Products related to Simulation:
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Introduction to Business Analytics Using Simulation
Introduction to Business Analytics Using Simulation, Second Edition employs an innovative strategy to teach business analytics.The book uses simulation modeling and analysis as mechanisms to introduce and link predictive and prescriptive modeling. Because managers can't fully assess what will happen in the future, but must still make decisions, the book treats uncertainty as an essential element in decision-making.Its use of simulation gives readers a superior way of analyzing past data, understanding an uncertain future, and optimizing results to select the best decision.With its focus on uncertainty and variability, this book provides a comprehensive foundation for business analytics. Students will gain a better understanding of fundamental statistical concepts that are essential to marketing research, Six-Sigma, financial analysis, and business analytics.
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Business Process Analytics : Modeling, Simulation and Design
The fourth edition of this widely used textbook offers a new perspective.Previously titled Business Process Modeling, Simulation and Design, as the new title suggests, this book is about analytical business process modeling and design.However, this new edition introduces analytics to the title and to the presentation. The main objective of this book is to provide students with a comprehensive understanding of the multitude of analytical tools that can be used to model, analyse, understand, and ultimately design business processes. The most flexible and powerful of these tools, although not always the most appropriate, is discrete-event simulation.The wide range of approaches covered in this book include graphical flowcharting tools, deterministic models for cycle time analysis and capacity decisions, and analytical queuing methods, as well as data mining. The authors focus on business processes as opposed to just manufacturing processes or general operations management problems and emphasise on simulation modeling using state-of-the-art commercial simulation software. BUSINESS PROCESS ANALYTICS Modeling, Simulation, and Design can be thought of as a hybrid between traditional books on process management, operations management, and simulation.The growing interest in simulation-based tools suggests an understanding of simulation modeling, its potential as well as its limitations for analysing and designing processes, is of key importance to students looking for a future career in operations management. Changes from previous edition. § New Section on data-driven process improvement (with data visualization)§ Added a subsection of control charts to the 6-sigma section§ Replaced business process reengineering with business process management§ Updated all text, figures and examples, and exercises to ExtendSim10 (current version)§ More coverage on design of experiments§ More coverage of machine learning and neural networks
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Atmospheric Turbulence Simulation Using Liquid Crystal Spatial Light Modulators
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Data Analytics & Visualization All-in-One For Dummies
Install data analytics into your brain with this comprehensive introduction Data Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place.Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling.Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one.You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau.You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level.With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sourcesOrganize and analyze data Use data to tell a story with TableauExpand your know-how with Python and R New and novice data analysts will love this All-in-One reference on how to make sense of data.Get ready to watch as your career in data takes off.
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What is spatial visualization ability?
Spatial visualization ability refers to the capacity to mentally manipulate and comprehend spatial relationships between objects. Individuals with strong spatial visualization skills can easily visualize and understand how objects relate to each other in space, such as rotating or manipulating shapes in their mind. This ability is crucial in various fields such as engineering, architecture, and mathematics, as it allows individuals to solve complex problems and understand spatial concepts more effectively. Improving spatial visualization ability can enhance problem-solving skills and overall cognitive performance.
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Is life a simulation?
The question of whether life is a simulation is a philosophical and scientific debate that has been explored by many thinkers. Some argue that the complexity and order of the universe suggest that it could be a simulation created by a higher intelligence or advanced civilization. Others believe that life is not a simulation, but rather a product of natural processes and evolution. Ultimately, the answer to this question is still unknown and may remain a topic of speculation and debate for years to come.
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Is spatial visualization important for engineers?
Yes, spatial visualization is important for engineers as it allows them to mentally manipulate and understand complex 3D objects and structures. Engineers often need to design and analyze various components and systems, and spatial visualization skills help them to conceptualize and communicate their ideas effectively. Whether it's designing a new product, creating blueprints for a building, or solving complex problems, spatial visualization is a crucial skill that allows engineers to think critically and innovate in their field.
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Is this world a simulation?
The question of whether our world is a simulation is a topic of philosophical debate. Some argue that advancements in technology and the possibility of creating realistic simulations suggest that it is possible our reality is simulated. Others believe that there is no concrete evidence to support this theory and that our world is real. Ultimately, the answer to this question remains unknown and is open to interpretation.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Spatial Analysis with R : Statistics, Visualization, and Computational Methods
In the five years since the publication of the first edition of Spatial Analysis: Statistics, Visualization, and Computational Methods, many new developments have taken shape regarding the implementation of new tools and methods for spatial analysis with R.The use and growth of artificial intelligence, machine learning and deep learning algorithms with a spatial perspective, and the interdisciplinary use of spatial analysis are all covered in this second edition along with traditional statistical methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis.Spatial Analysis with R: Statistics, Visualization, and Computational Methods, Second Edition provides a balance between concepts and practicums of spatial statistics with a comprehensive coverage of the most important approaches to understand spatial data, analyze spatial relationships and patterns, and predict spatial processes.New in the Second Edition: Includes new practical exercises and worked-out examples using R Presents a wide range of hands-on spatial analysis worktables and lab exercises All chapters are revised and include new illustrations of different concepts using data from environmental and social sciences Expanded material on spatiotemporal methods, visual analytics methods, data science, and computational methods Explains big data, data management, and data miningThis second edition of an established textbook, with new datasets, insights, excellent illustrations, and numerous examples with R, is perfect for senior undergraduate and first-year graduate students in geography and the geosciences.
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Spatial Epidemiological Approaches in Disease Mapping and Analysis
Containing method descriptions and step-by-step procedures, the Spatial Epidemiological Approaches in Disease Mapping and Analysis equips readers with skills to prepare health-related data in the proper format, process these data using relevant functions and software, and display the results as mapped or statistical summaries.Describing the wide range of available methods and key GIS concepts for spatial epidemiology, this book illustrates the utilities of the software using real-world data.Additional topics include geographic data models, address matching, geostatistical analysis, universal kriging, point pattern analysis, kernel density, spatio-temporal display, and disease surveillance.
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Crime Mapping and Spatial Data Analysis using R
Crime mapping and analysis sit at the intersection of geocomputation, data visualisation and cartography, spatial statistics, environmental criminology, and crime analysis.This book brings together relevant knowledge from these fields into a practical, hands-on guide, providing a useful introduction and reference material for topics in crime mapping, the geography of crime, environmental criminology, and crime analysis.It can be used by students, practitioners, and academics alike, whether to develop a university course, to support further training and development, or to hone skills in self-teaching R and crime mapping and spatial data analysis.It is not an advanced statistics textbook, but rather an applied guide and later useful reference books, intended to be read and for readers to practice the learnings from each chapter in sequence.In the first part of this volume we introduce key concepts for geographic analysis and representation and provide the reader with the foundations needed to visualise spatial crime data.We then introduce a series of tools to study spatial homogeneity and dependence.A key focus in this section is how to visualise and detect local clusters of crime and repeat victimisation.The final chapters introduce the use of basic spatial models, which account for the distribution of crime across space.In terms of spatial data analysis the focus of the book is on spatial point pattern analysis and lattice or area data analysis.
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Can life be a simulation?
The concept of life being a simulation is a philosophical idea that has been debated for years. Some argue that it is possible, given the advancements in technology and the potential for creating highly realistic simulations. Others believe that the complexity and unpredictability of life make it unlikely to be simulated. Ultimately, the question of whether life is a simulation remains unanswered and is open to interpretation and speculation.
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What is a driving simulation?
A driving simulation is a virtual environment that replicates real-world driving scenarios and conditions. It allows users to experience driving a vehicle in a controlled setting without the risks associated with actual driving. These simulations can be used for training purposes, research, entertainment, or testing new vehicle technologies. Users interact with the simulation through a steering wheel, pedals, and a screen that displays the virtual environment, providing a realistic driving experience.
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Search for a manager simulation.
You can search for a manager simulation game on various platforms such as Steam, Google Play Store, or the Apple App Store. Some popular manager simulation games include Football Manager, Motorsport Manager, and SimCity. These games allow players to take on the role of a manager and make decisions that impact the success of their team or city. Whether you're interested in sports, business, or city-building, there are manager simulation games available to suit your interests.
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What does simulation seriously mean?
Simulation seriously means creating a model or representation of a real-world system or process in order to understand, analyze, and predict its behavior. It involves using mathematical and computational techniques to mimic the dynamics and interactions of the system, allowing for experimentation and scenario analysis without the need for real-world implementation. Simulation is a powerful tool for decision-making, risk assessment, and optimization in various fields such as engineering, healthcare, and finance. It allows for testing different strategies and understanding the potential outcomes of complex systems, ultimately leading to more informed and effective decision-making.
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