Design and analysis of experimentsExtensively revised, this edition of the outstanding textbook features increased emphasis on the connection between the experiment and the model that the experimenter can develop from the results of the experiment. Material on factorial and fractional factorial designs has been expanded. Contains a new chapter on experiments with random factors which includes new material on variance component estimation. Problem sets vary in scope from computational exercises (designed to reinforce the fundamentals) to extensions or elaboration of basic principles. 
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Review: Design and Analysis of Experiments
User Review  Kalidhass Balasubramanian  GoodreadsThis is the Best book available on the subject.The book covers less theory and more pratcial examples.The book contains DoE ouput files from Design expert and Minitab, which helps the readers to ... Read full review
Review: Design and Analysis of Experiments
User Review  Andrea Ratti  GoodreadsI think that this text is one of the best to approach design of experiment topic. Every chapter is described quantitatively, with a lot of real examples. The weak part of the text, is represented by regression chapter, described in a too BOS way, with few quantitative demostrations. Read full review
Contents
Simple Comparative Experiments  20 
Randomized Designs  33 
Experiments with a Single Factor The Analysis  63 
Copyright  
15 other sections not shown
Common terms and phrases
23 design ABCD aliased analysis of variance Analyze the data average block design central composite design column computed confidence interval confounded Consider contour plot covariance defining relation degrees of freedom effect estimates engineer error expected mean squares experimental design factor levels factorial experiment fN fN fN four fractional factorial design I++I Latin square least squares linear main effects method normal distribution normal equations normal probability plot null hypothesis observations obtained orthogonal Pvalue parameter percent confidence interval Problem procedure quadratic random variables regression coefficients regression model replicates residuals versus response surface response variable rO rO rO runs sample shown in Figure shown in Table significant SSAB standard deviation sum of squares Suppose temperature tensile strength test statistic threefactor treatment combinations treatment means Tt Tt Tt twofactor interactions usually variance components yield