Design and Analysis of Experiments"The eighth edition of Design and Analysis of Experiments continues to provide extensive and in-depth information on engineering, business, and statistics-as well as informative ways to help readers design and analyze experiments for improving the quality, efficiency and performance of working systems. Furthermore, the text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book"-- |
Contents
Preface iii | 1 |
k | 8 |
Simple Comparative Experiments | 23 |
Center points in the 2k design 285 513 | 31 |
Chisquare test on the variance of a normal | 53 |
Completely randomized design | 68 |
Confidence interval 40 41 | 77 |
Plot of Residuals in Time Sequence | 81 |
Additional Design and Analysis Topics for Factorial | 405 |
Confounding 311 320 | 413 |
Fitting Regression Models | 460 |
11 | 486 |
Constant variance assumption in ANOVA 81 | 508 |
Construction of optimal designs | 524 |
308 | 593 |
Other Design and Analysis Topics | 656 |
Contrasts | 89 |
4 | 114 |
Randomized Blocks Latin Squares and Related Designs | 135 |
Estimating the Model Parameters | 194 |
1 | 230 |
1 | 308 |
TwoLevel Fractional Factorial Designs | 328 |
Confirmation runs 18 | 343 |
Appendix online at www wiley comcollegemontgomery | 697 |
657 | 700 |
Percentage Points of the Studentized Range Statistic | 707 |
| 724 | |
| 732 | |
| 733 | |
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Common terms and phrases
16 runs 23 design ABCD alias aliased analysis of variance Analyze the residuals ANOVA average Batch blocks center points central composite design Chapter column confidence interval confounded Consider construct contour plot D-optimal design defining relation degrees of freedom designed experiments error etch rate Example experimental design factor levels factorial experiment first-order model fractional factorial design Latin square linear main effects matrix Mean Square method Minitab noise variables normal distribution normal probability plot null hypothesis observations obtained optimal orthogonal output P-value percent confidence interval prediction variance Problem procedure quadratic random variables regression coefficients regression model replicates response surface response variable sample second-order model shown in Table standard deviation sum of squares Suppose t-test temperature test statistic treatment combinations treatment means two-factor interactions type I error variance component x₁ y₁ β₁ μ₁ σ² ΣΣ τ₁