Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

This book depicts a wide range of situations in which there exist finite form representations for the Meijer G and the Fox H functions. Accordingly, it will be of interest to researchers and graduate students who, when implementing likelihood ratio tests in multivariate analysis, would like to know if there exists an explicit manageable finite form for the distribution of the test statistics. In these cases, both the exact quantiles and the exact p-values of the likelihood ratio tests can be computed quickly and efficiently. The test statistics in question range from common ones, such as those used to test e.g. the equality of means or the independence of blocks of variables in real or complex normally distributed random vectors; to far more elaborate tests on the structure of covariance matrices and equality of mean vectors. The book also provides computational modules in Mathematica (R), MAXIMA and R, which allow readers to easily implement, plot and compute the distributions of any of these statistics, or any other statistics that fit into the general paradigm described here.

This textbook offers an easy-to-follow, practical guide to modern data analysis using the programming language R. The chapters cover topics such as the fundamentals of programming in R, data collection and preprocessing, including web scraping, data visualization, and statistical methods, including multivariate analysis, and feature exercises at the end of each section. The text requires only basic statistics skills, as it strikes a balance between statistical and mathematical understanding and implementation in R, with a special emphasis on reproducible examples and real-world applications. This textbook is primarily intended for undergraduate students of mathematics, statistics, physics, economics, finance and business who are pursuing a career in data analytics. It will be equally valuable for master students of data science and industry professionals who want to conduct data analyses.

This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.

This book sheds light on cross-industry and industry-specific trends in today's digital economy. Prepared by a group of international researchers, experts and practitioners under the auspices of SAP's Digital Thought Leadership & Enablement team within SAP's Business Transformation Services (BTS) unit, the book furthermore presents relevant use cases in digital transformation and innovation. The book argues that breakthrough technologies have matured and hit scale together, enabling five defining trends: hyper-connectivity, supercomputing, cloud computing, a smarter world, and cyber security. It presents in detail how companies are now reimagining their products and services, business models and processes, showcasing how every business today is a digital business. Digitalization, defined as the process of moving to a digital business, is no longer a choice but an imperative for all businesses across all industries and regions. Taking a step toward becoming a digital enterprise is demanding and challenging. The dimensions of customer centricity, leadership and strategy, business models, including offerings (products and services), processes, structure and governance, people and skills, culture, and technology foundation can serve as orientation for digitalization. The articles in this book touch on all dimensions of this digital innovation and transformation framework and offer possible answers to some of the pressing questions that arise when practitioners seek to digitalize their business.

The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.

Understand ABAP objects-the object-oriented extension of the SAP language ABAP-in the latest release of SAP NetWeaver 7.5, and its newest advancements. This book begins with the programming of objects in general and the basics of the ABAP language that a developer needs to know to get started. The most important topics needed to perform daily support jobs and ensure successful projects are covered. ABAP is a vast community with developers working in a variety of functional areas. You will be able to apply the concepts in this book to your area. SAP ABAP Objects is goal directed, rather than a collection of theoretical topics. It doesn't just touch on the surface of ABAP objects, but goes in depth from building the basic foundation (e.g., classes and objects created locally and globally) to the intermediary areas (e.g., ALV programming, method chaining, polymorphism, simple and nested interfaces), and then finally into the advanced topics (e.g., shared memory, persistent objects). You will know how to use best practices to make better programs via ABAP objects. What You'll Learn Know the latest advancements in ABAP objects with the new SAP Netweaver system Understand object-oriented ABAP classes and their components Use object creation and instance-methods calls Be familiar with the functions of the global class builder Be exposed to advanced topics Incorporate best practices for making object-oriented ABAP programs Who This Book Is For ABAP developers, ABAP programming analysts, and junior ABAP developers. Included are: ABAP developers for all modules of SAP, both new learners and developers with some experience or little programming experience in general; students studying ABAP at the college/university level; senior non-ABAP programmers with considerable experience who are willing to switch to SAP/ABAP; and any functional consultants who want or have recently switched to ABAP technical.

This book focuses on solving optimization problems with MATLAB. Descriptions and solutions of nonlinear equations of any form are studied first. Focuses are made on the solutions of various types of optimization problems, including unconstrained and constrained optimizations, mixed integer, multiobjective and dynamic programming problems. Comparative studies and conclusions on intelligent global solvers are also provided.

The book is aimed at Project Management Professionals who are casual or new users and understand the software basics but require a short and snappy guide. It is the sort of book that may be read without a computer on the bus, train or plane. This book quickly gets down to the issues that many people grapple with when trying to use some of the more advanced features of the software and enlightens readers on the traps that some users fall into and how to avoid them. It demonstrates how the software ticks and explains some tricks that may be used to become more productive with the software and generate better schedules. Suitable for people who understand the basics of Microsoft Project but want a short guide to give them insight into the less intuitive features of the software. It is packed with screen shots, constructive tips and is written in plain English. The book is based on the Microsoft Project 365 and 2021 but may be used with earlier versions of Microsoft Project as this book points out the differences where appropriate. The book picks out many of the key aspects from the author's exiting books and adds a substantial amount of new and original text to produce a pocket guide that omits describing the intuitive and obvious functions and concentrates on the issues that many users get stuck on or find hard to understand.

This book presents fundamentals in MATLAB programming, including data and statement structures, control structures, function writing and bugging in MATLAB programming, followed by the presentations of algebraic computation, transcendental function evaluations and data processing. Advanced topics such as MATLAB interfacing, object-oriented programming and graphical user interface design are also addressed.

This book focuses the solutions of linear algebra and matrix analysis problems, with the exclusive use of MATLAB. The topics include representations, fundamental analysis, transformations of matrices, matrix equation solutions as well as matrix functions. Attempts on matrix and linear algebra applications are also explored.

This book focuses on solving practical problems in calculus with MATLAB. Descriptions and sketching of functions and sequences are introduced first, followed by the analytical solutions of limit, differentiation, integral and function approximation problems of univariate and multivariate functions. Advanced topics such as numerical differentiations and integrals, integral transforms as well as fractional calculus are also covered in the book.

This book introduces readers to statistical methodologies used to analyze doubly truncated data. The first book exclusively dedicated to the topic, it provides likelihood-based methods, Bayesian methods, non-parametric methods, and linear regression methods. These procedures can be used to effectively analyze continuous data, especially survival data arising in biostatistics and economics. Because truncation is a phenomenon that is often encountered in non-experimental studies, the methods presented here can be applied to many branches of science. The book provides R codes for most of the statistical methods, to help readers analyze their data. Given its scope, the book is ideally suited as a textbook for students of statistics, mathematics, econometrics, and other fields.

This text provides a practical, hands-on introduction to data conceptualization, measurement, and association through active learning. Students get step-by-step instruction on data analysis using the latest version of SPSS and the most current General Social Survey data. The text starts with an introduction to computerized data analysis and the social research process, then walks users through univariate, bivariate, and multivariate analysis using SPSS. The book contains applications from across the social sciences-sociology, political science, social work, criminal justice, health-so it can be used in courses offered in any of these departments. The Eleventh Edition uses the latest general Social Survey (GSS) data, and the latest available version of SPSS. The GSS datasets now offer additional variables for more possibilities in the demonstrations and exercises within each chapter.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification. Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Get up to speed on Microsoft Project 2013 and learn how to manage projects large and small. This crystal-clear book not only guides you step-by-step through Project 2013's new features, it also gives you real-world guidance: how to prep a project before touching your PC, and which Project tools will keep you on target. With this Missing Manual, you'll go from project manager to Project master.

The important stuff you need to knowLearn Project 2013 inside out. Get hands-on instructions for the Standard and Professional editions.Start with a project management primer. Discover what it takes to handle a project successfully.Build and refine your plan. Put together your team, schedule, and budget.Achieve the results you want. Build realistic schedules with Project, and learn how to keep costs under control.Track your progress. Measure your performance, make course corrections, and manage changes.Create attractive reports. Communicate clearly to stakeholders and team members using charts, tables, and dashboards.Use Project's power tools. Customize Project's features and views, and transfer info via the cloud, using Microsoft SkyDrive.

Cognitive Intelligence with Neutrosophic Statistics in Bioinformatics investigates and presents the many applications that have arisen in the last ten years using neutrosophic statistics in bioinformatics, medicine, agriculture and cognitive science. This book will be very useful to the scientific community, appealing to audiences interested in fuzzy, vague concepts from which uncertain data are collected, including academic researchers, practicing engineers and graduate students. Neutrosophic statistics is a generalization of classical statistics. In classical statistics, the data is known, formed by crisp numbers. In comparison, data in neutrosophic statistics has some indeterminacy. This data may be ambiguous, vague, imprecise, incomplete, and even unknown. Neutrosophic statistics refers to a set of data, such that the data or a part of it are indeterminate in some degree, and to methods used to analyze the data.

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others). Copulas are multivariate distribution functions with standard uniform univariate margins. They are increasingly applied to modeling dependence among random variables in fields such as risk management, actuarial science, insurance, finance, engineering, hydrology, climatology, and meteorology, to name a few. In the spirit of the Use R! series, each chapter combines key theoretical definitions or results with illustrations in R. Aimed at statisticians, actuaries, risk managers, engineers and environmental scientists wanting to learn about the theory and practice of copula modeling using R without an overwhelming amount of mathematics, the book can also be used for teaching a course on copula modeling.

See how custom chatbots and Azure Bot Service can resolve common business problems. This book takes you through the many possibilities of bot development from a business point of view, using Microsoft bot technology, and demonstrates how to connect, deploy, and manage them. Starting with an introduction to chatbots and their features you will go through the design and implementation of Azure chatbots. This will set the foundation for the rest of the book before you learn how to create and manage messages in chatbots. You'll then see how to deploy your chatbot in different business scenarios and how to integrate Azure chatbots with different applications such as Facebook and Twitter. To really allow you to demonstrate business value, Introducing Azure Bot Service covers tips on enhancing customer satisfaction and developing insights by analyzing customer behavior. This knowledge will help you understand how artificial intelligence techniques such as chatbots help your organization undergo digital transformation. After reading this book, you will be ready to build chatbots using Microsoft Azure, deploy them in different business scenarios, and measure the benefits of chatbots. What You Will Learn Build time-saving chatbots using Azure Bot Service Engage in proactive customer interaction Integrate chatbots as a key aspect of your business strategy Improve customer satisfaction Ease into digital transformation using Azure chatbots Who This Book Is For Developers who are interested in building chatbots.

Written at a readily accessible level, " Basic Data Analysis for Time Series with R" emphasizes the mathematical importance of collaborative analysis of data used to collect increments of time or space. Balancing a theoretical and practical approach to analyzing data within the context of serial correlation, the book presents a coherent and systematic regression-based approach to model selection. The book illustrates these principles of model selection and model building through the use of information criteria, cross validation, hypothesis tests, and confidence intervals.

Focusing on frequency- and time-domain and trigonometric regression as the primary themes, the book also includes modern topical coverage on Fourier series and Akaike's Information Criterion (AIC). In addition, "Basic Data Analysis for Time Series with R" also features:

Real-world examples to provide readers with practical hands-on experienceMultiple R software subroutines employed with graphical displaysNumerous exercise sets intended to support readers understanding of the core conceptsSpecific chapters devoted to the analysis of the Wolf sunspot number data and the Vostok ice core data sets

This text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gerard Biau is a professor at Universite Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).

This book is the modern first treatment of experimental designs, providing a comprehensive introduction to the interrelationship between the theory of optimal designs and the theory of cubature formulas in numerical analysis. It also offers original new ideas for constructing optimal designs. The book opens with some basics on reproducing kernels, and builds up to more advanced topics, including bounds for the number of cubature formula points, equivalence theorems for statistical optimalities, and the Sobolev Theorem for the cubature formula. It concludes with a functional analytic generalization of the above classical results. Although it is intended for readers who are interested in recent advances in the construction theory of optimal experimental designs, the book is also useful for researchers seeking rich interactions between optimal experimental designs and various mathematical subjects such as spherical designs in combinatorics and cubature formulas in numerical analysis, both closely related to embeddings of classical finite-dimensional Banach spaces in functional analysis and Hilbert identities in elementary number theory. Moreover, it provides a novel communication platform for "design theorists" in a wide variety of research fields.

SAP ABAP (Advanced Business Application Programming) offers a detailed tutorial on the numerous features of the core programming platform, used for development for the entire SAP software suite. SAP ABAP uses hands on business oriented use cases and a valuable dedicated e-resource to demonstrate the underlying advanced concepts of the OO ABAP environment and the SAP UI. SAP ABAP covers the latest version (NetWeaver 7.3 and SAP application programming release 6.0) of the platform for demonstrating the customization and implementation phases of the SAP software implementation. Void of theoretical treatments and preoccupation with language syntax, SAP ABAP is a comprehensive, practical one stop solution,which demonstrates and conveys the language's commands and features through hands on examples. The accompanying e-resource is a take off point to the book. SAP ABAP works in tandem with the accompanying e-resource to create an interactive learning environment where the book provides a brief description and an overview of a specified feature/command, showing and discussing the corresponding code. At the reader's option, the user can utilize the accompanying e-resource, where a step-by-step guide to creating and running the feature's object is available. The presentation of the features is scenario oriented, i.e. most of the features are demonstrated in terms of small business scenarios. The e-resource contains the scenario descriptions, screen shots, detailed screen cams and ABAP program source to enable the reader to create all objects related to the scenario and run/execute them. The underlying concepts of a feature/command are conveyed through execution of these hands-on programs. Further exercises to be performed independently by the reader are also proposed. The demonstration/illustration objects including the programs rely on some of the SAP application tables being populated, for example an IDES system which is now a de facto system for all SAP training related activities.

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 deals with problems related to the evaluation of customer satisfaction in very different contexts and ways. Often satisfaction about a product or service is investigated through suitable surveys which try to capture the satisfaction about several partial aspects which characterize the perceived quality of that product or service. This book presents a series of statistical techniques adopted to analyze data from real situations where customer satisfaction surveys were performed. The aim is to give a simple guide of the variety of analysis that can be performed when analyzing data from sample surveys: starting from latent variable models to heterogeneity in satisfaction and also introducing some testing methods for comparing different customers. The book also discusses the construction of composite indicators including different benchmarks of satisfaction. Finally, some rank-based procedures for analyzing survey data are also shown.