Basic Engineering Data Collection and Analysis
| Author | : Stephen B. Vardeman |
| Publisher | : |
| Total Pages | : |
| Release | : 2001-01 |
| Genre | : |
| ISBN | : 9780534382391 |
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Basic Engineering Data Collection and Analysis
| Author | : Stephen B. Vardeman |
| Publisher | : Cengage Learning |
| Total Pages | : 864 |
| Release | : 2001 |
| Genre | : Business & Economics |
| ISBN | : |
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This approach encourages students to work through the statistics by carrying data collection and analysis projects from problem formulation through preparation of professional technical reports - just as if they were on the job."--BOOK JACKET.
Warranty Data Collection and Analysis
| Author | : Wallace R. Blischke |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 594 |
| Release | : 2011-07-28 |
| Genre | : Technology & Engineering |
| ISBN | : 0857296477 |
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Warranty Data Collection and Analysis deals with warranty data collection and analysis and the problems associated with these activities. The book is a both a research monograph and a handbook for practitioners. As a research monograph, it unifies the literature on warranty data collection and analysis, and presents the important results in an integrated manner. In the process, it highlights topics that require further research. As a handbook, it provides the essential methodology needed by practitioners involved with warranty data collection and analysis, along with extensive references to further results. Models and techniques needed for proper and effective analysis of data are included, together with guidelines for their use in warranty management, product improvement, and new product development. Warranty Data Collection and Analysis will be of interest to researchers (engineers and statisticians) and practitioners (engineers, applied statisticians, and managers) involved with product warranty and reliability. It is also suitable for use as a reference text for graduate-level reliability programs in engineering, applied statistics, operations research, and management.
Data Analytics for Engineering and Construction Project Risk Management
| Author | : Ivan Damnjanovic |
| Publisher | : Springer |
| Total Pages | : 379 |
| Release | : 2019-05-23 |
| Genre | : Technology & Engineering |
| ISBN | : 3030142515 |
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This book provides a step-by-step guidance on how to implement analytical methods in project risk management. The text focuses on engineering design and construction projects and as such is suitable for graduate students in engineering, construction, or project management, as well as practitioners aiming to develop, improve, and/or simplify corporate project management processes. The book places emphasis on building data-driven models for additive-incremental risks, where data can be collected on project sites, assembled from queries of corporate databases, and/or generated using procedures for eliciting experts’ judgments. While the presented models are mathematically inspired, they are nothing beyond what an engineering graduate is expected to know: some algebra, a little calculus, a little statistics, and, especially, undergraduate-level understanding of the probability theory. The book is organized in three parts and fourteen chapters. In Part I the authors provide the general introduction to risk and uncertainty analysis applied to engineering construction projects. The basic formulations and the methods for risk assessment used during project planning phase are discussed in Part II, while in Part III the authors present the methods for monitoring and (re)assessment of risks during project execution.
Basic Experimental Strategies and Data Analysis for Science and Engineering
| Author | : John Lawson |
| Publisher | : CRC Press |
| Total Pages | : 445 |
| Release | : 2016-11-03 |
| Genre | : Business & Economics |
| ISBN | : 1466512180 |
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Every technical investigation involving trial-and-error experimentation embodies a strategy for deciding what experiments to perform, when to quit, and how to interpret the data. This handbook presents several statistically derived strategies which are more efficient than any intuitive approach and will get the investigator to their goal with the fewest experiments, give the greatest degree of reliability to their conclusions, and keep the risk of overlooking something of practical importance to a minimum. Features: Provides a comprehensive desk reference on experimental design that will be useful to practitioners without extensive statistical knowledge Features a review of the necessary statistical prerequisites Presents a set of tables that allow readers to quickly access various experimental designs Includes a roadmap for where and when to use various experimental design strategies Shows compelling examples of each method discussed Illustrates how to reproduce results using several popular software packages on a companion web site Following the outlines and examples in this book should quickly allow a working professional or student to select the appropriate experimental design for a research problem at hand, follow the design to conduct the experiments, and analyze and interpret the resulting data. John Lawson and John Erjavec have a combined 25 years of industrial experience and over 40 years of academic experience. They have taught this material to numerous practicing engineers and scientists as well as undergraduate and graduate students.
Data Analysis for Scientists and Engineers
| Author | : Edward L. Robinson |
| Publisher | : Princeton University Press |
| Total Pages | : 408 |
| Release | : 2016-10-04 |
| Genre | : Science |
| ISBN | : 0691169926 |
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Data Analysis for Scientists and Engineers is a modern, graduate-level text on data analysis techniques for physical science and engineering students as well as working scientists and engineers. Edward Robinson emphasizes the principles behind various techniques so that practitioners can adapt them to their own problems, or develop new techniques when necessary. Robinson divides the book into three sections. The first section covers basic concepts in probability and includes a chapter on Monte Carlo methods with an extended discussion of Markov chain Monte Carlo sampling. The second section introduces statistics and then develops tools for fitting models to data, comparing and contrasting techniques from both frequentist and Bayesian perspectives. The final section is devoted to methods for analyzing sequences of data, such as correlation functions, periodograms, and image reconstruction. While it goes beyond elementary statistics, the text is self-contained and accessible to readers from a wide variety of backgrounds. Specialized mathematical topics are included in an appendix. Based on a graduate course on data analysis that the author has taught for many years, and couched in the looser, workaday language of scientists and engineers who wrestle directly with data, this book is ideal for courses on data analysis and a valuable resource for students, instructors, and practitioners in the physical sciences and engineering. In-depth discussion of data analysis for scientists and engineers Coverage of both frequentist and Bayesian approaches to data analysis Extensive look at analysis techniques for time-series data and images Detailed exploration of linear and nonlinear modeling of data Emphasis on error analysis Instructor's manual (available only to professors)
Data Engineering
| Author | : Olaf Wolkenhauer |
| Publisher | : John Wiley & Sons |
| Total Pages | : 296 |
| Release | : 2004-04-07 |
| Genre | : Technology & Engineering |
| ISBN | : 0471464104 |
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Although data engineering is a multi-disciplinary field withapplications in control, decision theory, and the emerging hot areaof bioinformatics, there are no books on the market that make thesubject accessible to non-experts. This book fills the gap in thefield, offering a clear, user-friendly introduction to the maintheoretical and practical tools for analyzing complex systems. Anftp site features the corresponding MATLAB and Mathematical toolsand simulations. Market: Researchers in data management, electrical engineering,computer science, and life sciences.
Enhancing the Community College Pathway to Engineering Careers
| Author | : National Research Council |
| Publisher | : National Academies Press |
| Total Pages | : 119 |
| Release | : 2006-01-01 |
| Genre | : Education |
| ISBN | : 0309095344 |
Download Enhancing the Community College Pathway to Engineering Careers Book in PDF, ePub and Kindle
Community colleges play an important role in starting students on the road to engineering careers, but students often face obstacles in transferring to four-year educational institutions to continue their education. Enhancing the Community College Pathway to Engineering Careers, a new book from the National Academy of Engineering and the National Research Council, discusses ways to improve the transfer experience for students at community colleges and offers strategies to enhance partnerships between those colleges and four-year engineering schools to help students transfer more smoothly. In particular, the book focuses on challenges and opportunities for improving transfer between community colleges and four-year educational institutions, recruitment and retention of students interested in engineering, the curricular content and quality of engineering programs, opportunities for community colleges to increase diversity in the engineering workforce, and a review of sources of information on community college and transfer students. It includes a number of current policies, practices, and programs involving community collegeâ€"four-year institution partnerships.
Guide to Advanced Empirical Software Engineering
| Author | : Forrest Shull |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 393 |
| Release | : 2007-11-21 |
| Genre | : Computers |
| ISBN | : 1848000448 |
Download Guide to Advanced Empirical Software Engineering Book in PDF, ePub and Kindle
This book gathers chapters from some of the top international empirical software engineering researchers focusing on the practical knowledge necessary for conducting, reporting and using empirical methods in software engineering. Topics and features include guidance on how to design, conduct and report empirical studies. The volume also provides information across a range of techniques, methods and qualitative and quantitative issues to help build a toolkit applicable to the diverse software development contexts
Principles of Marketing Engineering, 2nd Edition
| Author | : Gary L. Lilien |
| Publisher | : DecisionPro |
| Total Pages | : 287 |
| Release | : 2013 |
| Genre | : Business & Economics |
| ISBN | : 0985764805 |
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The 21st century business environment demands more analysis and rigor in marketing decision making. Increasingly, marketing decision making resembles design engineering-putting together concepts, data, analyses, and simulations to learn about the marketplace and to design effective marketing plans. While many view traditional marketing as art and some view it as science, the new marketing increasingly looks like engineering (that is, combining art and science to solve specific problems). Marketing Engineering is the systematic approach to harness data and knowledge to drive effective marketing decision making and implementation through a technology-enabled and model-supported decision process. (For more information on Excel-based models that support these concepts, visit DecisionPro.biz.) We have designed this book primarily for the business school student or marketing manager, who, with minimal background and technical training, must understand and employ the basic tools and models associated with Marketing Engineering. We offer an accessible overview of the most widely used marketing engineering concepts and tools and show how they drive the collection of the right data and information to perform the right analyses to make better marketing plans, better product designs, and better marketing decisions. What's New In the 2nd Edition While much has changed in the nearly five years since the first edition of Principles of Marketing Engineering was published, much has remained the same. Hence, we have not changed the basic structure or contents of the book. We have, however Updated the examples and references. Added new content on customer lifetime value and customer valuation methods. Added several new pricing models. Added new material on "reverse perceptual mapping" to describe some exciting enhancements to our Marketing Engineering for Excel software. Provided some new perspectives on the future of Marketing Engineering. Provided better alignment between the content of the text and both the software and cases available with Marketing Engineering for Excel 2.0.
