Experiments Planning, Analysis, and

Wiley Series in Probabilty and StatisticsSecond EditionExperimentsPlanning, Analysis, and OptimizationC. F. Jeff Wu and Michael S. HamadaPraise for the First Edition:"If you . . . want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library." -Journal of the American Statistical AssociationFully updated to reflect the major progress in the use of statistically designed experiments for product and process improvement, Experiments, Second Edition introduces some of the newest discoveries-and sheds further light on existing ones-on the design and analysis of experiments and their applications in system optimization, robustness, and treatment comparison. Maintaining the same easy-to-follow style as the previous edition while also including modern updates, this book continues to present a new and integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences.The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, reliability improvement, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. Along with a new chapter that focuses on regression analysis, the Second Edition features expanded and new coverage of additional topics, including:Expected mean squares and sample size determinationOne-way and two-way ANOVA with random effectsSplit-plot designsANOVA treatment of factorial effectsResponse surface modeling for related factorsDrawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study.Experiments, Second Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.

C. F. Jeff Wu, PhD, is Coca-Cola Professor in Engineering Statistics at the Georgia Institute of Technology. Dr. Wu has published more than 130 papers and is the recipient of numerous accolades, including the including the National Academy of Engineering membership and the COPSS Presidents' Award.

Michael S. Hamada, PhD, is Statistical Scientist at Los Alamos National Laboratory in New Mexico. Dr. Hamada is a Fellow of the American Statistical Association and the author of more than seventy published papers.

Basic Concepts for Experimental Design and Introductory Regression Analysis
Introduction and Historical Perspective
A Systematic Approach to the Planning and Implementation of Experiments
Fundamental Principles: Replication, Randomization, and Blocking
Simple Linear Regression
Testing of Hypothesis and Interval Estimation
Multiple Linear Regression
Variable Selection in Regression Analysis
Analysis of Air Pollution Data
Practical Summary
Experiments with a Single Factor
One-Way Layout
Multiple Comparisons
Quantitative Factors and Orthogonal Polynomials
Expected Mean Squares and Sample Size Determination
One-Way Random Effects Model
Residual Analysis: Assessment of Model Assumptions
Practical Summary
Experiments with More Than One Factor
Paired Comparison Designs
Randomized Block Designs
Two-Way Layout: Factors With Fixed Levels
Two-Way Layout: Factors With Random Levels
Multi-Way Layout
Latin Square Designs: Two Blocking Variables
Graeco-Latin Square Designs
Balanced Incomplete Block Designs
Split-Plot Designs
Analysis of Covariance: Incorporating Auxiliary Information
Transformation of the Response
Practical Summary
Full Factorial Experiments at Two Levels
An Epitaxial Layer Growth Experiment
Full Factorial Designs at Two Levels: A General Discussion
Factorial Effects and Plots
Using Regression to Compute Factorial Effects
ANOVA Treatment of Factorial Effects
Fundamental Principles for Factorial Effects: Effect Hierarchy, Effect Sparsity, and Effect Heredity
Comparisons with the One-Factor-At-A-Time" Approach
Normal and Half-Normal Plots for Judging Effect Significance
Lenth's Method: Testing Effect Significance for Experiments Without Variance Estimates
Nominal-the-Best Problem and Quadratic Loss Function
Use of Log Sample Variance for Dispersion Analysis
Analysis of Location and Dispersion: Revisiting the Epitaxial Layer Growth Experiment
Test of Variance Homogeneity and Pooled Estimate of Variance
Studentized Maximum Modulus Test: Testing Effect Significance for Experiments With Variance Estimates
Blocking and Optimal Arrangement of 2k Factorial Designs in 2q Blocks
Practical Summary
Fractional Factorial Experiments at Two Levels
A Leaf Spring Experiment
Fractional Factorial Designs: Effect Aliasing and the Criteria Of Resolution and Minimum Aberration
Analysis of Fractional Factorial Experiments
Techniques for Resolving the Ambiguities in Aliased Effects
Selection of 2kp Designs Using Minimum Aberration and Related Criteria
Blocking in Fractional Factorial Designs
Practical Summary
Full Factorial and Fractional Factorial Experiments at Three.Levels
A Seat-Belt Experiment
Larger-the-Better and Smaller-the-Better Problems
3k Full Factorial Designs
3kp Fractional Factorial Designs
Simple Analysis Methods: Plots and Analysis of Variance
An Alternative Analysis Method
Analysis Strategies for Multiple Responses I: Out-Of-Spec Probabilities
Blocking in 3k and 3kp Designs
Practical Summary
Other Design and Analysis Techniques for Experiments at More Than Two Levels
A Router Bit Experiment Based on a Mixed Two-Level and Four-Level Design
Method of Replacement and Construction of 2m4n Designs
Minimum Aberration 2m4n Designs
Table of Contents provided by Publisher. All Rights Reserved.

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Experiments Planning, Analysis, and Optimization

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