When a stray dog helps himself to a bone from a display in the supermarket, he doesn't know his actions will brand him a criminal and spark a romance between two humans. Stewart Coolidge works as a bag boy at the store, and his outraged boss offers a reward and demands Stewart catch the thieving animal. He fails at that, but now he finally has the perfect excuse to talk to his cute neighbor, Lisa. Lisa has always dreamed of being a journalist and asks Stewart if she can interview him for the local paper. As she gets to know Stewart, she likes him more and more, and she's delighted to learn he shares her Christian faith. Stewart can't bring himself to tell her she's mistaken, that he's not religious at all. And that's not Stewart's only deception. When the dog shows up on the doorstep of their building, Stewart decides to harbor the furry fugitive. But this is no ordinary dog, he has a divine sense of how things should be--and recognizing how much Stewart needs Lisa, he decides to do all he can to bring them together.

    Beginning with a risky business venture inspired by British fast food, Mole attempts to submerge himself in Russian culture—but often finds himself in the middle of a fiasco instead.

    Readers profit from its verbal-conceptual exposition and frequent use of examples.The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible. Researchers learn how to specify regression models that directly address their research questions. An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text. The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics. An introductory knowledge of statistics is required. Self-standing chapters minimize the need for researchers to refer to previous chapters.

    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.

    Masterfully combining the results of years of teaching microeconomics at Harvard University, Andreu Mas-Colell, Michael Whinston, and Jerry Green have filled that conspicuous vacancy with their groundbreaking text, Microeconomic Theory.The authors set out to create a solid organizational foundation upon which to build the effective teaching tool for microeconomic theory. The result presents unprecedented depth of coverage in all the essential topics, while allowing professors to tailor-make their course to suit personal priorities and style. Topics such as noncooperative game theory, information economics, mechanism design, and general equilibrium under uncertainty receive the attention that reflects their stature within the discipline. The authors devote an entire section to game theory alone, making it free-standing to allow instructors to return to it throughout the course when convenient. Discussion is clear, accessible, and engaging, enabling the student to gradually acquire confidence as well as proficiency. Extensive exercises within each chapter help students to hone their skills, while the text's appendix of terms, fully cross-referenced throughout the previous five sections, offers an accessible guide to the subject matter's terminology. Teachers of microeconomics need no longer rely upon scattered lecture notes to supplement their textbooks. Deftly written by three of the field's most influential scholars, Microeconomic Theory brings the readability, comprehensiveness, and versatility to the first-year graduate classroom that has long been missing.

    We all have one, but most of us will never see one. The heart surgeon now has that privilege but, for centuries, the heart was out of reach even for surgeons. So when a surgeon nowadays opens up a ribcage and mends a heart, it remains something of a miracle, even if, to some, it is merely plumbing. As with plumbers, the quality of surgeons’ work varies. As with plumbers, surgeons’ opinion of their own prowess and their own attitude to risk are not always reliable. Measurement is key. We’ve had a century of effective evidence-based medicine. We’ve had barely a decade of thorough monitoring of clinical outcomes. Thanks to the ground-breaking risk modelling of pioneering surgeons like Samer Nashef, we at last know how to judge whether an operation is in a patient’s best interest, which hospital and surgeon would be best for that operation, when it might best be performed and what the exact level of risk is. We have at last made what is important in surgery measurable. But how should surgeons, and their patients, use these newfound insights? Ever since his days as a medical student, Samer Nashef has challenged the medical profession to be more open and more accurate about the success of surgical procedures, for the sake of the patients. In The Naked Surgeon, he unclothes his own profession to demonstrate to his reader (and prospective patient) many revelations, such as the paradox at the heart of the cardiac surgeon’s craft: the more an operation is likely to kill you, the better it is for you. And he does so with absolute clarity, fluency and not a little wit.

    Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

    Reflecting the need for even minor programming in today's model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work.The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation.By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling.Web ResourceThe book is accompanied by an R package (rethinking) that is available on the author's website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

    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 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

    The prewritten progress notes can be easily and quickly adapted to fit a particular client need or treatment situation. Saves you hours of time-consuming paperwork, yet offers the freedom to develop customized progress notes Organized around 43 behaviorally based presenting problems, including depression, intimate relationship conflicts, chronic pain, anxiety, substance abuse, borderline personality, and more Features over 1,000 prewritten progress notes (summarizing patient presentation, themes of session, and treatment delivered) Provides an array of treatment approaches that correspond with the behavioral problems and DSM-5™ diagnostic categories in The Complete Adult Psychotherapy Treatment Planner, Fifth Edition Offers sample progress notes that conform to the requirements of most third-party payors and accrediting agencies, including CARF, The Joint Commission (TJC), COA, and the NCQA Identifies the latest evidence-based care treatments with treatment language following specific guidelines set by managed care and accrediting agencies

    An abundance of applications to current economic analysis, illustrative diagrams, thought-provoking exercises, careful proofs, and a flexible organization-these are the advantages that Mathematics for Economists brings to today’s classroom.

     Two hot topics team up in The Baseball Economist, and the result is a refreshing, clear- eyed survey of a playing field that has changed radically in recent years. Utilizing the latest economic methods and statistical analysis, writer, economics professor, and popular blogger J. C. Bradbury dissects burning baseball topics with his original Sabernomic perspective, such as: • Did steroids have nothing to do with the recent home run records? Incredibly, Bradbury's research, reviewed by Stanford economists, reveals steroids had little statistical significance. • Is the big-city versus small-city competition really lopsided? Bradbury shows why the Marlins and Indians are likely to dominate big-city franchises in the coming years. • Which players are ridiculously overvalued? Bradbury lists all players by team with their revenue value to the team listed in dollars—including a dishonor role of those players with negative values. • Is major league baseball a monopoly that can't govern itself? Bradbury sets out what rules the owners really need to play by, and what the players' union should be doing. • Does it help to lobby for balls and strikes? How would Babe Ruth perform in today's game? And who killed all the left-handed catchers, anyway? The Baseball Economist knows. Providing far more than a mere collection of numbers, Bradbury shines the light of his economic thinking on baseball, exposing the power of tradeoffs, competition, and incentives. Statistics alone aren't enough anymore. Fans, fantasy buffs, and players, as well as coaches at all levels who want to grasp what is really happening on the field today and in the coming years, will use and enjoy Bradbury's brilliant new understanding of the national pastime.

    "Another""What It Means When a Crowd in a Faraway Nation Takes a Soldier Representing Your Own Nation, Shoots Him, Drags Him from His Vehicle and Then Mutilates Him in the Dust""The Only Meaning of the Oil-Wet Water""On Wanting to Have Three Walls Up Before She Gets Home""Climbing to the Window, Pretending to Dance""She Waits, Seething, Blooming""Quiet""Your Mother and I""Naveed""Notes for a Story of a Man Who Will Not Die Alone""About the Man Who Began Flying After Meeting Her""Up the Mountain Coming Down Slowly""After I Was Thrown in the River and Before I Drowned"From the Trade Paperback edition.

    No statistical knowledge is required, and your programming skills can range from hobbyist to pro.Along the way, you'll learn about functional and object-oriented programming, running mathematical simulations, and rearranging complex data into simpler, more useful formats. You'll also learn to: Create artful graphs to visualize complex data sets and functions Write more efficient code using parallel R and vectorization Interface R with C/C++ and Python for increased speed or functionality Find new R packages for text analysis, image manipulation, and more Squash annoying bugs with advanced debugging techniques Whether you're designing aircraft, forecasting the weather, or you just need to tame your data, The Art of R Programming is your guide to harnessing the power of statistical computing.