When the pressure is on to resolve an elusive software or hardware glitch, what's needed is a cool head courtesy of a set of rules guaranteed to work on any system, in any circumstance. Written in a frank but engaging style, this book provides simple, foolproof principles guaranteed to help find any bug quickly. Recognized tech expert and author David Agans changes the way you think about debugging, making those pesky problems suddenly much easier to find and fix. Agans identifies nine simple, practical rules that are applicable to any software application or hardware system, which can help detect any bug, no matter how tricky or obscure. Illustrating the rules with real-life bug-detection war stories, Debugging shows you how to: Understand the system: how perceiving the ""roadmap"" can hasten your journey Quit thinking and look: when hands-on investigation can't be avoided Isolate critical factors: why changing one element at a time can be an essential tool Keep an audit trail: how keeping a record of the debugging process can win the day Whether the system or program you're working on has been designed wrong, built wrong, or used wrong, Debugging helps you think correctly about bugs, so the problems virtually reveal themselves.

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

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

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.

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

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

A MATLAB (R) Primer for Technical Programming for Materials Science and Engineering draws on examples from the field, providing the latest information on this programming tool that is targeted towards materials science. The book enables non-programmers to master MATLAB (R) in order to solve problems in materials science, assuming only a modest mathematical background. In addition, the book introduces programming and technical concepts in a logical manner to help students use MATLAB (R) for subsequent projects. This title offers materials scientists who are non-programming specialists with a coherent and focused introduction to MATLAB (R).

Applied Computing in Medicine and Health is a comprehensive presentation of on-going investigations into current applied computing challenges and advances, with a focus on a particular class of applications, primarily artificial intelligence methods and techniques in medicine and health. Applied computing is the use of practical computer science knowledge to enable use of the latest technology and techniques in a variety of different fields ranging from business to scientific research. One of the most important and relevant areas in applied computing is the use of artificial intelligence (AI) in health and medicine. Artificial intelligence in health and medicine (AIHM) is assuming the challenge of creating and distributing tools that can support medical doctors and specialists in new endeavors. The material included covers a wide variety of interdisciplinary perspectives concerning the theory and practice of applied computing in medicine, human biology, and health care. Particular attention is given to AI-based clinical decision-making, medical knowledge engineering, knowledge-based systems in medical education and research, intelligent medical information systems, intelligent databases, intelligent devices and instruments, medical AI tools, reasoning and metareasoning in medicine, and methodological, philosophical, ethical, and intelligent medical data analysis.

This advanced textbook explores small area estimation techniques, covers the underlying mathematical and statistical theory and offers hands-on support with their implementation. It presents the theory in a rigorous way and compares and contrasts various statistical methodologies, helping readers understand how to develop new methodologies for small area estimation. It also includes numerous sample applications of small area estimation techniques. The underlying R code is provided in the text and applied to four datasets that mimic data from labor markets and living conditions surveys, where the socioeconomic indicators include the small area estimation of total unemployment, unemployment rates, average annual household incomes and poverty indicators. Given its scope, the book will be useful for master and PhD students, and for official and other applied statisticians.

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

Complexity and Complex Thermoeconomic Systems describes the properties of complexity and complex thermo-economic systems as the consequence of formulations, definitions, tools, solutions and results consistent with the best performance of a system. Applying to complex systems contemporary advanced techniques, such as static optimization, optimal control, and neural networks, this book treats the systems theory as a science of general laws for functional integrities. It also provides a platform for the discussion of various definitions of complexity, complex hierarchical structures, self-organization examples, special references, and historical issues. This book is a valuable reference for scientists, engineers and graduated students in chemical, mechanical, and environmental engineering, as well as those in physics, ecology and biology, helping them better understand the complex thermodynamic systems and enhance their technical skills in research.

Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program.

- includes MATLABr fundamentals, matrices, arrays, general graphics and specialized plots in quality assurance problems, script files, ordinary and partial differential equations - gives calculation of six sigma, total quality management, time series forecasting, reliability, process improvement, metrology, quality control and assurance, measurement and testing techniques - provides tools for graphical presentation, basic and special statistics and testing, ordinary and partial differential solvers, and fitting tools - includes comprehensive command information in tables Many books are available on MATLABr programming for engineers in general or in some specific area, but none in the highly topical field of quality assurance (QA). MATLABr in quality assurance sciences fills this gap as a compact guide for students, engineers, and scientists in this field. It concentrates on MATLABr fundamentals with examples of application to a wide range of current problems from general, nano and bio-technology, and statistical control, to medicine and industrial management. Examples cover both the school and advanced level; comprising calculations of total quality management, six sigma, time series, process improvement, metrology, quality control, human factors in quality assurance, measurement and testing techniques, quality project and function management, and customer satisfaction. The book covers key topics, including: the basics of software with examples; graphics and representations; numerical computation, scripts and functions for QA calculations; ODE and PDEPE solvers applied to QA problems; curve fitting and time series tool interfaces in calculations of quality; and statistics calculations applied to quality testing.

This book highlights recent advances in natural computing, including biology and its theory, bio-inspired computing, computational aesthetics, computational models and theories, computing with natural media, philosophy of natural computing and educational technology. It presents extended versions of the best papers selected from the symposium "7th International Workshop on Natural Computing" (IWNC7), held in Tokyo, Japan, in 2013. The target audience is not limited to researchers working in natural computing but also those active in biological engineering, fine/media art design, aesthetics and philosophy.

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

Computational Methods in Drug Discovery and Repurposing for Cancer Therapy provides knowledge about ongoing research and computational approaches for drug discovery and repurposing for cancer therapy. The book provides detailed descriptions about target molecules and pathways and their inhibitors for easy understanding and applicability. Users will find discussions on tools and techniques such as integrated bioinformatics approaches, systems biology tools, molecular docking, computational chemistry, artificial intelligence, machine learning, structure-based virtual screening, biomarkers and transcriptome which are discussed in the context of different cancer types, such as colon, glioblastoma, endometrial and retinoblastoma, amongst others. This book will be a valuable resource for researchers, students and member of the biomedical and medical fields who want to learn more about the use of computational modeling to better tailor treatments for cancer patients.

Biostatistics Manual for Health Research: A Practical Guide to Data Analysis is a guide for researchers on how to apply biostatistics on different types of data. The book approaches biostatistics and its application from medical and health researcher's point-of-view and has real and mostly published data for practice and understanding. The interpretation and meaning of the statistical results, reporting guidelines and mistakes are taught with real world examples. This is a valuable resource for biostaticians, students and researchers from medical and biomedical fields who need to learn how to apply statistical approaches to improve their research.

Model Management and Analytics for Large Scale Systems covers the use of models and related artefacts (such as metamodels and model transformations) as central elements for tackling the complexity of building systems and managing data. With their increased use across diverse settings, the complexity, size, multiplicity and variety of those artefacts has increased. Originally developed for software engineering, these approaches can now be used to simplify the analytics of large-scale models and automate complex data analysis processes. Those in the field of data science will gain novel insights on the topic of model analytics that go beyond both model-based development and data analytics. This book is aimed at both researchers and practitioners who are interested in model-based development and the analytics of large-scale models, ranging from big data management and analytics, to enterprise domains. The book could also be used in graduate courses on model development, data analytics and data management.

Applied Statistics for Environmental Science with R presents the theory and application of statistical techniques in environmental science and aids researchers in choosing the appropriate statistical technique for analyzing their data. Focusing on the use of univariate and multivariate statistical methods, this book acts as a step-by-step resource to facilitate understanding in the use of R statistical software for interpreting data in the field of environmental science. Researchers utilizing statistical analysis in environmental science and engineering will find this book to be essential in solving their day-to-day research problems.

Partial least squares structural equation modeling (PLS-SEM) has become a standard approach for analyzing complex inter-relationships between observed and latent variables. Researchers appreciate the many advantages of PLS-SEM such as the possibility to estimate very complex models and the method's flexibility in terms of data requirements and measurement specification. This practical open access guide provides a step-by-step treatment of the major choices in analyzing PLS path models using R, a free software environment for statistical computing, which runs on Windows, macOS, and UNIX computer platforms. Adopting the R software's SEMinR package, which brings a friendly syntax to creating and estimating structural equation models, each chapter offers a concise overview of relevant topics and metrics, followed by an in-depth description of a case study. Simple instructions give readers the "how-tos" of using SEMinR to obtain solutions and document their results. Rules of thumb in every chapter provide guidance on best practices in the application and interpretation of PLS-SEM.

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.

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