Evo Devo Evolutionary Developmental Biology is the new science that has finally cracked open the box. Within the pages of his rich and riveting book, Sean B. Carroll explains how we are discovering that complex life is ironically much simpler than anyone ever expected.

    Updated web links and expanded appendices provide cutting edge information on action research along with new case studies and examples.

    With this book you will learn how to tap into every Kindle Fire feature, including many of the hidden ones not discussed in other books. From setting up your Kindle Page, managing your music, watching movies, and downloading content - this book covers everything. The task based full-color format allows you to quickly and easily find the exact task you want to accomplish and walks you through it in a delightfully concise and visual manner. My Kindle Fire makes it easy for you to:• Master all the basics, fast: reading, playing, watching, browsing, and more• Tweak your Kindle Fire for quicker access and longer battery life• Sample best-seller book chapters for free• Mark up any eBook with highlights, notes, and bookmarks• Convert your personal documents for use on Kindle Fire• Discover Calibre, a powerful eBook management tool• Control even the largest music libraries• Get instant answers from Wikipedia, and from Kindle Fire’s built-in dictionary• Listen to personalized Internet radio stations created just for you• Use your Kindle Fire as a digital photo frame• Prevent unwanted subscription charges• Set up any email account to work on your Kindle Fire• Explore any web content with Amazon’s innovative Silk browser• Use Amazon Cloud to get your stuff anywhere—even if you left your Kindle at home• And much much more…Unlike many other guides which might only briefly mention or skip over some very import Kindle Fire features My Kindle Fire covers everything. Here are just a few of the things you'll find in My Kindle Fire that aren't covered in other guides: • Full coverage of Calibre, a free application for Mac or PC that helps manage your eBook library• A large number of walkthroughs for managing music playlists, including how to use cloud playlists. • How to reinstall multiple apps at once in case you reset your Kindle Fire. • Step-by-step walkthroughs on configuring all types of email accounts. • Coverage on handling attachments in your email application. • Importing contacts from your existing email application or cloud service and how you can export your contacts in order to back them up. • How to use Copy and Paste on the Kindle Fire. • How to use social networking integration with Facebook and others. • Walkthroughs on using the Gallery app, Pandora, Audible, and other popular apps. • Shows you how to access files on other computers in your house right from your Kindle Fire.

    But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like nonparametric curve estimation, bootstrapping, and clas- sification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analyzing data. For some time, statistics research was con- ducted in statistics departments while data mining and machine learning re- search was conducted in computer science departments. Statisticians thought that computer scientists were reinventing the wheel. Computer scientists thought that statistical theory didn't apply to their problems. Things are changing. Statisticians now recognize that computer scientists are making novel contributions while computer scientists now recognize the generality of statistical theory and methodology. Clever data mining algo- rithms are more scalable than statisticians ever thought possible. Formal sta- tistical theory is more pervasive than computer scientists had realized.

    Primarily written for pre-service teachers, corporate trainers, and staff development programs that discuss teaching distant learners or managing distance education systems. Readers will be better equipped with the knowledge and ability to select appropriate strategies for teaching the distant learner and how to implement this new knowledge in a distance learning program, so important to today's educational culture. Five over-arching themes support the text: 1) defining distance education, 2) the importance of research development, 3) distance learning is a viable and increasingly important alternative for teaching and learning, 4) equivalency theory, and 5) the book should be comprehensive-which means it should cover as much as is possible of the various ways instruction is made available to distant learners. Significantly revised and updated, the fifth edition now includes these noteworthy new features and more: chapter goals and objectives, chapter discussion questions, a look at best practice issues, new strategies and techniques, over thirty-percent new resources and references, and a stronger emphasis on how to design, deliver, and evaluate online instruction.

    How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    Several of the data display strategies from previous editions are now presented in re-envisioned and reorganized formats to enhance reader accessibility and comprehension. The Third Edition's presentation of the fundamentals of research design and data management is followed by five distinct methods of analysis: exploring, describing, ordering, explaining, and predicting. Miles and Huberman′s original research studies are profiled and accompanied with new examples from Salda�a′s recent qualitative work. The book′s most celebrated chapter, Drawing and Verifying Conclusions, is retained and revised, and the chapter on report writing has been greatly expanded, and is now called Writing About Qualitative Research. Comprehensive and authoritative, Qualitative Data Analysis has been elegantly revised for a new generation of qualitative researchers.

    This guidebook is aimed clearly at the needs of the student, with a thorough understanding of, and provision for, the potential conceptual difficulties he or she is likely to encounter.

    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

    They reveal why general anesthesia is as dangerous as a parachute jump, giving birth in the US is nearly twice as risky as in the UK, and that the radiation from eating a banana shaves 3 seconds off your life. An entertaining guide to the statistics of personal risk, The Norm Chronicles will enlighten anyone who has ever worried about the dangers we encounter in our daily lives.

    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.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    Trisha Greenhalgh's ability to explain the basics of evidence-based medicine in an accessible and readable way means the book is an ideal introduction for all, from first year students to experienced practitioners. This is a text that explains the meaning of critical appraisal and terms such as 'numbers needed to treat', 'how to search the literature', 'evaluate the different types of papers' and 'put the conclusions to clinical use'.New features of the third edition include: New discussion putting evidence-based medicine into the current context, with more emphasis on patient perspectivesIncreased coverage of qualitative research in evidence-based medicineNew information on literature sources and search mechanisms

    Features include:Environmental maps covering the oceans and forestsWorld comparison charts and maps30,000-entry pronunciation guide109-page index