Tony Greenfield - Böcker

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

This book covers all the latest advances, as well as more established methods, in the application of statistical and optimisation methods within modern industry. These include applications from a range of industries that include micro-electronics, chemical, automotive, engineering, food, component assembly, household goods and plastics. Methods range from basic graphical approaches to generalised modelling, from designed experiments to process control. Solutions cover produce and process design, through manufacture to packaging and delivery, from single responses to multivariate problems.

Progress in engineering and the physical sciences, agriculture and the biological sciences, and to some extent social science, depends on experiments. The design of such experiments is crucial. If they are poorly designed they will be inefficient and may lead to misleading conclusions. Nevertheless, many investigators and researchers in industry and universities are expected to design and analyze their own experiments. Even if investigators do have access to statistical advice, they will be expected to have some basic knowledge of the issues. This book aims to help. Covering all the most commonly used designs of experiments, the methods and the potential pitfalls are described in clear English. The techniques are introduced with case studies of practical significance. The cases are based on real experiments but are described in the context of three fictitious organizations: an engineering company, SeaDragon; a pharmaceuticals and chemicals manufacturer AgroPharm; and the Department of Social Studies at the University of Erewhon. All technical terms are defined and the mathematical development is restricted to that which is needed to use MINITAB.To note: the text makes reference to the following URL: www.greenfieldresearch.co.uk/doe/data.htm.However, this URL has since been updated to the following: https://web.archive.org/web/20161117011155/http://www.greenfieldresearch.co.uk/doe/data.htm Please use this link to have access to the supplementary material.

An indispensable reference for postgraduates, providing up to date guidance in all subject areas Methods for Postgraduates brings together guidance for postgraduate students on how to organise, plan and do research from an interdisciplinary perspective. In this new edition, the already wide-ranging coverage is enhanced by the addition of new chapters on social media, evaluating the research process, Kansei engineering and medical research reporting. The extensive updates also provide the latest guidance on issues relevant to postgraduates in all subject areas, from writing a proposal and securing research funds, to data analysis and the presentation of research, through to intellectual property protection and career opportunities.This thoroughly revised new edition provides: Clear and concise advice from distinguished international researchers on how to plan, organise and conduct research.New chapters explore social media in research, evaluate the research process, Kansei engineering and discuss the reporting of medical research.Check lists and diagrams throughout. Praise for the second edition:“... the most useful book any new postgraduate could ever buy.” (New Scientist)“The book certainly merits its acceptance as essential reading for postgraduates and will be valuable to anyone associated in any way with research or with presentation of technical or scientific information of any kind.”(Robotica) Like its predecessors, the third edition of Research Methods for Postgraduates is accessible and comprehensive, and is a must-read for any postgraduate student.

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.