Selected for originality, general interest, or because they demonstrate valuable techniques, the problems are ideal as a supplement to courses in probability or statistics, or as stimulating recreation for the mathematically minded. Detailed solutions. Illustrated.

    Psychology of Learning for Instruction, Third Edition, focuses on the applications and implications of the learning theories. Using excellent examples ranging from primary school instruction to corporate training, this text combines the latest thinking and research to give readers the opportunity to explore the individual theories as viewed by the experts. Readers are encouraged to apply "reflective practice," which is designed to foster a critical and reflective mode of thinking when considering any particular approach to learning and instruction. Provides readers with the practical knowledge needed to apply learning theories to instruction. KEY TOPICS: This text addresses learning as it relates to behavior, cognition, development, biology, motivation and instruction. MARKET: Pre-service and in-service teachers, and educational psychologists.

    With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    An evolutionary perspective forms the foundation of the entire discussion. The book begins with the natural history of the planet, considers portions of the whole in the middle chapters, and ends with another perspective of the entire planet in the concluding chapter. Its unique organization of focusing only on several key concepts in each chapter sets it apart from the competition. .

    Included are practical, classroom-tested strategies and step-by-step instructions for how to use them. The new edition provides information on using technology for accelerated learning, managing cluster grouping, increasing curriculum rigor, improving assessments, boosting critical and creative thinking skills, and addressing gifted kids with special needs. Already a perennial best seller, this guide’s third edition is sure to be welcomed with open arms by teachers everywhere. Digital content provides a PowerPoint presentation for professional development, customizable reproducible forms from the book, additional extension menus for students in the primary and upper-elementary grades, and a special supplement for parents of gifted children.

    Theory and practice in the design of data graphics, 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Design of the high-resolution displays, small multiples. Editing and improving graphics. The data-ink ratio. Time-series, relational graphics, data maps, multivariate designs. Detection of graphical deception: design variation vs. data variation. Sources of deception. Aesthetics and data graphical displays. This is the second edition of The Visual Display of Quantitative Information. Recently published, this new edition provides excellent color reproductions of the many graphics of William Playfair, adds color to other images, and includes all the changes and corrections accumulated during 17 printings of the first edition.

    Inside: Logic Resource CD-ROM

    The complexity of nature's shapes differs in kind, not merely degree, from that of the shapes of ordinary geometry, the geometry of fractal shapes.Now that the field has expanded greatly with many active researchers, Mandelbrot presents the definitive overview of the origins of his ideas and their new applications. The Fractal Geometry of Nature is based on his highly acclaimed earlier work, but has much broader and deeper coverage and more extensive illustrations.

    Updated information is included about the associated features, culture, age & gender features, prevalence, course & familial pattern of mental disorders. The DSM-IV-TR(R) brings this essential diagnostic tool to-date, to promote effective diagnosis, treatment & quality of care. One can get all the essential diagnostic information from the DSM-IV(R) along with important updates not in the '94 edition. Benefit from new research into Schizophrenia, Asperger's Disorder & other conditions. Utilize additional information about the epidemiology & other facets of DSM conditions. Update ICD-9-CM codes implemented since 1994 including Conduct Disorder, Dementia, Somatoform Disorders.Use of the manual DSM-IV-TR classification Multiaxial assessment Disorders usually 1st diagnosed in infancy, childhood or adolescenceDelirium, dementia & amnestic & other cognitive disordersMental disorders due to a general medical condition Substance-related disorders Schizophrenia & other psychotic disordersMood disorders Anxiety disordersSomatoform disordersFactitious disordersDissociative disordersSexual & gender identity disordersEating disorders Sleep disorders Impulse-control disorders not elsewhere classifiedAdjustment disordersPersonality disordersOther conditions that may be a focus of clinical attentionAdditional codes Appendix A: Decision trees for differential diagnosis Appendix B: Criteria sets & axes provided for further study Appendix C: Glossary of technical terms Appendix D: Highlights of changes in DSM-IV text revisionAppendix E: Alphabetical listing of DSM-IV-TR diagnoses & codesAppendix F: Numerical listing of DSM-IV-TR diagnoses & codesAppendix G: ICD-9-CM codes for selected general medical conditions & medication-induced disordersAppendix H: DSM-IV classification with ICD-10 codesAppendix I: Outline for cultural formulation & glossary of culture-bound syndromesAppendix J: DSM-IV contributorsAppendix K: DSM-IV text revision advisers

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

    Each chapter is relatively self-contained and can be used as a unit of study. The algorithms are described in English and in a pseudocode designed to be readable by anyone who has done a little programming. The explanations have been kept elementary without sacrificing depth of coverage or mathematical rigor.

    This long-awaited revision contains changes throughout the text. There are new implementations of most of the major programming systems in the book, including the interpreters and compilers, and the authors have incorporated many small changes that reflect their experience teaching the course at MIT since the first edition was published. A new theme has been introduced that emphasizes the central role played by different approaches to dealing with time in computational models: objects with state, concurrent programming, functional programming and lazy evaluation, and nondeterministic programming. There are new example sections on higher-order procedures in graphics and on applications of stream processing in numerical programming, and many new exercises. In addition, all the programs have been reworked to run in any Scheme implementation that adheres to the IEEE standard.

    It runs on PCs rather than servers and is ideal for small- to mid-sized businesses and households. But Access is still intimidating to learn. It doesn't help that each new version crammed in yet another set of features; so many, in fact, that even the pros don't know where to find them all. Access 2007 breaks this pattern with some of the most dramatic changes users have seen since Office 95. Most obvious is the thoroughly redesigned user interface, with its tabbed toolbar (or "Ribbon") that makes features easy to locate and use. The features list also includes several long-awaited changes. One thing that hasn't improved is Microsoft's documentation. To learn the ins and outs of all the features in Access 2007, Microsoft merely offers online help.Access 2007: The Missing Manual was written from the ground up for this redesigned application. You will learn how to design complete databases, maintain them, search for valuable nuggets of information, and build attractive forms for quick-and-easy data entry. You'll even delve into the black art of Access programming (including macros and Visual Basic), and pick up valuable tricks and techniques to automate common tasks -- even if you've never touched a line of code before. You will also learn all about the new prebuilt databases you can customize to fit your needs, and how the new complex data feature will simplify your life. With plenty of downloadable examples, this objective and witty book will turn an Access neophyte into a true master.

    Rothman's acclaimed Modern Epidemiology reflects the remarkable conceptual development of this evolving science and the engagement of epidemiologists with an increasing range of current public health concerns. This landmark work is the most comprehensive and cohesive text on the principles and methods of contemporary epidemiologic research.Coauthored by two leading epidemiologists, with 15 additional contributors, the Second Edition presents a much broader range of concepts and methods than Dr. Rothman's single-authored original edition. Coverage of basic measures and study types is more thorough and includes a new chapter on field methods. New chapters on advanced topics in data analysis, such as hierarchical regression, are also included. A new section covers specific areas of research such as infectious disease epidemiology, ecologic studies, disease surveillance, analysis of vital statistics, screening, clinical epidemiology, environmental and occupational epidemiology, reproductive and perinatal epidemiology, genetic epidemiology, and nutritional epidemiology.

    With its unique combination of Japanese-style comics called manga and serious educational content, the EduManga format is already a hit in Japan.In The Manga Guide to Statistics, our heroine Rui is determined to learn about statistics to impress the dreamy Mr. Igarashi and begs her father for a tutor. Soon she's spending her Saturdays with geeky, bespectacled Mr. Yamamoto, who patiently teaches her all about the fundamentals of statistics: topics like data categorization, averages, graphing, and standard deviation.After all her studying, Rui is confident in her knowledge of statistics, including complex concepts like probability, coefficients of correlation, hypothesis tests, and tests of independence. But is it enough to impress her dream guy? Or maybe there's someone better, right in front of her?Reluctant statistics students of all ages will enjoy learning along with Rui in this charming, easy-to-read guide, which uses real-world examples like teen magazine quizzes, bowling games, test scores, and ramen noodle prices. Examples, exercises, and answer keys help you follow along and check your work. An appendix showing how to perform statistics calculations in Microsoft Excel makes it easy to put Rui's lessons into practice.This EduManga book is a translation from a bestselling series in Japan, co-published with Ohmsha, Ltd. of Tokyo, Japan.