Features an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference Illuminating Statistical Analysis Using Scenarios and Simulations presents the basic concepts of statistics and statistical inference using the dual mechanisms of scenarios and simulations. This approach helps readers develop key intuitions and deep understandings of statistical analysis. Scenario-specific sampling simulations depict the results that would be obtained by a very large number of individuals investigating the same scenario, each with their own evidence, while graphical depictions of the simulation results present clear and direct pathways to intuitive methods for statistical inference. These intuitive methods can then be easily linked to traditional formulaic methods, and the author does not simply explain the linkages, but rather provides demonstrations throughout for a broad range of statistical phenomena. In addition, induction and deduction are repeatedly interwoven, which fosters a natural "need to know basis" for ordering the topic coverage. Examining computer simulation results is central to the discussion and provides an illustrative way to (re)discover the properties of sample statistics, the role of chance, and to (re)invent corresponding principles of statistical inference. In addition, the simulation results foreshadow the various mathematical formulas that underlie statistical analysis. In addition, this book: • Features both an intuitive and analytical perspective and includes a broad introduction to the use of Monte Carlo simulation and formulaic methods for statistical analysis • Presents straight-forward coverage of the essentials of basic statistics and ensures proper understanding of key concepts such as sampling distributions, the effects of sample size and variance on uncertainty, analysis of proportion, mean and rank differences, covariance, correlation, and regression • Introduces advanced topics such as Bayesian statistics, data mining, model cross-validation, robust regression, and resampling • Contains numerous example problems in each chapter with detailed solutions as well as an appendix that serves as a manual for constructing simulations quickly and easily using Microsoft® Office Excel® Illuminating Statistical Analysis Using Scenarios and Simulations is an ideal textbook for courses, seminars, and workshops in statistics and statistical inference and is appropriate for self-study as well. The book also serves as a thought-provoking treatise for researchers, scientists, managers, technicians, and others with a keen interest in statistical analysis. Jeffrey E. Kottemann, Ph.D., is Professor in the Perdue School at Salisbury University. Dr. Kottemann has published articles in a wide variety of academic research journals in the fields of business administration, computer science, decision sciences, economics, engineering, information systems, psychology, and public administration. He received his Ph.D. in Systems and Quantitative Methods from the University of Arizona.

Features an integrated approach of statistical scenarios and simulations to aid readers in developing key intuitions needed to understand the wide ranging concepts and methods of statistics and inference Illuminating Statistical Analysis Using Scenarios and Simulations presents the basic concepts of statistics and statistical inference using the dual mechanisms of scenarios and simulations. This approach helps readers develop key intuitions and deep understandings of statistical analysis. Scenario-specific sampling simulations depict the results that would be obtained by a very large number of individuals investigating the same scenario, each with their own evidence, while graphical depictions of the simulation results present clear and direct pathways to intuitive methods for statistical inference. These intuitive methods can then be easily linked to traditional formulaic methods, and the author does not simply explain the linkages, but rather provides demonstrations throughout for a broad range of statistical phenomena. In addition, induction and deduction are repeatedly interwoven, which fosters a natural "need to know basis" for ordering the topic coverage. Examining computer simulation results is central to the discussion and provides an illustrative way to (re)discover the properties of sample statistics, the role of chance, and to (re)invent corresponding principles of statistical inference. In addition, the simulation results foreshadow the various mathematical formulas that underlie statistical analysis. In addition, this book: • Features both an intuitive and analytical perspective and includes a broad introduction to the use of Monte Carlo simulation and formulaic methods for statistical analysis • Presents straight-forward coverage of the essentials of basic statistics and ensures proper understanding of key concepts such as sampling distributions, the effects of sample size and variance on uncertainty, analysis of proportion, mean and rank differences, covariance, correlation, and regression • Introduces advanced topics such as Bayesian statistics, data mining, model cross-validation, robust regression, and resampling • Contains numerous example problems in each chapter with detailed solutions as well as an appendix that serves as a manual for constructing simulations quickly and easily using Microsoft® Office Excel® Illuminating Statistical Analysis Using Scenarios and Simulations is an ideal textbook for courses, seminars, and workshops in statistics and statistical inference and is appropriate for self-study as well. The book also serves as a thought-provoking treatise for researchers, scientists, managers, technicians, and others with a keen interest in statistical analysis. Jeffrey E. Kottemann, Ph.D., is Professor in the Perdue School at Salisbury University. Dr. Kottemann has published articles in a wide variety of academic research journals in the fields of business administration, computer science, decision sciences, economics, engineering, information systems, psychology, and public administration. He received his Ph.D. in Systems and Quantitative Methods from the University of Arizona.

From modeling and simulation to games and entertainment With contributions from leaders in systems and organizational modeling, behavioral and social sciences, computing and visualization, and gaming and entertainment, Organizational Simulation both articulates the grand vision of immersive environments and shows, in detail, how to realize it. This book offers unparalleled insight into the cutting edge of the field, since it was written by those who actually researched, designed, developed, deployed, marketed, sold, and critiqued today's best organizational simulations. The coverage is divided into four sections: * Introduction outlines the need for organizational simulation to support strategic thinking, design of unprecedented systems, and organizational learning, including the functionality and technology required to enable this support * Behaviors covers the state of knowledge of individual, group, and team behaviors and performance, how performance can best be supported, how performance is affected by national differences, and how organizational performance can best be measured * Modeling describes the latest approaches to modeling and simulating people, groups, teams, and organizations, as well as narrative contexts and organizational environments within which these entities act, drawing from a rich set of modeling methods and tools * Simulations and Games illustrates a wide range of fielded simulations, games, and entertainment, including the methods and tools employed for designing, developing, deploying, and evaluating these systems, as well as the social implications for the associated communities that have emerged Addressing all levels of organizational simulation architecture with theories and applications, and enabling technologies for each, Organizational Simulation offers students and professionals the premier reference and practical toolbox for this dynamic field.

The field of visible light communication (VLC) has diverse applications to the end user including streaming audio, video, high-speed data browsing, voice over internet and online gaming. This comprehensive textbook discusses fundamental aspects, research activities and modulation techniques in the field of VLC. Visible Light Communication: A Comprehensive Theory and Applications with MATLAB® discusses topics including line of sight (LOS) propagation model, non-line of sight (NLOS) propagation model, carrier less amplitude and phase modulation, multiple-input-multiple-output (MIMO), non-linearities of optical sources, orthogonal frequency-division multiple access, non-orthogonal multiple access and single-carrier frequency-division multiple access in depth. Primarily written for senior undergraduate and graduate students in the field of electronics and communication engineering for courses on optical wireless communication and VLC, this book: Provides up-to-date literature in the field of VLC Presents MATLAB codes and simulations to help readers understand simulations Discusses applications of VLC in enabling vehicle to vehicle (V2V) communication Covers topics including radio frequency (RF) based wireless communications and VLC Presents modulation formats along with the derivations of probability of error expressions pertaining to different variants of optical OFDM

In today's data-driven world, understanding and interpreting statistical information is more critical than ever, especially in medicine, where statistical methods are used to design and analyze clinical trials, study the distribution of disease in populations, and develop new treatments. In the era of evidence-based medicine, Exploring Medical Statistics: Biostatistics, Clinical Trials, and Epidemiology addresses the critical need for a grasp of statistical concepts. This book delves into biostatistics, clinical trials, and epidemiology, offering a robust foundation for understanding and interpreting statistical information in medicine. It explores biostatistics, elucidating fundamental elements such as probability, sampling, and hypothesis testing. The section on clinical trials covers the entire spectrum from trial design to ethical considerations, providing an invaluable resource for researchers navigating the complexities of medical research. Epidemiology, a cornerstone of public health, is examined in the book, offering insights into the distribution and determinants of diseases in populations. The application-focused section further extends the utility of medical statistics, encompassing public health, healthcare policy, and drug development.

This innovative volume introduces Trajectory Analysis, a new systems-based approach to measuring nonlinear dynamics in continuous change, to public health and epidemiology. It synthesizes influential strands of statistical and probability science (including chaos theory and catastrophe theory) to complement existing methods and models used in the health fields. The computational framework featured here pinpoints complex cause-and-effect processes in behavioral change as individuals and populations adjust to health interventions, with examples from neuroscience and cardiology. But this is no mere academic exercise, as the author illustrates how these methods can be harnessed toward finding real-world answers to longstanding public health problems, starting with treatment recidivism. Included in the coverage: · The universality of physical principles in the analysis of health and disease · The problem of recidivism in healthcare intervention studies · Stability and reversibility/irreversibility of health conditions · Chaos theory and sensitive dependence on initial conditions · Applications in health monitoring and geographic systems · Simulations, applications, and the challenge for public health A stimulating new take on statistics with powerful implications for future study, practice, and policy, Trajectory Analysis in Health Care should interest public health epidemiologists, researchers, clinicians, and policymakers.

Autonomous robot vehicles are vehicles capable of intelligent motion and action without requiring either a guide or teleoperator control. The recent surge of interest in this subject will grow even grow further as their potential applications increase. Autonomous vehicles are currently being studied for use as reconnaissance/exploratory vehicles for planetary exploration, undersea, land and air environments, remote repair and maintenance, material handling systems for offices and factories, and even intelligent wheelchairs for the disabled. This reference is the first to deal directly with the unique and fundamental problems and recent progress associated with autonomous vehicles. The editors have assembled and combined significant material from a multitude of sources, and, in effect, now conviniently provide a coherent organization to a previously scattered and ill-defined field.

This book gives practical advice and ready to use tips on the design and construction of subsurface reservoir models. The design elements cover rock architecture, petrophysical property modelling, multi-scale data integration, upscaling and uncertainty analysis. Philip Ringrose and Mark Bentley share their experience, gained from over a hundred reservoir modelling studies in 25 countries covering clastic, carbonate and fractured reservoir types, and for a range of fluid systems – oil, gas and CO2, production and injection, and effects of different mobility ratios. The intimate relationship between geology and fluid flow is explored throughout, showing how the impact of fluid type, displacement mechanism and the subtleties of single- and multi-phase flow combine to influence reservoir model design. The second edition updates the existing sections and adds sections on the following topics: · A new chapter on modelling for CO2 storage · A new chapter on modelling workflows · An extended chapter on fractured reservoir modelling · An extended chapter on multi-scale modelling · An extended chapter on the quantification of uncertainty · A revised section on the future of modelling based on recently published papers by the authors The main audience for this book is the community of applied geoscientists and engineers involved in understanding fluid flow in the subsurface: whether for the extraction of oil or gas or the injection of CO2 or the subsurface storage of energy in general. We will always need to understand how fluids move in the subsurface and we will always require skills to model these quantitatively. The second edition of this reference book therefore aims to highlight the modelling skills developed for the current energy industry which will also be required for the energy transition of the future. The book is aimed at technical-professional practitioners in the energy industry and is also suitable for a range of Master’s level courses in reservoir characterisation, modelling and engineering. • Provides practical advice and guidelines for users of 3D reservoir modelling packages • Gives advice on reservoir model design for the growing world-wide activity in subsurface reservoir modelling • Covers rock modelling, property modelling, upscaling, fluid flow and uncertainty handling • Encompasses clastic, carbonate and fractured reservoirs • Applies to multi-fluid cases and applications: hydrocarbons and CO2, production and storage; rewritten for use in the Energy Transition.