality. Students will find both important scientific basics as well as direct applications of the techniques presented. And last but not least, the book will offer scientists a comprehensive overview of the current state of development in the three core topics it covers.

This book presents a variant of UML that is especially suitable for agile development of high-quality software. It adjusts the language UML profile, called UML/P, for optimal assistance for the design, implementation, and agile evolution to facilitate its use especially in agile, yet model based development methods for data intensive or control driven systems. After a general introduction to UML and the choices made in the development of UML/P in Chapter 1, Chapter 2 includes a definition of the language elements of class diagrams and their forms of use as views and representations. Next, Chapter 3 introduces the design and semantic facets of the Object Constraint Language (OCL), which is conceptually improved and syntactically adjusted to Java for better comfort. Subsequently, Chapter 4 introduces object diagrams as an independent, exemplary notation in UML/P, and Chapter 5 offers a detailed introduction to UML/P Statecharts. Lastly, Chapter 6 presents a simplified form of sequence diagrams for exemplary descriptions of object interactions. For completeness, appendixes A-C describe the full syntax of UML/P, and appendix D explains a sample application from the E-commerce domain, which is used in all chapters. This book is ideal for introductory courses for students and practitioners alike.

This book is a valuable read for a diverse group of researchers and practitioners who analyze assessment data and construct test instruments. It focuses on the use of classical test theory (CTT) and item response theory (IRT), which are often required in the fields of psychology (e.g. for measuring psychological traits), health (e.g. for measuring the severity of disorders), and education (e.g. for measuring student performance), and makes these analytical tools accessible to a broader audience. Having taught assessment subjects to students from diverse backgrounds for a number of years, the three authors have a wealth of experience in presenting educational measurement topics, in-depth concepts and applications in an accessible format. As such, the book addresses the needs of readers who use CTT and IRT in their work but do not necessarily have an extensive mathematical background. The book also sheds light on common misconceptions in applying measurement models, and presents an integrated approach to different measurement methods, such as contrasting CTT with IRT and multidimensional IRT models with unidimensional IRT models. Wherever possible, comparisons between models are explicitly made. In addition, the book discusses concepts for test equating and differential item functioning, as well as Bayesian IRT models and plausible values using simple examples. This book can serve as a textbook for introductory courses on educational measurement, as supplementary reading for advanced courses, or as a valuable reference guide for researchers interested in analyzing student assessment data.