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Dodgers in the Hall of Fame (Images of Baseball)
by David Hickey Kerry Keene Raymond P. SinibaldiAmong the most successful franchises in the long and glorious history of baseball, the Dodgers have captured 25 pennants and have been crowned world champions seven times; only five teams in history have claimed more World Series titles. The Dodgers are baseball's most transformative franchise. In 1947, Jackie Robinson changed the face of baseball and America. They built Dodgertown in 1948; became the first major-league team to own a plane; and spurred the move west in 1958, where Sandy Koufax redefined pitching dominance. Herein lies the story of the men who have worn Dodger blue on their way to becoming baseball immortals, forever enshrined in Cooperstown's Baseball Hall of Fame.
Does Measurement Measure Up?: How Numbers Reveal and Conceal the Truth
by John M. HenshawA critical perspective of how measurements have come to affect our lives—from reasonable doubt to No Child Left Behind.There was once a time when we could not measure sound, color, blood pressure, or even time. We now find ourselves in the throes of a measurement revolution, from the laboratory to the sports arena, from the classroom to the courtroom, from a strand of DNA to the far reaches of outer space. Measurement controls our lives at work, at school, at home, and even at play. But does all this measurement really measure up? Here, John Henshaw examines the ways in which measurement makes sense or creates nonsense. Henshaw tells the controversial story of intelligence measurement from Plato to Binet to the early days of the SAT to today's super-quantified world of No Child Left Behind. He clears away the fog on issues of measurement in the environment, such as global warming, hurricanes, and tsunamis, and in the world of computers, from digital photos to MRI to the ballot systems used in Florida during the 2000 presidential election. From cycling and car racing to baseball, tennis, and track-and-field, he chronicles the ever-growing role of measurement in sports, raising important questions about performance and the folly of comparing today's athletes to yesterday's records.We can't quite measure everything, at least not yet. What could be more difficult to quantify than reasonable doubt? However, even our justice system is yielding to the measurement revolution with new forensic technologies such as DNA fingerprinting. As we evolve from unquantified ignorance to an imperfect but everpresent state of measured awareness, Henshaw gives us a critical perspective from which we can "measure up" the measurements that have come to affect our lives so greatly.
Dog's Colorful Day: A Messy Story about Colors and Counting
by Emma DoddThe story of a dog that wanders along picking up spots of different colors. Meant for kids to learn numbers and colors the fun way.
Doggone Dogs!
by David Catrow Karen BeaumontFrom the "New York Times"-bestselling creators of "I Ain't Gonna Paint No More!" comes a tail of 10 mischievous, lovable, and delightfully ungainly dogs, who find themselves in one hilarious antic adventure after another.
Doing Critical Research (Sage Series In Management Research Ser.)
by Mats Alvesson Stanley DeetzThis title builds on the success of Doing Critical Management Research which has proven to be a seminal text in the 20 years since publication. In 2020, Alvesson and Deetz have broadened their focus and updated the original book to offer relevance to critical research across all of the social sciences. In reflecting contemporary theoretical and methodological turns over the past few decades, it includes coverage of key contemporary topics such as race, gender, postmodernism and intersectionality. With examples throughout, the authors provide an authoritative and insightful framework for navigating critical theories and methods and sets out a new agenda for critical research undertaken today.
Doing Critical Research (Sage Series In Management Research Ser.)
by Mats Alvesson Stanley DeetzThis title builds on the success of Doing Critical Management Research which has proven to be a seminal text in the 20 years since publication. In 2020, Alvesson and Deetz have broadened their focus and updated the original book to offer relevance to critical research across all of the social sciences. In reflecting contemporary theoretical and methodological turns over the past few decades, it includes coverage of key contemporary topics such as race, gender, postmodernism and intersectionality. With examples throughout, the authors provide an authoritative and insightful framework for navigating critical theories and methods and sets out a new agenda for critical research undertaken today.
Doing Data Science in R: An Introduction for Social Scientists
by Mark AndrewsThis approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. This book: Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.
Doing Data Science in R: An Introduction for Social Scientists
by Mark AndrewsThis approachable introduction to doing data science in R provides step-by-step advice on using the tools and statistical methods to carry out data analysis. Introducing the fundamentals of data science and R before moving into more advanced topics like Multilevel Models and Probabilistic Modelling with Stan, it builds knowledge and skills gradually. This book: Focuses on providing practical guidance for all aspects, helping readers get to grips with the tools, software, and statistical methods needed to provide the right type and level of analysis their data requires Explores the foundations of data science and breaks down the processes involved, focusing on the link between data science and practical social science skills Introduces R at the outset and includes extensive worked examples and R code every step of the way, ensuring students see the value of R and its connection to methods while providing hands-on practice in the software Provides examples and datasets from different disciplines and locations demonstrate the widespread relevance, possible applications, and impact of data science across the social sciences.
Doing Data Science: Straight Talk from the Frontline
by Cathy O'Neil Rachel SchuttNow that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.
Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!
by Amit SahaDoing Math with Python shows you how to use Python to delve into high school–level math topics like statistics, geometry, probability, and calculus. You’ll start with simple projects, like a factoring program and a quadratic-equation solver, and then create more complex projects once you’ve gotten the hang of things.Along the way, you’ll discover new ways to explore math and gain valuable programming skills that you’ll use throughout your study of math and computer science. Learn how to:–Describe your data with statistics, and visualize it with line graphs, bar charts, and scatter plots–Explore set theory and probability with programs for coin flips, dicing, and other games of chance–Solve algebra problems using Python’s symbolic math functions–Draw geometric shapes and explore fractals like the Barnsley fern, the Sierpinski triangle, and the Mandelbrot set–Write programs to find derivatives and integrate functionsCreative coding challenges and applied examples help you see how you can put your new math and coding skills into practice. You’ll write an inequality solver, plot gravity’s effect on how far a bullet will travel, shuffle a deck of cards, estimate the area of a circle by throwing 100,000 "darts" at a board, explore the relationship between the Fibonacci sequence and the golden ratio, and more.Whether you’re interested in math but have yet to dip into programming or you’re a teacher looking to bring programming into the classroom, you’ll find that Python makes programming easy and practical. Let Python handle the grunt work while you focus on the math.Uses Python 3
Doing Meta-Analysis with R: A Hands-On Guide
by Mathias Harrer Pim Cuijpers Toshi A. Furukawa David D. EbertDoing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features• Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises• Describes statistical concepts clearly and concisely before applying them in R• Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book
Doing Physics with Scientific Notebook
by Joseph GallantThe goal of this book is to teach undergraduate students how to use Scientific Notebook (SNB) to solve physics problems. SNB software combines word processing and mathematics in standard notation with the power of symbolic computation. As its name implies, SNB can be used as a notebook in which students set up a math or science problem, write and solve equations, and analyze and discuss their results.Written by a physics teacher with over 20 years experience, this text includes topics that have educational value, fit within the typical physics curriculum, and show the benefits of using SNB.This easy-to-read text:Provides step-by-step instructions for using Scientific Notebook (SNB) to solve physics problems Features examples in almost every section to enhance the reader's understanding of the relevant physics and to provide detailed instructions on using SNB Follows the traditional physics curriculum, so it can be used to supplement teaching at all levels of undergraduate physics Includes many problems taken from the author's class notes and research Aimed at undergraduate physics and engineering students, this text teaches readers how to use SNB to solve some everyday physics problems.
Doing Qualitative Data Analysis with NVivo (Springer Texts in Social Sciences)
by Dimitri MortelmansThis open access textbook provides an introduction to the software program NVivo, the most widely used qualitative analysis program. It is a versatile program with an extensive range of accessible analysis tools, flexibly deployable in the diversity of qualitative analysis approaches. Qualitative analysis is almost standard practice today with the help of a software program. Yet there are many misunderstandings about qualitative software. They support the qualitative researcher but never take over their manual and theoretical work. An in-depth understanding of the possibilities of a qualitative software program helps to free up time for the analysis itself. The possibilities of NVivo in this book are approached from a researcher's perspective. That is precisely why gaining efficiency in using the software tools gets a prominent place in the chapters. The author examines basic skills, such as managing data, working with memos and coding qualitative data. This includes textual data (such as transcripts from interviews and focus groups) and audiovisual material (sound, video and images). The book also discusses more advanced analysis tools, such as case coding, queries, AI tools, matrices and models (maps). This textbook is intended for all users of NVivo, both early career researchers and more advanced analysts, who want to further discover the secrets of this software package along the way.
Doing Quantitative Research in Education with IBM SPSS Statistics
by Daniel MuijsThis essential guide for education students and researchers explains how to use quantitative methods for analysing educational data using IBM SPSS Statistics. By using datasets from real-life educational research, it demonstrates key statistical techniques that you will need to know, explaining how each procedure can by run on IBM SPSS Statistics. Datasets discussed in the book are downloadable, allowing you to hone your skills as you read. In this third edition, explanations have been updated with figures and screenshots from SPSS version 28, alongside a range of new research examples and updated further reading. Daniel Muijs is Dean of the Faculty of Education and Society at Academica University of Applied Sciences in Amsterdam.
Doing Quantitative Research in Education with IBM SPSS Statistics
by Daniel MuijsThis essential guide for education students and researchers explains how to use quantitative methods for analysing educational data using IBM SPSS Statistics. By using datasets from real-life educational research, it demonstrates key statistical techniques that you will need to know, explaining how each procedure can by run on IBM SPSS Statistics. Datasets discussed in the book are downloadable, allowing you to hone your skills as you read. In this third edition, explanations have been updated with figures and screenshots from SPSS version 28, alongside a range of new research examples and updated further reading. Daniel Muijs is Dean of the Faculty of Education and Society at Academica University of Applied Sciences in Amsterdam.
Doing Research: A New Researcher’s Guide (Research in Mathematics Education)
by James Hiebert Stephen Hwang Jinfa Cai Charles Hohensee Anne K MorrisThis book is about scientific inquiry. Designed for early and mid-career researchers, it is a practical manual for conducting and communicating high-quality research in (mathematics) education. Based on the authors’ extensive experience as researchers, as mentors, and as members of the editorial team for the Journal for Research in Mathematics Education (JRME), this book directly speaks to researchers and their communities about each phase of the process for conceptualizing, conducting, and communicating high-quality research in (mathematics) education.In the late 2010s, both JRME and Educational Studies in Mathematics celebrated 50 years of publishing high-quality research in mathematics education. Many advances in the field have occurred since the establishment of these journals, and these anniversaries marked a milestone in research in mathematics education. Indeed, fifty years represents a small step for human history but a giant leap for mathematics education. The educational research community in general (and the mathematics education community in particular) has strongly advocated for original research, placing great emphasis on building knowledge and capacity in the field. Because it is an interdisciplinary field, mathematics education has integrated means and methods for scientific inquiry from multiple disciplines. Now that the field is gaining maturity, it is a good time to take a step back and systematically consider how mathematics education researchers can engage in significant, impactful scientific inquiry.
Doing Secondary Analysis (Contemporary Social Research)
by Angela Dale Sara Arber Michael ProcterOriginally published in 1988 Doing Secondary Analysis is a practical guide to the secondary analysis of large-scale survey data.At a time when funding for primary data collection was increasingly constrained, the secondary analysis of high-quality government surveys offered the social scientist an unrivalled opportunity. This volume provided a guide which moves through every stage of ‘doing secondary analysis’. The authors begin with the conceptualization of the research problem and examine all the practicalities of using both standard rectangular data and hierarchical data, and of deriving simple and complex variables. They also provide a lucid description of the hardware and software available to the secondary analyst at the time.This book successfully demonstrates the way in which secondary analysis can contribute both to the development of sociological theory and to social policy formation. The authors emphasize throughout that secondary analysis cannot be used as a short cut to quick ‘results’, but that as much care over defining the research problem and understanding the categories of data is needed as for any other kind of research.While Doing Secondary Analysis was invaluable to those about to embark upon social research, it also offered many challenges to more experienced researchers.
Doing Simple Math in Your Head
by W. J. HowardAlmost all adults suffer a little math anxiety, especially when it comes to everyday problems they think they should be able to figure out in their heads. Want to figure the six percent sales tax on a $34.50 item? A 15 percent tip for a $13.75 check? The carpeting needed for a 12½-by-17-foot room? No one learns how to do these mental calculations in school, where the emphasis is on paper-and-pencil techniques. With no math background required and no long list of rules to memorize, this book teaches average adults how to simplify their math problems, provides ample real-life practice problems and solutions, and gives grown-ups the necessary background in basic arithmetic to handle everyday problems quickly.
Doing Statistical Analysis: A Student’s Guide to Quantitative Research
by Christer ThraneDoing Statistical Analysis looks at three kinds of statistical research questions – descriptive, associational, and inferential – and shows students how to conduct statistical analyses and interpret the results. Keeping equations to a minimum, it uses a conversational style and relatable examples such as football, COVID-19, and tourism, to aid understanding. Each chapter contains practice exercises, and a section showing students how to reproduce the statistical results in the book using Stata and SPSS. Digital supplements consist of data sets in Stata, SPSS, and Excel, and a test bank for instructors. Its accessible approach means this is the ideal textbook for undergraduate students across the social and behavioral sciences needing to build their confidence with statistical analysis.
Doing Transitions in the Life Course: Processes and Practices (Life Course Research and Social Policies #16)
by Barbara Stauber Andreas Walther Richard A. SetterstenThis open access book provides a unique research perspective on life course transitions. Here, transitions are understood as social processes and practices. Leveraging the recent “practice turn” in the social sciences, the contributors analyze how life course transitions are “done.” This book introduces the concept of “doing transitions” and its implications for theories and methods. It presents fresh empirical research on “doing transitions” in different life phases (e.g., childhood, young adulthood, later life) and life domains (e.g., education, work, family, health, migration). It also emphasizes themes related to institutions and organizations, time and normativity, materialities (such as bodies, spaces, and artifacts), and the reproduction of social inequalities in education and welfare. In coupling this new perspective with empirical illustrations, this book is an indispensable resource for scholars from demography, sociology, psychology, social work and other scientific fields, as well as for students, counselors and practitioners, and policymakers.
Domain Conditions and Social Rationality
by Satish Kumar JainThis book primarily focuses on the domain conditions under which a number of important classes of binary social decision rules give rise to rational social preferences. One implication of the Arrow and Gibbard theorems is that every non-oligarchic social decision rule that satisfies the condition of independence of irrelevant alternatives, a requirement crucial for the unambiguity of social choices, and the weak Pareto criterion fails to generate quasi-transitive social preferences for some configurations of individual preferences. The problem is exemplified by the famous voting paradox associated with the majority rule. Thus, in the context of rules that do not give rise to transitive (quasi-transitive) social preferences for every configuration of individual preferences, an important problem is that of formulating Inada-type necessary and sufficient conditions for transitivity (quasi-transitivity). This book formulates conditions for transitivity and quasi-transitivity for several classes of social decision rules, including majority rules, non-minority rules, Pareto-inclusive non-minority rules, and social decision rules that are simple games. It also analyzes in detail the conditions for transitivity and quasi-transitivity under the method of the majority decision, and derives the maximally sufficient conditions for transitivity under the class of neutral and monotonic binary social decision rules and one of its subclasses. The book also presents characterizations of some of the classes of rules for which domain conditions have been derived. The material covered is relevant to anyone interested in studying the structure of voting rules, particularly those interested in social choice theory. Providing the necessary social choice theoretic concepts, definitions, propositions and theorems, the book is essentially self-contained. The treatment throughout is rigorous, and unlike most of the literature on domain conditions, care is taken regarding the number of individuals in the 'necessity' proofs. As such it is an invaluable resource for students of economics and political science, with takeaways for everyone – from first-year postgraduates to more advanced doctoral students and scholars.
Domain Decomposition Methods in Science and Engineering XXII
by Thomas Dickopf Martin J. Gander Laurence Halpern Rolf Krause Luca F. PavarinoThese are the proceedings of the 22nd International Conference on Domain Decomposition Methods, which was held in Lugano, Switzerland. With 172 participants from over 24 countries, this conference continued a long-standing tradition of internationally oriented meetings on Domain Decomposition Methods. The book features a well-balanced mix of established and new topics, such as the manifold theory of Schwarz Methods, Isogeometric Analysis, Discontinuous Galerkin Methods, exploitation of modern HPC architectures and industrial applications. As the conference program reflects, the growing capabilities in terms of theory and available hardware allow increasingly complex non-linear and multi-physics simulations, confirming the tremendous potential and flexibility of the domain decomposition concept.
Domain Decomposition Methods in Science and Engineering XXIII
by Chang-Ock Lee Xiao-Chuan Cai David E. Keyes Hyea Hyun Kim Axel Klawonn Eun-Jae Park Olof B. WidlundThis book is a collection of papers presented at the 23rd International Conference on Domain Decomposition Methods in Science and Engineering, held on Jeju Island, Korea on July 6-10, 2015. Domain decomposition methods solve boundary value problems by splitting them into smaller boundary value problems on subdomains and iterating to coordinate the solution between adjacent subdomains. Domain decomposition methods have considerable potential for a parallelization of the finite element methods, and serve a basis for distributed, parallel computations.
Domain Decomposition Methods in Science and Engineering XXIV (Lecture Notes in Computational Science and Engineering #125)
by Susanne C. Brenner Hyea Hyun Kim Olof B. Widlund Petter E. Bjørstad Lawrence Halpern Ralf Kornhuber Talal RahmanThese are the proceedings of the 24th International Conference on Domain Decomposition Methods in Science and Engineering, which was held in Svalbard, Norway in February 2017. Domain decomposition methods are iterative methods for solving the often very large systems of equations that arise when engineering problems are discretized, frequently using finite elements or other modern techniques. These methods are specifically designed to make effective use of massively parallel, high-performance computing systems. The book presents both theoretical and computational advances in this domain, reflecting the state of art in 2017.
Domain Generalization with Machine Learning in the NOvA Experiment (Springer Theses)
by Andrew T.C. SuttonThis thesis presents significant advances in the use of neural networks to study the properties of neutrinos. Machine learning tools like neural networks (NN) can be used to identify the particle types or determine their energies in detectors such as those used in the NOvA neutrino experiment, which studies changes in a beam of neutrinos as it propagates approximately 800 km through the earth. NOvA relies heavily on simulations of the physics processes and the detector response; these simulations work well, but do not match the real experiment perfectly. Thus, neural networks trained on simulated datasets must include systematic uncertainties that account for possible imperfections in the simulation. This thesis presents the first application in HEP of adversarial domain generalization to a regression neural network. Applying domain generalization to problems with large systematic variations will reduce the impact of uncertainties while avoiding the risk of falsely constraining the phase space. Reducing the impact of systematic uncertainties makes NOvA analysis more robust, and improves the significance of experimental results.