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Preface. <p>PART I: FUNDAMENTAL STATISTICAL CONCEPTS.</p> <p>Statistics in Engineering and Science.</p> <p>Fundamentals of Statistical Inference.</p> <p>Inferences on Means and Standard Deviations.</p> <p>PART II: DESIGN AND ANALYSIS WITH FACTORIAL STRUCTURE.</p> <p>Statistical Principles in Experimental Design.</p> <p>Factorial Experiments in Completely Randomized Designs.</p> <p>Analysis of Completely Randomized Designs.</p> <p>Fractional Factorial Experiments.</p> <p>Analysis of Fractional Factorial Experiments.</p> <p>PART III: DESIGN AND ANALYSIS WITH RANDOM EFFECTS.</p> <p>Experiments in Randomized Block Designs.</p> <p>Analysis of Designs with Random Factor Levels.</p> <p>Nested Designs.</p> <p>Special Designs for Process Improvement.</p> <p>Analysis of Nested Designs and Designs for Process Improvement.</p> <p>PART IV: DESIGN AND ANALYSIS WITH QUANTITATIVE PREDICTORS AND FACTORS.</p> <p>Linear Regression with One Predicator Variables.</p> <p>Linear Regression with Several Predicator Variables.</p> <p>Linear Regression with Factors and Covariates as Predictors.</p> <p>Designs and Analyses for Fitting Re sponse Surfaces.</p> <p>Model Assessment.</p> <p>Variable Selection Techniques.</p> <p>Appendix: Statistical Tables.</p> <p>Index.</p>

"With an excellent presentation, this is suitable as a textbook in a graduate level course in design of experiments." (<i>Journal of Statistical Computation and Simulation</i>, April 2005) <p>"...can really provide useful information for the intended audience..." (<i>Zentralblatt Math</i>, Vol. 1029, 2004)</p> <p>“...a practitioner’s guide to statistical methods for designing and analyzing experiments...” (<i>Quarterly of Applied Mathematics</i>, Vol. LXI, No. 3, September 2003)</p> <p>"...a perfect desktop reference..." (<i>Technometrics</i>, Vol. 45, No. 3, August 2003)</p>

ROBERT L. MASON, PhD, is Institute Analyst at Southwest Research Institute in San Antonio, Texas. <p>RICHARD F. GUNST, PhD, is a professor in the Department of Statistical Science at Southern Methodist University in Dallas, Texas.</p> <p>JAMES L. HESS, PhD, is Staff Vice President, Operations, at Leggett & Platt Inc. in Carthage, Missouri.</p>

<b>Praise for the First Edition</b> <br /> <b><i>Statistical Design and Analysis of Experiments</i></b> <p>"A very useful book for self study and reference."<br /> –<i>Journal of Quality Technology</i></p> <p>"Very well written. It is concise and really packs a lot of material in a valuable reference book."<br /> –<i>Technometrics</i></p> <p>"An informative and well-written book . . . presented in an easy-to-understand style with many illustrative numerical examples taken from engineering and scientific studies."<br /> –<i>Choice</i> (American Library Association)</p> <p>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.</p> <p>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.</p> <p>Giving an overview of the conceptual foundations of modern statistical practice, the revised text features discussions of:</p> <ul> <li>The distinctions between populations or processes and samples; parameters and statistics; and mathematical and statistical modeling</li> <li>The design and analysis of experiments with factorial structures, unbalanced experiments, crossed and nested factors, and random factor effects</li> <li>Confidence-interval and hypothesis-testing procedures for single-factor and multifactor experiments</li> <li>Quantitative predictors and factors, including linear regression modeling using least-squares estimators, with diagnostic techniques for assessing model assumptions</li> </ul> <p>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.</p>

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