Design and analysis of experiments
Extensively 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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Simple Comparative Experiments
Experiments with a Single Factor The Analysis
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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 P-value 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 three-factor treatment combinations treatment means Tt Tt Tt two-factor interactions usually variance components yield