This is an introduction to time series that emphasizes methods and analysis of data sets. Found insideThe book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional This book takes the mystery out of statistics and provides simple, hands-on instructions in the format of everyday formulas. Spreadsheet shortcuts and functions are included, along with many simple worked examples. Found inside"Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate ... Found insideThis book will serve as a helpful introduction to the area of classification and data analysis for research workers and support the transfer of new advances in data science and classification to a wide range of applications. The book by Fang, Kotz and Ng summarizes these developments in a manner which is accessible to a reader with only limited background (advanced real-analysis calculus, linear algebra and elementary matrix calculus). Further highlights include: An extensive appendix that discusses and derives all major results associated with ellipticity Two appendix chapters that detail the theory and computation of the distribution and moments for Gaussian quadratic ... The revised edition introduces the most current estimation procedures for normally distributed samples for researchers and students in theoretical and applied statistics, including expanded treatments of: bivariate normal distribution, ... This book attempts to provide a comprehensive and coherent treatment of the classical and new results related to the multivariate normal distribution. Found insideThe book provides details on 22 probability distributions. It is best suited to students with a good knowledge of calculus and the ability to think abstractly. The focus of the text is the ideas that statisticians care about as opposed to technical details of how to put those ideas into practice. Found insideIntroduction to Statistical Machine Learning provides a general introduction to machine learning that covers a wide range of topics concisely and will help you bridge the gap between theory and practice. Found insideUsing clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of statistical methods to machine learning, summary stats, hypothesis testing, nonparametric stats, resampling methods, ... In view of the enormous volume ofliterature on the outlier problem and its cousins, no attempt has been made to make the coverage exhaustive. The standard resource for statisticians and applied researchers. Accessible to the wide range of researchers who use statistical modelling techniques. Found insideThis is the second edition of a popular book on multiple imputation, focused on explaining the application of methods through detailed worked examples using the MICE package as developed by the author. In the past, however, the techniques used by scientists to interpret this data have not progressed as quickly. This is a book of modern statistical methods for analysis of practical problems in water quality and water resources. This text reflects current change in the research and practice of teaching statistics. The approach emphasizes the conceptual understanding of statistics and relies on computers to do most of the computational work. Found insideNo fear? this friendly guide offers clear, practical explanations of statistical ideas, techniques, formulas, and calculations, with lots of examples that show you how these concepts apply to your everyday life. You've come to the right place! Statistics For Big Data For Dummies breaks this often-overwhelming subject down into easily digestible parts, offering new and aspiring data analysts the foundation they need to be successful in the field. Found insideNew to this edition: Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. "This book focuses on the practical aspects of modern and robust statistical methods. Found insideIts philosophy is that the best way to learn probability is to see it in action, so there are 200 examples and 450 problems. The fourth edition begins with a short chapter on measure theory to orient readers new to the subject. Found inside – Page 1This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. A second goal of this book is to present work in the field without bias toward any particular statistical paradigm. Broadly speaking, the essays in this Handbook are concerned with problems of induction, statistics and probability. This book is a concise presentation of the normal distribution on the real line and its counterparts on more abstract spaces, which we shall call the Gaussian distributions. This text assumes students have been exposed to intermediate algebra, and it focuses on the applications of statistical knowledge rather than the theory behind it. In this revised text, master expositor Sheldon Ross has produced a unique work in introductory statistics. The book is based on the authors’ experience teaching Liberal Arts Math and other courses to students of various backgrounds and majors, and is also appropriate for preparing students for Florida’s CLAST exam or similar core ... Students using this book should have some familiarity with algebra and precalculus. The Probability Lifesaver not only enables students to survive probability but also to achieve mastery of the subject for use in future courses. Found insideStatistics is hard, but you no longer need to be deluged by data. With U Can, studying the key concepts from your class just got easier than ever before. Simply open this book to find help on all of the topics in your statistics class. Found insideThis new edition is useful for students, residents and fellows in clinical chemistry and pathology, presenting an introduction and overview of the field to assist readers as they in review and prepare for board certification examinations. Found insideOptimal Sports Math, Statistics, and Fantasy provides the sports community—students, professionals, and casual sports fans—with the essential mathematics and statistics required to objectively analyze sports teams, evaluate player ... Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Found insideThis book enables professionals to connect their knowledge of mathematics to either or both of the symbolic languages Maple and Mathematica. Found insideThis book describes the inferential and modeling advantages that this distribution, together with its generalizations and modifications, offers. Found insideImportant Notice: Media content referenced within the product description or the product text may not be available in the ebook version. This book is designed to educate senior medical students, residents, and fellows, and to "refresh" the knowledge base of practicing clinicians on how tests are performed in their laboratories (i.e., method principles, interferences, and ... Found inside – Page iUnivariate Stable Distributions is ideal for advanced undergraduate or graduate students in mathematics, as well as many other fields, such as statistics, economics, engineering, physics, and more. Immediate applications of this line of work include different scenarios such as economics, environmental sciences, biometrics, engineering, health, etc. A useful random variable arising from the application of the symmetric normal distribution to the solution of many problems (e.g., normally distributed errors) is the radius drawn from the center of the distribution to any point therein. The text is backed up by numerous exercises and worked examples throughout, firmly rooted in engineering practice, ensuring that all mathematical theory introduced is directly relevant to real-world engineering. This is a book about statistical distributions, their properties, and their application to modelling the dependence of the location, scale, and shape of the distribution of a response variable on explanatory variables. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. Found insideHigh-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Essays in this revised text, master expositor Sheldon Ross has produced a unique work in introductory statistics the range... And the ability to think abstractly symbolic languages Maple and Mathematica range of researchers who statistical... Results related to the subject problems of induction, statistics and provides,..., offers modern and robust statistical methods for analysis of data sets to this... 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