This book will offer students a broad outline of essential mathematics and will help to fill in the gaps in their knowledge. The author explains the basic points and a few key results of all the most important undergraduate topics in mathematics, emphasizing the intuitions behind the subject. The topics include linear algebra, vector calculus, differential and analytical geometry, real analysis, point-set topology, probability, complex analysis, set theory, algorithms, and more. An annotated bibliography offers a guide to further reading and to more rigorous foundations.

    The book will also be extremely useful for those preparing for other MBA entrance examinations like XAT, SNAP, CMAT, NMAT, etc. Quantitative Aptitude is quite challenging component of the CAT question paper and the other mentioned MBA entrance examinations. In his inimitable style, Arun Sharma, an acknowledged authority on the topic, provides a comprehensive package of theory and practice problems to enable aspirants to attempt questions with extra speed and confidence.

    Included are step-by-step instructions on how to carry out Bayesian data analyses.Download Link : readbux.com/download?i=0124058884            0124058884 Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan PDF by John Kruschke

    The novel approach taken here banishes determinants to the end of the book and focuses on the central goal of linear algebra: understanding the structure of linear operators on vector spaces. The author has taken unusual care to motivate concepts and to simplify proofs. For example, the book presents - without having defined determinants - a clean proof that every linear operator on a finite-dimensional complex vector space (or an odd-dimensional real vector space) has an eigenvalue. A variety of interesting exercises in each chapter helps students understand and manipulate the objects of linear algebra. This second edition includes a new section on orthogonal projections and minimization problems. The sections on self-adjoint operators, normal operators, and the spectral theorem have been rewritten. New examples and new exercises have been added, several proofs have been simplified, and hundreds of minor improvements have been made throughout the text.

    Competition Book

    With an emphasis on critical self-reflection, Reflect & Relate gives students the practical skills to work through life's many challenges using better interpersonal communication. The sound theory, clear explanations, lively writing, practical activities, and vibrant design all work toward a single goal: Teaching students to make better communication choices so they can build happier and healthier interpersonal relationships.

    Yet the realities of bureaucracies, deadlines, budgets, and demands for quick results make the payoffs for dealing formally with ethics seem unclear. Since its original publication, The Responsible Administrator has guided professionals and students alike as they grapple with the challenges of making ethical, responsible decisions in real world situations. This new edition includes information on coping with new demands for accountability, as well as new cases and examples, an examination of current issues relevant to administrative ethics, and supplementary materials for professors.Cooper's theoretical framework and practical applications and techniques will help you consider all of the factors involved in a decision, ensuring that you balance professional, personal, and organizational values. Case studies and examples illustrate what works and what does not. The Responsible Administrator helps both experienced and novice public administrators and students become effective decision makers, provides them with a solid understanding of the role of ethics in public service and the framework to incorporate ethical and values-based decision making in day-to-day management.

    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

    The coloring book helps you memorize the anatomy content you need to know in both veterinary medicine and veterinary technology and gives you a fun way to review the information you have studied. All illustrations in the book are suitable for coloring and are of the highest quality, created by expert medical illustrators.Organized by body region, the book is divided into sections devoted to the head and neck; neck, back, and vertebral column; thorax; abdomen; pelvis; forlimb; and hindlimb.Numbered lead lines clearly identify structures to be colored and correspond to a numbered list beneath the illustration so you can easily visualize the veterinary anatomy. Plus, you can create your own color code using the numbered boxes provided for each illustration.Comprehensive veterinary anatomy coverage helps you reinforce your understanding of canine, feline, equine, porcine, ruminants, and avian anatomy.High quality illustrations make it easy for you to color in each anatomic region and review anatomic details.Self-study format provides a fun and interactive way to prepare for exams throughout your veterinary courses.Part of Elsevier's complete veterinary anatomy learning system, integrating core anatomy knowledge, engaging review, hands-on practice, and clinical application to give you a solid foundation for success!

    Hemostasis and coagulation theory, testing, and instrumentation are also thoroughly discussed. Beautiful full-color illustrations throughout the text enhance comprehension and allow students to realistically visualize hematology concepts.Detailed, full-color illustrations appear near hematology concept discussions to visually support comprehension and recognition.Opening Case Studies present real-life scenarios to apply concepts presented in each chapter.Learning Objectives and Review Questions work together to list expected outcomes and then test those concepts for each chapter.A Bulleted Summary highlights the key concepts at the end of each chapter.Hematology/Hemostasis Reference Ranges inside the front and back covers provide quick reference.Three new chapters detail significant information: An Overview of Clinical Laboratory Hematology, Hematology in Selected Populations, and Monitoring Anticoagulant Therapy.All chapters have been revised to feature the most current technology and research updates.The included Glossary of Terms and consistent pedagogy ensure a cohesive, effective tool for learning.Two well-respected editors contributed more significantly to this third edition: George Fritsma shared his expertise for the Hemostasis section and Kathy Doig managed the pedagogical features.

    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.

    The cases, content, and features of the exciting new ninth edition have been thoroughly updated to represent the latest developments in the business law environment. An excellent assortment of cases ranges from precedent-setting landmarks to important recent decisions, and ethical, global, and corporate themes are integrated throughout. In addition, numerous features and exercises help you master key concepts and apply what you've learned to real-world issues, and the book offers an unmatched range of support resources, including innovative online review tools.

    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.

    Packed with the latest findings on inflammation, oxidative stress, and degenerative disease, this guide is the definitive resource for anyone serious about optimal nutrition and anti-aging defence. Inside, you will find over 1,500 US and Canadian supplements scientifically rated and compared, 18 critical Health Support Criteria required to evaluate supplements, 300 full-colour graphs so you can easily compare top-rated products. Discover recent scientific evidence that shows supplements can optimize health and retard the aging process. Determine which supplement is best for you and your family, using a scientifically-based approach. Now in its 4th edition and representing thousands of hours of research, the NutriSearch Comparative Guide to Nutritional Supplements is the way for you to find a quality nutritional supplement that best suits your needs. For those interested in optimizing their health and warding off the aging process, this guide is a must!

    Real-life cases and situations demonstrate the significance of ethics in the world of criminal justice today for those studying or contemplating a career in criminal justice.