Written especially for exercise science and physical education students, this text provides a solid foundation in theory illuminated by application and performance models to increase understanding and to help students apply what they've learned in the classroom and beyond.

    Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment.The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

    It follows a more quantitative and design-oriented approach than other texts in the market, helping readers gain a better understanding of important concepts. They'll also discover how material properties relate to the process variables in a given process as well as how to perform manufacturing science and quantitative engineering analysis of manufacturing processes.

    The eighth edition has been revised to include updated coverage of NMR Spectroscopy and UV spectroscopy. New questions at the end of chapters reinforce the skills and techniques learned. Emphasis is placed on green chemistry in the lab, focusing on the more environmentally friendly materials that can be used. In addition, updated discussions are included on safety, distillation, gas chromatography, and liquid chromatography. This gives organic chemists the most up-to-date information to enhance their lab skills.

    Sipser's candid, crystal-clear style allows students at every level to understand and enjoy this field. His innovative "proof idea" sections explain profound concepts in plain English. The new edition incorporates many improvements students and professors have suggested over the years, and offers updated, classroom-tested problem sets at the end of each chapter.

    Scientists have marveled at the beauty and elegance of his analysis, while ordinary programmers have successfully applied his "cookbook" solutions to their day-to-day problems. All have admired Knuth for the breadth, clarity, accuracy, and good humor found in his books. "I can't begin to tell you how many pleasurable hours of study and recreation they have afforded me I have pored over them in cars, restaurants, at work, at home... and even at a Little League game when my son wasn't in the line-up.""--"Charles Long Primarily written as a reference, some people have nevertheless found it possible and interesting to read each volume from beginning to end. A programmer in China even compared the experience to reading a poem. "If you think you're a really good programmer... read Knuth's] "Art of Computer Programming.".. You should definitely send me a resume if you can read the whole thing.""--"Bill Gates Whatever your background, if you need to do any serious computer programming, you will find your own good reason to make each volume in this series a readily accessible part of your scholarly or professional library. "It's always a pleasure when a problem is hard enough that you have to get the Knuths off the shelf. I find that merely opening one has a very useful terrorizing effect on computers.""--"Jonathan LaventholIn describing the new fourth volume, one reviewer listed the qualities that distinguish all of Knuth's work. In sum: ] "detailed coverage of the basics, illustrated with well-chosen examples; occasional forays into more esoteric topics and problems at the frontiers of research; impeccable writing peppered with occasional bits of humor; extensive collections of exercises, all with solutions or helpful hints; a careful attention to history; implementations of many of the algorithms in his classic step-by-step form."--Frank RuskeyThese four books comprise what easily could be the most important set of information on any serious programmer's bookshelf.

    Introducing students to the many facets of neural networks, this text provides many case studies to illustrate their real-life, practical applications.

    Plus, it helps you identify areas of weakness so you can focus your study time. Sample problems and step-by-step examples with explanations in the math and physics sections show you how to work through each problem so you understand the steps it takes to complete the equation. Practice tests with answer keys for each topic - located in the appendices for quick access - help you assess your understanding of each topic and familiarize you with the types of questions you're likely to encounter on the actual exam. HESI Hints boxes offer valuable test-taking tips, as well as rationales, suggestions, examples, and reminders for specific topics.End-of-chapter review questions help you gauge your understanding of chapter content.A full-color layout and more illustrations in the life science chapters visually reinforce key concepts for better understanding.Expanded and updated content in each chapter ensures you're studying the most current content.Basic algebra review in the math section offers additional review and practice.Color-coded chapters help you quickly find specific topic sections.Helpful organizational features in each chapter include an introduction, key terms, chapter outline, and a bulleted chapter summary to help you focus your study.A glossary at the end of the text offers quick access to key terms and their definitions.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    It uses a taxonomic approach for the study of pathogens.