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Four books in the series lay a solid foundation for the math skills needed for standardized tests, covering everything from basic computation to the fundamentals of algebra and geometry.
A comprehensive and user-friendly introduction to statistics for behavioral science students revised and updated Refined over seven editions by master teachers, this book gives instructors and students alike clear examples and carefully crafted exercises to support the teaching and learning of statistics for both manipulating and consuming data. One of the most popular and respected statistics texts in the behavioral sciences, the Seventh Edition of Introductory Statistics for the Behavioral Sciences has been fully revised. The new edition presents all the topics students in the behavioral sciences need in a uniquely accessible and easy-to-understand format, aiding in the comprehension and implementation of the statistical analyses most commonly used in the behavioral sciences. The Seventh Edition features: A continuous narrative that clearly explains statistics while tracking a common data set throughout, making the concepts unintimidating and memorable, and providing a framework that connects all of the topics and allows for easy comparison of different statistical analyses Coverage of important aspects of research design throughout the text, such as the "correlation is not causality" principle Updated and annotated SPSS output at the end of each chapter with step-by-step instructions Updated examples and exercises An expanded website, at www.wiley.com/go/welkowitz, with test bank, chapter quizzes, and PowerPoint slides for instructors, as well as a second website for students with additional basic math coverage, math review exercises, a study guide, a set of additional SPSS exercises, and more downloadable data sets
Data Structures & Theory of Computation
Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online. This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.
Foundations of Algorithms Using C++ Pseudocode, Third Edition offers a well-balanced presentation on designing algorithms, complexity analysis of algorithms, and computational complexity. The volume is accessible to mainstream computer science students who have a background in college algebra and discrete structures. To support their approach, the authors present mathematical concepts using standard English and a simpler notation than is found in most texts. A review of essential mathematical concepts is presented in three appendices. The authors also reinforce the explanations with numerous concrete examples to help students grasp theoretical concepts.
CD-ROM contains: "SPSS and SAS data sets fpr ,amu pf tje text exercoses as we;; as titorials reviewing basic statistics and simple and multiple regression."
WINNER OF: Frantz Fanon Outstanding Book from the Caribbean Philosophical Association Canadian Political Science Association’s C.B. MacPherson Prize Studies in Political Economy Book Prize Over the past forty years, recognition has become the dominant mode of negotiation and decolonization between the nation-state and Indigenous nations in North America. The term “recognition” shapes debates over Indigenous cultural distinctiveness, Indigenous rights to land and self-government, and Indigenous peoples’ right to benefit from the development of their lands and resources. In a work of critically engaged political theory, Glen Sean Coulthard challenges recognition as a method of organizing difference and identity in liberal politics, questioning the assumption that contemporary difference and past histories of destructive colonialism between the state and Indigenous peoples can be reconciled through a process of acknowledgment. Beyond this, Coulthard examines an alternative politics—one that seeks to revalue, reconstruct, and redeploy Indigenous cultural practices based on self-recognition rather than on seeking appreciation from the very agents of colonialism. Coulthard demonstrates how a “place-based” modification of Karl Marx’s theory of “primitive accumulation” throws light on Indigenous–state relations in settler-colonial contexts and how Frantz Fanon’s critique of colonial recognition shows that this relationship reproduces itself over time. This framework strengthens his exploration of the ways that the politics of recognition has come to serve the interests of settler-colonial power. In addressing the core tenets of Indigenous resistance movements, like Red Power and Idle No More, Coulthard offers fresh insights into the politics of active decolonization.
A concise and practical introduction to the foundations and engineering principles of self-adaptation Though it has recently gained significant momentum, the topic of self-adaptation remains largely under-addressed in academic and technical literature. This book changes that. Using a systematic and holistic approach, An Introduction to Self-adaptive Systems: A Contemporary Software Engineering Perspective provides readers with an accessible set of basic principles, engineering foundations, and applications of self-adaptation in software-intensive systems. It places self-adaptation in the context of techniques like uncertainty management, feedback control, online reasoning, and machine learning while acknowledging the growing consensus in the software engineering community that self-adaptation will be a crucial enabling feature in tackling the challenges of new, emerging, and future systems. The author combines cutting-edge technical research with basic principles and real-world insights to create a practical and strategically effective guide to self-adaptation. He includes features such as: An analysis of the foundational engineering principles and applications of self-adaptation in different domains, including the Internet-of-Things, cloud computing, and cyber-physical systems End-of-chapter exercises at four different levels of complexity and difficulty An accompanying author-hosted website with slides, selected exercises and solutions, models, and code Perfect for researchers, students, teachers, industry leaders, and practitioners in fields that directly or peripherally involve software engineering, as well as those in academia involved in a class on self-adaptivity, this book belongs on the shelves of anyone with an interest in the future of software and its engineering.