Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Modeling for Insight PDF full book. Access full book title Modeling for Insight by Stephen G. Powell. Download full books in PDF and EPUB format.
Author: Stephen G. Powell Publisher: John Wiley & Sons ISBN: 1118210484 Category : Mathematics Languages : en Pages : 392
Book Description
Praise for Modeling for Insight "Most books on modeling are either too theoretical or too focused on the mechanics of programming. Powell and Batt's emphasis on using simple spreadsheet models to gain business insight (which is, after all, the name of the game) is what makes this book stand head and shoulders above the rest. This clear and practical book deserves a place on the shelf of every business analyst." —Jonathan Koomey, PhD, Lawrence Berkeley National Laboratory and Stanford University, author of Turning Numbers into Knowledge: Mastering the Art of Problem Solving Most business analysts are familiar with using spreadsheets to organize data and build routine models. However, analysts often struggle when faced with examining new and ill-structured problems. Modeling for Insight is a one-of-a-kind guide to building effective spreadsheet models and using them to generate insights. With its hands-on approach, this book provides readers with an effective modeling process and specific modeling tools to become a master modeler. The authors provide a structured approach to problem-solving using four main steps: frame the problem, diagram the problem, build a model, and generate insights. Extensive examples, graduated in difficulty, help readers to internalize this modeling process, while also demonstrating the application of important modeling tools, including: Influence diagrams Spreadsheet engineering Parameterization Sensitivity analysis Strategy analysis Iterative modeling The real-world examples found in the book are drawn from a wide range of fields such as financial planning, insurance, pharmaceuticals, advertising, and manufacturing. Each chapter concludes with a discussion on how to use the insights drawn from these models to create an effective business presentation. Microsoft Office Excel and PowerPoint are used throughout the book, along with the add-ins Premium Solver, Crystal Ball, and Sensitivity Toolkit. Detailed appendices guide readers through the use of these software packages, and the spreadsheet models discussed in the book are available to download via the book's related Web site. Modeling for Insight is an ideal book for courses in engineering, operations research, and management science at the upper-undergraduate and graduate levels. It is also a valuable resource for consultants and business analysts who often use spreadsheets to better understand complex problems.
Author: Stephen G. Powell Publisher: John Wiley & Sons ISBN: 1118210484 Category : Mathematics Languages : en Pages : 392
Book Description
Praise for Modeling for Insight "Most books on modeling are either too theoretical or too focused on the mechanics of programming. Powell and Batt's emphasis on using simple spreadsheet models to gain business insight (which is, after all, the name of the game) is what makes this book stand head and shoulders above the rest. This clear and practical book deserves a place on the shelf of every business analyst." —Jonathan Koomey, PhD, Lawrence Berkeley National Laboratory and Stanford University, author of Turning Numbers into Knowledge: Mastering the Art of Problem Solving Most business analysts are familiar with using spreadsheets to organize data and build routine models. However, analysts often struggle when faced with examining new and ill-structured problems. Modeling for Insight is a one-of-a-kind guide to building effective spreadsheet models and using them to generate insights. With its hands-on approach, this book provides readers with an effective modeling process and specific modeling tools to become a master modeler. The authors provide a structured approach to problem-solving using four main steps: frame the problem, diagram the problem, build a model, and generate insights. Extensive examples, graduated in difficulty, help readers to internalize this modeling process, while also demonstrating the application of important modeling tools, including: Influence diagrams Spreadsheet engineering Parameterization Sensitivity analysis Strategy analysis Iterative modeling The real-world examples found in the book are drawn from a wide range of fields such as financial planning, insurance, pharmaceuticals, advertising, and manufacturing. Each chapter concludes with a discussion on how to use the insights drawn from these models to create an effective business presentation. Microsoft Office Excel and PowerPoint are used throughout the book, along with the add-ins Premium Solver, Crystal Ball, and Sensitivity Toolkit. Detailed appendices guide readers through the use of these software packages, and the spreadsheet models discussed in the book are available to download via the book's related Web site. Modeling for Insight is an ideal book for courses in engineering, operations research, and management science at the upper-undergraduate and graduate levels. It is also a valuable resource for consultants and business analysts who often use spreadsheets to better understand complex problems.
Author: Gary Klein Publisher: PublicAffairs ISBN: 1610392752 Category : Business & Economics Languages : en Pages : 306
Book Description
Insights -- like Darwin's understanding of the way evolution actually works, and Watson and Crick's breakthrough discoveries about the structure of DNA -- can change the world. We also need insights into the everyday things that frustrate and confuse us so that we can more effectively solve problems and get things done. Yet we know very little about when, why, or how insights are formed -- or what blocks them. In Seeing What Others Don't, renowned cognitive psychologist Gary Klein unravels the mystery. Klein is a keen observer of people in their natural settings -- scientists, businesspeople, firefighters, police officers, soldiers, family members, friends, himself -- and uses a marvelous variety of stories to illuminate his research into what insights are and how they happen. What, for example, enabled Harry Markopolos to put the finger on Bernie Madoff? How did Dr. Michael Gottlieb make the connections between different patients that allowed him to publish the first announcement of the AIDS epidemic? What did Admiral Yamamoto see (and what did the Americans miss) in a 1940 British attack on the Italian fleet that enabled him to develop the strategy of attack at Pearl Harbor? How did a "smokejumper" see that setting another fire would save his life, while those who ignored his insight perished? How did Martin Chalfie come up with a million-dollar idea (and a Nobel Prize) for a natural flashlight that enabled researchers to look inside living organisms to watch biological processes in action? Klein also dissects impediments to insight, such as when organizations claim to value employee creativity and to encourage breakthroughs but in reality block disruptive ideas and prioritize avoidance of mistakes. Or when information technology systems are "dumb by design" and block potential discoveries. Both scientifically sophisticated and fun to read, Seeing What Others Don't shows that insight is not just a "eureka!" moment but a whole new way of understanding.
Author: Dilip Chhajed Publisher: Springer Science & Business Media ISBN: 0387736999 Category : Business & Economics Languages : en Pages : 195
Book Description
This is the first book in the field that uses the power of the basic models and principles to provide students and managers with an "intuitive understanding" of operations management. The book touches on nine fundamental models and principles, and outlines the key insights behind each one. Some of the very biggest names in the Management Science field have developed and carefully written these chapters on the field’s basic models.
Author: Andrew Gelman Publisher: Cambridge University Press ISBN: 9780521686891 Category : Mathematics Languages : en Pages : 654
Book Description
This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models.
Author: Darryl I. MacKenzie Publisher: Elsevier ISBN: 0124072453 Category : Mathematics Languages : en Pages : 668
Book Description
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition, provides a synthesis of model-based approaches for analyzing presence-absence data, allowing for imperfect detection. Beginning from the relatively simple case of estimating the proportion of area or sampling units occupied at the time of surveying, the authors describe a wide variety of extensions that have been developed since the early 2000s. This provides an improved insight about species and community ecology, including, detection heterogeneity; correlated detections; spatial autocorrelation; multiple states or classes of occupancy; changes in occupancy over time; species co-occurrence; community-level modeling, and more. Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence, Second Edition has been greatly expanded and detail is provided regarding the estimation methods and examples of their application are given. Important study design recommendations are also covered to give a well rounded view of modeling. Provides authoritative insights into the latest in occupancy modeling Examines the latest methods in analyzing detection/no detection data surveys Addresses critical issues of imperfect detectability and its effects on species occurrence estimation Discusses important study design considerations such as defining sample units, sample size determination and optimal effort allocation
Author: Prakasha, G. S. Publisher: IGI Global ISBN: 1668495783 Category : Computers Languages : en Pages : 427
Book Description
Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems.
Author: Thomas W. Miller Publisher: Pearson Education ISBN: 0133412938 Category : Business & Economics Languages : en Pages : 347
Book Description
Today, successful firms compete and win based on analytics. Modeling Techniques in Predictive Analytics brings together all the concepts, techniques, and R code you need to excel in any role involving analytics. Thomas W. Miller's unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business challenges and business cases, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and even spatio-temporal data. For each problem, Miller explains why the problem matters, what data is relevant, how to explore your data once you've identified it, and then how to successfully model that data. You'll learn how to model data conceptually, with words and figures; and then how to model it with realistic R programs that deliver actionable insights and knowledge. Miller walks you through model construction, explanatory variable subset selection, and validation, demonstrating best practices for improving out-of-sample predictive performance. He employs data visualization and statistical graphics in exploring data, presenting models, and evaluating performance. All example code is presented in R, today's #1 system for applied statistics, statistical research, and predictive modeling; code is set apart from other text so it's easy to find for those who want it (and easy to skip for those who don't).
Author: Max Kuhn Publisher: Springer Science & Business Media ISBN: 1461468493 Category : Medical Languages : en Pages : 595
Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.
Author: Saikou Y. Diallo Publisher: Springer ISBN: 303017090X Category : Philosophy Languages : en Pages : 241
Book Description
This uniquely inspirational and practical book explores human simulation, which is the application of computational modeling and simulation to research subjects in the humanities disciplines. It delves into the fascinating process of collaboration among experts who usually don’t have much to do with one another – computer engineers and humanities scholars – from the perspective of the humanities scholars. It also explains the process of developing models and simulations in these interdisciplinary teams. Each chapter takes the reader on a journey, presenting a specific theory about the human condition, a model of that theory, discussion of its implementation, analysis of its results, and an account of the collaborative experience. Contributing authors with different fields of expertise share how each model was validated, discuss relevant datasets, explain development strategies, and frankly discuss the ups and downs of the process of collaborative development. Readers are given access to the models and will also gain new perspectives from the authors’ findings, experiences, and recommendations. Today we are in the early phases of an information revolution, combining access to vast computing resources, large amounts of human data through social media, and an unprecedented richness of methods and tools to capture, analyze, explore, and test hypotheses and theories of all kinds. Thus, this book’s insights will be valuable not only to students and scholars of humanities subjects, but also to the general reader and researchers from other disciplines who are intrigued by the expansion of the information revolution all the way into the humanities departments of modern universities.
Author: Gerard Blokdijk Publisher: Complete Publishing ISBN: 9781488896231 Category : Reference Languages : en Pages : 208
Book Description
The one-stop-source powering Data Modeling success, jam-packed with ready to use insights for success, loaded with all the data you need to decide how to gain and move ahead. An one-of-a-kind book, based on extensive research, this reveals the best practices of the most successful Data Modeling knowledge mavens, those who are adept at continually innovating and seeing opportunity where others do not. This is the first place to go for Data Modeling innovation, in today's knowledge-driven business environment, professionals face particular challenges as their purpose is to discover or develop new concepts, products, or processes; the pressure to perform is intense. This title is the entryway to a single source for innovation. BONUS: Included with the book come numerous real-world Data Modeling blueprints, presentations and templates ready for you to download and use. This book addresses the crucial issue of Data Modeling adoption by presenting the facts to move beyond general observation. The model underpinning this book has been used as a predictive decision tool, tracking thousands of innovations for over more than a decade. And...this all-encompassing analysis focuses on key areas of future Data Modeling growth.
Author: Stephen G. Powell
Author: Stephen G. Powell
Author: Gary Klein
Author: Dilip Chhajed
Author: Andrew Gelman
Author: Darryl I. MacKenzie
Author: Prakasha, G. S.
Author: Thomas W. Miller
Author: Max Kuhn
Author: Saikou Y. Diallo
Author: Gerard Blokdijk