Statistics for Engineers (PDF)

This practical text is an essential source of information for those
wanting to know how to deal with the variability that exists in
every engineering situation. Using typical engineering data,
it presents the basic statistical methods that are relevant, in
simple numerical terms. In addition, statistical terminology is
translated into basic English.

In the past, a lack of communication between engineers and
statisticians, coupled with poor practical skills in quality
management and statistical engineering, was damaging to products
and to the economy. The disastrous consequence of setting tight
tolerances without regard to the statistical aspect of process data
is demonstrated.

This book offers a solution, bridging the gap between
statistical science and engineering technology to ensure that the
engineers of today are better equipped to serve the manufacturing
industry.

Inside, you will find coverage on:

* the nature of variability, describing the use of formulae to
pin down sources of variation;

* engineering design, research and development, demonstrating the
methods that help prevent costly mistakes in the early stages of a
new product;

* production, discussing the use of control charts, and;

* management and training, including directing and controlling
the quality function.

The Engineering section of the index identifies the role of
engineering technology in the service of industrial quality
management. The Statistics section identifies points in the text
where statistical terminology is used in an explanatory
context.

Engineers working on the design and manufacturing of new
products find this book invaluable as it develops a statistical
method by which they can anticipate and resolve quality problems
before launching into production. This book appeals to
students in all areas of engineering and also managers concerned
with the quality of manufactured products.

Academic engineers can use this text to teach their students
basic practical skills in quality management and statistical
engineering, without getting involved in the complex mathematical
theory of probability on which statistical science is
dependent.