Engineering Mathematics Volume - III (Statistical and Numerical Methods) (For 1st Year - 2nd Semester of JNTU, Hyderabad) PDF Download

Author: Iyenger T.K.V./ Gandhi, Krishna B./ Ranganatham S. & Prasad M.V.S.S.N.
Publisher: S. Chand Publishing
ISBN: 9352832442
Category : Mathematics
Languages : en
Pages :

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Engineering Mathematics

Author: A. P. Dwivedi
Publisher: PHI Learning Pvt. Ltd.
ISBN: 8120342356
Category : Engineering mathematics
Languages : en
Pages : 418

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Author: Bilal Ayyub
Publisher: CRC Press
ISBN: 1482250365
Category : Mathematics
Languages : en
Pages : 444

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Book Description
Numerical Analysis for Engineers: Methods and Applications demonstrates the power of numerical methods in the context of solving complex engineering and scientific problems. The book helps to prepare future engineers and assists practicing engineers in understanding the fundamentals of numerical methods, especially their applications, limitations, and potentials. Each chapter contains many computational examples, as well as a section on applications that contain additional engineering examples. Each chapter also includes a set of exercise problems. The problems are designed to meet the needs of instructors in assigning homework and to help students with practicing the fundamental concepts. Although the book was developed with emphasis on engineering and technological problems, the numerical methods can also be used to solve problems in other fields of science.

Author: Carl .E. Pearson
Publisher: CRC Press
ISBN: 9780442273446
Category : Mathematics
Languages : en
Pages : 228

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Book Description
This book is designed for an introductory course in numerical methods for students of engineering and science at universities and colleges of advanced education.

Author: Nicholas P. Cheremisinoff
Publisher: Routledge
ISBN: 1351450972
Category : Mathematics
Languages : en
Pages : 117

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Book Description
This pocket handbook is intended as a handy reference guide for engineers, scientists and students on widely used mathematical relationships, statistical formulas and problem-solving methods. It is a compilation of useful formulas and generalised problem-solving techniques employed by practitioners in the analysis and interpretation of scientific data and problem solving. Written in short note form, it is intended to provide the user with a quick, easy reference to information with ample references provided for further readings. Illustrated examples are included for more involved problem-solving methods. Many of the techniques are well suited to adaptatation on personal computers and there are more detailed instructions included to guide and illustrate computer aided problem solving.

Author: Bilal M. Ayyub
Publisher: Pearson
ISBN:
Category : Computers
Languages : en
Pages : 394

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Book Description
Appropriate for a one- or two-semester introductory course in Numerical Analysis with an emphasis on applications. This text introduces numerical methods by emphasizing the practical aspects of their use. In the process the book establishes their limitations, advantages and disadvantages. It is intended to assist future as well as practicing engineers in fully understanding the fundamentals of numerical methods.

Author: Andrew Metcalfe
Publisher: CRC Press
ISBN: 1439895481
Category : Mathematics
Languages : en
Pages : 792

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Book Description
Engineers are expected to design structures and machines that can operate in challenging and volatile environments, while allowing for variation in materials and noise in measurements and signals. Statistics in Engineering, Second Edition: With Examples in MATLAB and R covers the fundamentals of probability and statistics and explains how to use these basic techniques to estimate and model random variation in the context of engineering analysis and design in all types of environments. The first eight chapters cover probability and probability distributions, graphical displays of data and descriptive statistics, combinations of random variables and propagation of error, statistical inference, bivariate distributions and correlation, linear regression on a single predictor variable, and the measurement error model. This leads to chapters including multiple regression; comparisons of several means and split-plot designs together with analysis of variance; probability models; and sampling strategies. Distinctive features include: All examples based on work in industry, consulting to industry, and research for industry Examples and case studies include all engineering disciplines Emphasis on probabilistic modeling including decision trees, Markov chains and processes, and structure functions Intuitive explanations are followed by succinct mathematical justifications Emphasis on random number generation that is used for stochastic simulations of engineering systems, demonstration of key concepts, and implementation of bootstrap methods for inference Use of MATLAB and the open source software R, both of which have an extensive range of statistical functions for standard analyses and also enable programing of specific applications Use of multiple regression for times series models and analysis of factorial and central composite designs Inclusion of topics such as Weibull analysis of failure times and split-plot designs that are commonly used in industry but are not usually included in introductory textbooks Experiments designed to show fundamental concepts that have been tested with large classes working in small groups Website with additional materials that is regularly updated Andrew Metcalfe, David Green, Andrew Smith, and Jonathan Tuke have taught probability and statistics to students of engineering at the University of Adelaide for many years and have substantial industry experience. Their current research includes applications to water resources engineering, mining, and telecommunications. Mahayaudin Mansor worked in banking and insurance before teaching statistics and business mathematics at the Universiti Tun Abdul Razak Malaysia and is currently a researcher specializing in data analytics and quantitative research in the Health Economics and Social Policy Research Group at the Australian Centre for Precision Health, University of South Australia. Tony Greenfield, formerly Head of Process Computing and Statistics at the British Iron and Steel Research Association, is a statistical consultant. He has been awarded the Chambers Medal for outstanding services to the Royal Statistical Society; the George Box Medal by the European Network for Business and Industrial Statistics for Outstanding Contributions to Industrial Statistics; and the William G. Hunter Award by the American Society for Quality.

Author: Thomas P. Ryan
Publisher: John Wiley & Sons
ISBN: 0470096071
Category : Mathematics
Languages : en
Pages : 198

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Book Description
An introductory perspective on statistical applications in the field of engineering Modern Engineering Statistics presents state-of-the-art statistical methodology germane to engineering applications. With a nice blend of methodology and applications, this book provides and carefully explains the concepts necessary for students to fully grasp and appreciate contemporary statistical techniques in the context of engineering. With almost thirty years of teaching experience, many of which were spent teaching engineering statistics courses, the author has successfully developed a book that displays modern statistical techniques and provides effective tools for student use. This book features: Examples demonstrating the use of statistical thinking and methodology for practicing engineers A large number of chapter exercises that provide the opportunity for readers to solve engineering-related problems, often using real data sets Clear illustrations of the relationship between hypothesis tests and confidence intervals Extensive use of Minitab and JMP to illustrate statistical analyses The book is written in an engaging style that interconnects and builds on discussions, examples, and methods as readers progress from chapter to chapter. The assumptions on which the methodology is based are stated and tested in applications. Each chapter concludes with a summary highlighting the key points that are needed in order to advance in the text, as well as a list of references for further reading. Certain chapters that contain more than a few methods also provide end-of-chapter guidelines on the proper selection and use of those methods. Bridging the gap between statistics education and real-world applications, Modern Engineering Statistics is ideal for either a one- or two-semester course in engineering statistics.

Author: Bilal M. Ayyub
Publisher: CRC Press
ISBN: 9781584882862
Category : Mathematics
Languages : en
Pages : 660

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Book Description
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. The second edition of this bestselling text introduces 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. It 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 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. Probability, Statistics, and Reliability for Engineers and Scientists, Second Edition, was designed as both a reference and as a textbook, and it serves each purpose well. Ultimately, readers will find its content of great value in problem solving and decision making, particularly in practical applications.

Author: Robert M. Bethea
Publisher: CRC Press
ISBN: 9780824793357
Category : Mathematics
Languages : en
Pages : 686

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Book Description
This work details the fundamentals of applied statistics and experimental design, presenting a unified approach to data handling that emphasizes the analysis of variance, regression analysis and the use of Statistical Analysis System computer programs. This edition: discusses modern nonparametric methods; contains information on statistical process control and reliability; supplies fault and event trees; furnishes numerous additional end-of-chapter problems and worked examples; and more.