| dc.description.abstract | Since writing the first edition of Design and Analysis of Experiments, there
have been a number of additions to the research investigator’s toolbox. In
this second edition, we have incorporated a few of these modern topics.
Small screening designs are now becoming prevalent in industry for
aiding the search for a few influential factors from amongst a large pool of
factors of potential interest. In Chap. 15, we have expanded the material on
saturated designs and introduced the topic of supersaturated designs which
have fewer observations than the number of factors being investigated. We
have illustrated that useful information can be gleaned about influential
factors through the use of supersaturated designs even though their contrast
estimators are correlated. When curvature is of interest, we have described
definitive screening designs which have only recently been introduced in the
literature, and which allow second order effects to be measured while
retaining independence of linear main effects and requiring barely more than
twice as many observations as factors.
Another modern set of tools, now used widely in areas such as biomedical
and materials engineering, the physical sciences, and the life sciences, is that
of computer experiments. To give a flavor of this topic, a new Chap. 20 has
been added. Computer experiments are typically used when a mathematical
description of a physical process is available, but a physical experiment
cannot be run for ethical or cost reasons. We have discussed the major issues
in both the design and analysis of computer experiments. While the complete
treatment of the theoretical background for the analysis is beyond the scope
of this book, we have provided enough technical details of the statistical
model, as well as an intuitive explanation, to make the analysis accessible to
the intended reader. We have also provided computer code needed for both
design and analysis.
Chapter 19 has been expanded to include two new experiments involving
split-plot designs from the discipline of human factors engineering. In one
case, imbalance due to lost data, coupled with a mixed model, motivates
introduction of restricted-maximum-likelihood-based methods implemented
in the computer software sections, including a comparison of these methods
to those based on least squares estimation | en_US |
