There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.

    Although dashboards are potentially powerful, this potential is rarely realized. The greatest display technology in the world won't solve this if you fail to use effective visual design. And if a dashboard fails to tell you precisely what you need to know in an instant, you'll never use it, even if it's filled with cute gauges, meters, and traffic lights. Don't let your investment in dashboard technology go to waste.This book will teach you the visual design skills you need to create dashboards that communicate clearly, rapidly, and compellingly. Information Dashboard Design will explain how to:Avoid the thirteen mistakes common to dashboard design Provide viewers with the information they need quickly and clearly Apply what we now know about visual perception to the visual presentation of information Minimize distractions, cliches, and unnecessary embellishments that create confusion Organize business information to support meaning and usability Create an aesthetically pleasing viewing experience Maintain consistency of design to provide accurate interpretation Optimize the power of dashboard technology by pairing it with visual effectiveness Stephen Few has over 20 years of experience as an IT innovator, consultant, and educator. As Principal of the consultancy Perceptual Edge, Stephen focuses on data visualization for analyzing and communicating quantitative business information. He provides consulting and training services, speaks frequently at conferences, and teaches in the MBA program at the University of California in Berkeley. He is also the author of Show Me the Numbers: Designing Tables and Graphs to Enlighten. Visit his website at www.perceptualedge.com.

    Readers will see how the choice of topic influences question wording and how the questions asked influence the analysis. The authors have improved the presentation of matters that students have most trouble with, such as finding an interesting and viable topic, recognizing concepts, learning when and how aggressively to follow up, and figuring out ways to get published.

    In this comprehensive anthology, authors selected for their distinction in their chosen careers offer their professional - and personal - perspectives on 19 different graduate-level careers in psychology. Few fields of study offer more career opportunities than does psychology, and readers will find thoughtful discussions, leavened with tips and insights gained from personal experience, on the full range, including (to name only a few) academia, clinical psychology, health and school psychology, clinical neuropsychology, and government service. Each chapter discusses the nature of the career, its advantages and disadvantages, how to prepare for it, typical activities, ranges of financial compensation, opportunities for employment, and the personal attributes needed for success in the career. realities, challenges, and rewards of each career that the lab or lecture hall rarely provides. Reprinted eight times since the publication of the first edition in 1997, Career Paths in Psychology is recognized as the go-to sourcebook for anyone seeking a candid portrait of different careers in this ever-changing field. The second edition has been expanded (discussions of five new careers added) and updated to reflect current trends and changes in the field.

    Here, an indigenous researcher issues a clarion call for the decolonization of research methods.The book is divided into two parts. In the first, the author critically examines the historical and philosophical base of Western research. Extending the work of Foucault, she explores the intersections of imperialism, knowledge and research, and the different ways in which imperialism is embedded in disciplines of knowledge and methodologies as 'regimes of truth'. Providing a history of knowledge from the Enlightenment to Postcoloniality, she also discusses the fate of concepts such as 'discovery, 'claiming' and 'naming' through which the west has incorporated and continues to incorporate the indigenous world within its own web.The second part of the book meets the urgent need for people who are carrying out their own research projects, for literature which validates their frustrations in dealing with various western paradigms, academic traditions and methodologies, which continue to position the indigenous as 'Other'. In setting an agenda for planning and implementing indigenous research, the author shows how such programmes are part of the wider project of reclaiming control over indigenous ways of knowing and being.Exploring the broad range of issues which have confronted, and continue to confront, indigenous peoples, in their encounters with western knowledge, this book also sets a standard for truly emancipatory research. It brilliantly demonstrates that "when indigenous peoples become the researchers and not merely the researched, the activity of research is transformed."

    If it takes a book to get it across, I hope this book will do it. It ought to.”—Thomas Schelling, Distinguished University Professor, School of Public Policy, University of Maryland, and 2005 Nobel Prize Laureate in Economics “With humor, insight, piercing logic and a nod to history, Ziliak and McCloskey show how economists—and other scientists—suffer from a mass delusion about statistical analysis. The quest for statistical significance that pervades science today is a deeply flawed substitute for thoughtful analysis. . . . Yet few participants in the scientific bureaucracy have been willing to admit what Ziliak and McCloskey make clear: the emperor has no clothes.”—Kenneth Rothman, Professor of Epidemiology, Boston University School of Health The Cult of Statistical Significance shows, field by field, how “statistical significance,” a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ “testing” that doesn’t test and “estimating” that doesn’t estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots. Stephen T. Ziliak is the author or editor of many articles and two books. He currently lives in Chicago, where he is Professor of Economics at Roosevelt University. Deirdre N. McCloskey, Distinguished Professor of Economics, History, English, and Communication at the University of Illinois at Chicago, is the author of twenty books and three hundred scholarly articles. She has held Guggenheim and National Humanities Fellowships. She is best known for How to Be Human* Though an Economist (University of Michigan Press, 2000) and her most recent book, The Bourgeois Virtues: Ethics for an Age of Commerce (2006).

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    Incorporating OD ethics and values into each chapter, Donald L. Anderson provides discussion of the real-world application of these theoretical ideas. In-depth case studies that follow major content chapters allow students to immediately apply what they have learned. In today's challenging environment of increased globalization, rapidly changing technologies, economic pressures, and expectations in the contemporary workforce, this book is an essential tool.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    Designed primarily for undergraduates, this self-help manual offers straightforward solutions to common problems and an overview of the diversity of writing tasks faced by professional biologists.

    From a leading figure in the field, this introductory text forms an elegant overview of social constructionism that is at once wide-ranging and accessible.

    This guide to stage management, aimed at aspiring stage managers, features all they need to know when staging a production, including methods which employ computer technology.

    Rossi, Mark W. Lipsey, and Howard E. Freeman first published Evaluation: A Systematic Approach, more than 90,000 readers have considered it the premier text on how to design, implement, and appraise social programs through evaluation. In this, the completely revised Seventh Edition, authors Rossi and Lipsey include the latest techniques and approaches to evaluation as well as guidelines to tailor evaluations to fit programs and social contexts.With decades of hands-on experience conducting evaluations, the authors provide scores of examples to help students understand how evaluators deal with various critical issues. They include a glossary of key terms and concepts, making this the most comprehensive and authoritative evaluation text available.Thoroughly revised, the Seventh Edition now includes* Substantially more attention to outcome measurement* Lengthy discussions of program theory, including a section about detecting program effects and interpreting their practical significance* An augmented and updated discussion of major evaluation designs* A detailed exposition of meta-analysis as an approach to the synthesis of evaluation studies* Alternative approaches to evaluation* Examples of successful evaluations* Discussions of the political and social contexts of evaluation

    For each question, there is a concise explanation of how to apply it in the evaluation of research reports.Numerous examples from journals in the social and behavioral sciences illustrate the application of the evaluation questions. Students see actual examples of strong and weak features of published reports.Commonsense models for evaluation combined with a lack of jargon make it possible for students to start evaluating research articles the first week of class.The structure of this book enables students to work with confidence while evaluating articles for homework.Avoids oversimplification in the evaluation process by describing the nuances that may make an article publishable even though it has serious methodological flaws. Students learn when and why certain types of flaws may be tolerated. They learn why evaluation should not be performed mechanically.This book received very high student evaluations when field-tested with students just beginning their study of research methods.Contains more than 60 new examples from recently published research. In addition, minor changes have been made throughout for consistency with the latest edition of the Publication Manual of the American Psychological Association."

    The rise of the Internet and the wide availability of inexpensive computers have made it possible to gather and analyze network data on a large scale, and the development of a variety of new theoretical tools has allowed us to extract new knowledge from many different kinds of networks.The study of networks is broadly interdisciplinary and important developments have occurred in many fields, including mathematics, physics, computer and information sciences, biology, and the social sciences. This book brings together for the first time the most important breakthroughs in each of these fields and presents them in a coherent fashion, highlighting the strong interconnections between work in different areas.Subjects covered include the measurement and structure of networks in many branches of science, methods for analyzing network data, including methods developed in physics, statistics, and sociology, the fundamentals of graph theory, computer algorithms, and spectral methods, mathematical models of networks, including random graph models and generative models, and theories of dynamical processes taking place on networks.