The MATLAB (R) programming environment is often perceived as a platform suitable for prototyping and modeling but not for "serious" applications. One of the main complaints is that MATLAB is just too slow. Accelerating MATLAB Performance aims to correct this perception by describing multiple ways to greatly improve MATLAB program speed. Packed with thousands of helpful tips, it leaves no stone unturned, discussing every aspect of MATLAB. Ideal for novices and professionals alike, the book describes MATLAB performance in a scale and depth never before published. It takes a comprehensive approach to MATLAB performance, illustrating numerous ways to attain the desired speedup. The book covers MATLAB, CPU, and memory profiling and discusses various tradeoffs in performance tuning. It describes both the application of standard industry techniques in MATLAB, as well as methods that are specific to MATLAB such as using different data types or built-in functions. The book covers MATLAB vectorization, parallelization (implicit and explicit), optimization, memory management, chunking, and caching. It explains MATLAB's memory model and details how it can be leveraged. It describes the use of GPU, MEX, FPGA, and other forms of compiled code, as well as techniques for speeding up deployed applications. It details specific tips for MATLAB GUI, graphics, and I/O. It also reviews a wide variety of utilities, libraries, and toolboxes that can help to improve performance. Sufficient information is provided to allow readers to immediately apply the suggestions to their own MATLAB programs. Extensive references are also included to allow those who wish to expand the treatment of a particular topic to do so easily. Supported by an active website, and numerous code examples, the book will help readers rapidly attain significant reductions in development costs and program run times.

Experience learning made easy-and quickly teach yourself how to manage your projects with Project 2007. With Step By Step, you set the pace-building and practicing the skills you need, just when you need them! Build a project plan and fine-tune the details Schedule tasks, assign resources, and manage dependencies Monitor progress and costs-and keep your project on track Format Gantt charts and other views to communicate project data Begin exploring enterprise project management systems Your all-in-one learning experience includes: Files for building skills and practicing the book's lessons Fully searchable eBook Bonus guide to the Ribbon, the new Microsoft Office interface Quick course on project management in the Appendix Windows Vista Product Guide eReference-plus other resources on CD For customers who purchase an ebook version of this title, instructions for downloading the CD files can be found in the ebook.

This book is open access under a CC BY 4.0 license This open access book brings together the latest genome base prediction models currently being used by statisticians, breeders and data scientists. It provides an accessible way to understand the theory behind each statistical learning tool, the required pre-processing, the basics of model building, how to train statistical learning methods, the basic R scripts needed to implement each statistical learning tool, and the output of each tool. To do so, for each tool the book provides background theory, some elements of the R statistical software for its implementation, the conceptual underpinnings, and at least two illustrative examples with data from real-world genomic selection experiments. Lastly, worked-out examples help readers check their own comprehension.The book will greatly appeal to readers in plant (and animal) breeding, geneticists and statisticians, as it provides in a very accessible way the necessary theory, the appropriate R code, and illustrative examples for a complete understanding of each statistical learning tool. In addition, it weighs the advantages and disadvantages of each tool.

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors' own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.

If you want to learn to use R for data analysis but aren't sure how to get started, this practical book will help you find the right path through your data. Drawing on real-world data to show you how to use different techniques in practice, it helps you progress your programming and statistics knowledge so you can apply the most appropriate tools in your research. It starts with descriptive statistics and moves through regression to advanced techniques such as structural equation modelling and Bayesian statistics, all with digestible mathematical detail for beginner researchers. The book: Shows you how to use R packages and apply functions, adjusting them to suit different datasets. Gives you the tools to try new statistical techniques and empowers you to become confident using them. Encourages you to learn by doing when running and adapting the authors' own code. Equips you with solutions to overcome the potential challenges of working with real data that may be messy or imperfect. Accompanied by online resources including screencast tutorials of R that give you step by step guidance and R scripts and datasets for you to practice with, this book is a perfect companion for any student of applied statistics or quantitative research methods courses.

Multiple factor analysis (MFA) enables users to analyze tables of individuals and variables in which the variables are structured into quantitative, qualitative, or mixed groups. Written by the co-developer of this methodology, Multiple Factor Analysis by Example Using R brings together the theoretical and methodological aspects of MFA. It also includes examples of applications and details of how to implement MFA using an R package (FactoMineR). The first two chapters cover the basic factorial analysis methods of principal component analysis (PCA) and multiple correspondence analysis (MCA). The next chapter discusses factor analysis for mixed data (FAMD), a little-known method for simultaneously analyzing quantitative and qualitative variables without group distinction. Focusing on MFA, subsequent chapters examine the key points of MFA in the context of quantitative variables as well as qualitative and mixed data. The author also compares MFA and Procrustes analysis and presents a natural extension of MFA: hierarchical MFA (HMFA). The final chapter explores several elements of matrix calculation and metric spaces used in the book.

Microsoft Windows 8.1 and Windows Server 2012 R2 are designed to be the best performing operating systems to date, but even the best systems can be overwhelmed with load and/or plagued with poorly performing code. Windows Performance Analysis Field Guide gives you a practical field guide approach to performance monitoring and analysis from experts who do this work every day. Think of this book as your own guide to "What would Microsoft support do?" when you have a Windows performance issue. Author Clint Huffman, a Microsoft veteran of over fifteen years, shows you how to identify and alleviate problems with the computer resources of disk, memory, processor, and network. You will learn to use performance counters as the initial indicators, then use various tools to "dig in" to the problem, as well as how to capture and analyze boot performance problems.

John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. This is the only advanced programming book on R, written by the author of the S language from which R evolved.

Choose the Proper Statistical Method for Your Sensory Data Issue Analyzing Sensory Data with R gives you the foundation to analyze and interpret sensory data. The book helps you find the most appropriate statistical method to tackle your sensory data issue. Covering quantitative, qualitative, and affective approaches, the book presents the big picture of sensory evaluation. Through an integrated approach that connects the different dimensions of sensory evaluation, you'll understand: The reasons why sensory data are collected The ways in which the data are collected and analyzed The intrinsic meaning of the data The interpretation of the data analysis results Each chapter corresponds to one main sensory topic. The chapters start with presenting the nature of the sensory evaluation and its objectives, the sensory particularities related to the sensory evaluation, details about the data set obtained, and the statistical analyses required. Using real examples, the authors then illustrate step by step how the analyses are performed in R. The chapters conclude with variants and extensions of the methods that are related to the sensory task itself, the statistical methodology, or both.

Data Analytics for the Social Sciences is an introductory, graduate-level treatment of data analytics for social science. It features applications in the R language, arguably the fastest growing and leading statistical tool for researchers. The book starts with an ethics chapter on the uses and potential abuses of data analytics. Chapters 2 and 3 show how to implement a broad range of statistical procedures in R. Chapters 4 and 5 deal with regression and classification trees and with random forests. Chapter 6 deals with machine learning models and the "caret" package, which makes available to the researcher hundreds of models. Chapter 7 deals with neural network analysis, and Chapter 8 deals with network analysis and visualization of network data. A final chapter treats text analysis, including web scraping, comparative word frequency tables, word clouds, word maps, sentiment analysis, topic analysis, and more. All empirical chapters have two "Quick Start" exercises designed to allow quick immersion in chapter topics, followed by "In Depth" coverage. Data are available for all examples and runnable R code is provided in a "Command Summary". An appendix provides an extended tutorial on R and RStudio. Almost 30 online supplements provide information for the complete book, "books within the book" on a variety of topics, such as agent-based modeling. Rather than focusing on equations, derivations, and proofs, this book emphasizes hands-on obtaining of output for various social science models and how to interpret the output. It is suitable for all advanced level undergraduate and graduate students learning statistical data analysis.

About this book * Gives the reader hands on example-base experience for simulating dynamical models in MATLAB (R)/Simulink (R) and animating them in VRML * More than 150 images describe each step in the model realizations helping readers to understand them visually * Diverse examples and profound problem treatment enable the reader to animate complex dynamical problems m-files, Simulink models, VRML files and jpegs available for download provide full solutions for the end-of-chapter problems Virtual Reality and Animation for MATLAB (R) and Simulink (R) Users demonstrates the simulation and animation of physical systems using the MATLAB (R) Virtual Reality Toolbox (virtual models are created in V-Realm Builder). The book is divided into two parts; the first addresses MATLAB (R) and the second Simulink (R). The presentation is problem-based with each chapter teaching the reader a group of essential principles in the context of a step-by-step solution to a particular issue. Examples of the systems covered include mass-spring-dampers, a crank-slider mechanism and a moving vehicle. The examples are given in ascending level of difficulty and contain MATLAB (R)/Simulink (R) codes deliberately simplified so that readers can focus on: * understanding how to link a 3-d virtual scene to MATLAB (R)/Simulink (R); and * manipulating the 3-d virtual scene in MATLAB (R)/Simulink (R). When studied in sequence, the chapters of this text form a coherent whole enabling the reader to gain a thorough expertise in virtual simulation and animation of dynamical models using MATLAB (R)/Simulink (R). Individual chapters stand on their own, however, so that readers interested in a particular system can concentrate on it easily. Problems are provided in each chapter to give practice in the techniques demonstrated and to extend the range of the systems studied, for example, into the control sphere. Solution code for these problems can be downloaded from insert URL. Whether modeling the dynamics of a simple pendulum, a robot arm or a moving car, animation of a dynamical model can enliven and encourage understanding of mechanical systems and thus contribute to control design. Virtual Reality and Animation for MATLAB (R) and Simulink (R) Users will be instructive and interesting to anyone, researcher or student, working with the dynamics of physical systems. Readers are assumed to have some familiarity with MATLAB (R).

Second Edition

Learn How to Use Growth Curve Analysis with Your Time Course Data An increasingly prominent statistical tool in the behavioral sciences, multilevel regression offers a statistical framework for analyzing longitudinal or time course data. It also provides a way to quantify and analyze individual differences, such as developmental and neuropsychological, in the context of a model of the overall group effects. To harness the practical aspects of this useful tool, behavioral science researchers need a concise, accessible resource that explains how to implement these analysis methods. Growth Curve Analysis and Visualization Using R provides a practical, easy-to-understand guide to carrying out multilevel regression/growth curve analysis (GCA) of time course or longitudinal data in the behavioral sciences, particularly cognitive science, cognitive neuroscience, and psychology. With a minimum of statistical theory and technical jargon, the author focuses on the concrete issue of applying GCA to behavioral science data and individual differences. The book begins with discussing problems encountered when analyzing time course data, how to visualize time course data using the ggplot2 package, and how to format data for GCA and plotting. It then presents a conceptual overview of GCA and the core analysis syntax using the lme4 package and demonstrates how to plot model fits. The book describes how to deal with change over time that is not linear, how to structure random effects, how GCA and regression use categorical predictors, and how to conduct multiple simultaneous comparisons among different levels of a factor. It also compares the advantages and disadvantages of approaches to implementing logistic and quasi-logistic GCA and discusses how to use GCA to analyze individual differences as both fixed and random effects. The final chapter presents the code for all of the key examples along with samples demonstrating how to report GCA results. Throughout the book, R code illustrates how to implement the analyses and generate the graphs. Each chapter ends with exercises to test your understanding. The example datasets, code for solutions to the exercises, and supplemental code and examples are available on the author's website.

For courses in Graduate MIS, Decision Support Systems, and courses covering the principles of enterprise resource planning systems. This text takes a generic approach to enterprise resource planning systems and their interrelationships, covering all functional areas of this new type of management challenge. It discusses the re-design of business processes, changes in organizational structure, and effective management strategies that will help assure competitiveness, responsiveness, productivity, and global impact for many organizations in the years ahead.

This book provides a full representation of Inverse Synthetic Aperture Radar (ISAR) imagery, which is a popular and important radar signal processing tool. The book covers all possible aspects of ISAR imaging. The book offers a fair amount of signal processing techniques and radar basics before introducing the inverse problem of ISAR and the forward problem of Synthetic Aperture Radar (SAR). Important concepts of SAR such as resolution, pulse compression and image formation are given together with associated MATLAB codes.

After providing the fundamentals for ISAR imaging, the book gives the detailed imaging procedures for ISAR imaging with associated MATLAB functions and codes. To enhance the image quality in ISAR imaging, several imaging tricks and fine-tuning procedures such as zero-padding and windowing are also presented. Finally, various real applications of ISAR imagery, like imaging the antenna-platform scattering, are given in a separate chapter. For all these algorithms, MATLAB codes and figures are included. The final chapter considers advanced concepts and trends in ISAR imaging.

Designed for a graduate course in applied statistics, Nonparametric Methods in Statistics with SAS Applications teaches students how to apply nonparametric techniques to statistical data. It starts with the tests of hypotheses and moves on to regression modeling, time-to-event analysis, density estimation, and resampling methods. The text begins with classical nonparametric hypotheses testing, including the sign, Wilcoxon sign-rank and rank-sum, Ansari-Bradley, Kolmogorov-Smirnov, Friedman rank, Kruskal-Wallis H, Spearman rank correlation coefficient, and Fisher exact tests. It then discusses smoothing techniques (loess and thin-plate splines) for classical nonparametric regression as well as binary logistic and Poisson models. The author also describes time-to-event nonparametric estimation methods, such as the Kaplan-Meier survival curve and Cox proportional hazards model, and presents histogram and kernel density estimation methods. The book concludes with the basics of jackknife and bootstrap interval estimation. Drawing on data sets from the author's many consulting projects, this classroom-tested book includes various examples from psychology, education, clinical trials, and other areas. It also presents a set of exercises at the end of each chapter. All examples and exercises require the use of SAS 9.3 software. Complete SAS codes for all examples are given in the text. Large data sets for the exercises are available on the author's website.

Using the same accessible, hands-on approach as its best-selling predecessor, the Handbook of Univariate and Multivariate Data Analysis with IBM SPSS, Second Edition explains how to apply statistical tests to experimental findings, identify the assumptions underlying the tests, and interpret the findings. This second edition now covers more topics and has been updated with the SPSS statistical package for Windows. New to the Second Edition Three new chapters on multiple discriminant analysis, logistic regression, and canonical correlation New section on how to deal with missing data Coverage of tests of assumptions, such as linearity, outliers, normality, homogeneity of variance-covariance matrices, and multicollinearity Discussions of the calculation of Type I error and the procedure for testing statistical significance between two correlation coefficients obtained from two samples Expanded coverage of factor analysis, path analysis (test of the mediation hypothesis), and structural equation modeling Suitable for both newcomers and seasoned researchers in the social sciences, the handbook offers a clear guide to selecting the right statistical test, executing a wide range of univariate and multivariate statistical tests via the Windows and syntax methods, and interpreting the output results. The SPSS syntax files used for executing the statistical tests can be found in the appendix. Data sets employed in the examples are available on the book's CRC Press web page.

Basic Statistics provides an accessible and comprehensive introduction to statistics using the free, state-of-the-art, powerful software program R. This book is designed to both introduce students to key concepts in statistics and to provide simple instructions for using R. This concise book: *Teaches essential concepts in statistics, assuming little background knowledge on the part of the reader *Introduces students to R with as few sub-commands as possible for ease of use *Provides practical examples from the educational, behavioral, and social sciences With clear explanations of statistical processes and step-by-step commands in R, Basic Statistics will appeal to students and professionals across the social and behavioral sciences.

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

Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting.

The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling.

The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them.

See Professor Hilbe discuss the book.

Ever-changing business needs have prompted large companies to rethink their enterprise IT. Today, businesses must allow interaction with their customers, partners, and employees at more touch points and at a depth never thought previously. At the same time, rapid advances in information technologies, like business digitization, cloud computing, and Web 2.0, demand fundamental changes in the enterprises management practices. These changes have a drastic effect not only on IT and business, but also on policies, processes, and people. Many companies therefore embark on enterprise-wide transformation initiatives. The role of Enterprise Architecture (EA) is to architect and supervise this transformational journey. Unfortunately, today s EA is often a ponderous and detached exercise, with most of the EA initiatives failing to create visible impact. The enterprises need an EA that is agile and responsive to business dynamics. "Collaborative Enterprise Architecture" provides the innovative solutions today s enterprises require, informed by real-world experiences and experts insights. This book, in its first part, provides a systematic compendium of the current best practices in EA, analyzes current ways of doing EA, and identifies its constraints and shortcomings. In the second part, it leaves the beaten tracks of EA by introducing Lean, Agile, and Enterprise 2.0 concepts to the traditional EA methods. This blended approach to EA focuses on practical aspects, with recommendations derived from real-world experiences. A truly thought provoking and pragmatic guide to manage EA, "Collaborative Enterprise Architecture" effectively merges the long-term oriented top-down approach with pragmatic bottom-up thinking, and that way offers real solutions to businesses undergoing enterprise-wide change.
Covers the latest emerging technologies affecting business practice, including digitization, cloud computing, agile software development, and Web 2.0 Focuses on the practical implementation of EAM rather than theory, with recommendations based on real-world case studies Addresses changing business demands and practices, including Enterprise 2.0, open source, global sourcing, and more Takes an innovative approach to EAM, merging standard top-down and pragmatic, bottom-up strategies, offering real solutions to businesses undergoing enterprise-wide changes "

PAMIR (Parameterized Adaptive Multidimensional Integration Routines) is a suite of Fortran programs for multidimensional numerical integration over hypercubes, simplexes, and hyper-rectangles in general dimension p, intended for use by physicists, applied mathematicians, computer scientists, and engineers. The programs, which are available on the internet at www.pamir-integrate.com and are free for non-profit research use, are capable of following localized peaks and valleys of the integrand. Each program comes with a Message-Passing Interface (MPI) parallel version for cluster use as well as serial versions.The first chapter presents introductory material, similar to that on the PAMIR website, and the next is a "manual" giving much more detail on the use of the programs than is on the website. They are followed by many examples of performance benchmarks and comparisons with other programs, and a discussion of the computational integration aspects of PAMIR, in comparison with other methods in the literature. The final chapter provides details of the construction of the algorithms, while the Appendices give technical details and certain mathematical derivations.

PAMIR (Parameterized Adaptive Multidimensional Integration Routines) is a suite of Fortran programs for multidimensional numerical integration over hypercubes, simplexes, and hyper-rectangles in general dimension p, intended for use by physicists, applied mathematicians, computer scientists, and engineers. The programs, which are available on the internet at www.pamir-integrate.com and are free for non-profit research use, are capable of following localized peaks and valleys of the integrand. Each program comes with a Message-Passing Interface (MPI) parallel version for cluster use as well as serial versions.The first chapter presents introductory material, similar to that on the PAMIR website, and the next is a "manual" giving much more detail on the use of the programs than is on the website. They are followed by many examples of performance benchmarks and comparisons with other programs, and a discussion of the computational integration aspects of PAMIR, in comparison with other methods in the literature. The final chapter provides details of the construction of the algorithms, while the Appendices give technical details and certain mathematical derivations.

Enterprise Resource Planning (ERP), Supply Chain Management (SCM), Customer Relationship Management (CRM), Business Intelligence (BI) and Big Data analytics (BDA) are business related tasks and processes, which are supported by standardized software solutions. The book explains that this requires business-oriented thinking and acting from IT specialists and data scientists. It is a good idea to let students experience this directly from the business perspective, for example as executives of a virtual company in a role-playing game. The second edition of the book has been completely revised, restructured and supplemented with actual topics such as blockchains in supply chains and the correlation between Big Data analytics, artificial intelligence and machine learning. The structure of the book is based on the gradual implementation and integration of the respective information systems from the business and management perspectives. Part I contains chapters with detailed descriptions of the topics supplemented by online tests and exercises. Part II introduces role play and the online gaming and simulation environment. Supplementary teaching material, presentations, templates, and video clips are available online in the gaming area. The gaming and business simulation Kdibisglobal.com, newly created for this book, now includes a beer division, a bottled water division, a soft drink division and a manufacturing division for barcode cash register scanner with their specific business processes and supply chains.

These lecture notes provide a rapid, accessible introduction to Bayesian statistical methods. The course covers the fundamental philosophy and principles of Bayesian inference, including the reasoning behind the prior/likelihood model construction synonymous with Bayesian methods, through to advanced topics such as nonparametrics, Gaussian processes and latent factor models. These advanced modelling techniques can easily be applied using computer code samples written in Python and Stan which are integrated into the main text. Importantly, the reader will learn methods for assessing model fit, and to choose between rival modelling approaches.