The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/

    Students will receive a grounding in areas that vary from treatment to organizational and community settings. This edition also includes of new case studies, practice examples and guiding principles.

    With an accessible and lively writing style, it introduces modern techniques to what can often be a formal and intimidating subject. Readers are led from the physics of flat spacetime (special relativity), through the intricacies of differential geometry and Einstein's equations, and on to exciting applications such as black holes, gravitational radiation, and cosmology.

    With this smart cookbook, readers learn Bob's secret to making rich, creamy Vanilla Bean Cheesecake in just 25 minutes. He's even got a recipe for Most Excellent Macaroni and Cheese that tastes just like it was oven baked — but takes only six minutes in the pressure cooker! In all, this cookbook contains 117 time-saving ways for readers to treat loved ones to the goodness of home-cooked food and still have time to sit down and enjoy it with them. Enhanced with over 50 full-page color photos, Smyth sewn binding, and plenty of tips from Bob, this cookbook is a must-have for pressure cooker novices and pros alike.

    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.

    The 9th edition continues to examine the strategic choices in managing total compensation. The total compensation model introduced in chapter one serves as an integrating framework throughout the book. The authors discuss major compensation issues in the context of current theory, research, and real-business practices. Milkovich and Newman strive to differentiate beliefs and opinions from facts and scholarly research. They illustrate new developments in compensation practices as well as established approaches to compensation decisions.

    Oriented toward traditional black and white photography, the book also explores digital techniques and web photography resources, equipment, the exposure and development of film, and the making and finishing of prints.

    Time-saving study tools help readers arrive at a complete understanding of human anatomy. KEY TOPICS: An Introduction to Anatomy, The Cell, Tissues and Early Embryology, The Integumentary System, The Skeletal System: Osseous Tissue and Skeletal Structure, The Skeletal System: Axial Division, The Skeletal System: Appendicular Division, The Skeletal System: Articulations, The Muscular System: Skeletal Muscle Tissue and Muscle Organization, The Muscular System: Axial Musculature, The Muscular System: Appendicular Musculature, Surface Anatomy and Cross-Sectional Anatomy, The Nervous System: Neural Tissue, The Nervous System: The Spinal Cord and Spinal Nerves, The Nervous System: The Brain and Cranial Nerves, The Nervous System: Pathways And Higher-Order Functions, The Nervous System:  Autonomic Division, The Nervous System: General and Special Senses, The Endocrine System, The Cardiovascular System: Blood, The Cardiovascular System: The Heart, The Cardiovascular System: Vessels and Circulation, The Lymphoid System, The Respiratory System, The Digestive System, The Urinary System, The Reproductive System, The Reproductive System: Embryology and HumanDevelopment. MARKET: For all readers interested in human anatomy.

    The text progresses from a micro- to macro-level of analysis, focusing first on such problems as illness and health care, drugs and alcohol, and family problems and then broadening to the larger issues of poverty and inequality, population growth, environmental problems, and conflict around the world. The social problem in each chapter is framed in a global as well as U.S. context. In every chapter, the three major theoretical perspectives are applied to the social problem under discussion, and the consequences of the problem, as well as alternative solutions, are explored. Pedagogical features such as The Human Side and Self and Society enable students to grasp how social problems affect the lives of individuals and apply their understanding of social problems to their own lives.

    He proposes his solution.

    Language is informal, examples are vivid and lively, and the perspectivie is fresh. Based on lectures delivered to engineering students, this work will also be valued by scientists, engineers, technicians, businessmen, anyone facing energy challenges of the future.

    . . Nothing less than a major contribution to the scientific culture of this world." — The New York Times Book ReviewThis major survey of mathematics, featuring the work of 18 outstanding Russian mathematicians and including material on both elementary and advanced levels, encompasses 20 prime subject areas in mathematics in terms of their simple origins and their subsequent sophisticated developement. As Professor Morris Kline of New York University noted, "This unique work presents the amazing panorama of mathematics proper. It is the best answer in print to what mathematics contains both on the elementary and advanced levels."Beginning with an overview and analysis of mathematics, the first of three major divisions of the book progresses to an exploration of analytic geometry, algebra, and ordinary differential equations. The second part introduces partial differential equations, along with theories of curves and surfaces, the calculus of variations, and functions of a complex variable. It furthur examines prime numbers, the theory of probability, approximations, and the role of computers in mathematics. The theory of functions of a real variable opens the final section, followed by discussions of linear algebra and nonEuclidian geometry, topology, functional analysis, and groups and other algebraic systems.Thorough, coherent explanations of each topic are further augumented by numerous illustrative figures, and every chapter concludes with a suggested reading list. Formerly issued as a three-volume set, this mathematical masterpiece is now available in a convenient and modestly priced one-volume edition, perfect for study or reference."This is a masterful English translation of a stupendous and formidable mathematical masterpiece . . ." — Social Science

    More importantly, this book provides a framework for thinking about computer organization and design that will enable the reader to continue the lifetime of learning necessary for staying at the forefront of this competitive discipline. --John Crawford Intel Fellow Director of Microprocessor Architecture, Intel The performance of software systems is dramatically affected by how well software designers understand the basic hardware technologies at work in a system. Similarly, hardware designers must understand the far reaching effects their design decisions have on software applications. For readers in either category, this classic introduction to the field provides a deep look into the computer. It demonstrates the relationship between the software and hardware and focuses on the foundational concepts that are the basis for current computer design. Using a distinctive learning by evolution approach the authors present each idea from its first principles, guiding readers through a series of worked examples that incrementally add more complex instructions until they ha

    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.

    These sections together make an appropriate one-term advanced/graduate level course (mathematical prerequisites: vector analysis and simple partial-differential equations). The book is printed to make it easy for readers to identify these sections.• The remaining Track 2 material provides a wealth of advanced topics instructors can draw from to flesh out a two-term course, with Track 1 sections serving as prerequisites.