This short handbook is a practical and accessible guide to the
statistical design and analysis of 2-level, multi-factor
experiments of the kind widely used in industry and business.
Written for technologists and researchers, it forgoes the usual
heavy statistical overlay of typical texts on this subject by
focusing on a limited catalog of standard designs that are useful
for commonly encountered problems. These design choices are based
on relatively recent developments in design projectivity, and their
analysis requires nothing more than simple plots of the data:
neither special expertise nor complex software is needed. Numerous
examples show how to carry out this program in practice.
Even though the statistical content of the handbook has been
deliberately limited, it nevertheless discusses several practical
matters that are rarely included in more comprehensive treatments,
but which are vital for experimental success. Among these are the
realities of randomization versus split-plotting, the importance of
identifying the experimental unit, and a discussion of replication
that argues that it is generally not worth the effort. Readers with
some prior statistical exposure -- and statisticians -- may also be
surprised to find that p-values do not appear anywhere in the book,
and that in fact the authors explicitly argue against their
use.
Those new to the ideas of Statistical Design of Experiments
(DOE)-- or even those who have some familiarity but would like
greater insight and simplicity -- should find this handbook an
effective way to learn about and apply this powerful technology in
their own work.
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