statics learning from engineering examples

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Author : Igor Emri
ISBN : 9781493921010
Genre : Technology & Engineering
File Size : 63. 24 MB
Format : PDF, Docs
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This textbook introduces and explains the basic concepts on which statics is based utilizing real engineering examples. The authors emphasize the learning process by showing a real problem, analyzing it, simplifying it, and developing a way to solve it. This feature teaches students intuitive thinking in solving real engineering problems using the fundamentals of Newton’s laws. This book also: · Stresses representation of physical reality in ways that allow students to solve problems and obtain meaningful results · Emphasizes identification of important features of the structure that should be included in a model and which features may be omitted · Facilitates students' understanding and mastery of the "flow of thinking" practiced by professional engineers

Statistics For Engineers And Scientists

Author : William Navidi
ISBN : 0073376337
Genre : Technology & Engineering
File Size : 54. 10 MB
Format : PDF, Docs
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Statistics for Engineers and Scientists stands out for its crystal clear presentation of applied statistics. Suitable for a one or two semester course, the book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work. Statistics for Engineers and Scientists features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.

Engineering Statics 2 Edition

Author : F.E. Archer
ISBN : 9781742246970
Genre : Technology & Engineering
File Size : 54. 58 MB
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A useful book for anyone interested in engineering mechnanics. It is primary intended to be a textbook for undergraduate engineering students and is treasured both for its brevity and clarity of expression.

Probability And Statistics For Engineering And The Sciences

Author : Jay Devore
ISBN : 9780538733526
Genre : Mathematics
File Size : 51. 33 MB
Format : PDF, Mobi
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This market-leading text provides a comprehensive introduction to probability and statistics for engineering students in all specialties. Proven, accurate, and lauded for its excellent examples, PROBABILITY AND STATISTICS FOR ENGINEERING AND THE SCIENCES evidences Jay Devore’s reputation as an outstanding author and leader in the academic community. Devore emphasizes concepts, models, methodology, and applications as opposed to rigorous mathematical development and derivations. Aided by his lively and realistic examples, students go beyond simply learning about statistics--they also learn how to put statistical methods to use. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

The Elements Of Statistical Learning

Author : Trevor Hastie
ISBN : 9780387216065
Genre : Mathematics
File Size : 76. 10 MB
Format : PDF, ePub
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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.

An Introduction To Statistical Learning

Author : Gareth James
ISBN : 9781461471387
Genre : Mathematics
File Size : 59. 23 MB
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An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. 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.

Engineering Statics In Modular Learning Units

Author : Daniel Edward Alexander
ISBN : UOM:39076005268482
Genre : Science
File Size : 54. 37 MB
Format : PDF
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Fundamentals Of Probability And Statistics For Engineers

Author : T. T. Soong
ISBN : 9780470868157
Genre : Mathematics
File Size : 59. 12 MB
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This textbook differs from others in the field in that it has been prepared very much with students and their needs in mind, having been classroom tested over many years. It is a true “learner’s book” made for students who require a deeper understanding of probability and statistics. It presents the fundamentals of the subject along with concepts of probabilistic modelling, and the process of model selection, verification and analysis. Furthermore, the inclusion of more than 100 examples and 200 exercises (carefully selected from a wide range of topics), along with a solutions manual for instructors, means that this text is of real value to students and lecturers across a range of engineering disciplines. Key features: Presents the fundamentals in probability and statistics along with relevant applications. Explains the concept of probabilistic modelling and the process of model selection, verification and analysis. Definitions and theorems are carefully stated and topics rigorously treated. Includes a chapter on regression analysis. Covers design of experiments. Demonstrates practical problem solving throughout the book with numerous examples and exercises purposely selected from a variety of engineering fields. Includes an accompanying online Solutions Manual for instructors containing complete step-by-step solutions to all problems.

Engineering Mechanics

Author :
ISBN : 9780471787020
Genre : Dynamics
File Size : 24. 72 MB
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Engineering Mechanics Statics

Author : Andrew Pytel
ISBN : 9781305856240
Genre : Technology & Engineering
File Size : 70. 91 MB
Format : PDF, Kindle
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ENGINEERING MECHANICS: STATICS, 4E, written by authors Andrew Pytel and Jaan Kiusalaas, provides readers with a solid understanding of statics without the overload of extraneous detail. The authors use their extensive teaching experience and first-hand knowledge to deliver a presentation that's ideally suited to the skills of today's learners. This edition clearly introduces critical concepts using features that connect real problems and examples with the fundamentals of engineering mechanics. Readers learn how to effectively analyze problems before substituting numbers into formulas -- a skill that will benefit them tremendously as they encounter real problems that do not always fit into standard formulas. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.

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