The authors take time to explain statistical procedures so that you can go beyond memorizing formulas and gain a conceptual understanding of statistics. The authors also take care to show you how having an understanding of statistical procedures will help you comprehend published findings and will lead you to become a savvy consumer of information. Known for its exceptional accuracy and examples, this text also has a complete supplements package to support your learning.

    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.

    At a summer tea party in Cambridge, England, a guest states that tea poured into milk tastes different from milk poured into tea. Her notion is shouted down by the scientific minds of the group. But one man, Ronald Fisher, proposes to scientifically test the hypothesis. There is no better person to conduct such an experiment, for Fisher is a pioneer in the field of statistics.The Lady Tasting Tea spotlights not only Fisher's theories but also the revolutionary ideas of dozens of men and women which affect our modern everyday lives. Writing with verve and wit, David Salsburg traces breakthroughs ranging from the rise and fall of Karl Pearson's theories to the methods of quality control that rebuilt postwar Japan's economy, including a pivotal early study on the capacity of a small beer cask at the Guinness brewing factory. Brimming with intriguing tidbits and colorful characters, The Lady Tasting Tea salutes the spirit of those who dared to look at the world in a new way.

    You'll find complete coverage of all exam objectives, including key topics such as project planning, execution, delivery, closure, and others. CompTIA's Project+ is the foundation-level professional exam in the complex world of project management; certified project managers often choose to go on and obtain their Project Management Professional (PMP) certifications as well Provides complete coverage of all exam objectives for CompTIA's first update to the Project+ exam in six years Covers project planning, execution, delivery, change, control, communication, and closure Demonstrates and reinforces exam preparation with practical examples and real-word scenarios Includes a CD with Sybex test engine, practice exams, electronic flashcards, and a PDF of the book Approach the new Project+ exam with confidence with this in-depth study guide! Reviews

    But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science.Topics include:Statistical inference, exploratory data analysis, and the data science processAlgorithmsSpam filters, Naive Bayes, and data wranglingLogistic regressionFinancial modelingRecommendation engines and causalityData visualizationSocial networks and data journalismData engineering, MapReduce, Pregel, and HadoopDoing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

    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.

    This text aims to motivate the need for tools with concrete applications, providing simple assumptions that match the application.

    Marieb and Katja Hoehn have produced the most accessible, comprehensive, up-to-date, and visually stunning anatomy & physiology textbook on the market. Marieb draws on her career as an A&P professor and her experience as a part-time nursing student, while Hoehn relies on her medical education and classroom experience to explain concepts and processes in a meaningful and memorable way. The most significant revision to date, the Eighth Edition makes it easier for you to learn key concepts in A&P. The new edition features a whole new art program that is not only more visually dynamic and vibrant than in previous editions but is also much more pedagogically effective for today's students, including new Focus figures, which guide you through the toughest concepts in A&P. The text has been edited to make it easier than ever to study from and navigate, with integrated objectives, new concept check questions, and a new design program.

    Offers students with the tools necessary to do ethical research in psychology and to understand the research they learn about in psychology courses and in the media.

    This work offers accesible coverage of the fundamentals of electrodynamics, enhanced with with discussion points, examples and exercises.

    Since no single or monolithic theoretical perspective orients this book, instructors with a wide range of views and approaches can use it effectively. The combination of brevity and readability make "Mirror for Humanity" a perfect match for cultural anthropology courses that use readings or ethnographies along with a main text.

    The best-selling brief introduction to public speaking, this succinct and inexpensive guide offers practical coverage of the material typically covered in a full-sized text -- from invention, research, and organization to practice and delivery -- in a concise, inexpensive format perfect for any setting across the curriculum, on the job, or in the community.

    The new edition is an even more useful reference-with more on research and documentation and helpful disicipline-focused advice on writing.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    Reviewed are fundamental statistical concepts--such as correlation, regressions, data preparation and screening, path analysis, and confirmatory factor analysis--as well as more advanced methods, including the evaluation of nonlinear effects, measurement models and structural regression models, latent growth models, and multilevel SEM. The companion Web page offers data and program syntax files for many of the research examples, electronic overheads that can be downloaded and printed by instructors or students, and links to SEM-related resources.