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STATISTICAL METHODS FOR ENGINEERS, 3e, International Edition offers
a balanced, streamlined one-semester introduction to Engineering
Statistics that emphasizes the statistical tools most needed by
practicing engineers. Using real engineering problems with real
data based on actual journals and consulting experience in the
field, students see how statistics fits within the methods of
engineering problem solving. The text teaches students how to think
like an engineer at analyzing real data and planning a project the
same way they will in their careers. Case studies simulate problems
students will encounter professionally and tackle on long-term job
projects. The presentation makes extensive use of graphical
analysis, and use of statistical software is encouraged for
problem-solving to illustrate how engineers rely on computers for
data analysis. The authors relate their own extensive professional
experience as engineers in short margin notes called Voice of
Experience that lend valuable context to how students will apply
concepts in the field and why they're important to learn. And a
rich companion website provides hours of multimedia lecture
presentation narrated by the authors to show the material related
live by different voices, simulating how students will listen and
learn from multiple colleagues in their jobs.
Praise for the Fourth Edition "As with previous editions, the
authors have produced a leading textbook on regression." -- Journal
of the American Statistical Association A comprehensive and
up-to-date introduction to the fundamentals of regression analysis
Introduction to Linear Regression Analysis, Fifth Edition continues
to present both the conventional and less common uses of linear
regression in today's cutting-edge scientific research. The authors
blend both theory and application to equip readers with an
understanding of the basic principles needed to apply regression
model-building techniques in various fields of study, including
engineering, management, and the health sciences. Following a
general introduction to regression modeling, including typical
applications, a host of technical tools are outlined such as basic
inference procedures, introductory aspects of model adequacy
checking, and polynomial regression models and their variations.
The book then discusses how transformations and weighted least
squares can be used to resolve problems of model inadequacy and
also how to deal with influential observations. The Fifth Edition
features numerous newly added topics, including: A chapter on
regression analysis of time series data that presents the
Durbin-Watson test and other techniques for detecting
autocorrelation as well as parameter estimation in time series
regression models Regression models with random effects in addition
to a discussion on subsampling and the importance of the mixed
model Tests on individual regression coefficients and subsets of
coefficients Examples of current uses of simple linear regression
models and the use of multiple regression models for understanding
patient satisfaction data. In addition to Minitab, SAS, and S-PLUS,
the authors have incorporated JMP and the freely available R
software to illustrate the discussed techniques and procedures in
this new edition. Numerous exercises have been added throughout,
allowing readers to test their understanding of the material, and a
related FTP site features the presented data sets, extensive
problem solutions, software hints, and PowerPoint slides to
facilitate instructional use of the book. Introduction to Linear
Regression Analysis, Fifth Edition is an excellent book for
statistics and engineering courses on regression at the
upper-undergraduate and graduate levels. The book also serves as a
valuable, robust resource for professionals in the fields of
engineering, life and biological sciences, and the social sciences.
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