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| example of a model in science: R for Data Science Hadley Wickham, Garrett Grolemund, 2016-12-12 Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true signals in your dataset Communicate—learn R Markdown for integrating prose, code, and results |
| example of a model in science: A Framework for K-12 Science Education National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on a Conceptual Framework for New K-12 Science Education Standards, 2012-02-28 Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments. |
| example of a model in science: Modelling Learners and Learning in Science Education Keith S. Taber, 2013-12-11 This book sets out the necessary processes and challenges involved in modeling student thinking, understanding and learning. The chapters look at the centrality of models for knowledge claims in science education and explore the modeling of mental processes, knowledge, cognitive development and conceptual learning. The conclusion outlines significant implications for science teachers and those researching in this field. This highly useful work provides models of scientific thinking from different field and analyses the processes by which we can arrive at claims about the minds of others. The author highlights the logical impossibility of ever knowing for sure what someone else knows, understands or thinks, and makes the case that researchers in science education need to be much more explicit about the extent to which research onto learners’ ideas in science is necessarily a process of developing models. Through this book we learn that research reports should acknowledge the role of modeling and avoid making claims that are much less tentative than is justified as this can lead to misleading and sometimes contrary findings in the literature. In everyday life we commonly take it for granted that finding out what another knows or thinks is a relatively trivial or straightforward process. We come to take the ‘mental register’ (the way we talk about the ‘contents’ of minds) for granted and so teachers and researchers may readily underestimate the challenges involved in their work. |
| example of a model in science: Scientific Representation James Nguyen, Roman Frigg, 2022-09-01 This Element presents a philosophical exploration of the notion of scientific representation. It does so by focussing on an important class of scientific representations, namely scientific models. Models are important in the scientific process because scientists can study a model to discover features of reality. But what does it mean for something to represent something else? This is the question discussed in this Element. The authors begin by disentangling different aspects of the problem of representation and then discuss the dominant accounts in the philosophical literature: the resemblance view and inferentialism. They find them both wanting and submit that their own preferred option, the so-called DEKI account, not only eschews the problems that beset these conceptions, but further provides a comprehensive answer to the question of how scientific representation works. This title is also available as Open Access on Cambridge Core. |
| example of a model in science: Model Based Inference in the Life Sciences David R. Anderson, 2007-12-22 This textbook introduces a science philosophy called information theoretic based on Kullback-Leibler information theory. It focuses on a science philosophy based on multiple working hypotheses and statistical models to represent them. The text is written for people new to the information-theoretic approaches to statistical inference, whether graduate students, post-docs, or professionals. Readers are however expected to have a background in general statistical principles, regression analysis, and some exposure to likelihood methods. This is not an elementary text as it assumes reasonable competence in modeling and parameter estimation. |
| example of a model in science: Body Physics Lawrence Davis, 201? Body Physics was designed to meet the objectives of a one-term high school or freshman level course in physical science, typically designed to provide non-science majors and undeclared students with exposure to the most basic principles in physics while fulfilling a science-with-lab core requirement. The content level is aimed at students taking their first college science course, whether or not they are planning to major in science. However, with minor supplementation by other resources, such as OpenStax College Physics, this textbook could easily be used as the primary resource in 200-level introductory courses. Chapters that may be more appropriate for physics courses than for general science courses are noted with an asterisk symbol (*). Of course this textbook could be used to supplement other primary resources in any physics course covering mechanics and thermodynamics--Textbook Web page. |
| example of a model in science: Modern Data Science with R Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton, 2021-03-31 From a review of the first edition: Modern Data Science with R... is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician). Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions. The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to understand. New functionality from packages like sf, purrr, tidymodels, and tidytext is now integrated into the text. All chapters have been revised, and several have been split, re-organized, or re-imagined to meet the shifting landscape of best practice. |
| example of a model in science: Biomedical Models and Resources National Research Council, Commission on Life Sciences, Institute for Laboratory Animal Research, Committee on New and Emerging Models in Biomedical and Behavioral Research, 1998-02-16 Printbegrænsninger: Der kan printes 10 sider ad gangen og max. 40 sider pr. session. |
| example of a model in science: Scientific Models Philip Gerlee, Torbjörn Lundh, 2016-07-07 A zebrafish, the hull of a miniature ship, a mathematical equation and a food chain - what do these things have in common? They are examples of models used by scientists to isolate and study particular aspects of the world around us. This book begins by introducing the concept of a scientific model from an intuitive perspective, drawing parallels to mental models and artistic representations. It then recounts the history of modelling from the 16th century up until the present day. The iterative process of model building is described and discussed in the context of complex models with high predictive accuracy versus simpler models that provide more of a conceptual understanding. To illustrate the diversity of opinions within the scientific community, we also present the results of an interview study, in which ten scientists from different disciplines describe their views on modelling and how models feature in their work. Lastly, it includes a number of worked examples that span different modelling approaches and techniques. It provides a comprehensive introduction to scientific models and shows how models are constructed and used in modern science. It also addresses the approach to, and the culture surrounding modelling in different scientific disciplines. It serves as an inspiration for model building and also facilitates interdisciplinary collaborations by showing how models are used in different scientific fields. The book is aimed primarily at students in the sciences and engineering, as well as students at teacher training colleges but will also appeal to interested readers wanting to get an overview of scientific modelling in general and different modelling approaches in particular. |
| example of a model in science: Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices Christina V. Schwarz, Cynthia Passmore, Brian J. Reiser , 2017-01-31 When it’s time for a game change, you need a guide to the new rules. Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices provides a play-by-play understanding of the practices strand of A Framework for K–12 Science Education (Framework) and the Next Generation Science Standards (NGSS). Written in clear, nontechnical language, this book provides a wealth of real-world examples to show you what’s different about practice-centered teaching and learning at all grade levels. The book addresses three important questions: 1. How will engaging students in science and engineering practices help improve science education? 2. What do the eight practices look like in the classroom? 3. How can educators engage students in practices to bring the NGSS to life? Helping Students Make Sense of the World Using Next Generation Science and Engineering Practices was developed for K–12 science teachers, curriculum developers, teacher educators, and administrators. Many of its authors contributed to the Framework’s initial vision and tested their ideas in actual science classrooms. If you want a fresh game plan to help students work together to generate and revise knowledge—not just receive and repeat information—this book is for you. |
| example of a model in science: Understanding by Design Grant P. Wiggins, Jay McTighe, 2005 What is understanding and how does it differ from knowledge? How can we determine the big ideas worth understanding? Why is understanding an important teaching goal, and how do we know when students have attained it? How can we create a rigorous and engaging curriculum that focuses on understanding and leads to improved student performance in today's high-stakes, standards-based environment? Authors Grant Wiggins and Jay McTighe answer these and many other questions in this second edition of Understanding by Design. Drawing on feedback from thousands of educators around the world who have used the UbD framework since its introduction in 1998, the authors have greatly revised and expanded their original work to guide educators across the K-16 spectrum in the design of curriculum, assessment, and instruction. With an improved UbD Template at its core, the book explains the rationale of backward design and explores in greater depth the meaning of such key ideas as essential questions and transfer tasks. Readers will learn why the familiar coverage- and activity-based approaches to curriculum design fall short, and how a focus on the six facets of understanding can enrich student learning. With an expanded array of practical strategies, tools, and examples from all subject areas, the book demonstrates how the research-based principles of Understanding by Design apply to district frameworks as well as to individual units of curriculum. Combining provocative ideas, thoughtful analysis, and tested approaches, this new edition of Understanding by Design offers teacher-designers a clear path to the creation of curriculum that ensures better learning and a more stimulating experience for students and teachers alike. |
| example of a model in science: Ambitious Science Teaching Mark Windschitl, Jessica Thompson, Melissa Braaten, 2020-08-05 2018 Outstanding Academic Title, Choice Ambitious Science Teaching outlines a powerful framework for science teaching to ensure that instruction is rigorous and equitable for students from all backgrounds. The practices presented in the book are being used in schools and districts that seek to improve science teaching at scale, and a wide range of science subjects and grade levels are represented. The book is organized around four sets of core teaching practices: planning for engagement with big ideas; eliciting student thinking; supporting changes in students’ thinking; and drawing together evidence-based explanations. Discussion of each practice includes tools and routines that teachers can use to support students’ participation, transcripts of actual student-teacher dialogue and descriptions of teachers’ thinking as it unfolds, and examples of student work. The book also provides explicit guidance for “opportunity to learn” strategies that can help scaffold the participation of diverse students. Since the success of these practices depends so heavily on discourse among students, Ambitious Science Teaching includes chapters on productive classroom talk. Science-specific skills such as modeling and scientific argument are also covered. Drawing on the emerging research on core teaching practices and their extensive work with preservice and in-service teachers, Ambitious Science Teaching presents a coherent and aligned set of resources for educators striving to meet the considerable challenges that have been set for them. |
| example of a model in science: Mega Physical Education (044) Secrets Study Guide: Mega Test Review for the Missouri Educator Gateway Assessments Mega Exam Secrets Test Prep, 2018-04-12 ***Includes Practice Test Questions*** MEGA Physical Education (044) Secrets helps you ace the Missouri Educator Gateway Assessments, without weeks and months of endless studying. Our comprehensive MEGA Physical Education (044) Secrets study guide is written by our exam experts, who painstakingly researched every topic and concept that you need to know to ace your test. Our original research reveals specific weaknesses that you can exploit to increase your exam score more than you've ever imagined. MEGA Physical Education (044) Secrets includes: The 5 Secret Keys to MEGA Success: Time is Your Greatest Enemy, Guessing is Not Guesswork, Practice Smarter, Not Harder, Prepare, Don't Procrastinate, Test Yourself; A comprehensive General Strategy review including: Make Predictions, Answer the Question, Benchmark, Valid Information, Avoid Fact Traps, Milk the Question, The Trap of Familiarity, Eliminate Answers, Tough Questions, Brainstorm, Read Carefully, Face Value, Prefixes, Hedge Phrases, Switchback Words, New Information, Time Management, Contextual Clues, Don't Panic, Pace Yourself, Answer Selection, Check Your Work, Beware of Directly Quoted Answers, Slang, Extreme Statements, Answer Choice Families; Along with a complete, in-depth study guide for your specific MEGA exam, and much more... |
| example of a model in science: Testing the Standard Model Mirjam Cvetič, 1991 |
| example of a model in science: Category Theory for the Sciences David I. Spivak, 2014-10-17 An introduction to category theory as a rigorous, flexible, and coherent modeling language that can be used across the sciences. Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences. Information is inherently dynamic; the same ideas can be organized and reorganized in countless ways, and the ability to translate between such organizational structures is becoming increasingly important in the sciences. Category theory offers a unifying framework for information modeling that can facilitate the translation of knowledge between disciplines. Written in an engaging and straightforward style, and assuming little background in mathematics, the book is rigorous but accessible to non-mathematicians. Using databases as an entry to category theory, it begins with sets and functions, then introduces the reader to notions that are fundamental in mathematics: monoids, groups, orders, and graphs—categories in disguise. After explaining the “big three” concepts of category theory—categories, functors, and natural transformations—the book covers other topics, including limits, colimits, functor categories, sheaves, monads, and operads. The book explains category theory by examples and exercises rather than focusing on theorems and proofs. It includes more than 300 exercises, with solutions. Category Theory for the Sciences is intended to create a bridge between the vast array of mathematical concepts used by mathematicians and the models and frameworks of such scientific disciplines as computation, neuroscience, and physics. |
| example of a model in science: Dependence Modeling Harry Joe, Dorota Kurowicka, 2011 1. Introduction : Dependence modeling / D. Kurowicka -- 2. Multivariate copulae / M. Fischer -- 3. Vines arise / R.M. Cooke, H. Joe and K. Aas -- 4. Sampling count variables with specified Pearson correlation : A comparison between a naive and a C-vine sampling approach / V. Erhardt and C. Czado -- 5. Micro correlations and tail dependence / R.M. Cooke, C. Kousky and H. Joe -- 6. The Copula information criterion and Its implications for the maximum pseudo-likelihood estimator / S. Gronneberg -- 7. Dependence comparisons of vine copulae with four or more variables / H. Joe -- 8. Tail dependence in vine copulae / H. Joe -- 9. Counting vines / O. Morales-Napoles -- 10. Regular vines : Generation algorithm and number of equivalence classes / H. Joe, R.M. Cooke and D. Kurowicka -- 11. Optimal truncation of vines / D. Kurowicka -- 12. Bayesian inference for D-vines : Estimation and model selection / C. Czado and A. Min -- 13. Analysis of Australian electricity loads using joint Bayesian inference of D-vines with autoregressive margins / C. Czado, F. Gartner and A. Min -- 14. Non-parametric Bayesian belief nets versus vines / A. Hanea -- 15. Modeling dependence between financial returns using pair-copula constructions / K. Aas and D. Berg -- 16. Dynamic D-vine model / A. Heinen and A. Valdesogo -- 17. Summary and future directions / D. Kurowicka |
| example of a model in science: Science in the Age of Computer Simulation Eric Winsberg, 2010-10-30 Digital computer simulation helps study phenomena of great complexity, but how much do we know about the limits and possibilities of this new scientific practice? How do simulations compare to traditional experiments? And are they reliable? Scrutinizing these issues with a philosophical lens, Eric Winsberg explores the impact of simulation on such issues as the nature of scientific evidence, the role of values in science, the nature and role of fictions in science, and the relationship between simulation and experiment, theories and data, and theories at different levels of description--Cover. |
| example of a model in science: The Grand Design Stephen W. Hawking, Stephen Hawking, Leonard Mlodinow, 2011 Relativity physics. |
| example of a model in science: Communicating Science Effectively National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on the Science of Science Communication: A Research Agenda, 2017-03-08 Science and technology are embedded in virtually every aspect of modern life. As a result, people face an increasing need to integrate information from science with their personal values and other considerations as they make important life decisions about medical care, the safety of foods, what to do about climate change, and many other issues. Communicating science effectively, however, is a complex task and an acquired skill. Moreover, the approaches to communicating science that will be most effective for specific audiences and circumstances are not obvious. Fortunately, there is an expanding science base from diverse disciplines that can support science communicators in making these determinations. Communicating Science Effectively offers a research agenda for science communicators and researchers seeking to apply this research and fill gaps in knowledge about how to communicate effectively about science, focusing in particular on issues that are contentious in the public sphere. To inform this research agenda, this publication identifies important influences †psychological, economic, political, social, cultural, and media-related †on how science related to such issues is understood, perceived, and used. |
| example of a model in science: A Vast Machine Paul N. Edwards, 2010-03-12 The science behind global warming, and its history: how scientists learned to understand the atmosphere, to measure it, to trace its past, and to model its future. Global warming skeptics often fall back on the argument that the scientific case for global warming is all model predictions, nothing but simulation; they warn us that we need to wait for real data, “sound science.” In A Vast Machine Paul Edwards has news for these skeptics: without models, there are no data. Today, no collection of signals or observations—even from satellites, which can “see” the whole planet with a single instrument—becomes global in time and space without passing through a series of data models. Everything we know about the world's climate we know through models. Edwards offers an engaging and innovative history of how scientists learned to understand the atmosphere—to measure it, trace its past, and model its future. |
| example of a model in science: The Structure of Scientific Revolutions Thomas S. Kuhn, 1969 |
| example of a model in science: How People Learn National Research Council, Division of Behavioral and Social Sciences and Education, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Developments in the Science of Learning with additional material from the Committee on Learning Research and Educational Practice, 2000-08-11 First released in the Spring of 1999, How People Learn has been expanded to show how the theories and insights from the original book can translate into actions and practice, now making a real connection between classroom activities and learning behavior. This edition includes far-reaching suggestions for research that could increase the impact that classroom teaching has on actual learning. Like the original edition, this book offers exciting new research about the mind and the brain that provides answers to a number of compelling questions. When do infants begin to learn? How do experts learn and how is this different from non-experts? What can teachers and schools do-with curricula, classroom settings, and teaching methodsâ€to help children learn most effectively? New evidence from many branches of science has significantly added to our understanding of what it means to know, from the neural processes that occur during learning to the influence of culture on what people see and absorb. How People Learn examines these findings and their implications for what we teach, how we teach it, and how we assess what our children learn. The book uses exemplary teaching to illustrate how approaches based on what we now know result in in-depth learning. This new knowledge calls into question concepts and practices firmly entrenched in our current education system. Topics include: How learning actually changes the physical structure of the brain. How existing knowledge affects what people notice and how they learn. What the thought processes of experts tell us about how to teach. The amazing learning potential of infants. The relationship of classroom learning and everyday settings of community and workplace. Learning needs and opportunities for teachers. A realistic look at the role of technology in education. |
| example of a model in science: The Great Mental Models, Volume 1 Shane Parrish, Rhiannon Beaubien, 2024-10-15 Discover the essential thinking tools you’ve been missing with The Great Mental Models series by Shane Parrish, New York Times bestselling author and the mind behind the acclaimed Farnam Street blog and “The Knowledge Project” podcast. This first book in the series is your guide to learning the crucial thinking tools nobody ever taught you. Time and time again, great thinkers such as Charlie Munger and Warren Buffett have credited their success to mental models–representations of how something works that can scale onto other fields. Mastering a small number of mental models enables you to rapidly grasp new information, identify patterns others miss, and avoid the common mistakes that hold people back. The Great Mental Models: Volume 1, General Thinking Concepts shows you how making a few tiny changes in the way you think can deliver big results. Drawing on examples from history, business, art, and science, this book details nine of the most versatile, all-purpose mental models you can use right away to improve your decision making and productivity. This book will teach you how to: Avoid blind spots when looking at problems. Find non-obvious solutions. Anticipate and achieve desired outcomes. Play to your strengths, avoid your weaknesses, … and more. The Great Mental Models series demystifies once elusive concepts and illuminates rich knowledge that traditional education overlooks. This series is the most comprehensive and accessible guide on using mental models to better understand our world, solve problems, and gain an advantage. |
| example of a model in science: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| example of a model in science: Interpretable Machine Learning Christoph Molnar, 2020 This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project. |
| example of a model in science: Principles and Practice of Structural Equation Modeling Rex B. Kline, 2015-10-08 This book has been replaced by Principles and Practice of Structural Equation Modeling, Fifth Edition, ISBN 978-1-4625-5191-0. |
| example of a model in science: Understanding Scientific Understanding Henk W. de Regt, 2017-07-24 It is widely acknowledged that a central aim of science is to achieve understanding of the world around us, and that possessing such understanding is highly important in our present-day society. But what does it mean to achieve this understanding? What precisely is scientific understanding? These are philosophical questions that have not yet received satisfactory answers. While there has been an ongoing debate about the nature of scientific explanation since Carl Hempel advanced his covering-law model in 1948, the related notion of understanding has been largely neglected, because most philosophers regarded understanding as merely a subjective by-product of objective explanations. By contrast, this book puts scientific understanding center stage. It is primarily a philosophical study, but also contains detailed historical case studies of scientific practice. In contrast to most existing studies in this area, it takes into account scientists' views and analyzes their role in scientific debate and development. The aim of Understanding Scientific Understanding is to develop and defend a philosophical theory of scientific understanding that can describe and explain the historical variation of criteria for understanding actually employed by scientists. The theory does justice to the insights of such famous physicists as Werner Heisenberg and Richard Feynman, while bringing much-needed conceptual rigor to their intuitions. The scope of the proposed account of understanding is the natural sciences: while the detailed case studies derive from physics, examples from other sciences are presented to illustrate its wider validity. |
| example of a model in science: Data Science Live Book Pablo Casas, 2018-03-16 This book is a practical guide to problems that commonly arise when developing a machine learning project. The book's topics are: Exploratory data analysis Data Preparation Selecting best variables Assessing Model Performance More information on predictive modeling will be included soon. This book tries to demonstrate what it says with short and well-explained examples. This is valid for both theoretical and practical aspects (through comments in the code). This book, as well as the development of a data project, is not linear. The chapters are related among them. For example, the missing values chapter can lead to the cardinality reduction in categorical variables. Or you can read the data type chapter and then change the way you deal with missing values. You¿ll find references to other websites so you can expand your study, this book is just another step in the learning journey. It's open-source and can be found at http://livebook.datascienceheroes.com |
| example of a model in science: How Nature Works Per Bak, 2013-11-11 Self-organized criticality, the spontaneous development of systems to a critical state, is the first general theory of complex systems with a firm mathematical basis. This theory describes how many seemingly desperate aspects of the world, from stock market crashes to mass extinctions, avalanches to solar flares, all share a set of simple, easily described properties. ...a'must read'...Bak writes with such ease and lucidity, and his ideas are so intriguing...essential reading for those interested in complex systems...it will reward a sufficiently skeptical reader. -NATURE ...presents the theory (self-organized criticality) in a form easily absorbed by the non-mathematically inclined reader. -BOSTON BOOK REVIEW I picture Bak as a kind of scientific musketeer; flamboyant, touchy, full of swagger and ready to join every fray... His book is written with panache. The style is brisk, the content stimulating. I recommend it as a bracing experience. -NEW SCIENTIST |
| example of a model in science: Drive Daniel H. Pink, 2011-04-05 The New York Times bestseller that gives readers a paradigm-shattering new way to think about motivation from the author of When: The Scientific Secrets of Perfect Timing Most people believe that the best way to motivate is with rewards like money—the carrot-and-stick approach. That's a mistake, says Daniel H. Pink (author of To Sell Is Human: The Surprising Truth About Motivating Others). In this provocative and persuasive new book, he asserts that the secret to high performance and satisfaction-at work, at school, and at home—is the deeply human need to direct our own lives, to learn and create new things, and to do better by ourselves and our world. Drawing on four decades of scientific research on human motivation, Pink exposes the mismatch between what science knows and what business does—and how that affects every aspect of life. He examines the three elements of true motivation—autonomy, mastery, and purpose-and offers smart and surprising techniques for putting these into action in a unique book that will change how we think and transform how we live. |
| example of a model in science: Encyclopedia of Research Design Neil J. Salkind, 2010-06-22 Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases.--Publisher's description. |
| example of a model in science: An Introduction to Statistical Learning Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor, 2023-08-01 An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users. |
| example of a model in science: Social Science Research Anol Bhattacherjee, 2012-04-01 This book is designed to introduce doctoral and graduate students to the process of conducting scientific research in the social sciences, business, education, public health, and related disciplines. It is a one-stop, comprehensive, and compact source for foundational concepts in behavioral research, and can serve as a stand-alone text or as a supplement to research readings in any doctoral seminar or research methods class. This book is currently used as a research text at universities on six continents and will shortly be available in nine different languages. |
| example of a model in science: Numerical Computing with MATLAB Cleve B. Moler, 2010-08-12 A revised textbook for introductory courses in numerical methods, MATLAB and technical computing, which emphasises the use of mathematical software. |
| example of a model in science: Science Learning, Science Teaching Jerry Wellington, Gren Ireson, 2013-02-28 Now fully updated in its third edition, Science Learning, Science Teaching offers an accessible, practical guide to creative classroom teaching and a comprehensive introduction to contemporary issues in science education. Aiming to encourage and assist professionals with the process of reflection in the science classroom, the new edition examines the latest research in the field, changes to curriculum and the latest standards for initial teacher training. Including two brand new chapters, key topics covered include: the science curriculum and science in the curriculum planning and managing learning learning in science – including consideration of current ‘fads’ in learning safety in the science laboratory exploring how science works using ICT in the science classroom teaching in an inclusive classroom the role of practical work and investigations in science language and literacy in science citizenship and sustainability in science education. Including useful references, further reading lists and recommended websites, Science Learning, Science Teaching is an essential source of support, guidance and inspiration all students, teachers, mentors and those involved in science education wishing to reflect upon, improve and enrich their practice. |
| example of a model in science: Investigative Science Learning Environment Eugenia Etkina, David T Brookes, Gorazd Planinsic, 2019-11-15 The goal of this book is to introduce a reader to a new philosophy of teaching and learning physics - Investigative Science Learning Environment, or ISLE (pronounced as a small island). ISLE is an example of an intentional approach to curriculum design and learning activities (MacMillan and Garrison 1988 A Logical Theory of Teaching: Erotetics and Intentionality). Intentionality means that the process through which the learning occurs is as crucial for learning as the final outcome or learned content. In ISLE, the process through which students learn mirrors the practice of physics. |
| example of a model in science: Reproducibility and Replicability in Science National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Engineering, Medicine, and Public Policy, Board on Research Data and Information, Division on Engineering and Physical Sciences, Committee on Applied and Theoretical Statistics, Board on Mathematical Sciences and Analytics, Division on Earth and Life Studies, Nuclear and Radiation Studies Board, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Board on Behavioral, Cognitive, and Sensory Sciences, Committee on Reproducibility and Replicability in Science, 2019-10-20 One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some fear that it may be a symptom of a lack of rigor in science, while others argue that such an observed inconsistency can be an important precursor to new discovery. Concerns about reproducibility and replicability have been expressed in both scientific and popular media. As these concerns came to light, Congress requested that the National Academies of Sciences, Engineering, and Medicine conduct a study to assess the extent of issues related to reproducibility and replicability and to offer recommendations for improving rigor and transparency in scientific research. Reproducibility and Replicability in Science defines reproducibility and replicability and examines the factors that may lead to non-reproducibility and non-replicability in research. Unlike the typical expectation of reproducibility between two computations, expectations about replicability are more nuanced, and in some cases a lack of replicability can aid the process of scientific discovery. This report provides recommendations to researchers, academic institutions, journals, and funders on steps they can take to improve reproducibility and replicability in science. |
| example of a model in science: Philosophy of Science for Biologists Kostas Kampourakis, Tobias Uller, 2020-09-24 A short and accessible introduction to philosophy of science for students and researchers across the life sciences. |
| example of a model in science: Python Data Science Handbook Jake VanderPlas, 2016-11-21 For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms |
| example of a model in science: Models and Inferences in Science Emiliano Ippoliti, Fabio Sterpetti, Tom Nickles, 2016-01-27 The book answers long-standing questions on scientific modeling and inference across multiple perspectives and disciplines, including logic, mathematics, physics and medicine. The different chapters cover a variety of issues, such as the role models play in scientific practice; the way science shapes our concept of models; ways of modeling the pursuit of scientific knowledge; the relationship between our concept of models and our concept of science. The book also discusses models and scientific explanations; models in the semantic view of theories; the applicability of mathematical models to the real world and their effectiveness; the links between models and inferences; and models as a means for acquiring new knowledge. It analyzes different examples of models in physics, biology, mathematics and engineering. Written for researchers and graduate students, it provides a cross-disciplinary reference guide to the notion and the use of models and inferences in science. |
Developing and Using Models to Make Sense of Science
The Role of Models in Science erstanding, doing, and communicating about science. Scientists use models to make predictions and construct explanations for how and why natural henomena (i.e., …
A Considered Perspective on the Nature and Structure of …
Models are a central topic of discussion in contemporary science education with debates centering on the pros and cons of including a modeling perspective in science curricula and on pragmatic …
Completed Frayer Model: Science Example
Completed Frayer Model: Science Example ... Frayer Model adapted from Frayer, D. A., Frederick, W. C., & Klausmeier, H. G. (1969). A schema for testing the level of concept mastery (Technical …
Lecture 2 - Modeling and Simulation - Stanford University
BIG question: Why controller designed for a model would ever work with a real system?
Modeling Instruction: An Effective Model for Science Education
Modeling Instruction: An Effective Model for Science Education The authors describe a Modeling Instruction program that places an emphasis on the construction and application of conceptual …
Models and Modeling- An Introduction - Ambitious Science …
A scientific model is a representation of a system (such as the human respiratory system, the solar system, a system of electrical circuits) or a phenomenon (such as the changing seasons, the …
Introduction and Examples: Physical Models
n be modelled via differential equations. Applications arise in almost all branches of science and engineering–radiation decay, aging, tumor growth, population growth, electrical circuits, …
Activity Name - Science4Inquiry
Modeling is the process of building, using and evaluating external representations of systems. Modeling is an important practice that scientists use to understand complex systems, from atoms …
Models and modelling in science and science education
One of the most famous examples of a scientific model is the molecular model of the structure of DNA built by Francis Crick and James Watson at the Cavendish Laboratory in the 1950s (Watson, …
Models and analogies in science - HYLE
Abstract: Science makes extensive use of models, i.e. simplified or idealized representations of the systems found in the physical world. Models fall into at least two categories: mathematical and …
Model Reading PreCSI - teachers.oregon.k12.wi.us
Scientists develop models in many different forms. Models may be actual physical constructions of mental images. They can also be mathematical models. A model of the Earth, moon and sun, …
Chapter 1 An Introduction to Modeling Science: Basic Model
structural units of science (e.g., research teams, specialties) or their interlinkages. Different model types are discussed in the next section. Recent work aims to develop models that describe the …
Models in Science Education: Applications of Models in …
In science, a model is the outcome of representing an object, phenomenon or idea (the target) with a more familiar one (the source) (Tregidgo & Ratcliffe, 2000). For example, one model of the …
Scientific Models - Erie City School District
Scientists use models to study things that are very small, like atoms, or things that are very large, like Earth. Some scientists use models to predict things that haven’t happened yet, or to study …
Scientific Models.docx - Monash University
scientific model is a physical and/or mathematical and/or conceptual representation of a system of ideas, events or processes. Scientists seek to identify and understand patterns in our world by …
Introduction to Mathematical Modeling - Oregon State University
A mechanistic or scientific model begins with a description of how nature might work, and proceeds from this description to a set of predictions relating the independent and dependent variables.
Engaging Students in Scientific Practices: What does …
model. For instance, the model shows particles that collide with the imaginary side of the magnified section. The model, however, is consistent with the evidence the student has coll
Models and Fictions in Science
Standard examples are ideal gases and frictionless planes. Examples also include infinitely large populations in biology, neural networks which learn using biologically unrealistic rules, and the …
Microsoft Word - ModSciBook-Ch1-12-09.doc - IU
This chapter provides a general introduction to the modeling of science together with a discussion of different model types, basic definitions, and an overview and comparison of major predictive …
USING MODELS TO EXPLAIN SCIENCE - Open Scholarship
What specific kind of models does the scientist construct? The following definition contains the answer to that question. This definition is proposed as a conceptualization which has enough …
Developing and Using Models to Make Sense of Science
The Role of Models in Science erstanding, doing, and communicating about science. Scientists use models to make predictions and construct explanations for how and why natural …
A Considered Perspective on the Nature and Structure of …
Models are a central topic of discussion in contemporary science education with debates centering on the pros and cons of including a modeling perspective in science curricula and on …
Completed Frayer Model: Science Example
Completed Frayer Model: Science Example ... Frayer Model adapted from Frayer, D. A., Frederick, W. C., & Klausmeier, H. G. (1969). A schema for testing the level of concept …
Lecture 2 - Modeling and Simulation - Stanford University
BIG question: Why controller designed for a model would ever work with a real system?
Modeling Instruction: An Effective Model for Science Education
Modeling Instruction: An Effective Model for Science Education The authors describe a Modeling Instruction program that places an emphasis on the construction and application of conceptual …
Models and Modeling- An Introduction - Ambitious Science …
A scientific model is a representation of a system (such as the human respiratory system, the solar system, a system of electrical circuits) or a phenomenon (such as the changing seasons, …
Introduction and Examples: Physical Models
n be modelled via differential equations. Applications arise in almost all branches of science and engineering–radiation decay, aging, tumor growth, population growth, electrical circuits, …
Activity Name - Science4Inquiry
Modeling is the process of building, using and evaluating external representations of systems. Modeling is an important practice that scientists use to understand complex systems, from …
Models and modelling in science and science education
One of the most famous examples of a scientific model is the molecular model of the structure of DNA built by Francis Crick and James Watson at the Cavendish Laboratory in the 1950s …
Models and analogies in science - HYLE
Abstract: Science makes extensive use of models, i.e. simplified or idealized representations of the systems found in the physical world. Models fall into at least two categories: mathematical …
Model Reading PreCSI - teachers.oregon.k12.wi.us
Scientists develop models in many different forms. Models may be actual physical constructions of mental images. They can also be mathematical models. A model of the Earth, moon and …
Chapter 1 An Introduction to Modeling Science: Basic Model …
structural units of science (e.g., research teams, specialties) or their interlinkages. Different model types are discussed in the next section. Recent work aims to develop models that describe the …
Models in Science Education: Applications of Models in …
In science, a model is the outcome of representing an object, phenomenon or idea (the target) with a more familiar one (the source) (Tregidgo & Ratcliffe, 2000). For example, one model of …
Scientific Models - Erie City School District
Scientists use models to study things that are very small, like atoms, or things that are very large, like Earth. Some scientists use models to predict things that haven’t happened yet, or to study …
Scientific Models.docx - Monash University
scientific model is a physical and/or mathematical and/or conceptual representation of a system of ideas, events or processes. Scientists seek to identify and understand patterns in our world by …
Introduction to Mathematical Modeling - Oregon State …
A mechanistic or scientific model begins with a description of how nature might work, and proceeds from this description to a set of predictions relating the independent and dependent …
Engaging Students in Scientific Practices: What does …
model. For instance, the model shows particles that collide with the imaginary side of the magnified section. The model, however, is consistent with the evidence the student has coll
Models and Fictions in Science
Standard examples are ideal gases and frictionless planes. Examples also include infinitely large populations in biology, neural networks which learn using biologically unrealistic rules, and the …
Microsoft Word - ModSciBook-Ch1-12-09.doc - IU
This chapter provides a general introduction to the modeling of science together with a discussion of different model types, basic definitions, and an overview and comparison of major predictive …
USING MODELS TO EXPLAIN SCIENCE - Open Scholarship
What specific kind of models does the scientist construct? The following definition contains the answer to that question. This definition is proposed as a conceptualization which has enough …
