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Experimentation is one of the most common activities in which all people engage. In this thoroughly updated Second Edition, Experiments presents the most modern, up-to-date treatment in the design and analysis of experiment topics currently available. The authors#151highly recognized researchers in the field#151introduce some of the newest discoveries and shed further light on existing ones. Drawing from their impressive roster of industrial clients, the authors modernize accepted methodologies while refining many cutting-edge topics in a single, easily accessible source suitable for upper-undergraduate or beginning-graduate students, practicing engineers, and statisticians.
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Table of Content
1. Basic Concepts for Experimental Design and Introductory Regression Analysis.1.1 Introduction and Historical Perspective.1.2 A Systematic Approach to the Planning and Implementation of Experiments.1.3 Fundamental Principles: Replication, Randomization, and Blocking.1.4 Simple Linear Regression.1.5 Testing of Hypothesis and Interval Estimation.1.6 Multiple Linear Regression.1.7 Variable Selection in Regression Analysis.1.8 Analysis of Air Pollution Data.1.9 Practical Summary.2. Experiments with a Single Factor.2.1 One-Way Layout.2.2 Multiple Comparisons.2.3 Quantitative Factors and Orthogonal Polynomials.2.4 Expected Mean Squares and Sample Size Determination.2.5 One-Way Random Effects Model.2.6 Residual Analysis: Assessment of Model Assumptions.2.7 Practical Summary.3. Experiments with More Than One Factor.3.1 Paired Comparison Designs.3.2 Randomized Block Designs.3.3 Two-Way Layout: Factors With Fixed Levels.3.4 Two-Way Layout: Factors With Random Levels.3.5 Multi-Way Layout.3.6 Latin Square Designs: Two Blocking Variables.3.7 Graeco-Latin Square Designs.3.8 Balanced Incomplete Block Designs.3.9 Split-Plot Designs.3.10 Analysis of Covariance: Incorporating Auxiliary Information.3.11 Transformation of the Response.3.12 Practical Summary.4. Full Factorial Experiments at Two Levels.4.1 An Epitaxial Layer Growth Experiment.4.2 Full Factorial Designs at Two Levels: A General Discussion.4.3 Factorial Effects and Plots.4.4 Using Regression to Compute Factorial Effects.4.5 ANOVA Treatment of Factorial Effects.4.6 Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity.4.7 Comparisons with the \One-Factor-At-A-Time" Approach.4.8 Normal and Half-Normal Plots for Judging Effect Significance.4.9 Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates.4.10 Nominal-the-Best Problem and Quadratic Loss Function.4.11 Use of Log Sample Variance for Dispersion Analysis.4.12 Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment.4.13 Test of Variance Homogeneity and Pooled Estimate of Variance.4.14 Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates.4.15 Blocking and Optimal Arrangement of 2k Factorial Designs in 2q Blocks.4.16 Practical Summary.5. Fractional Factorial Experiments at Two Levels.5.1 A Leaf Spring Experiment.5.2 Fractional Factorial Designs: Effect Aliasing and the Criteria Of Resolution and Minimum Aberration.5.3 Analysis of Fractional Factorial Experiments.5.4 Techniques for Resolving the Ambiguities in Aliased Effects.5.5 Selection of 2kp Designs Using Minimum Aberration and Related Criteria.5.6 Blocking in Fractional Factorial Designs.5.7 Practical Summary.6. Full Factorial and Fractional Factorial Experiments at Three.Levels.6.1 A Seat-Belt Experiment.6.2 Larger-the-Better and Smaller-the-Better Problems.6.3 3k Full Factorial Designs.6.4 3kp Fractional Factorial Designs.6.5 Simple Analysis Methods: Plots and Analysis of Variance.6.6 An Alternative Analysis Method.6.7 Analysis Strategies for Multiple Responses I: Out-Of-Spec Probabilities.6.8 Blocking in 3k and 3kp Designs.6.9 Practical Summary.7. Other Design and Analysis Techniques for Experiments at More Than Two Levels.7.1 A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design.7.2 Method of Replacement and Construction of 2m4n Designs.7.3 Minimum Aberration 2m4n Designs
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Wiley Series in Probability and Statistics
C. F. Jeff Wu, Michaels. Hamada
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On the whole, I think the book is ideal for a year-longcourse at the graduate level (there is much more material in thebook than can be reasonably covered even in a year-long course),but is still advanced for undergraduates. ( Zentralblatt MATH , 2012), ?On the whole, I think the book is ideal for a year-long course at the graduate level (there is much more material in the book than can be reasonably covered even in a year-long course), but is still advanced for undergraduates.' ( Zentralblatt MATH , 2012)