This "hands-on" book is for people who are interested in immediately putting Maple to work. The reader is provided with a compact, fast and surveyable guide that introduces them to the extensive capabilities of the software. The book is sufficient for standard use of Maple and will provide techniques for extending Maple for more specialized work. The author discusses the reliability of results systematically and presents ways of testing questionable results. The book allows a reader to become a user almost immediately and helps him/her to grow gradually to a broader and more proficient use. As a consequence, some subjects are dealt with in an introductory way early in the book, with references to a more detailed discussion later on.

This book presents a study of the COVID-19 pandemic using computational social scientific analysis that draws from, and employs, statistics and simulations. Combining approaches in crisis management, risk assessment and mathematical modelling, the work also draws from the philosophy of sacrifice and futurology. It makes an original contribution to the important issue of the stability of society by highlighting two significant factors: the COVID-19 crisis as a catalyst for change and the rise of AI and Big Data in managing society. It also emphasizes the nature and importance of sacrifices and the role of politics in the distribution of sacrifices. The book considers the treatment of AI and Big Data and their use to both "good" and "bad" ends, exposing the inevitability of these tools being used. Relevant to both policymakers and social scientists interested in the influence of AI and Big Data on the structure of society, the book re-evaluates the ways we think of lifestyles, economic systems and the balance of power in tandem with digital transformation.

Geocomputation with R is for people who want to analyze, visualize and model geographic data with open source software. It is based on R, a statistical programming language that has powerful data processing, visualization, and geospatial capabilities. The book equips you with the knowledge and skills to tackle a wide range of issues manifested in geographic data, including those with scientific, societal, and environmental implications. This book will interest people from many backgrounds, especially Geographic Information Systems (GIS) users interested in applying their domain-specific knowledge in a powerful open source language for data science, and R users interested in extending their skills to handle spatial data. The book is divided into three parts: (I) Foundations, aimed at getting you up-to-speed with geographic data in R, (II) extensions, which covers advanced techniques, and (III) applications to real-world problems. The chapters cover progressively more advanced topics, with early chapters providing strong foundations on which the later chapters build. Part I describes the nature of spatial datasets in R and methods for manipulating them. It also covers geographic data import/export and transforming coordinate reference systems. Part II represents methods that build on these foundations. It covers advanced map making (including web mapping), "bridges" to GIS, sharing reproducible code, and how to do cross-validation in the presence of spatial autocorrelation. Part III applies the knowledge gained to tackle real-world problems, including representing and modeling transport systems, finding optimal locations for stores or services, and ecological modeling. Exercises at the end of each chapter give you the skills needed to tackle a range of geospatial problems. Solutions for each chapter and supplementary materials providing extended examples are available at https://geocompr.github.io/geocompkg/articles/. Dr. Robin Lovelace is a University Academic Fellow at the University of Leeds, where he has taught R for geographic research over many years, with a focus on transport systems. Dr. Jakub Nowosad is an Assistant Professor in the Department of Geoinformation at the Adam Mickiewicz University in Poznan, where his focus is on the analysis of large datasets to understand environmental processes. Dr. Jannes Muenchow is a Postdoctoral Researcher in the GIScience Department at the University of Jena, where he develops and teaches a range of geographic methods, with a focus on ecological modeling, statistical geocomputing, and predictive mapping. All three are active developers and work on a number of R packages, including stplanr, sabre, and RQGIS.

This adapted version of CBSD for the Fundamentals Series explores the characteristics of IT-driven business services, their requirements and how to gather the right requirements to improve the service lifecycle throughout design, development and maintenance until decommissioning. By understanding IT-driven business services and anchoring them in a service design statement (SDS), you will be able to accelerate the translation of the needs of the business to the delivery of IT-intensive business services. Product overview CBSD supports portfolio, programme and project management by identifying key questions and structuring the creative process of designing services. Insight into the CBSD approach to deriving an SDS is therefore a practical and powerful tool to help you: - Promote a coherent design so that fundamental issues and requirements of needs are mapped, based on different perspectives between demand and supply; - Gain insight into the dynamics between stakeholders within an enterprise; - Reflect on and formulate a practical and realistic roadmap; and - Guide the development, build, programme management and maintenance of IT-driven business services. CBSD complements existing frameworks such as TOGAF(R), IT4IT, BiSL(R) Next and ITIL(R) by focusing on business architecture, a subject rarely discussed before designing an IT-intensive, complex business service. Who should read this book This book is intended for anyone responsible for designing and implementing IT-driven services or involved in their operation. This includes: - Internal and external service providers, such as service managers, contract managers, bid managers, lead architects and requirement analysts; - Business, financial, sales, marketing and operations managers who are responsible for output and outcome; - Sales and product managers who need to present and improve service offerings; - Developers who need to develop new and improved services; - Contract managers and those responsible for purchasing; and - Consultants, strategists, business managers, business process owners, business architects, business information managers, chief information officers, information systems owners and information architects. Collaborative Business Design: The Fundamentals is part of the Fundamentals Series. Authors Brian Johnson has published more than 30 books, including a dozen official titles in the IT Infrastructure Library (ITIL), all of which are used worldwide. He designed and led the programme for ITIL version 2. He has fulfilled many roles during his career, including vice president, chief architect, senior director and executive consultant. One of his current roles is chief architect at the ASL BiSL Foundation, which provides guidance on business information management to a wide range of public and private-sector businesses in the Benelux region. Brian is chief architect for the redesign of all guidance and is the author of new strategic publications. Leon-Paul de Rouw studied technical management and organisation sociology. He worked for several years as a consultant and researcher in the private sector. Since 2003, he has been a programme manager with the central government in the Netherlands. He is responsible for all types of projects and programmes that focus on business enabled by IT.

This book introduces readers to various signal processing models that have been used in analyzing periodic data, and discusses the statistical and computational methods involved. Signal processing can broadly be considered to be the recovery of information from physical observations. The received signals are usually disturbed by thermal, electrical, atmospheric or intentional interferences, and due to their random nature, statistical techniques play an important role in their analysis. Statistics is also used in the formulation of appropriate models to describe the behavior of systems, the development of appropriate techniques for estimation of model parameters and the assessment of the model performances. Analyzing different real-world data sets to illustrate how different models can be used in practice, and highlighting open problems for future research, the book is a valuable resource for senior undergraduate and graduate students specializing in mathematics or statistics.

This book is a result of a workshop, the 8th of the successful TopoInVis workshop series, held in 2019 in Nykoeping, Sweden. The workshop regularly gathers some of the world's leading experts in this field. Thereby, it provides a forum for discussions on the latest advances in the field with a focus on finding practical solutions to open problems in topological data analysis for visualization. The contributions provide introductory and novel research articles including new concepts for the analysis of multivariate and time-dependent data, robust computational approaches for the extraction and approximations of topological structures with theoretical guarantees, and applications of topological scalar and vector field analysis for visualization. The applications span a wide range of scientific areas comprising climate science, material sciences, fluid dynamics, and astronomy. In addition, community efforts with respect to joint software development are reported and discussed.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

The Definitive Guide to Interwoven TeamSite is the first comprehensive book on this enterprise-level content management system, guiding the reader through the product Architecture, key features, and a detailed implementation of the product by way of a case-study involving a hypothetical financial services firm. Along the way, the authors share key development and deployment principles gained throughout their several years of working in enterprise CMS environments.

Divided into five parts, the material is presented in much the same way one might expect when considering a large-scale CMS project: Introduction, Inception, Elaboration, Construction, and Transition. Each part introduces the concepts and TeamSite features readers will need to understand in order to carry out that aspect of the project. Complete with a working implementation and numerous visual guides, the authors painstakingly layout the project process. Finally an Epilogue which discusses future product releases.

Learn Management Information Systems YOUR Way with MIS!

MIS’s easy-reference, paperback textbook presents course content through visually-engaging chapters as well as Chapter Review Cards that consolidate the best review material into a ready-made study tool. With the textbook or on its own, MIS Online allows easy exploration of MIS anywhere, anytime - including on your device! Collect your notes and create StudyBits™ from interactive content as you go to remember what’s important. Then, either use preset study resources, or personalize the product through easy-to-use tags and filters to prioritize your study time. Make and review flashcards, review related content, and track your progress with Concept Tracker, all in one place and at an affordable price!

New to this edition:

• NEW 4LTR PRESS ONLINE: Created through a deep investigation of students' challenges and workflows, MIS Online lets students study how and when they want−including on their devices!
• With the unique StudyBits™ functionality, students easily collect notes and create StudyBits™ from interactive content to collect what’s important. And intuitive tagging and filtering options helps students maximize their study efforts as they make flashcards, take practice quizzes, view related content, and track their progress all in one place!
• Coupled with straightforward course management, assessment, and analytics for instructors, MIS with MIS Online engages students of all generations and learning styles, and integrates seamlessly into your course, setting the stage for thinking critically about Management Information Systems.

Statistics is made simple with this award-winning guide to using R and applied statistical methods. With a clear step-by-step approach explained using real world examples, learn the practical skills you need to use statistical methods in your research from an expert with over 30 years of teaching experience. With a wealth of hands-on exercises and online resources created by the author, practice your skills using the data sets and R scripts from the book with detailed screencasts that accompany each script. This book is ideal for anyone looking to: * Complete an introductory course in statistics * Prepare for more advanced statistical courses * Gain the transferable analytical skills needed to interpret research from across the social sciences * Learn the technical skills needed to present data visually * Acquire a basic competence in the use of R and RStudio. This edition also includes a gentle introduction to Bayesian methods integrated throughout. The author has created a wide range of online resources, including: over 90 R scripts, 36 datasets, 37 screen casts, complete solutions for all exercises, and 130 multiple-choice questions to test your knowledge.

Understanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways. Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed "just-in-time" within chapters Simple mathematical explanations ("baby proofs") of key concepts Clear explanations and applications of statistical significance (p-values), incorporating the American Statistical Association guidelines Use of "data-generating process" terminology rather than "population" Random-X framework is assumed throughout (the fixed-X case is presented as a special case of the random-X case) Clear explanations of probabilistic modelling, including likelihood-based methods Use of simulations throughout to explain concepts and to perform data analyses This book has a strong orientation towards science in general, as well as chapter-review and self-study questions, so it can be used as a textbook for research-oriented students in the social, biological and medical, and physical and engineering sciences. As well, its mathematical emphasis makes it ideal for a text in mathematics and statistics courses. With its numerous worked examples, it is also ideally suited to be a reference book for all scientists.

A comprehensive guide to automated statistical data cleaning The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy. Key features: Focuses on the automation of data cleaning methods, including both theory and applications written in R. Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis. Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring. Supported by an accompanying website featuring data and R code. This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.

Continuous improvements in digitized practices have created opportunities for businesses to develop more streamlined processes. This not only leads to higher success in day-to-day production, but it increases the overall success of businesses. Always-On Enterprise Information Systems for Modern Organizations is a critical scholarly resource that examines how EIS implementations support business processes and facilitate this in today's e-business environment. Featuring coverage on a broad range of topics such as customer relations management, supply chain management, and business intelligence, this book is geared towards professionals, researchers, managers, consultants, and university students interested in emerging developments for business process management.

This book provides comprehensive guidance on leveraging SAP IBP technology to connect strategic (to be understood as long term SC&O), tactical and operational planning into one coherent process framework, presenting experience shared by practitioners in workshops, customer presentations, business, and IT transformation projects. It offers use cases and a wealth of practical tips to ensure that readers understand the challenges and advantages of IBP implementation. The book starts by characterizing disconnected planning and contrasting this with key elements of a transformation project approach. It explains the functional foundations and SAP Hybris, Trade Promotion Planning, Customer Business Planning, ARIBA, and S/4 integration with SAP IBP. It then presents process for integrating finance in IBP. Annual planning and monthly planning are taken as examples of explain Long term planning (in some companies labeled as strategic). The core of the book is about sales and operations planning (S&OP) and its process steps, product demand, supply review, integrated reconciliation and management business review, illustrating all steps with use cases. It describes unconstrained and constrained optimized supply planning, inventory optimization, shelf life planning. We explain how to improve responsiveness with order-based allocation planning, sales order confirmation, and big deal / tender management coupled with simultaneous re-planning of supply. The book closes with a chapter on performance measurement, measurement of effectiveness, efficiency, and adherence.

This book gathers a selection of invited and contributed lectures from the European Conference on Numerical Mathematics and Advanced Applications (ENUMATH) held in Lausanne, Switzerland, August 26-30, 2013. It provides an overview of recent developments in numerical analysis, computational mathematics and applications from leading experts in the field. New results on finite element methods, multiscale methods, numerical linear algebra and discretization techniques for fluid mechanics and optics are presented. As such, the book offers a valuable resource for a wide range of readers looking for a state-of-the-art overview of advanced techniques, algorithms and results in numerical mathematics and scientific computing.

Discover how SAP S/4HANA transforms your supply chain! Explore functionalities for sourcing and procurement, production execution, plant maintenance, sales order management, transportation management, warehouse management, and more. See how intelligent technologies elevate your logistics operations with SAP Business Technology Platform and learn about complementary cloud solutions like SAP Ariba and SAP IBP. This is your starting point for logistics with SAP S/4HANA!In this book, you'll learn about: a. Key Functionality See what SAP S/4HANA 2021 has to offer! Walk through your logistics business processes, from production planning to inventory valuation and beyond. Learn about new features such as predictive MRP, centralized procurement, and production engineering and operations. b. Logistics Innovations Your supply chain is getting smarter. Discover intelligent technologies enabled by SAP BTP: blockchain, intelligent robotic process automation, machine learning, and more. c. Planning Your Migration Prepare for your logistics transformation. Plan your roadmap to SAP S/4HANA, evaluate your implementation approaches, and get insight into the new RISE with SAP offering. Highlights include: 1) Planning and scheduling 2) Sourcing and procurement 3) Manufacturing operations 4) Quality management 5) Plant maintenance 6) Sales order management 7) Transportation management 8) Inventory management 9) Warehouse management 10) Intelligent technologies 11) Reporting and analytics 12) Industry use cases

Statistical Analysis of Questionnaires: A Unified Approach Based on R and Stata presents special statistical methods for analyzing data collected by questionnaires. The book takes an applied approach to testing and measurement tasks, mirroring the growing use of statistical methods and software in education, psychology, sociology, and other fields. It is suitable for graduate students in applied statistics and psychometrics and practitioners in education, health, and marketing. The book covers the foundations of classical test theory (CTT), test reliability, validity, and scaling as well as item response theory (IRT) fundamentals and IRT for dichotomous and polytomous items. The authors explore the latest IRT extensions, such as IRT models with covariates, multidimensional IRT models, IRT models for hierarchical and longitudinal data, and latent class IRT models. They also describe estimation methods and diagnostics, including graphical diagnostic tools, parametric and nonparametric tests, and differential item functioning. Stata and R software codes are included for each method. To enhance comprehension, the book employs real datasets in the examples and illustrates the software outputs in detail. The datasets are available on the authors' web page.

Straightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: * Original case studies and data sets * Practical exercises and lists of commands for each chapter * Downloadable Stata programmes created to work alongside chapters * A wide range of detailed applications using Stata * Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis.

What happens when a researcher and a practitioner spend hours crammed in a Fiat discussing data visualization? Beyond creating beautiful charts, they found greater richness in the craft as an integrated whole. Drawing from their unconventional backgrounds, these two women take readers through a journey around perception, semantics, and intent as the triad that influences visualization. This visually engaging book blends ideas from theory, academia, and practice to craft beautiful, yet meaningful visualizations and dashboards. How do you take your visualization skills to the next level? The book is perfect for analysts, research and data scientists, journalists, and business professionals. Functional Aesthetics for Data Visualization is also an indispensable resource for just about anyone curious about seeing and understanding data. Think of it as a coffee book for the data geek in you. https: //www.functionalaestheticsbook.com

This book covers all the topics found in introductory descriptive statistics courses, including simple linear regression and time series analysis, the fundamentals of inferential statistics (probability theory, random sampling and estimation theory), and inferential statistics itself (confidence intervals, testing). Each chapter starts with the necessary theoretical background, which is followed by a variety of examples. The core examples are based on the content of the respective chapter, while the advanced examples, designed to deepen students' knowledge, also draw on information and material from previous chapters. The enhanced online version helps students grasp the complexity and the practical relevance of statistical analysis through interactive examples and is suitable for undergraduate and graduate students taking their first statistics courses, as well as for undergraduate students in non-mathematical fields, e.g. economics, the social sciences etc.

For the engineering and scientific professional, A Physicist's Guide to Mathematica, 2/e provides an updated reference guide based on the 2007 new 6.0 release, providing an organized and integrated desk reference with step by step instructions for the most often used features of the software as it applies to research in physics.
For Professors teaching physics and other science courses using the Mathematica software, A Physicist's Guide to Mathematica, 2/e is the only fully compatible (new software release) Mathematica text that engages students by providing complete topic coverage, new applications, exercises and examples that enable the user to solve a wide range of physics problems.
- Does not require prior knowledge of Mathematica or computer programming
- Can be used as either a primary or supplemental text for upper-division physics majors and an Instructor's Solutions Manual is available
- Provides over 450 end-of-section exercises and end-of-chapter problems
- Serves as a reference suitable for chemists, physical scientists, and engineers
- Compatible with Mathematica Version 6, a recent major release
- Compact disk contains all of the Mathematica input and output in this book