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| bias definition in math: Statistics For Dummies Deborah J. Rumsey, 2016-06-07 The fun and easy way to get down to business with statistics Stymied by statistics? No fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life. Statistics For Dummies shows you how to interpret and critique graphs and charts, determine the odds with probability, guesstimate with confidence using confidence intervals, set up and carry out a hypothesis test, compute statistical formulas, and more. Tracks to a typical first semester statistics course Updated examples resonate with today's students Explanations mirror teaching methods and classroom protocol Packed with practical advice and real-world problems, Statistics For Dummies gives you everything you need to analyze and interpret data for improved classroom or on-the-job performance. |
| bias definition in math: Anti-Bias Education for Young Children and Ourselves Louise Derman-Sparks, Julie Olsen Edwards, 2020-04-07 Anti-bias education begins with you! Become a skilled anti-bias teacher with this practical guidance to confronting and eliminating barriers. |
| bias definition in math: Weapons of Math Destruction Cathy O'Neil, 2016 A former Wall Street quantitative analyst sounds an alarm on mathematical modeling, a pervasive new force in society that threatens to undermine democracy and widen inequality,--NoveList. |
| bias definition in math: Math in Society David Lippman, 2012-09-07 Math in Society is a survey of contemporary mathematical topics, appropriate for a college-level topics course for liberal arts major, or as a general quantitative reasoning course.This book is an open textbook; it can be read free online at http://www.opentextbookstore.com/mathinsociety/. Editable versions of the chapters are available as well. |
| bias definition in math: MCAT Physics and Math Review 2018-2019 Kaplan Test Prep, 2017-07-04 Kaplan's MCAT Complete 7-Book Set Subject Review has all the information and strategies you need to score higher on the MCAT. These books feature more practice than any other guide, plus targeted strategy review, opportunities for self-analysis, and thorough information on all of the critical thinking skills necessary for MCAT success -- from the creators of the #1 MCAT prep course. -- From publisher's description. |
| bias definition in math: 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. |
| bias definition in math: Mathematics for Human Flourishing Francis Su, 2020-01-07 Winner of the Mathematics Association of America's 2021 Euler Book Prize, this is an inclusive vision of mathematics—its beauty, its humanity, and its power to build virtues that help us all flourish“This is perhaps the most important mathematics book of our time. Francis Su shows mathematics is an experience of the mind and, most important, of the heart.”—James Tanton, Global Math ProjectA good book is an entertaining read. A great book holds up a mirror that allows us to more clearly see ourselves and the world we live in. Francis Su’s Mathematics for Human Flourishing is both a good book and a great book.—MAA Reviews For mathematician Francis Su, a society without mathematical affection is like a city without concerts, parks, or museums. To miss out on mathematics is to live without experiencing some of humanity’s most beautiful ideas.In this profound book, written for a wide audience but especially for those disenchanted by their past experiences, an award‑winning mathematician and educator weaves parables, puzzles, and personal reflections to show how mathematics meets basic human desires—such as for play, beauty, freedom, justice, and love—and cultivates virtues essential for human flourishing. These desires and virtues, and the stories told here, reveal how mathematics is intimately tied to being human. Some lessons emerge from those who have struggled, including philosopher Simone Weil, whose own mathematical contributions were overshadowed by her brother’s, and Christopher Jackson, who discovered mathematics as an inmate in a federal prison. Christopher’s letters to the author appear throughout the book and show how this intellectual pursuit can—and must—be open to all. |
| bias definition in math: MCAT Physics and Math Review 2020-2021 Kaplan Test Prep, 2019-08-06 Kaplan’s MCAT Physics and Math Review 2020-2021 is updated to reflect the latest, most accurate, and most testable materials on the MCAT. A new layout makes our book even more streamlined and intuitive for easier review. You’ll get efficient strategies, detailed subject review, and hundreds of practice questions—all authored by the experts behind the MCAT prep course that has helped more people get into medical school than all other major courses combined. Efficient Strategies and In-Depth Review High Yield badges indicate the most testable content based on AAMC materials Concept summaries that boil down the need-to-know information in each chapter, including any necessary equations to memorize Chapter Profiles indicate the degree to which each chapter is tested and the testmaker content categories to which it aligns Charts, graphs, diagrams, and full-color, 3-D illustrations from Scientific American help turn even the most complex science into easy-to-visualize concepts Realistic Practice One-year online access to instructional videos, practice questions, and quizzes Hundreds of practice questions show you how to apply concepts and equations 15 multiple-choice “Test Your Knowledge” questions at the end of each chapter Learning objectives and concept checks ensure you’re focusing on the most important information in each chapter Expert Guidance Sidebars illustrate connections between concepts and include references to more information, real-world tie ins, mnemonics, and MCAT-specific tips Comprehensive subject review written by top-rated, award-winning Kaplan instructors who guide you on where to focus your efforts and how to organize your review. All material is vetted by editors with advanced science degrees and by a medical doctor. We know the test: The Kaplan MCAT team has spent years studying every MCAT-related document available, and our experts ensure our practice questions and study materials are true to the test |
| bias definition in math: Ideological and Political Bias in Psychology Craig L. Frisby, Richard E. Redding, William T. O'Donohue, Scott O. Lilienfeld, 2023-09-13 This book examines the traditional assumptions made by academics and professionals alike that have embedded sociopolitical biases that impede practice. and undermine efforts to achieve an objective scientific status. If allowed to go unchallenged, the credibility of psychology as a discipline is compromised. This contributed volume thoroughly and comprehensively examines this concern in a conceptually and empirically rigorous manner and offers constructive solutions for minimizing undue political influences within the field of psychology. Societies in the 21st century desperately need reliable psychological science, but we don’t have it. This important volume explains one of the main reasons why we are making little progress on any issue that gets contaminated by the left-right culture war: because the field of psychology is an enthusiastic member of one of the two teams, so it rejects findings and researchers who question its ideological commitments. The authors of this engaging volume also show us the way out. They diagnose the social dynamics of bias and point to reforms that would give us the psychology that we need to address 21st century problems. Jonathan Haidt, Thomas Cooley Professor of Ethical Leadership, NYU—Stern School of Business and author of The Righteous Mind The boundaries of free speech, censorship, moral cultures, social justice, and ideological biases are among the many incendiary topics discussed in this book. If you are looking for a deep-dive into real-world contemporary controversies, Ideological and Political Bias in Psychology fits the bill. The chapters are thoughtful and thought-provoking. Most readers will find something to agree with and something to rage at in almost every chapter. It just may change how you think about some of these topics. Diane F. Halpern, Professor of Psychology Emerita, Claremont McKenna College and Past President, American Psychological Association Unless the political left is always correct about everything (in which case, we wouldn’t need to do research; we could just ask a leftist), the growing political monoculture of social science is a major barrier to our search for the truth. This volume shows how ideological bias should be treated as a source of research error, up there with classic methodological flaws like non-random assignment and non-blind measurement. Steven Pinker, Johnstone Professor of Psychology, Harvard University, and the author of Rationality An important read for academics curious about how their politics fashions beliefs that too often are uncritically taken for granted, and for non-academics wondering why we can't shake off the politics that so influences scientific work. Vernon Smith, Nobel Laureate in Economic Sciences and George L. Argyros Chair in Finance and Economics, Chapman University Advances and deepens empirically rigorous scholarship into biased political influences affecting academic and professional psychology. Offers constructive solutions for minimizing undue political influences within psychology and moving the field forward. Serves as a resource for psychological academicians, researchers, practitioners, and consultants seeking to restore the principles of accurate science and effective practice to their respective areas of research. |
| bias definition in math: MCAT Physics and Math Review 2021-2022 Kaplan Test Prep, 2020-07-07 Always study with the most up-to-date prep! Look for MCAT Physics and Math Review 2022-2023, ISBN 9781506276731, on sale July 06, 2021. Publisher's Note: Products purchased from third-party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitles included with the product. |
| bias definition in math: Unconscious Bias in Schools Tracey A. Benson, Sarah E. Fiarman, 2020-07-22 In Unconscious Bias in Schools, two seasoned educators describe the phenomenon of unconscious racial bias and how it negatively affects the work of educators and students in schools. “Regardless of the amount of effort, time, and resources education leaders put into improving the academic achievement of students of color,” the authors write, “if unconscious racial bias is overlooked, improvement efforts may never achieve their highest potential.” In order to address this bias, the authors argue, educators must first be aware of the racialized context in which we live. Through personal anecdotes and real-life scenarios, Unconscious Bias in Schools provides education leaders with an essential roadmap for addressing these issues directly. The authors draw on the literature on change management, leadership, critical race theory, and racial identity development, as well as the growing research on unconscious bias in a variety of fields, to provide guidance for creating the conditions necessary to do this work—awareness, trust, and a “learner’s stance.” Benson and Fiarman also outline specific steps toward normalizing conversations about race; reducing the influence of bias on decision-making; building empathic relationships; and developing a system of accountability. All too often, conversations about race become mired in questions of attitude or intention–“But I’m not a racist!” This book shows how information about unconscious bias can help shift conversations among educators to a more productive, collegial approach that has the potential to disrupt the patterns of perception that perpetuate racism and institutional injustice. Tracey A. Benson is an assistant professor of educational leadership at the University of North Carolina at Charlotte. Sarah E. Fiarman is the director of leadership development for EL Education, and a former public school teacher, principal, and lecturer at Harvard Graduate School of Education. |
| bias definition in math: Algorithms of Oppression Safiya Umoja Noble, 2018-02-20 Acknowledgments -- Introduction: the power of algorithms -- A society, searching -- Searching for Black girls -- Searching for people and communities -- Searching for protections from search engines -- The future of knowledge in the public -- The future of information culture -- Conclusion: algorithms of oppression -- Epilogue -- Notes -- Bibliography -- Index -- About the author |
| bias definition in math: MCAT Physics and Math Review 2025-2026 Kaplan Test Prep, 2024-08-13 Kaplan’s MCAT Physics and Math Review 2025-2026 offers an expert study plan, detailed subject review, and hundreds of online and in-book practice questions—all authored by the experts behind Kaplan's score-raising MCAT prep course. Prepping for the MCAT is a true challenge. Kaplan can be your partner along the way—offering guidance on where to focus your efforts and how to organize your review. This book has been updated to match the AAMC’s guidelines precisely—no more worrying about whether your MCAT review is comprehensive! The Most Practice More than 350 questions in the book and access to even more online—more practice than any other MCAT physics and math book on the market. The Best Practice Comprehensive physics and math subject review is written by top-rated, award-winning Kaplan instructors. Full-color, 3-D illustrations, charts, graphs and diagrams help turn even the most complex science into easy-to-visualize concepts. All material is vetted by editors with advanced science degrees and by a medical doctor. Online resources, including a full-length practice test, help you practice in the same computer-based format you’ll see on Test Day. Expert Guidance High-yield badges throughout the book identify the topics most frequently tested by the AAMC. We know the test: The Kaplan MCAT team has spent years studying every MCAT-related document available. Kaplan’s expert psychometricians ensure our practice questions and study materials are true to the test. |
| bias definition in math: MCAT Physics and Math Review 2023-2024 Kaplan Test Prep, 2022-08-02 Kaplan’s MCAT Physics and Math Review 2023–2024 offers an expert study plan, detailed subject review, and hundreds of online and in-book practice questions—all authored by the experts behind the MCAT prep course that has helped more people get into medical school than all other major courses combined. Prepping for the MCAT is a true challenge. Kaplan can be your partner along the way—offering guidance on where to focus your efforts and how to organize your review. This book has been updated to match the AAMC’s guidelines precisely—no more worrying about whether your MCAT review is comprehensive! The Most Practice More than 350 questions in the book and access to even more online—more practice than any other MCAT physics and math book on the market. The Best Practice Comprehensive physics and math subject review is written by top-rated, award-winning Kaplan instructors. Full-color, 3-D illustrations from Scientific American, charts, graphs and diagrams help turn even the most complex science into easy-to-visualize concepts. All material is vetted by editors with advanced science degrees and by a medical doctor. Online resources, including a full-length practice test, help you practice in the same computer-based format you’ll see on Test Day. Expert Guidance High-yield badges throughout the book identify the topics most frequently tested by the AAMC. We know the test: The Kaplan MCAT team has spent years studying every MCAT-related document available. Kaplan’s expert psychometricians ensure our practice questions and study materials are true to the test. |
| bias definition in math: MCAT Physics and Math Review 2024-2025 Kaplan Test Prep, 2023-07-04 Kaplan’s MCAT Physics and Math Review 2024-2025 offers an expert study plan, detailed subject review, and hundreds of online and in-book practice questions—all authored by the experts behind the MCAT prep course that has helped more people get into medical school than all other major courses combined. Prepping for the MCAT is a true challenge. Kaplan can be your partner along the way—offering guidance on where to focus your efforts and how to organize your review. This book has been updated to match the AAMC’s guidelines precisely—no more worrying about whether your MCAT review is comprehensive! The Most Practice More than 350 questions in the book and access to even more online—more practice than any other MCAT physics and math book on the market. The Best Practice Comprehensive physics and math subject review is written by top-rated, award-winning Kaplan instructors. Full-color, 3-D illustrations, charts, graphs and diagrams help turn even the most complex science into easy-to-visualize concepts. All material is vetted by editors with advanced science degrees and by a medical doctor. Online resources, including a full-length practice test, help you practice in the same computer-based format you’ll see on Test Day. Expert Guidance High-yield badges throughout the book identify the topics most frequently tested by the AAMC. We know the test: The Kaplan MCAT team has spent years studying every MCAT-related document available. Kaplan’s expert psychometricians ensure our practice questions and study materials are true to the test. |
| bias definition in math: Mathematics Anxiety Irene C. Mammarella, Sara Caviola, Ann Dowker, 2019-02-18 Feelings of apprehension and fear brought on by mathematical performance can affect correct mathematical application and can influence the achievement and future paths of individuals affected by it. In recent years, mathematics anxiety has become a subject of increasing interest both in educational and clinical settings. This ground-breaking collection presents theoretical, educational and psychophysiological perspectives on the widespread phenomenon of mathematics anxiety. Featuring contributions from leading international researchers, Mathematics Anxiety challenges preconceptions and clarifies several crucial areas of research, such as the distinction between mathematics anxiety from other forms of anxiety (i.e., general or test anxiety); the ways in which mathematics anxiety has been assessed (e.g. throughout self-report questionnaires or psychophysiological measures); the need to clarify the direction of the relationship between math anxiety and mathematics achievement (which causes which). Offering a revaluation of the negative connotations usually associated with mathematics anxiety and prompting avenues for future research, this book will be invaluable to academics and students in the field psychological and educational sciences, as well as teachers working with students who are struggling with mathematics anxiety |
| bias definition in math: Mathematics Dictionary R.C. James, 1992-07-31 For more than 50 years, this classic reference has provided fundamental data in an accessible, concise form. This edition of the Mathematics Dictionary incorporates updated terms and concepts in its span of more than 8,000 topics from a broad spectrum of mathematical specialties. It features review-length descriptions of theories, practices and principles as well as a multilingual index. |
| bias definition in math: MCAT Physics and Math Review 2022-2023 Kaplan Test Prep, 2021-07-06 Kaplan's MCAT Physics and Math Review 2022-2023 offers an expert study plan, detailed subject review, and hundreds of online and in-book practice questions--all authored by the experts behind the MCAT prep course that has helped more people get into medical school than all other major courses combined. Prepping for the MCAT is a true challenge. Kaplan can be your partner along the way--offering guidance on where to focus your efforts and how to organize your review. This book has been updated to match the AAMC's guidelines precisely--no more worrying about whether your MCAT review is comprehensive The Most Practice More than 350 questions in the book and access to even more online--more practice than any other MCAT physics and math book on the market. The Best Practice Comprehensive physics and math subject review is written by top-rated, award-winning Kaplan instructors. Full-color, 3-D illustrations from Scientific American, charts, graphs and diagrams help turn even the most complex science into easy-to-visualize concepts. All material is vetted by editors with advanced science degrees and by a medical doctor. Online resources, including a full-length practice test, help you practice in the same computer-based format you'll see on Test Day. Expert Guidance High-yield badges throughout the book identify the top 100 topics most tested by the AAMC. We know the test: The Kaplan MCAT team has spent years studying every MCAT-related document available. Kaplan's expert psychometricians ensure our practice questions and study materials are true to the test. |
| bias definition in math: Meaning in Mathematics Education Jeremy Kilpatrick, Celia Hoyles, Ole Skovsmose, 2006-03-30 What does it mean to know mathematics? How does meaning in mathematics education connect to common sense or to the meaning of mathematics itself? How are meanings constructed and communicated and what are the dilemmas related to these processes? There are many answers to these questions, some of which might appear to be contradictory. Thus understanding the complexity of meaning in mathematics education is a matter of huge importance. There are twin directions in which discussions have developed—theoretical and practical—and this book seeks to move the debate forward along both dimensions while seeking to relate them where appropriate. A discussion of meaning can start from a theoretical examination of mathematics and how mathematicians over time have made sense of their work. However, from a more practical perspective, anybody involved in teaching mathematics is faced with the need to orchestrate the myriad of meanings derived from multiple sources that students develop of mathematical knowledge. This book presents a wide variety of theoretical reflections and research results about meaning in mathematics and mathematics education based on long-term and collective reflection by the group of authors as a whole. It is the outcome of the work of the BACOMET (BAsic COmponents of Mathematics Education for Teachers) group who spent several years deliberating on this topic. The ten chapters in this book, both separately and together, provide a substantial contribution to clarifying the complex issue of meaning in mathematics education. This book is of interest to researchers in mathematics education, graduate students of mathematics education, under graduate students in mathematics, secondary mathematics teachers and primary teachers with an interest in mathematics. |
| bias definition in math: Principles of Test Theories Hoi K. Suen, 2012-11-12 Based on a tremendous increase in the development of psychometric theories in the past decade -- ranging from techniques for criterion-referenced testing to behavioral assessment, generalizability, and item response theory -- this book offers a summary of core issues. In so doing, it provides a comprehensive survey of reliability, validity, and item analysis from the perspectives of classical true-score model, generalizability theory, item response theory, criterion-referenced testing, and behavioral assessment. Related theoretical issues such as item bias, equating, and cut-score determination are also discussed. This is an excellent text for courses in statistics, research methods, behavioral medicine and cognitive science as well as educational, school, experimental, counseling/social, clinical, developmental, and personality psychology. |
| bias definition in math: Mathematics for Human Flourishing Francis Su, 2020-01-07 The ancient Greeks argued that the best life was filled with beauty, truth, justice, play and love. The mathematician Francis Su knows just where to find them.--Kevin Hartnett, Quanta Magazine This is perhaps the most important mathematics book of our time. Francis Su shows mathematics is an experience of the mind and, most important, of the heart.--James Tanton, Global Math Project For mathematician Francis Su, a society without mathematical affection is like a city without concerts, parks, or museums. To miss out on mathematics is to live without experiencing some of humanity's most beautiful ideas. In this profound book, written for a wide audience but especially for those disenchanted by their past experiences, an award-winning mathematician and educator weaves parables, puzzles, and personal reflections to show how mathematics meets basic human desires--such as for play, beauty, freedom, justice, and love--and cultivates virtues essential for human flourishing. These desires and virtues, and the stories told here, reveal how mathematics is intimately tied to being human. Some lessons emerge from those who have struggled, including philosopher Simone Weil, whose own mathematical contributions were overshadowed by her brother's, and Christopher Jackson, who discovered mathematics as an inmate in a federal prison. Christopher's letters to the author appear throughout the book and show how this intellectual pursuit can--and must--be open to all. |
| bias definition in math: Mathematical Statistics with Applications in R Kandethody M. Ramachandran, Chris P. Tsokos, 2014-09-14 Mathematical Statistics with Applications in R, Second Edition, offers a modern calculus-based theoretical introduction to mathematical statistics and applications. The book covers many modern statistical computational and simulation concepts that are not covered in other texts, such as the Jackknife, bootstrap methods, the EM algorithms, and Markov chain Monte Carlo (MCMC) methods such as the Metropolis algorithm, Metropolis-Hastings algorithm and the Gibbs sampler. By combining the discussion on the theory of statistics with a wealth of real-world applications, the book helps students to approach statistical problem solving in a logical manner.This book provides a step-by-step procedure to solve real problems, making the topic more accessible. It includes goodness of fit methods to identify the probability distribution that characterizes the probabilistic behavior or a given set of data. Exercises as well as practical, real-world chapter projects are included, and each chapter has an optional section on using Minitab, SPSS and SAS commands. The text also boasts a wide array of coverage of ANOVA, nonparametric, MCMC, Bayesian and empirical methods; solutions to selected problems; data sets; and an image bank for students.Advanced undergraduate and graduate students taking a one or two semester mathematical statistics course will find this book extremely useful in their studies. - Step-by-step procedure to solve real problems, making the topic more accessible - Exercises blend theory and modern applications - Practical, real-world chapter projects - Provides an optional section in each chapter on using Minitab, SPSS and SAS commands - Wide array of coverage of ANOVA, Nonparametric, MCMC, Bayesian and empirical methods |
| bias definition in math: Mathematical Constants II Steven R. Finch, 2003 Famous mathematical constants include the ratio of circular circumference to diameter, π = 3.14 ..., and the natural logarithm base, e = 2.718 .... Students and professionals can often name a few others, but there are many more buried in the literature and awaiting discovery. How do such constants arise, and why are they important? Here the author renews the search he began in his book Mathematical Constants, adding another 133 essays that broaden the landscape. Topics include the minimality of soap film surfaces, prime numbers, elliptic curves and modular forms, Poisson-Voronoi tessellations, random triangles, Brownian motion, uncertainty inequalities, Prandtl-Blasius flow (from fluid dynamics), Lyapunov exponents, knots and tangles, continued fractions, Galton-Watson trees, electrical capacitance (from potential theory), Zermelo's navigation problem, and the optimal control of a pendulum. Unsolved problems appear virtually everywhere as well. This volume continues an outstanding scholarly attempt to bring together all significant mathematical constants in one place. |
| bias definition in math: The Concise Encyclopedia of Statistics Yadolah Dodge, 2008-04-15 The Concise Encyclopedia of Statistics presents the essential information about statistical tests, concepts, and analytical methods in language that is accessible to practitioners and students of the vast community using statistics in medicine, engineering, physical science, life science, social science, and business/economics. The reference is alphabetically arranged to provide quick access to the fundamental tools of statistical methodology and biographies of famous statisticians. The more than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings. All entries include cross-references as well as the key citations. The back matter includes a timeline of statistical inventions. This reference will be an enduring resource for locating convenient overviews about this essential field of study. |
| bias definition in math: A Course in Statistics with R Prabhanjan N. Tattar, Suresh Ramaiah, B. G. Manjunath, 2016-03-15 Integrates the theory and applications of statistics using R A Course in Statistics with R has been written to bridge the gap between theory and applications and explain how mathematical expressions are converted into R programs. The book has been primarily designed as a useful companion for a Masters student during each semester of the course, but will also help applied statisticians in revisiting the underpinnings of the subject. With this dual goal in mind, the book begins with R basics and quickly covers visualization and exploratory analysis. Probability and statistical inference, inclusive of classical, nonparametric, and Bayesian schools, is developed with definitions, motivations, mathematical expression and R programs in a way which will help the reader to understand the mathematical development as well as R implementation. Linear regression models, experimental designs, multivariate analysis, and categorical data analysis are treated in a way which makes effective use of visualization techniques and the related statistical techniques underlying them through practical applications, and hence helps the reader to achieve a clear understanding of the associated statistical models. Key features: Integrates R basics with statistical concepts Provides graphical presentations inclusive of mathematical expressions Aids understanding of limit theorems of probability with and without the simulation approach Presents detailed algorithmic development of statistical models from scratch Includes practical applications with over 50 data sets |
| bias definition in math: Methods for Identifying Biased Test Items Gregory Camilli, Lorrie A. Shepard, 1994-04-06 In this book, the authors provide a cogent review of statistical and interpretive procedures that, in combination, can be used to reduce the likelihood that tests contain items that favor members of one gender, age, racial, or ethnic group over equally able members of another group, for reasons that are unrelated to the objectives and purposes of measurement. Such test items are said to be biased against the equally able members of the group that is not favored. The methods described and illustrated in this book have the potential to reducing the incidence of tests that are, in their construction, biased against members of one or more groups. These methods have the potential of controlling an important source of invalidity when test results are interpreted. |
| bias definition in math: Introductory Statistics 2e Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| bias definition in math: Arthur Jensen: Consensus And Controversy Sohan Modgil, Celia Modgil, 2005-11-04 First Published in 1987. During the last thirty years, Arthur Jensen’s brilliant contribution to knowledge has been well-known world-wide. From its early transmission, his work has not been without its critics. Naturally, criticisms persist, although his work continues to be frequently acknowledged with great admiration in the channels of psychology. With such prolific work, it would seem justified to consider the discrepancies, the omissions, together with the various interpretations which have been and are currently being highlighted. No theory or practice in modern psychology has been the object of more stringent attack than mental testing, and among the most severe criticisms is that of cultural bias. |
| bias definition in math: Theory Construction and Model-Building Skills James Jaccard, Jacob Jacoby, 2020-02-06 This book provides young scientists with tools to assist them in the practical aspects of theory construction. We take an informal journey through the cognitive heuristics, tricks of the trade, and ways of thinking that we have found to be useful in developing theories-essentially, conceptualizations-that can advance knowledge in the social sciences. This book is intended to provide the instructor with a useful source for helping students come up with ideas for research and for fine-tuning the resultant theories that emerge from such thinking. An objective of this book is to move toward a needed balance in the emphases given to theory construction and theory testing-- |
| bias definition in math: OECD Glossary of Statistical Terms OECD, 2008-09-01 The OECD Glossary contains a comprehensive set of over 6 700 definitions of key terminology, concepts and commonly used acronyms derived from existing international statistical guidelines and recommendations. |
| bias definition in math: Observational Studies Paul R. Rosenbaum, 2013-06-29 An observational study is an empirical investigation of the effects of treatments, policies, or exposures. It differes from an experiment in that the investigator cannot control the assignments of treatments to subjects. Scientists across a wide range of disciplines undertake such studies, and the aim of this book is to provide a sound statistical account of the principles and methods for the design and analysis of observational studies. Readers are assumed to have a working knowledge of basic probability and statistics, but otherwise the account is reasonably self-contained. Throughout there are extended discussions of actual observational studies to illustrate the ideas discussed. These are drawn from topics as diverse as smoking and lung cancer, lead in children, nuclear weapons testing, and placement programs for students. As a result, many researchers involved in observational studes will find this an invaluable companion to their work. |
| bias definition in math: Naked Statistics: Stripping the Dread from the Data Charles Wheelan, 2013-01-07 A New York Times bestseller Brilliant, funny…the best math teacher you never had. —San Francisco Chronicle Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called sexy. From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more. For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions. And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life. |
| bias definition in math: Introduction to Educational Research W. Newton Suter, 2012 W. Newton Suter argues that what is important in a changing education landscape is the ability to think clearly about research methods, reason through complex problems and evaluate published research. He explains how to evaluate data and establish its relevance. |
| bias definition in math: Mastering Assessment W. James Popham, 2011-07 Mastering Assessment: A Self-Service System for Educators, 2/e (hereafter referred to as MA) is a set of 15 booklets intended to be the grist for a wide variety of professional development programs focused on educational assessment. Each of the MA booklets was deliberately written to permit a one- sitting or two- sittings reading by busy educators. The resultant brevity of the MA booklets, coupled with their being provided as separate documents, is intended to provide users of the MA system with considerable latitude in determining how best to use the booklets. A Facilitator's Guide is available to guide educators in using the 15 booklets in their professional development programs and can be downloaded at no additional charge from Pearson's Instructor Resource Center. Mastering Assessment boxset incudes: * Appropriate and Inappropriate Tests for Evaluating Schools * Assessing Students' Affect * Assessing Students with Disabilities * Assessment Bias: How to Banish It * Classroom Evidence of Successful Teaching * College Entrance Examinations: The SAT and the ACT * Constructed-Response Tests: Building and Bettering * How Testing Can Help Teaching * Interpreting the Results of Large-Scale Assessments * Portfolio Assessment and Performance Testing * Reliability: What Is It and Is It Necessary? * Selected-Response Tests: Building and Bettering * The Role of Rubrics in Testing and Teaching * Test Preparation: Sensible or Sordid? * Validity: Assessment's Cornerstone |
| bias definition in math: Signal Processing in Radar Systems Vyacheslav Tuzlukov, 2017-12-19 An essential task in radar systems is to find an appropriate solution to the problems related to robust signal processing and the definition of signal parameters. Signal Processing in Radar Systems addresses robust signal processing problems in complex radar systems and digital signal processing subsystems. It also tackles the important issue of defining signal parameters. The book presents problems related to traditional methods of synthesis and analysis of the main digital signal processing operations. It also examines problems related to modern methods of robust signal processing in noise, with a focus on the generalized approach to signal processing in noise under coherent filtering. In addition, the book puts forth a new problem statement and new methods to solve problems of adaptation and control by functioning processes. Taking a systems approach to designing complex radar systems, it offers readers guidance in solving optimization problems. Organized into three parts, the book first discusses the main design principles of the modern robust digital signal processing algorithms used in complex radar systems. The second part covers the main principles of computer system design for these algorithms and provides real-world examples of systems. The third part deals with experimental measurements of the main statistical parameters of stochastic processes. It also defines their estimations for robust signal processing in complex radar systems. Written by an internationally recognized professor and expert in signal processing, this book summarizes investigations carried out over the past 30 years. It supplies practitioners, researchers, and students with general principles for designing the robust digital signal processing algorithms employed by complex radar systems. |
| bias definition in math: Women in Engineering, Science and Technology: Education and Career Challenges Cater-Steel, Aileen, Cater, Emily, 2010-05-31 This book discusses increasing the participation of women in science, engineering and technology professions, educating the stakeholders - citizens, scholars, educators, managers and policy makers - how to be part of the solution--Provided by publisher. |
| bias definition in math: Uses of Artificial Intelligence in STEM Education Xiaoming Zhai, Joseph Krajcik, 2024-10-24 In the age of rapid technological advancements, the integration of Artificial Intelligence (AI), machine learning (ML), and large language models (LLMs) in Science, Technology, Engineering, and Mathematics (STEM) education has emerged as a transformative force, reshaping pedagogical approaches and assessment methodologies. Uses of AI in STEM Education, comprising 25 chapters, delves deep into the multifaceted realm of AI-driven STEM education. It begins by exploring the challenges and opportunities of AI-based STEM education, emphasizing the intricate balance between human tasks and technological tools. As the chapters unfold, readers learn about innovative AI applications, from automated scoring systems in biology, chemistry, physics, mathematics, and engineering to intelligent tutors and adaptive learning. The book also touches upon the nuances of AI in supporting diverse learners, including students with learning disabilities, and the ethical considerations surrounding AI's growing influence in educational settings. It showcases the transformative potential of AI in reshaping STEM education, emphasizing the need for adaptive pedagogical strategies that cater to diverse learning needs in an AI-centric world. The chapters further delve into the practical applications of AI, from scoring teacher observations and analyzing classroom videos using neural networks to the broader implications of AI for STEM assessment practices. Concluding with reflections on the new paradigm of AI-based STEM education, this book serves as a comprehensive guide for educators, researchers, and policymakers, offering insights into the future of STEM education in an AI-driven world. |
| bias definition in math: Tests & Measurement for People Who (Think They) Hate Tests & Measurement Neil J. Salkind, Bruce B. Frey, 2022-09-25 With a signature, conversational writing style and straightforward presentation, Neil J. Salkind’s best-selling Tests & Measurement for People Who (Think They) Hate Tests & Measurement guides readers through an overview of categories of tests, the design of tests, the use of tests, and some of the basic social, political, and legal issues that the process of testing involves. New co-author Bruce B. Frey has streamlined the table of contents for ease of use; added more content on validity and reliability throughout; more closely connected standardized tests to classroom instruction, adding more on classroom assessment; and added a chapter on surveys and scale development. An instructor website includes a test bank and PowerPoint slides. |
| bias definition in math: Psychometrics R. Michael Furr, 2017-11-17 Using a meaning-based approach that emphasizes the why over the how to, Psychometrics: An Introduction provides thorough coverage of fundamental issues in psychological measurement. Author R. Michael Furr discusses traditional psychometric perspectives and issues including reliability, validity, dimensionality, test bias, and response bias as well as advanced procedures and perspectives including item response theory and generalizability theory. The substantially updated Third Edition includes broader and more in-depth coverage with new references, a glossary summarizing over 200 key terms, and expanded suggested readings consisting of highly relevant papers to enhance the book’s overall accessibility, scope, and usability for both instructors and students. Online Resources Free PowerPoint® slides for instructors are available with this text. Contact your rep to learn more. |
| bias definition in math: Essential Math for AI Hala Nelson, 2023-01-04 Companies are scrambling to integrate AI into their systems and operations. But to build truly successful solutions, you need a firm grasp of the underlying mathematics. This accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI--including regression, neural networks, optimization, backpropagation, convolution, Markov chains, and more--through popular applications such as computer vision, natural language processing, and automated systems. And supplementary Jupyter notebooks shed light on examples with Python code and visualizations. Whether you're just beginning your career or have years of experience, this book gives you the foundation necessary to dive deeper in the field. Understand the underlying mathematics powering AI systems, including generative adversarial networks, random graphs, large random matrices, mathematical logic, optimal control, and more Learn how to adapt mathematical methods to different applications from completely different fields Gain the mathematical fluency to interpret and explain how AI systems arrive at their decisions |
机器学习中的 Bias（偏差）、Error（误差）、Variance（方差）有 …
首先明确一点，Bias和Variance是针对Generalization（一般化，泛化）来说的。. 在机器学习中，我们用训练数据集去训练（学习）一个model（模型），通常的做法是定义一个Loss …
神经网络中的偏置（bias）究竟有什么用？ - 知乎
神经网络中的偏置（bias）究竟有什么用？ 最近写了一下模式识别的作业，简单的用python实现了一个三层神经网络，发现不加偏置的话，网络的训练精度一直不能够提升，加了偏执之后反而 …
偏差——bias与deviation的联系/区别？ - 知乎
各位同学，你们有没有想过‘偏见’在英语中是怎么说的？没错，答案就是'bias'！而且，我们这次还结合了一款超酷的桌面背单词软件，让你在学习单词的同时，也能感受到科技的魅
英文中prejudice和bias的区别? - 知乎
Bias：Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. 可见 bias 所表示的意思是“偏爱”，其本质是一种喜好，而非厌恶，所以没有偏见的意思。
sci投稿Declaration of interest怎么写? - 知乎
正在写SCI的小伙伴看到这篇回答有福了！作为一个在硕士阶段发表了4篇SCI（一区×2，二区×2）的人，本回答就好好给你唠唠究竟该如何撰写Declaration of interest利益声明部分。
确认偏误是什么？如何系统地克服确认偏误？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
Linear classifier 里的 bias 有什么用？ - 知乎
Oct 27, 2015 · 你想象一下一维的情况，如果有两个点 -1 是负类， -2 是正类。如果没有bias，你的分类边界只能是过远点的一条垂直线，没法区分出这两个类别，bias给你提供了在特征空间上 …
选择性偏差（selection bias）指的是什么？ - 知乎
选择性偏差指的是在研究过程中因样本选择的非随机性而导致得到的结论存在偏差，包括自选择偏差（self-selection bias）和样本选择偏差（sample-selection bias）。消除选择性偏差，我们 …
哪里有标准的机器学习术语(翻译)对照表？ - 知乎
预测偏差 (prediction bias) 一种值，用于表明预测平均值与数据集中标签的平均值相差有多大。 预训练模型 (pre-trained model) 已经过训练的模型或模型组件（例如嵌套）。有时，您需要将预 …
如何理解Adam算法(Adaptive Moment Estimation)？ - 知乎
完整的Adam更新算法也包含了一个偏置（bias）矫正机制，因为m,v两个矩阵初始为0，在没有完全热身之前存在偏差，需要采取一些补偿措施。 不同最优化方法效果
机器学习中的 Bias（偏差）、Error（误差）、Variance（方 …
首先明确一点，Bias和Variance是针对Generalization（一般化，泛化）来说的。. 在机器学习中，我们用训练数据集去训练（学习）一个model（模型），通常的做法是 …
神经网络中的偏置（bias）究竟有什么用？ - 知乎
神经网络中的偏置（bias）究竟有什么用？ 最近写了一下模式识别的作业，简单的用python实现了一个三层神经网络，发现不加偏置的话，网络的训练精度一直不能够提 …
偏差——bias与deviation的联系/区别？ - 知乎
各位同学，你们有没有想过‘偏见’在英语中是怎么说的？没错，答案就是'bias'！而且，我们这次还结合了一款超酷的桌面背单词软件，让你在学习单词的同时，也能感受到科技的魅
英文中prejudice和bias的区别? - 知乎
Bias：Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. 可见 bias 所表示的意思是“偏爱”，其本质是一种喜 …
sci投稿Declaration of interest怎么写? - 知乎
正在写SCI的小伙伴看到这篇回答有福了！作为一个在硕士阶段发表了4篇SCI（一区×2，二区×2）的人，本回答就好好给你唠唠究竟该如何撰写Declaration of interest利益声明部 …
