This book is a must for anyone who teaches statistics, particularly those who teach beginning statistics - mathematicians, social scientists, engineers - as well as graduate students and others new to the field. The authors focus on topics central to the teaching of statistics for beginners, and other expositions that are guided by the current state of statistical research and practice. Statistical practice has changed radically during the past generation under the impact of ever cheaper and more accessible computing power. Beginning instruction has lagged behind the evolution of the field. Software now enables students to shortcut unpleasant calculations, but this is only the most obvious consequences of changing statistical practice, the content and emphases of statistics instruction still needs much rethinking. This volume assembles nine new essays on important topics in present-day statistics at the college with various levels of mathematics preparation, and from diverse disciplinary backgrounds. Accordingly the chapters present modern perspectives on central aspects of statistics and emphasize the conceptual content that should accompany all varieties of beginning instruction. The books opens with a contemporary overview of statistics as the science of data - a view much broader than the "inference from data" emphasized by much traditional teaching. The next two chapters discuss the philosophy and some of the tools used in data analysis and describe the impact of computing on data analysis and inference and its implication for teaching. The book conlucdes with introductions to diagnostics and to the alternative approach embodied in resistent and robut procedures. -- from back cover.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in marketing (customer loyalty, identifying profitable customers, instore promotions, e-commerce populations); in business (teaching data mining, efficiency of the Chinese automobile industry, moderate asset allocation funds); and techniques (veterinary predictive models, data integrity in the cloud, irregular pattern detection in a mobility network and road safety modeling.)

The anniversary edition marks thirty years of offering an indispensable review and analysis of thinkers who have exerted a profound influence on contemporary rhetorical theory: I. A. Richards, Ernesto Grassi, Chaïm Perelman and Lucie Olbrechts-Tyteca, Stephen Toulmin, Richard Weaver, Kenneth Burke, Jürgen Habermas, bell hooks, Jean Baudrillard, and Michel Foucault. The brief biographical sketches locate the theorists in time and place, showing how life experiences influenced perspectives on rhetorical thought. The concise explanations of complex concepts are clear, engaging, insightful, and highly accessible, serving as an excellent primer for reading the major works of these scholars. The critical commentary is carefully chosen to highlight implications and to place the theories within a broader rhetorical context. Each chapter ends with a complete bibliography of works by the theorists.

The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted from this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are in finance (banking, brokerage, and insurance), marketing (customer relationships, retailing, logistics, and travel), as well as in manufacturing, health care, fraud detection, homeland security, and law enforcement.

The series, Contemporary Perspectives on Data Mining, is composed of blind refereed scholarly research methods and applications of data mining. This series will be targeted both at the academic community, as well as the business practitioner. Data mining seeks to discover knowledge from vast amounts of data with the use of statistical and mathematical techniques. The knowledge is extracted form this data by examining the patterns of the data, whether they be associations of groups or things, predictions, sequential relationships between time order events or natural groups. Data mining applications are seen in finance (banking, brokerage, insurance), marketing (customer relationships, retailing, logistics, travel), as well as in manufacturing, health care, fraud detection, home-land security, and law enforcement.

This open access book provides a comprehensive perspective on the concept of ageism, its origins, the manifestation and consequences of ageism, as well as ways to respond to and research ageism. The book represents a collaborative effort of researchers from over 20 countries and a variety of disciplines, including, psychology, sociology, gerontology, geriatrics, pharmacology, law, geography, design, engineering, policy and media studies. The contributors have collaborated to produce a truly stimulating and educating book on ageism which brings a clear overview of the state of the art in the field. The book serves as a catalyst to generate research, policy and public interest in the field of ageism and to reconstruct the image of old age and will be of interest to researchers and students in gerontology and geriatrics.