A indispensable guide to understanding and designing modern
experiments
The tools and techniques of Design of Experiments (DOE) allow
researchers to successfully collect, analyze, and interpret data
across a wide array of disciplines. Statistical Analysis of
Designed Experiments provides a modern and balanced treatment of
DOE methodology with thorough coverage of the underlying theory and
standard designs of experiments, guiding the reader through
applications to research in various fields such as engineering,
medicine, business, and the social sciences.
The book supplies a foundation for the subject, beginning with
basic concepts of DOE and a review of elementary normal theory
statistical methods. Subsequent chapters present a uniform,
model-based approach to DOE. Each design is presented in a
comprehensive format and is accompanied by a motivating example,
discussion of the applicability of the design, and a model for its
analysis using statistical methods such as graphical plots,
analysis of variance (ANOVA), confidence intervals, and hypothesis
tests.
Numerous theoretical and applied exercises are provided in each
chapter, and answers to selected exercises are included at the end
of the book. An appendix features three case studies that
illustrate the challenges often encountered in real-world
experiments, such as randomization, unbalanced data, and outliers.
Minitab(R) software is used to perform analyses throughout the
book, and an accompanying FTP site houses additional exercises and
data sets.
With its breadth of real-world examples and accessible treatment
of both theory and applications, Statistical Analysis of Designed
Experiments is a valuable book for experimental design courses at
the upper-undergraduate and graduate levels. It is also an
indispensable reference for practicing statisticians, engineers,
and scientists who would like to further their knowledge of
DOE.
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