Fundamental Concepts in the Design of Experiments
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Product details:
- Edition number 5
- Publisher OUP USA
- Date of Publication 1 July 1999
- ISBN 9780195122732
- Binding Hardback
- No. of pages576 pages
- Size 188x239x30 mm
- Weight 1148 g
- Language English
- Illustrations numerous line figures 0
Categories
Long description:
This text is a solid revision and redesign of Charles Hicks's comprehensive fourth edition of Fundamental Concepts in the Design of Experiments. It covers the essentials of experimental design used by applied researchers in solving problems in the field. It is appropriate for a variety of experimental methods courses found in engineering and statistics departments. Students learn to use applied statistics for planning, running, and analysing an experiment. The text includes 350+ problems taken from the author's actual industrial consulting experiences to give students valuable practice with real data and problem solving. About 60 new problems have been added for this edition. SAS (Statistical Analysis System) computer programs are incorporated to facilitate analysis. There is extensive coverage of the analysis of residuals, the concepts of resolution in fractional replications, the Plackett-Burman designs, and Taguchi techniques. The new edition will place a greater emphasis on computer use, include additional problems, and add computer outputs from statistical packages like Minitab, SPSS, and JMP.
The book is written for anyone engaged in experimental work who has a good background in statistical inference. It will be most profitable reading to those with a background in statistical methods including analysis of variance. This text is suitable for senior undergraduate/graduate level students in mathematics, statistics, or engineering. It is appropriate for a variety of experimental methods courses found in engineering and statistics deparmtents -- majors in this course are usually in applied statistics; non-majors, in industrial and electrical engineering, or education and life sciences.
Table of Contents:
The Experiment, the Design, and the Analysis
Introduction
The Experiment
The Design
The Analysis
Examples
Summary in Outline
Further Reading
Problems
Review of Statistical Inference
Introduction
Estimation
Tests of hypothesis
The Operating Characterisitc Curve
How Large a Sample?
Application to Tests on Variances
Application to Tests on Means
Assessing Normality
Applications to Tests on Proportions
Analysis of Experiments with SAS
Further Reading
Problems
Single-Factor Experiments with No Restrictions on Randomization
Introduction
Analysis of Variance Rationale
After ANOVA-What?
Tests of Means
Confidence Limits on Means
Components of Variance
Checking the Model
SAS Programs for ANOVA and Tests after ANOVA
Summary
Further Reading
Problems
Single-Factor Experiments -- Randomized Block and Latin Square Designs
Introduction
Randomized Complete Block Design
ANOVA Rationale
Missing Values
Latin Squares
Interpretations
Assessing the Model
Graeco-Latin Squares
Extensions
SAS Programs for Randomized Blocks and Latin Squares
Summary
Further Reading
Problems
Factorial Experiments
Introduction
Factorial Experiments: An Example
Interpretations
The Model and Its Assessment
ANOVA Rationale
One Observation Per Treatment
SAS Programs for Factorial Experiments
Summary
Further Reading
Summary
Fixed, Random, and Mixed Models
Introduction
Single-Factor Models
Two-Factor Models
EMS Rule
EMS Derivations
The Pseudo-F Test
Expected Mean Squares Via Statistical Computing Packages
Remarks
Repeatability and Reproducibility for a Measurement System
Further Reading
Problems
Nested and Nested-Factorial Experiments
Introduction
Nested Experiments
ANOVA Rationale
Nested-Factorial Experiments
Repeated-Measures Design and Nested-Factorial Experiments
SAS Programs for Nested and Nested-Factorial Experiments
Summary
Further Reading
Problems
Experiments of Two or More Factors -- Restrictions and Randomization
Introductin
Factorial Experiment in a Randomized Block Design
Factorial Experiment in a Latin Square Design
Remarks
SAS Programs
Summary
Further Reading
Problems
2 Squared Factorial
2 Cubed Factorial
2f Factorial
The Yates Method
Analysis of 2f Factorials When n=1
Summary
Further Reading
Problems
3f Factorial Experiments
Introduction
3 Squared Factorial
3 Cubed Factorial
Computer Programs
Summary
Further Reading
Problems
Factorial Experiment -- Split-Plot Design
Introduction
A Split-Plot Design
A Split-Split-Plot Design
Using SAS to Analyze a Split-Plot Experiment
Summary
Further Reading
Problems
Factorial Experiment -- Confounding in Blocks
Introduction
Confounding Systems
Block Confounding -- No Replication
Blcok Confounding with Replication
Confounding in 3F Factorials
SAS Progrms
Summary
Further Reading
Problems
Fractional Replication
Introduction
Aliases
2f Fractional Replication
Plackett-Burman Designs
Taguchi Approach to the Design of Experiments
Introduction
The L4 (2 Cubed) Orthogonal Array
Outer Arrays
Signal-To-Noise-Ratio
The L8 (2 7) Orthogonal Array
The L16 (2 15) Orthogonal Array
The L9 (3 4) Orthogonal Array
Some Other Taguchi Designs
Summary
Futher Reading
Problems
Regression
Introduction
Linear Regression
Curvilinear Regression
Orthogronal Polynomials
Multiple Regression
Summary
Further Reading
Summary
Miscellaneous Topics
Introduction
Covariance Analysis
Response-Surface Experimentation
Evolutionary Operation (EVOP)
Analysis of Attribute Data
Randomized Incomplete Blocks -- Restriction On Experimentation
Youden Squares
Further Reading
Problems
Summary and Special Problems
Glossary of Terms
References
Statistical Tables
Areas Under the Normal Curve
Student's t Distribution
Cumulative Chi-Square Distribution
Cumulative F Distribution
Upper 5 Percent of Studentized Range q
Upper 1 Percent of Studentized Range q
Coefficients of Orthogonal Polynomials
Answers to Selected Problems
Index
