Описание: The revised Fifth Edition of this popular textbook is redesigned with Excel 2019 and the new inclusion of interactive, user-friendly JMP to encourage business students to develop competitive advantages for use in their future careers.
Описание: This textbook provides an accessible and comprehensive introduction to the field of lesbian, gay, bisexual, transgender, intersex and queer psychology. Designed for undergraduate students taking LGBTIQ psychology, psychology of sexuality or related courses, it includes pedagogical features to support learning and to facilitate discussion and reflection.
Автор: Quirk Название: Excel 2016 for Business Statistics ISBN: 3319389580 ISBN-13(EAN): 9783319389585 Издательство: Springer Рейтинг: Цена: 9781.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows the capabilities of Microsoft Excel in teaching business statistics effectively. Similar to the previously published Excel 2010 for Business Statistics, this book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical business problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.Excel, a widely available computer program for students and managers, is also an effective teaching and learning tool for quantitative analyses in business courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Business Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel to statistical techniques necessary in their courses and work.Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand business problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.
Описание: This book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.Excel is an effective learning tool for quantitative analyses in biological and life sciences courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel 2016 to statistical techniques necessary in their courses and work.Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand biological and life science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.
Описание: Probabilistic Finite Element Model Updating Using Bayesian Statistics: Applications to Aeronautical and Mechanical Engineering Tshilidzi Marwala and Ilyes Boulkaibet, University of Johannesburg, South Africa Sondipon Adhikari, Swansea University, UK Covers the probabilistic finite element model based on Bayesian statistics with applications to aeronautical and mechanical engineering Finite element models are used widely to model the dynamic behaviour of many systems including in electrical, aerospace and mechanical engineering. The book covers probabilistic finite element model updating, achieved using Bayesian statistics. The Bayesian framework is employed to estimate the probabilistic finite element models which take into account of the uncertainties in the measurements and the modelling procedure.
The Bayesian formulation achieves this by formulating the finite element model as the posterior distribution of the model given the measured data within the context of computational statistics and applies these in aeronautical and mechanical engineering. Probabilistic Finite Element Model Updating Using Bayesian Statistics contains simple explanations of computational statistical techniques such as Metropolis-Hastings Algorithm, Slice sampling, Markov Chain Monte Carlo method, hybrid Monte Carlo as well as Shadow Hybrid Monte Carlo and their relevance in engineering. Key features: * Contains several contributions in the area of model updating using Bayesian techniques which are useful for graduate students.
* Explains in detail the use of Bayesian techniques to quantify uncertainties in mechanical structures as well as the use of Markov Chain Monte Carlo techniques to evaluate the Bayesian formulations. The book is essential reading for researchers, practitioners and students in mechanical and aerospace engineering.
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