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.

    Based on a course of lectures delivered by the author at Columbia University, the text is elementary in treatment and remarkable for its clarity and organization. Although it is assumed that the reader is familiar with the fundamental facts of thermometry and calorimetry, no advanced mathematics beyond calculus is assumed.Partial contents: thermodynamic systems, the first law of thermodynamics (application, adiabatic transformations), the second law of thermodynamics (Carnot cycle, absolute thermodynamic temperature, thermal engines), the entropy (properties of cycles, entropy of a system whose states can be represented on a (V, p) diagram, Clapeyron and Van der Waals equations), thermodynamic potentials (free energy, thermodynamic potential at constant pressure, the phase rule, thermodynamics of the reversible electric cell), gaseous reactions (chemical equilibria in gases, Van't Hoff reaction box, another proof of the equation of gaseous equilibria, principle of Le Chatelier), the thermodynamics of dilute solutions (osmotic pressure, chemical equilibria in solutions, the distribution of a solute between 2 phases vapor pressure, boiling and freezing points), the entropy constant (Nernst's theorem, thermal ionization of a gas, thermionic effect, etc.).

    Financial planning for life -- from career strategies and consumer credit to investments and taxes to retirement and estate planning -- this handbook covers everything for making those all-important decisions.

    It givessyntax and output for accomplishing many analyses through the mostrecent releases of SAS, SPSS, and SYSTAT, some not available insoftware manuals. The book maintains its practical approach, stillfocusing on the benefits and limitations of applications of a techniqueto a data set -- when, why, and how to do it. Overall, it providesadvanced students with a timely and comprehensive introduction totoday's most commonly encountered statistical and multivariatetechniques, while assuming only a limited knowledge of higher-levelmathematics.

    From stars, planets and galaxies, to black holes, the Big Bang and life in space, this title has been making it easy for beginners to quickly grasp the basic concepts of astronomy for over 25 years. Updated with the latest discoveries in astronomy and astrophysics, this newest edition of Dinah Moche's classic guide now includes many Web site addresses for spectacular images and news. And like all previous editions, it is packed with valuable tables, charts, star and moon maps and features simple activities that reinforce readers' grasp of basic concepts at their own pace, as well as objectives, reviews, and self-tests to monitor their progress. Dinah L. Moche, PhD (Rye, NY), is an award-winning author, educator, and lecturer. Her books have sold over nine million copies in seven languages.

    This social world is filled with paradox, mystery, suspense, and outright absurdity. Explore how social psychology can help you make sense of your own social world with this engaging and accessible book. Roy F. Baumeister and Brad J. Bushman's SOCIAL PSYCHOLOGY AND HUMAN NATURE can help you make sense of the always fascinating and sometimes bizarre and baffling diversity of human behavior-and it's also just plain interesting to learn about how and why people act the way they do.

    Bryan Derrickson of Valencia Community College in Orlando, Florida joins Jerry Tortora as a co-author, bringing his background and expertise in physiology in balance with Jerry's focus on anatomy. The authors have maintained in the text the superb balance between structure and function and continue to emphasize the correlations between normal physiology and pathophysiology, normal anatomy and pathology, and homeostasis and homeostatic imbalances. The acclaimed illustration program is now even better thanks to the input of hundreds of professors and students and the re-development of many of the figures depicting the toughest topics for students to grasp. The eleventh edition now fully integrates this exceptional text with a host of innovative electronic media, setting the standard once again for a rewarding and successful classroom experience for both students and instructors.

    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.

    Building from a review of the basic science to the how and why of clinical practice, this text is thorough guide to immunohematology and transfusion practices. It begins with six color plates of which Plate 2 - standardized grading of macroscopic red cell antigen-antibody reactions - is extraordinarily useful. These are actual photomicrographs of immediate spin reactions and at a glance, will automatically assure standardized reporting of the reactions. Chapter on medicolegal and ethical aspects of providing blood collection and transfusion service is simply fascinating reveting reading. An added bonus is the table of blood group characteri stics (antigen, ISBT number, frequency in different ethnicities, expression during life, etc.) on the inside covers at the front and back of the book. Nothing like having a quick complete reference when you need it! This is a great book. Valerie L. Ng, PhD, MD, University of California, San Francisco, California for Doody Review Service.

    Cases, datasets, and examples allow for a more real-world perspective and explore relevant uses of regression techniques in business today.

    Unique in basing relativity on the Principle of Equivalence of Gravitation and Inertia over Riemannian geometry, this book explores relativity experiments and observational cosmology to provide a sound foundation upon which analyses can be made. Covering special and general relativity, tensor analysis, gravitation, curvature, and more, this book provides an engaging, insightful introduction to the forces that shape the universe.

    What, really, does E=MC2 mean? More fun than a slide rule, Handy Physics tackles the big issues: Gravity. Magnetism. Matter. Sound. Light. And the smaller topics, like why do cats always land on their feet? Why don't birds or squirrels on power lines get electrocuted? Only Handy Physics combines elementary theory with heartwarming tales of small animals. For everyone who ever wondered how a light bulb works, The Handy Physics Answer Book examines more than 825 basic questions about physics and physicists, ranging from everyday life applications to the latest explorations in subatomic physics. The Handy Physics Answer Book disposes with the mathematical explanations and deep coma often associated with physics and instead takes a more conceptual approach – written in everyday English by yet another teacher. Other great stuff includes a list of the Nobel Prize winners in physics and suggestions for further reading. Ideal for students, science readers, theatergoers, and anyone reckoning with the essential questions about the universe we dwell within, Handy Physics is a friendly guide to the most significant scientific theories and discoveries of our time. And, we promise, no chalkboards.

    It has become known for its engaging writing style, instructional art program and focus on active learning. We are so excited to offer a robust learning program with student-focused learning activities, allowing the student to manage their learning while you easily manage their assessment. Detailed reports show how your assignments measure various learning objectives from the book (or input your own!), levels of Bloom's Taxonomy or other categories, and how your students are doing. The Cowan Learning program will save you time and improve your student's success in this course.

    The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality.Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.