Written as a study tool, the Lab Workbook is keyed directly to the text to provide section by section review and practice for the first ten chapters of Agresti/Franklin 2/e. Print outs of the activities found on the Student CD are included in the Lab Workbook.
Provides a conceptual introduction to introductory statistics that is accessible to students. This work is designed for algebra-based Introductory Statistics Courses.
With increased awareness of the role of plans in shaping urban and suburban landscapes has come increased criticism of planners and the planning profession. Developers, politicians, and citizens alike blame "poor planning" for a host of community ills. But what are plans really supposed to do? How do they work? What problems can they successfully address, and what is beyond their scope? In Urban Development, leading planning scholar Lewis Hopkins tackles these thorny issues as he explains the logic of plans for urban development and justifies prescriptions about when and how to make them. He explores the concepts behind plans, some that are widely accepted but seldom examined, and others that modify conventional wisdom about the use and usefulness of plans. The book: places the role of plans and planners within the complex system of urban development offers examples from the history of plans and planning discusses when plans should be made (and when they should not be made) gives a realistic idea of what can be expected from plans examines ways of gauging the success or failure of plansThe author supports his explanations with graphics, case examples, and hypothetical illustrations that enliven, clarify, and make concrete the discussions of how decisions about plans are and should be made.Urban Development will give all those involved with planning human settlements a more thorough understanding of why and how plans are made, enabling them to make better choices about using and making plans. It is an important contribution that will be essential for students and faculty in planning theory, land use planning, and planning project courses.
Shorn of all subtlety and led naked out of the protec tive fold of educational research literature, there comes a sheepish little fact: lectures don't work nearly as well as many of us would like to think. -George Cobb (1992) This book contains activities that guide students to discover statistical concepts, explore statistical principles, and apply statistical techniques. Students work toward these goals through the analysis of genuine data and through inter action with one another, with their instructor, and with technology. Providing a one-semester introduction to fundamental ideas of statistics for college and advanced high school students, Warkshop Statistics is designed for courses that employ an interactive learning environment by replacing lectures with hands on activities. The text contains enough expository material to stand alone, but it can also be used to supplement a more traditional textbook. Some distinguishing features of Workshop Statistics are its emphases on active learning, conceptual understanding, genuine data, and the use of technology. The following sections of this preface elaborate on each of these aspects and also describe the unusual organizational structure of this text.
By taking a thematic approach to the study of music appreciation, Music: A Social Experience, Second Edition demonstrates how music reflects and deepens both individual and cultural understandings. Musical examples are presented within universally experienced social frameworks (ethnicity, gender, spirituality, love, and more) to help students understand how music reflects and advances human experience. Students engage with multiple genres (Western art music, popular music, and world music) through lively narratives and innovative activities. A companion website features streaming audio and instructors' resources. New to this edition: Two additional chapters: "Music and the Life Cycle" and "Music and Technology" Essay questions and "key terms" lists at the ends of chapters Additional repertoire and listening guides covering all historical periods of Western art music Expanded instructors’ resources Many additional images Updated student web materials
For courses in introductory statistics. The Art and Science of Learning from Data Statistics: The Art and Science of Learning from Data, Fourth Edition, takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises. The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data. Also available with MyStatLab MyStatLab™ is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts. For this edition, new web apps with complementary exercises, a tightly integrated video program, and strong exercise coverage enhance student learning. Note: You are purchasing a standalone product; MyLab™ & Mastering™ does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase boththe physical text and MyLab & Mastering, search for: 0134101677 / 9780134101675 * Statistics Plus New MyStatLab with Pearson eText -- Access Card Package Package consists of: 0321847997 / 9780321847997 * My StatLab Glue-in Access Card 032184839X / 9780321848390 * MyStatLab Inside Sticker for Glue-In Packages 0321997832 / 9780321997838 * Statistics: The Art and Science of Learning from Data
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring in social science disciplines. With an emphasis on concepts and applications, this book assumes you have no previous knowledge of statistics and only a minimal mathematical background. It contains sufficient material for a two-semester course. The 5th Edition gives you examples and exercises with a variety of “real data.” It includes more illustrations of statistical software for computations and takes advantage of the outstanding applets to explain key concepts, such as sampling distributions and conducting basic data analyses. It continues to downplay mathematics–often a stumbling block for students–while avoiding reliance on an overly simplistic recipe-based approach to statistics.
Previously Published as A Field Guide to Lies We’re surrounded by fringe theories, fake news, and pseudo-facts. These lies are getting repeated. New York Times bestselling author Daniel Levitin shows how to disarm these socially devastating inventions and get the American mind back on track. Here are the fundamental lessons in critical thinking that we need to know and share now. Investigating numerical misinformation, Daniel Levitin shows how mishandled statistics and graphs can give a grossly distorted perspective and lead us to terrible decisions. Wordy arguments on the other hand can easily be persuasive as they drift away from the facts in an appealing yet misguided way. The steps we can take to better evaluate news, advertisements, and reports are clearly detailed. Ultimately, Levitin turns to what underlies our ability to determine if something is true or false: the scientific method. He grapples with the limits of what we can and cannot know. Case studies are offered to demonstrate the applications of logical thinking to quite varied settings, spanning courtroom testimony, medical decision making, magic, modern physics, and conspiracy theories. This urgently needed book enables us to avoid the extremes of passive gullibility and cynical rejection. As Levitin attests: Truth matters. A post-truth era is an era of willful irrationality, reversing all the great advances humankind has made. Euphemisms like “fringe theories,” “extreme views,” “alt truth,” and even “fake news” can literally be dangerous. Let's call lies what they are and catch those making them in the act.
This workbook is a study tool for the first 10 chapters of the text. This workbook provides section-by-section review and practice, and additional activities that cover fundamental statistical topics.
For courses in introductory statistics. The Art and Science of Learning from Data Statistics: The Art and Science of Learning from Data, Fourth Edition, takes a conceptual approach, helping students understand what statistics is about and learning the right questions to ask when analyzing data, rather than just memorizing procedures. This book takes the ideas that have turned statistics into a central science in modern life and makes them accessible, without compromising the necessary rigor. Students will enjoy reading this book, and will stay engaged with its wide variety of real-world data in the examples and exercises. The authors believe that it’s important for students to learn and analyze both quantitative and categorical data. As a result, the text pays greater attention to the analysis of proportions than many other introductory statistics texts. Concepts are introduced first with categorical data, and then with quantitative data. MyStatLab™ not included. Students, if MyStatLab is a recommended/mandatory component of the course, please ask your instructor for the correct ISBN and course ID. MyStatLab should only be purchased when required by an instructor. Instructors, contact your Pearson representative for more information. MyStatLab is an online homework, tutorial, and assessment product designed to personalize learning and improve results. With a wide range of interactive, engaging, and assignable activities, students are encouraged to actively learn and retain tough course concepts.
STATISTICS: LEARNING FROM DATA, Second Edition, addresses common problems faced by learners of elementary statistics with an innovative approach. The authors have paid particular attention to areas learners often struggle with -- probability, hypothesis testing, and selecting an appropriate method of analysis. Probability coverage is based on current research on how students best learn the subject. A unique chapter that provides an informal introduction to the ideas of statistical inference helps students to develop a framework for choosing an appropriate method. Supported by learning objectives, real-data examples and exercises, and technology notes, this book helps learners to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren't possible before. The 4th edition of this award-winning and widely adopted text adds content to bridge between the foundations of organizing systems and the new statistical and computational techniques of data science because at its core, data science is about how resources are described and organized. The 4th edition reframes descriptive statistics as organizing techniques, expands the treatment of classification to include computational methods, and incorporates many new examples of data-driven resource selection, organization, maintenance, and personalization. The Informatics edition contains all the new content related to data science, but omits the discipline-specific content about library science, museums, and document archives.
The fourth edition of this widely-used text relates theory to practice in the public policy process. In a clear, conversational style, author Tom Birkland conveys the best current thinking on the policy process with an emphasis on accessibility and synthesis. This new edition has been reorganized to better explain the role of policy analysis in the policy process. New to this edition: • A new section on the role of policy analysis and policy analysts in the policy process. • A revised and updated chapter surveying the social, economic, and demographic trends that are transforming the policy environment. • Fully updated references to help the advanced reader locate the most important theoretical literature in policy process studies. • New illustrations and an improved layout to clarify key ideas and stimulate classroom discussion. The book makes generous use of visual aids and examples that link policy theory to the concrete experience of practitioners. It includes chapter-at-a-glance outlines, definitions of key terms, provocative review questions, recommended reading, and online materials for professors and students.
This title is intended as a supplement for statistics or research methods courses, or for any course that uses statistics, or as a reference book to refresh one's memory about statistical concepts. The examples are varied so that it can be used in social sciences departments.
Each day we are faced with continuing claims made by media pundits, politicians, teachers, and friends, often quoting research. Consider also the numerous comments and posts on Internet blogs, Twitter, and Facebook that illustrate the confusion between opinion and factual data. How do we learn to interpret the research we hear about and read, to distinguish opinions from scientific facts, and to use this knowledge to conduct our own studies to answer the questions faced in everyday situations? Understanding the components that go into scientific research and learning how to do research, make decisions about which statistics to use, and analyze statistical findings are goals for everyone in today's research-oriented world. Questions about the reliability and validity of data from a study or public opinion poll come up routinely and need critical review. This book contributes to achieving these objectives. Doing Survey Research is intended for people who want to learn how to conduct quantitative studies for a project in an undergraduate course, a graduate-level thesis, or a survey that an employer may want completed. This brief, practical textbook prepares beginners to conduct their own survey research and write up the results, as well as read and interpret other people's research. It combines survey design with data analysis and interpretation. And it is for those who need to understand and critically interpret survey research found in scholarly journals, reports distributed in the workplace, and social scientific findings presented online in the media, on a blog, or in social media postings. Essential new updates to this edition include coverage of Big Data, Meta-Analysis, and A/B testing methodology—methods used by scholars as well as businesses like Netflix and Amazon. New to this Fourth Edition Each chapter and its exercises feature updated data and illustrations from current academic and popular articles relevant to today’s web-oriented students, including studies focused on topics related to social media. Update web site http://doingsurveyresearch.wordpress.com/ New Coverage of Big Data (used by popular web sites like Amazon and Netflix) and the ethical issues which emerge not only about privacy, but also how it relates to the methods discussed in this book about sampling, probability, and research design. New coverage of meta-data, and the increasingly popular method in many professional and other settings.
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
A good book may have the power to change the way we see the world, but a great book actually becomes part of our daily consciousness, pervading our thinking to the point that we take it for granted, and we forget how provocative and challenging its ideas once were—and still are. The Structure of Scientific Revolutions is that kind of book. When it was first published in 1962, it was a landmark event in the history and philosophy of science. Fifty years later, it still has many lessons to teach. With The Structure of Scientific Revolutions, Kuhn challenged long-standing linear notions of scientific progress, arguing that transformative ideas don’t arise from the day-to-day, gradual process of experimentation and data accumulation but that the revolutions in science, those breakthrough moments that disrupt accepted thinking and offer unanticipated ideas, occur outside of “normal science,” as he called it. Though Kuhn was writing when physics ruled the sciences, his ideas on how scientific revolutions bring order to the anomalies that amass over time in research experiments are still instructive in our biotech age. This new edition of Kuhn’s essential work in the history of science includes an insightful introduction by Ian Hacking, which clarifies terms popularized by Kuhn, including paradigm and incommensurability, and applies Kuhn’s ideas to the science of today. Usefully keyed to the separate sections of the book, Hacking’s introduction provides important background information as well as a contemporary context. Newly designed, with an expanded index, this edition will be eagerly welcomed by the next generation of readers seeking to understand the history of our perspectives on science.
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 to marketing to 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, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.