This book is a product of the Third International Conference on Computing, Mathematics and Statistics (iCMS2017) to be held in Langkawi in November 2017. It is divided into four sections according to the thrust areas: Computer Science, Mathematics, Statistics, and Multidisciplinary Applications. All sections sought to confront current issues that society faces today. The book brings collectively quantitative, as well as qualitative, research methods that are also suitable for future research undertakings. Researchers in Computer Science, Mathematics and Statistics can use this book as a sourcebook to enrich their research works.

Demonstrates the simplicity and effectiveness of Mathematica as the solution to practical problems in composite materials. Designed for those who need to learn how micromechanical approaches can help understand the behaviour of bodies with voids, inclusions, defects, this book is perfect for readers without a programming background. Thoroughly introducing the concept of micromechanics, it helps readers assess the deformation of solids at a localized level and analyse a body with microstructures. The author approaches this analysis using the computer algebra system Mathematica, which facilitates complex index manipulations and mathematical expressions accurately. The book begins by covering the general topics of continuum mechanics such as coordinate transformations, kinematics, stress, constitutive relationship and material symmetry. Mathematica programming is also introduced with accompanying examples. In the second half of the book, an analysis of heterogeneous materials with emphasis on composites is covered. Takes a practical approach by using Mathematica, one of the most popular programmes for symbolic computation * Introduces the concept of micromechanics with worked-out examples using Mathematica code for ease of understanding * Logically begins with the essentials of the topic, such as kinematics and stress, before moving to more advanced areas * Applications covered include the basics of continuum mechanics, Eshelby's method, analytical and semi-analytical approaches for materials with inclusions (composites) in both infinite and finite matrix media and thermal stresses for a medium with inclusions, all with Mathematica examples * Features a problem and solution section on the book s companion website, useful for students new to the programme

Statistical Models in S extends the S language to fit and analyze a variety of statistical models, including analysis of variance, generalized linear models, additive models, local regression, and tree-based models. The contributions of the ten authors-most of whom work in the statistics research department at AT&T Bell Laboratories-represent results of research in both the computational and statistical aspects of modeling data.

Signals and Systems: A Primer with MATLAB (R) provides clear, interesting, and easy-to-understand coverage of continuous-time and discrete-time signals and systems. Each chapter opens with a historical profile or career talk, followed by an introduction that states the chapter objectives and links the chapter to the previous ones. All principles are presented in a lucid, logical, step-by-step approach. As much as possible, the authors avoid wordiness and detail overload that could hide concepts and impede understanding. In recognition of the requirements by the Accreditation Board for Engineering and Technology (ABET) on integrating computer tools, the use of MATLAB (R) is encouraged in a student-friendly manner. MATLAB is introduced in Appendix B and applied gradually throughout the book. Each illustrative example is immediately followed by a practice problem along with its answer. Students can follow the example step by step to solve the practice problem without flipping pages or looking at the end of the book for answers. These practice problems test students' comprehension and reinforce key concepts before moving on to the next section. Toward the end of each chapter, the authors discuss some application aspects of the concepts covered in the chapter. The material covered in the chapter is applied to at least one or two practical problems or devices. This helps students see how the concepts are applied to real-life situations. In addition, thoroughly worked examples are given liberally at the end of every section. These examples give students a solid grasp of the solutions as well as the confidence to solve similar problems themselves. Some of the problems are solved in two or three ways to facilitate a deeper understanding and comparison of different approaches. Ten review questions in the form of multiple-choice objective items are provided at the end of each chapter with answers. The review questions are intended to cover the "little tricks" that the examples and end-of-chapter problems may not cover. They serve as a self-test device and help students determine chapter mastery. Each chapter also ends with a summary of key points and formulas. Designed for a three-hour semester course on signals and systems, Signals and Systems: A Primer with MATLAB (R) is intended as a textbook for junior-level undergraduate students in electrical and computer engineering. The prerequisites for a course based on this book are knowledge of standard mathematics (including calculus and differential equations) and electric circuit analysis.

The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an estimator and its large sample distribution, with suitable normalization, are discussed, the focus being on the consistent and asymptotically normal (CAN) estimators. It is shown that for the probability models belonging to an exponential family and a Cramer family, the maximum likelihood estimators of the indexing parameters are CAN. The book describes some large sample test procedures, in particular, the most frequently used likelihood ratio test procedure. Various applications of the likelihood ratio test procedure are addressed, when the underlying probability model is a multinomial distribution. These include tests for the goodness of fit and tests for contingency tables. The book also discusses a score test and Wald's test, their relationship with the likelihood ratio test and Karl Pearson's chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic and Karl Pearson's chi-square test statistic are identical. Numerous illustrative examples of differing difficulty level are incorporated to clarify the concepts. For better assimilation of the notions, various exercises are included in each chapter. Solutions to almost all the exercises are given in the last chapter, to motivate students towards solving these exercises and to enable digestion of the underlying concepts. The concepts from asymptotic inference are crucial in modern statistics, but are difficult to grasp in view of their abstract nature. To overcome this difficulty, keeping up with the recent trend of using R software for statistical computations, the book uses it extensively, for illustrating the concepts, verifying the properties of estimators and carrying out various test procedures. The last section of the chapters presents R codes to reveal and visually demonstrate the hidden aspects of different concepts and procedures. Augmenting the theory with R software is a novel and a unique feature of the book. The book is designed primarily to serve as a text book for a one semester introductory course in asymptotic statistical inference, in a post-graduate program, such as Statistics, Bio-statistics or Econometrics. It will also provide sufficient background information for studying inference in stochastic processes. The book will cater to the need of a concise but clear and student-friendly book introducing, conceptually and computationally, basics of asymptotic inference.

Accelerate your digital transformation and break down silos with Microsoft Dynamics 365 It's no secret that running a business involves several complex parts like managing staff, financials, marketing, and operations--just to name a few. That's where Microsoft Dynamics 365, the most profitable business management tool, comes in. In Microsoft Dynamics 365 For Dummies, you'll learn the aspects of the program and each of its applications from Customer Service to Financial Management. With expert author Renato Bellu's clear instructions and helpful tips, you'll be managing to your fullest advantage before you know it. Let's get started! Digitally transform your business by connecting CRM and ERP Use data to make decisions across all business functions Integrate Dynamics 365 with Office 365 and LinkedIn Manage financials and operations Are you running a dynamic business? This book shows you how!

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.

This book explores missing data techniques and provides a detailed and easy-to-read introduction to multiple imputation, covering the theoretical aspects of the topic and offering hands-on help with the implementation. It discusses the pros and cons of various techniques and concepts, including multiple imputation quality diagnostics, an important topic for practitioners. It also presents current research and new, practically relevant developments in the field, and demonstrates the use of recent multiple imputation techniques designed for situations where distributional assumptions of the classical multiple imputation solutions are violated. In addition, the book features numerous practical tutorials for widely used R software packages to generate multiple imputations (norm, pan and mice). The provided R code and data sets allow readers to reproduce all the examples and enhance their understanding of the procedures. This book is intended for social and health scientists and other quantitative researchers who analyze incompletely observed data sets, as well as master's and PhD students with a sound basic knowledge of statistics.

With the development of computing technologies in today's modernized world, software packages have become easily accessible. Open source software, specifically, is a popular method for solving certain issues in the field of computer science. One key challenge is analyzing big data due to the high amounts that organizations are processing. Researchers and professionals need research on the foundations of open source software programs and how they can successfully analyze statistical data. Open Source Software for Statistical Analysis of Big Data: Emerging Research and Opportunities provides emerging research exploring the theoretical and practical aspects of cost-free software possibilities for applications within data analysis and statistics with a specific focus on R and Python. Featuring coverage on a broad range of topics such as cluster analysis, time series forecasting, and machine learning, this book is ideally designed for researchers, developers, practitioners, engineers, academicians, scholars, and students who want to more fully understand in a brief and concise format the realm and technologies of open source software for big data and how it has been used to solve large-scale research problems in a multitude of disciplines.

Marketing researchers, companies and business schools need to be able to use statistical procedures correctly and accurately interpret the outputs, yet generally these people are scared off by the statistics behind the different analyses procedures, thus they often rely on external sources to come up with profound answers to the proposed research questions. In an accessible and step by step approach, the authors show readers which procedures to use in which particular situation and how to practically execute them using IBM (R) SPSS Statistics. IBM (R) is one of the largest statistical software providers world-wide and their IBM (R) SPSS Statistics software offers a very user-friendly environment. The program uses a simple drag-and-drop menu interface, which is also suitable for non-experienced programmers. It is widely employed in companies and many business schools also use this software package. This straightforward, pragmatic reference manual will help: professional marketers who use statistical procedures in in IBM (R) SPSS Statistics; undergraduate and postgraduate students where marketing research and research methodology are taught; all researchers analyzing survey-based data in a wide range of frontier domains like psychology, finance, accountancy, negotiation, communication, sociology, criminology, management, information systems, etc. IBM (R)'s next-generation business analytic solutions help organizations of all sizes make sense of information in the context of their business. You can uncover insights more quickly and easily from all types of data-even big data-and on multiple platforms and devices. And, with self-service and built-in expertise and intelligence, you have the freedom and confidence to make smarter decisions that better address your business imperatives.

Free Mathematica 10 Update Included! Now available from www.wiley.com/go/magrab Updated material includes: - Creating regions and volumes of arbitrary shape and determining their properties: arc length, area, centroid, and area moment of inertia - Performing integrations, solving equations, and determining the maximum and minimum values over regions of arbitrary shape - Solving numerically a class of linear second order partial differential equations in regions of arbitrary shape using finite elements An Engineer's Guide to Mathematica enables the reader to attain the skills to create Mathematica 9 programs that solve a wide range of engineering problems and that display the results with annotated graphics. This book can be used to learn Mathematica, as a companion to engineering texts, and also as a reference for obtaining numerical and symbolic solutions to a wide range of engineering topics. The material is presented in an engineering context and the creation of interactive graphics is emphasized. The first part of the book introduces Mathematica's syntax and commands useful in solving engineering problems. Tables are used extensively to illustrate families of commands and the effects that different options have on their output. From these tables, one can easily determine which options will satisfy one's current needs. The order of the material is introduced so that the engineering applicability of the examples increases as one progresses through the chapters. The second part of the book obtains solutions to representative classes of problems in a wide range of engineering specialties. Here, the majority of the solutions are presented as interactive graphics so that the results can be explored parametrically. Key features: * Material is based on Mathematica 9 * Presents over 85 examples on a wide range of engineering topics, including vibrations, controls, fluids, heat transfer, structures, statistics, engineering mathematics, and optimization * Each chapter contains a summary table of the Mathematica commands used for ease of reference * Includes a table of applications summarizing all of the engineering examples presented. * Accompanied by a website containing Mathematica notebooks of all the numbered examples An Engineer's Guide to Mathematica is a must-have reference for practitioners, and graduate and undergraduate students who want to learn how to solve engineering problems with Mathematica.

Create Top-Quality Figures Using SAS Producing High-Quality Figures Using SAS/GRAPH (R) and ODS Graphics Procedures gives statisticians and SAS programmers practical guidance on presenting research data in high-quality figures that meet the publication requirements of academic institutions and various industries, such as pharmaceutical companies, agricultural businesses, and financial organizations. Choose the Proper Graph Formats, Options, and Fonts The book provides all the details on selecting the right figure formats, options, and fonts to produce high-quality figures. In-depth instructions and SAS programs using procedures in both SAS/GRAPH and ODS Graphics show how to generate sample figures in listing graphics formats and ODS document files. Each chapter includes practical examples and SAS programs. Readers can easily modify the SAS programs to develop high-quality figures to meet their own needs. For those unfamiliar with SAS programming and SAS Graphics, the author describes how to produce sample figures in SAS ODS Graphics designer, a convenient tool for generating figures without having to write SAS programs. The book also discusses how to produce figures in a SAS Enterprise Guide project.

Easily Use SAS to Produce Your Graphics Diagrams, plots, and other types of graphics are indispensable components in nearly all phases of statistical analysis, from the initial assessment of the data to the selection of appropriate statistical models to the diagnosis of the chosen models once they have been fitted to the data. Harnessing the full graphics capabilities of SAS, A Handbook of Statistical Graphics Using SAS ODS covers essential graphical methods needed in every statistician's toolkit. It explains how to implement the methods using SAS 9.4. The handbook shows how to use SAS to create many types of statistical graphics for exploring data and diagnosing fitted models. It uses SAS's newer ODS graphics throughout as this system offers a number of advantages, including ease of use, high quality of results, consistent appearance, and convenient semiautomatic graphs from the statistical procedures. Each chapter deals graphically with several sets of example data from a wide variety of areas, such as epidemiology, medicine, and psychology. These examples illustrate the use of graphic displays to give an overview of data, to suggest possible hypotheses for testing new data, and to interpret fitted statistical models. The SAS programs and data sets are available online.

Formally validated training material to pass Microsoft certification Exam 70-178: Microsoft Project 2010, Managing Projects! Designed for the busy, practicing project manager, Dynamic Scheduling With Microsoft Project 2010 will help you get up to speed quickly with the new features of Project 2010 and enable you to create effective schedules more efficiently using best practices, tips & tricks, and step-by-step instruction. Through the use of helpful screen shots, hands-on exercises, illustrations, and review questions, this guide instructs you on how to build dynamic schedules that will allow you to explore what-if scenarios and dramatically decrease the time you spend making static schedule changes. ""A must read, re-read, and use daily for all project managers"" is what PMI's Project Management Journal had to say about the previous edition. This updated version is even better.

This book provides comprehensive coverage of the field of outlier analysis from a computer science point of view. It integrates methods from data mining, machine learning, and statistics within the computational framework and therefore appeals to multiple communities. The chapters of this book can be organized into three categories: Basic algorithms: Chapters 1 through 7 discuss the fundamental algorithms for outlier analysis, including probabilistic and statistical methods, linear methods, proximity-based methods, high-dimensional (subspace) methods, ensemble methods, and supervised methods. Domain-specific methods: Chapters 8 through 12 discuss outlier detection algorithms for various domains of data, such as text, categorical data, time-series data, discrete sequence data, spatial data, and network data. Applications: Chapter 13 is devoted to various applications of outlier analysis. Some guidance is also provided for the practitioner. The second edition of this book is more detailed and is written to appeal to both researchers and practitioners. Significant new material has been added on topics such as kernel methods, one-class support-vector machines, matrix factorization, neural networks, outlier ensembles, time-series methods, and subspace methods. It is written as a textbook and can be used for classroom teaching.

Enterprise 2.0 (E 2.0) has caught the collective imagination of executives who are innovating to radically change the face of business. E 2.0 takes full benefit of social networking, including blogs, discussion boards, mashups, and all that is sharable and combinable.
Examining organizations and their social activities, Enterprise 2.0: Social Networking Tools to Transform Your Organization considers the complete spectrum of social media and social activities available to your business. It not only offers a hands-on, practical assessment of "what to do," but also "how to do it." Demonstrating how to utilize social networking within diverse functional areas, the book:

• Describes the functions of social networking in the context of today's enterprise
• Details how to make the best use of blogs, discussion boards, and workspaces in an organizational setting
• Supplies a complete discussion on how to prepare staff for social enterprising
• Explains how to measure and manage social networking

This book includes discussions of social networking in enterprise activities such as human resources, risk management, operations, and project management. It describes the utility and role of social networking on a department by department basis, explains how to integrate social enterprising with knowledge management, and supplies helpful insights into legal, performance, and measurement issues. Touching on key security and privacy issues, the book clearly illustrates how social networking and E 2.0 can help improve risk management in your organization.

This is the first workbook that introduces the multilevel approach to modeling with categorical outcomes using IBM SPSS Version 20. Readers learn how to develop, estimate, and interpret multilevel models with categorical outcomes. The authors walk readers through data management, diagnostic tools, model conceptualization, and model specification issues related to single-level and multilevel models with categorical outcomes. Screen shots clearly demonstrate techniques and navigation of the program. Modeling syntax is provided in the appendix. Examples of various types of categorical outcomes demonstrate how to set up each model and interpret the output. Extended examples illustrate the logic of model development, interpretation of output, the context of the research questions, and the steps around which the analyses are structured. Readers can replicate examples in each chapter by using the corresponding data and syntax files available at www.psypress.com/9781848729568. The book opens with a review of multilevel with categorical outcomes, followed by a chapter on IBM SPSS data management techniques to facilitate working with multilevel and longitudinal data sets. Chapters 3 and 4 detail the basics of the single-level and multilevel generalized linear model for various types of categorical outcomes. These chapters review underlying concepts to assist with trouble-shooting common programming and modeling problems. Next population-average and unit-specific longitudinal models for investigating individual or organizational developmental processes are developed. Chapter 6 focuses on single- and multilevel models using multinomial and ordinal data followed by a chapter on models for count data. The book concludes with additional trouble shooting techniques and tips for expanding on the modeling techniques introduced. Ideal as a supplement for graduate level courses and/or professional workshops on multilevel, longitudinal, latent variable modeling, multivariate statistics, and/or advanced quantitative techniques taught in psychology, business, education, health, and sociology, this practical workbook also appeals to researchers in these fields. An excellent follow up to the authors' highly successful Multilevel and Longitudinal Modeling with IBM SPSS and Introduction to Multilevel Modeling Techniques, 2nd Edition, this book can also be used with any multilevel and/or longitudinal book or as a stand-alone text introducing multilevel modeling with categorical outcomes.

The overwhelming majority of a software system's lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google's Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems in the world. You'll learn the principles and practices that enable Google engineers to make systems more scalable, reliable, and efficient-lessons directly applicable to your organization. This book is divided into four sections: Introduction-Learn what site reliability engineering is and why it differs from conventional IT industry practices Principles-Examine the patterns, behaviors, and areas of concern that influence the work of a site reliability engineer (SRE) Practices-Understand the theory and practice of an SRE's day-to-day work: building and operating large distributed computing systems Management-Explore Google's best practices for training, communication, and meetings that your organization can use

This book provides application of multi criteria decision making techniques for managerial discretion. With this book, a concerted platform has been provided for several peers and other management organizations to understand and implement these tools and deal with the practical problems in a better way so as to provide more robust managerial decision making.

The fun and friendly guide to mastering IBM's Statistical Package for the Social Sciences Written by an author team with a combined 55 years of experience using SPSS, this updated guide takes the guesswork out of the subject and helps you get the most out of using the leader in predictive analysis. Covering the latest release and updates to SPSS 27.0, and including more than 150 pages of basic statistical theory, it helps you understand the mechanics behind the calculations, perform predictive analysis, produce informative graphs, and more. You'll even dabble in programming as you expand SPSS functionality to suit your specific needs. Master the fundamental mechanics of SPSS Learn how to get data into and out of the program Graph and analyze your data more accurately and efficiently Program SPSS with Command Syntax Get ready to start handling data like a pro--with step-by-step instruction and expert advice!

Improve your audit results and extend your capabilities with Mastering IDEAScript: The Definitive Guide

Run audit programs and data analysis projects with ease. Create a local automated audit system. Meet current audit standards. Do it all with Mastering IDEAScript, the official guide from CaseWare IDEA(R).

Designed to help the complete novice develop the skills required to write applications using IDEAScript, this resource starts with simple topics, progressively working up to more complex areas. It helps you understand what automation can help you do and set reasonable goals for working with IDEAScript.

Although basic familiarity with the IDEA(R) program is helpful in the use of this book's concepts, neither programming skills nor special equipment is required. Here, you'll find plain-English, easy-to-follow explanations for:

Creating your first IDEAScript application

Using the IDEAScript Editor

Writing code quickly and efficiently

Building complete applications without any code at all, using the Macro Recorder

Troubleshooting errors that occur in your application

Performing basic tasks, such as indexing, sorting, and closing your database

Finding information easily within databases

And much more

Along with a companion website containing all of the scripts found in this book, Mastering IDEAScript: The Definitive Guide is packed with practical techniques and rules of thumb to help you understand the workings of IDEAScript. The days of filling in forms and answering countless questions when running audit programs are over. From now on, let IDEAScript do the work

IDEA(R) is a leading provider of data analysis software targeted to auditors to use as a tool for fraud detection and internal control assessment. IDEA(R) software is used in sixteen languages in more than ninety countries, by major accounting firms, governments, and corporations in all industry sectors, as well as by universities as a teaching tool.

IDEA(R) is a registered trademark of CaseWare International Inc.

Maple is a powerful symbolic computation system that is widely used in universities around the world. This short introduction gives readers an insight into the rules that control how the system works, and how to understand, fix, and avoid common problems. Topics covered include algebra, calculus, linear algebra, graphics, programming, and procedures. Each chapter contains numerous illustrative examples, using mathematics that does not extend beyond first-year undergraduate material. Maple worksheets containing these examples are available for download from the author's personal website. The book is suitable for new users, but where advanced topics are central to understanding Maple they are tackled head-on. Many concepts which are absent from introductory books and manuals are described in detail. With this book, students, teachers and researchers will gain a solid understanding of Maple and how to use it to solve complex mathematical problems in a simple and efficient way.

In "Large-Scale Scrum," Craig Larman and Bas Vodde offer the most direct, concise, actionable guide to reaping the full benefits of agile in distributed, global enterprises. Larman and Vodde have distilled their immense experience helping geographically distributed development organizations move to agile. Going beyond their previous books, they offer today's fastest, most focused guidance: "brass tacks" advice and field-proven best practices for achieving value fast, and achieving even more value as you move forward. Targeted to enterprise project participants and stakeholders, "Large-Scale Scrum" offers straight-to-the-point insights for scaling Scrum across the entire project lifecycle, from sprint planning to retrospective. Larman and Vodde help you:

• Implement proven Scrum frameworks for large-scale developments
• Scale requirements, planning, and product management
• Scale design and architecture
• Effectively manage defects and interruptions
• Integrate Scrum into multisite and offshore projects
• Choose the right adoption strategies and organizational designs

Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research.

Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers:

• A refresher on basic statistics and an introduction to R
• Considerations and techniques for the visual display of data through graphics
• An overview of statistical hypothesis tests and the reasoning behind them
• A comprehensive guide to the use of the linear model, the foundation of most statistics encountered
• An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression
• Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist

Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.