Synopses & Reviews
Praise for the First EditionStatistical Design and Analysis of Experiments
"A very useful book for self study and reference."
–Journal of Quality Technology
"Very well written. It is concise and really packs a lot of material in a valuable reference book."
–Technometrics
"An informative and well-written book . . . presented in an easy-to-understand style with many illustrative numerical examples taken from engineering and scientific studies."
–Choice(American Library Association)
Practicing engineers and scientists often have a need to utilize statistical approaches to solving problems in an experimental setting. Yet many have little formal training in statistics. Statistical Design and Analysis of Experiments gives such readers a carefully selected, practical background in the statistical techniques that are most useful to experimenters and data analysts who collect, analyze, and interpret data.
The First Edition of this now-classic book garnered praise in the field. Now its authors update and revise their text, incorporating readers’ suggestions as well as a number of new developments. Statistical Design and Analysis of Experiments, Second Edition emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results, presenting statistics as an integral component of experimentation from the planning stage to the presentation of conclusions.
Giving an overview of the conceptual foundations of modern statistical practice, the revised text features discussions of:
- The distinctions between populations or processes and samples; parameters and statistics; and mathematical and statistical modeling
- The design and analysis of experiments with factorial structures, unbalanced experiments, crossed and nested factors, and random factor effects
- Confidence-interval and hypothesis-testing procedures for single-factor and multifactor experiments
- Quantitative predictors and factors, including linear regression modeling using least-squares estimators, with diagnostic techniques for assessing model assumptions
Ideal for both students and professionals, this focused and cogent reference has proven to be an excellent classroom textbook with numerous examples. It deserves a place among the tools of every engineer and scientist working in an experimental setting.
Review
"With an excellent presentation, this is suitable as a textbook in a graduate level course in design of experiments." (
Journal of Statistical Computation and Simulation, April 2005)
"...can really provide useful information for the intended audience..." (Zentralblatt Math, Vol. 1029, 2004)
“...a practitioner’s guide to statistical methods for designing and analyzing experiments...” (Quarterly of Applied Mathematics, Vol. LXI, No. 3, September 2003)
"...a perfect desktop reference..." (Technometrics, Vol. 45, No. 3, August 2003)
Synopsis
Emphasizes the strategy of experimentation, data analysis, and the interpretation of experimental results.
- Features numerous examples using actual engineering and scientific studies.
- Presents statistics as an integral component of experimentation from the planning stage to the presentation of the conclusions.
- Deep and concentrated experimental design coverage, with equivalent but separate emphasis on the analysis of data from the various designs.
- Topics can be implemented by practitioners and do not require a high level of training in statistics.
- New edition includes new and updated material and computer output.
Synopsis
A practical guide to statistical methods useful in designing and analyzing experiments. An introductory section provides background information. Part I presents elementary descriptive statistics and graphical displays. Part II addresses experimental design. Part III discusses analysis of data from each of the designs presented in Part II. Part IV is devoted to regression modelling.
About the Author
ROBERT L. MASON, PhD, is Institute Analyst at Southwest Research Institute in San Antonio, Texas.
RICHARD F. GUNST, PhD, is a professor in the Department of Statistical Science at Southern Methodist University in Dallas, Texas.
JAMES L. HESS, PhD, is Staff Vice President, Operations, at Leggett &Platt Inc. in Carthage, Missouri.
Table of Contents
Statistics in Engineering and Science.
Basic Statistical Concepts.
DESCRIBING VARIABILITY.
Descriptive Statistics.
Graphical Displays of Data.
Graphical Comparisons of Distributions.
EXPERIMENTAL DESIGN.
Statistical Principles in Experimental Design.
Factorial Experiments in Completely Randomized Designs.
Blocking Designs.
Fractional Factorial Experiments.
Nested Designs.
Response-Surface Designs.
ANALYSIS OF DESIGNED EXPERIMENTS.
Statistical Principles in Data Analysis.
Inferences on Means.
Inferences on Standard Deviations.
Analysis of Completely Randomized Designs.
Multiple Comparisons.
Analysis of Designs with Random Factor Levels.
Analysis of Blocking Designs and Fractional Factorials.
Analysis of Covariance.
Analysis of Count Data.
FITTING DATA.
Linear Regression with One Variable.
Linear Regression with Several Variables.
Polynomial Models.
Outlier Detection.
Assessment of Model Assumptions.
Model Respecification.
Variable Selection Techniques.
Alternative Regression Estimators.
Appendix.
Index.
Index of Examples and Data Sets.