Statistics for Experimenters : Design, Innovation, and Discovery

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  • Edition: 2nd
  • Format: Hardcover
  • Copyright: 5/31/2005
  • Publisher: Wiley-Interscience

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A Classic adapted to modern timesRewritten and updated, this new edition of Statistics for Experimenters adopts the same approaches as the landmark First Edition by teaching with examples, readily understood graphics, and the appropriate use of computers. Catalyzing innovation, problem solving, and discovery, the Second Edition provides experimenters with the scientific and statistical tools needed to maximize the knowledge gained from research data, illustrating how these tools may best be utilized during all stages of the investigative process. The authors' practical approach starts with a problem that needs to be solved and then examines the appropriate statistical methods of design and analysis.Providing even greater accessibility for its users, the Second Edition is thoroughly revised and updated to reflect the changes in techniques and technologies since the publication of the classic First Edition.Among the new topics included are: Graphical Analysis of Variance Computer Analysis of Complex Designs Simplification by transformation Hands-on experimentation using Response Service Methods Further development of robust product and process design using split plot arrangements and minimization of error transmission Introduction to Process Control, Forecasting and Time Series Illustrations demonstrating how multi-response problems can be solved using the concepts of active and inert factor spaces and canonical spaces Bayesian approaches to model selection and sequential experimentationAn appendix featuring Quaquaversal quotes from a variety of sources including noted statisticians and scientists to famous philosophers is provided to illustrate key concepts and enliven the learning process.All the computations in the Second Edition can be done utilizing the statistical language R. Functions for displaying ANOVA and lamba plots, Bayesian screening, and model building are all included and R packages are available online. All theses topics can also be applied utilizing easy-to-use commercial software packages.Complete with applications covering the physical, engineering, biological, and social sciences, Statistics for Experimenters is designed for individuals who must use statistical approaches to conduct an experiment, but do not necessarily have formal training in statistics. Experimenters need only a basic understanding of mathematics to master all the statistical methods presented. This text is an essential reference for all researchers and is a highly recommended course book for undergraduate and graduate students.

Author Biography

GEORGE E. P. BOX, PhD, DSc, is Ronald Aylmer Fisher Professor Emeritus of Statistics and Industrial Engineering at the University of Wisconsin–Madison. He is a Fellow of the Royal Society, an Honorary Fellow and Shewhart and Deming Medalist of the American Society for Quality and was awarded the Guy Medal in Gold of the Royal Statistical Society. He is also the recipient of the Samuel S. Wilks Memorial Medal of the American Statistical Association.

J. STUART HUNTER, PhD, DSc, is Professor Emeritus of Civil Engineering at Princeton University. Dr. Hunter is a member of the National Academy of Engineering and has served as consultant to many industries and government agencies. He has been a staff member of the National Academy of Sciences, Committee on National Statistics; statistician in residence at the University of Wisconsin; and is the founding editor of Technometrics.

The late WILLIAM G. HUNTER, PhD, was Professor of Statistics and Engineering at the University of Wisconsin–Madison.

Table of Contents

1. Catalizing the Generation of Knowledge.
2. Basics: probability, Parameters and Statistics.
3. Comparing Two Entities: Relevant Reference Distributions, Tests and Confidence Intervals.
4. Comparing a Number of Entities: Randomized Blocks and Latin Squares.
5. Factorial Designs at Two Levels: Advantages of Experimental Design.
6. Fraction Factorial Designs: Economy in Experimentation.
7. Other Fractionals, Analysis and Choosing Follow-up Runs.
8. Factorial Designs and Data Transformation.
9. Multiple Sources of Variation: Split Plot Designs, Variance Components and Error Transmission.
10. Least Squares and Why You Need to Design Experiments.
11. Modelling Relationships, Sequential Assembly: Basics for Response Surface Methods.
12. Some Applications of Response Surface Methods.
13. Designing Robust Products: An Introduction.
14. Process Control, Forecasting and Times Series: An Introduction.
15. Evolutionary Process Operation.

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