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Books > Science & Mathematics > Mathematics > Applied mathematics > Mathematical modelling
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Empirical Modeling and Data Analysis for Engineers and Applied Scientists (Hardcover, 1st ed. 2016)
Loot Price: R2,473
Discovery Miles 24 730
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Empirical Modeling and Data Analysis for Engineers and Applied Scientists (Hardcover, 1st ed. 2016)
Expected to ship within 10 - 15 working days
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This textbook teaches advanced undergraduate and first-year
graduate students in Engineering and Applied Sciences to gather and
analyze empirical observations (data) in order to aid in making
design decisions. While science is about discovery, the primary
paradigm of engineering and "applied science" is design. Scientists
are in the discovery business and want, in general, to understand
the natural world rather than to alter it. In contrast, engineers
and applied scientists design products, processes, and solutions to
problems. That said, statistics, as a discipline, is mostly
oriented toward the discovery paradigm. Young engineers come out of
their degree programs having taken courses such as "Statistics for
Engineers and Scientists" without any clear idea as to how they can
use statistical methods to help them design products or processes.
Many seem to think that statistics is only useful for demonstrating
that a device or process actually does what it was designed to do.
Statistics courses emphasize creating predictive or classification
models - predicting nature or classifying individuals, and
statistics is often used to prove or disprove phenomena as opposed
to aiding in the design of a product or process. In industry
however, Chemical Engineers use designed experiments to optimize
petroleum extraction; Manufacturing Engineers use experimental data
to optimize machine operation; Industrial Engineers might use data
to determine the optimal number of operators required in a manual
assembly process. This text teaches engineering and applied science
students to incorporate empirical investigation into such design
processes. Much of the discussion in this book is about models, not
whether the models truly represent reality but whether they
adequately represent reality with respect to the problems at hand;
many ideas focus on how to gather data in the most efficient way
possible to construct adequate models. Includes chapters on
subjects not often seen together in a single text (e.g.,
measurement systems, mixture experiments, logistic regression,
Taguchi methods, simulation) Techniques and concepts introduced
present a wide variety of design situations familiar to engineers
and applied scientists and inspire incorporation of experimentation
and empirical investigation into the design process. Software is
integrally linked to statistical analyses with fully worked
examples in each chapter; fully worked using several packages: SAS,
R, JMP, Minitab, and MS Excel - also including discussion questions
at the end of each chapter. The fundamental learning objective of
this textbook is for the reader to understand how experimental data
can be used to make design decisions and to be familiar with the
most common types of experimental designs and analysis methods.
General
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