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Design of Experiments: An Introduction Based on Linear Models

Publisher: 
Chapman & Hall/CRC
Number of Pages: 
355
Price: 
89.95
ISBN: 
9781584889236
Date Received: 
Tuesday, September 28, 2010
Reviewable: 
No
Include In BLL Rating: 
No
Reviewer Email Address: 
Max D.Morris
Series: 
Texts in Statistical Science
Publication Date: 
2010
Format: 
Hardcover
Category: 
Textbook

Introduction
Example: rainfall and grassland
Basic elements of an experiment
Experiments and experiment-like studies
Models and data analysis

Linear Statistical Models
Linear vector spaces
Basic linear model
The hat matrix, least-squares estimates, and design information matrix
The partitioned linear model
The reduced normal equations
Linear and quadratic forms
Estimation and information
Hypothesis testing and information
Blocking and information

Completely Randomized Designs
Introduction
Models
Matrix formulation
Influence of design on estimation
Influence of design on hypothesis testing

Randomized Complete Blocks and Related Designs
Introduction
A model
Matrix formulation
Influence of design on estimation
Influence of design on hypothesis testing
Orthogonality and "Condition E"

Latin Squares and Related Designs
Introduction
Replicated Latin squares
A model
Matrix formulation
Influence of design on quality of inference
More general constructions: Graeco-Latin squares

Some Data Analysis for CRDs and Orthogonally Blocked Designs
Introduction
Diagnostics
Power transformations
Basic inference
Multiple comparisons

Balanced Incomplete Block Designs
Introduction
A model
Matrix formulation
Influence of design on quality of inference
More general constructions

Random Block Effects
Introduction
Inter- and intra-block analysis
CBDs and augmented CBDs
BIBDs
Combined estimator
Why can information be "recovered"?
CBD reprise

Factorial Treatment Structure
Introduction
An overparameterized model
An equivalent full-rank model
Estimation
Partitioning of variability and hypothesis testing
Factorial experiments as CRDs, CBDs, LSDs, and BIBDs
Model reduction

Split-Plot Designs
Introduction
SPD(R,B)
SPD(B,B)
More than two experimental factors
More than two strata of experimental units

Two-Level Factorial Experiments: Basics
Introduction
Example: bacteria and nuclease
Two-level factorial structure
Estimation of treatment contrasts
Testing factorial effects
Additional guidelines for model editing

Two-Level Factorial Experiments: Blocking
Introduction
Complete blocks
Balanced incomplete block designs
Regular blocks of size 2f−1
Regular blocks of size 2f−2
Regular blocks: general case

Two-Level Factorial Experiments: Fractional Factorials
Introduction
Regular fractional factorial designs
Analysis
Example: bacteria and bacteriocin
Comparison of fractions
Blocking regular fractional factorial designs
Augmenting regular fractional factorial designs
Irregular fractional factorial designs

Factorial Group Screening Experiments
Introduction
Example: semiconductors and simulation
Factorial structure of group screening designs
Group screening design considerations
Case study

Regression Experiments: First-Order Polynomial Models
Introduction
Polynomial models
Designs for first-order models
Blocking experiments for first-order models
Split-plot regression experiments
Diagnostics

Regression Experiments: Second-Order Polynomial Models
Introduction
Quadratic polynomial models
Designs for second-order models
Design scaling and information
Orthogonal blocking
Split-plot designs
Bias due to omitted model terms

Introduction to Optimal Design
Introduction
Optimal design fundamentals
Optimality criteria
Algorithms

Appendices

References

Index

Publish Book: 
Modify Date: 
Wednesday, January 12, 2011

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