Probability, Statistics, and Reliability for Engineers and Scientists, Ayyub, Bilal , McCuen, Richard H.
Автор: Morgan Название: Counterfactuals and Causal Inference ISBN: 1107694167 ISBN-13(EAN): 9781107694163 Издательство: Cambridge Academ Рейтинг: Цена: 5702.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Cause-and-effect questions are the motivation for most research in the social, demographic, and health sciences. The counterfactual approach to causal analysis represents a unified framework for the prosecution of these questions. This second edition aims to convince more social scientists to take this approach when analyzing these core empirical questions.
In a technological society, virtually every engineer and scientist needs to be able to collect, analyze, interpret, and properly use vast arrays of data. This means acquiring a solid foundation in the methods of data analysis and synthesis. Understanding the theoretical aspects is important, but learning to properly apply the theory to real-world problems is essential.
Probability, Statistics, and Reliability for Engineers and Scientists, Third Edition introduces the fundamentals of probability, statistics, reliability, and risk methods to engineers and scientists for the purposes of data and uncertainty analysis and modeling in support of decision making.
The third edition of this bestselling text presents probability, statistics, reliability, and risk methods with an ideal balance of theory and applications. Clearly written and firmly focused on the practical use of these methods, it places increased emphasis on simulation, particularly as a modeling tool, applying it progressively with projects that continue in each chapter. This provides a measure of continuity and shows the broad use of simulation as a computational tool to inform decision making processes. This edition also features expanded discussions of the analysis of variance, including single- and two-factor analyses, and a thorough treatment of Monte Carlo simulation. The authors not only clearly establish the limitations, advantages, and disadvantages of each method, but also show that data analysis is a continuum rather than the isolated application of different methods.
Like its predecessors, this book continues to serve its purpose well as both a textbook and a reference. Ultimately, readers will find the content of great value in problem solving and decision making, particularly in practical applications.
Автор: Moulin Pierre Название: Statistical Inference for Engineers and Data Scientists ISBN: 1107185920 ISBN-13(EAN): 9781107185920 Издательство: Cambridge Academ Рейтинг: Цена: 10138.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An up-to-date and mathematically accessible introduction to the tools needed to address modern inference problems in engineering and data science. Richly illustrated with examples and exercises connecting the theory with practice, it is the `go to` guide for students studying the topic, and an excellent reference for researchers and practitioners.
Автор: Joseph K. Blitzstein, Jessica Hwang Название: Introduction to Probability, Second Edition ISBN: 1138369918 ISBN-13(EAN): 9781138369917 Издательство: Taylor&Francis Рейтинг: Цена: 11176.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Assumes one-semester of calculus. "Stories" make distributions (Normal, Binomial, Poisson that are widely-used in statistics) easier to remember, understand. Many books write down formulas without explaining clearly why these particular distributions are important or how they are all connected.
Автор: Joshi Название: Introduction to Mathematical Portfolio Theory ISBN: 1107042313 ISBN-13(EAN): 9781107042315 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A concise yet comprehensive guide to the mathematics of portfolio theory from a modelling perspective, with discussion of the assumptions, limitations and implementations of the models as well as the theory underlying them. Aimed at advanced undergraduates, this book can be used for self-study or as a course text.
Описание: Put statistical theories into practice with PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, INTERNATIONAL METRIC EDITION. Always a market favorite, this calculus-based book offers a comprehensive introduction to probability and statistics while demonstrating how to apply concepts, models, and methodologies in today's engineering and scientific workplaces. Jay Devore, an award-winning professor and internationally recognized author and statistician, stresses lively examples and engineering activities to drive home the numbers without exhaustive mathematical development and derivations.
Many examples, practice problems, sample tests, and simulations based on real data and issues help you build a more intuitive connection to the material. A proven and accurate book, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES, 9E, INTERNATIONAL METRIC EDITION also includes graphics and screen shots from SAS (R), MINITAB (R), and Java (TM) Applets to give you a solid perspective of statistics in action.
Описание: Probability, Statistics, and Information Theory for Scientists and Engineers offers students clear explanations of the most fundamental and useful statistical concepts. Extensively class-tested, the book is designed to appeal to the intuition, yet provide sound academic support, so that conclusions are clear and unambiguous.The text is a graphical introduction to the subject, which allows for ease of interpretation. In the first sections, students will explore the basic axioms and conclusions of probability and statistics. They will learn what can be expected in data taken from certain specified populations, and conversely, what can be inferred about a population from a data set. Unlike other standard texts, Probability, Statistics, and Information Theory for Scientists and Engineers also includes an introduction to information theory—the study of the structure of probability distributions. This allows for a new and useful perspective on hypothesis testing. Designed for science and engineering students, Probability, Statistics and Information Theory for Scientists and Engineers can be used in courses on engineering, math and statistics, and physics.
Описание: Featuring recent advances in probability, statistics, and stochastic processes, this new textbook presents Probability and Statistics, and an introduction to Stochastic Processes.
Описание: Featuring recent advances in probability, statistics, and stochastic processes, this new textbook presents Probability and Statistics, and an introduction to Stochastic Processes.
Автор: Dickson, David C. M. Название: Actuarial Mathematics for Life Contingent Risks ISBN: 1107044073 ISBN-13(EAN): 9781107044074 Издательство: Cambridge Academ Рейтинг: Цена: 12514.00 р. Наличие на складе: Поставка под заказ.
Описание: Actuarial Mathematics for Life Contingent Risks, 2nd edition, is the sole required text for the Society of Actuaries Exam MLC Fall 2015 and Spring 2016. It covers the entire syllabus for the SOA Exam MLC, including new sections for Spring 2016. It is ideal for university courses and for individuals preparing for professional actuarial examinations - especially the new, long-answer exam questions. Three leaders in actuarial science balance rigor with intuition and emphasize practical applications using computational techniques to provide a modern perspective on life contingencies and equip students for the products and risk structures of the future. The authors then develop a more contemporary outlook, introducing multiple state models, emerging cash flows and embedded options. The 210 exercises provide meaningful practice with both long-answer and multiple choice questions. Furthermore: • the book has been updated to include new material on discrete time Markov processes, on models involving joint lives, and on universal life insurance and participating traditional insurance • the Solutions Manual (ISBN 9781107620261), available for separate purchase, provides detailed solutions to the text's exercises.
Автор: Co Название: Methods of Applied Mathematics for Engineers and Scientists ISBN: 1107004128 ISBN-13(EAN): 9781107004122 Издательство: Cambridge Academ Рейтинг: Цена: 14571.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Based on course notes from over twenty years of teaching engineering and physical sciences at Michigan Technological University, Tomas Co's engineering mathematics textbook is rich with examples, applications and exercises. Professor Co uses analytical approaches to solve smaller problems to provide mathematical insight and understanding, and numerical methods for large and complex problems. The book emphasises applying matrices with strong attention to matrix structure and computational issues such as sparsity and efficiency. Chapters on vector calculus and integral theorems are used to build coordinate-free physical models with special emphasis on orthogonal co-ordinates. Chapters on ODEs and PDEs cover both analytical and numerical approaches. Topics on analytical solutions include similarity transform methods, direct formulas for series solutions, bifurcation analysis, Lagrange–Charpit formulas, shocks/rarefaction and others. Topics on numerical methods include stability analysis, DAEs, high-order finite-difference formulas, Delaunay meshes, and others. MATLAB® implementations of the methods and concepts are fully integrated.
Автор: Bradley Efron and Trevor Hastie Название: Computer Age Statistical Inference ISBN: 1107149894 ISBN-13(EAN): 9781107149892 Издательство: Cambridge Academ Рейтинг: Цена: 9029.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.
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