Fundamental Concepts in the Design of Experiments / Edition 5

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Overview


Fundamental Concepts in the Design of Experiments, 5/e offers comprehensive coverage of the key elements of experimental design used by applied researchers to solve problems in the field. Wide-ranging and accessible, it shows students how to use applied statistics for planning, running, and analyzing experiments. Featuring over 350 problems taken from the authors' actual industrial consulting experiences, the text gives students valuable practice with real data and problem solving. The problems emphasize the basic philosophy of design and are simple enough for students with limited mathematical backgrounds to understand. The authors provide extensive coverage of the analysis of residuals, the concept of resolution in fractional replications, Plackett-Burman designs, and Taguchi techniques. SAS (Statistical Analysis System) computer programs are incorporated to facilitate analysis.
Thoroughly revised and updated, this new edition includes sixty new problems, focuses more on computer use (adding computer outputs from statistical packages like Minitab, SPSS, and JMP), and emphasizes graphical procedures including residual plots and normal quantile plots. Ideal for various advanced undergraduate and graduate experimental methods courses taught in statistics, engineering, and mathematics departments, this book will also appeal to professionals and researchers doing experimental work.
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Product Details

  • ISBN-13: 9780195122732
  • Publisher: Oxford University Press, USA
  • Publication date: 3/25/1999
  • Edition description: REV
  • Edition number: 5
  • Pages: 576
  • Sales rank: 1,131,804
  • Product dimensions: 9.30 (w) x 7.70 (h) x 1.30 (d)

Table of Contents

Preface
1. The Experiment, the Design, and the Analysis
1.1. Introduction to Experimental Design
1.2. The Experiment
1.3. The Design
1.4. The Analysis
1.5. Examples
1.6. Summary in Outline
1.7. Further Reading
Problems
2. Review of Statistical Inference
2.1. Introduction
2.2. Estimation
2.3. Tests of Hypothesis
2.4. The Operating Characteristic Curve
2.5. How Large a Sample?
2.6. Application to Tests on Variances
2.7. Application to Tests on Means
2.8. Assessing Normality
2.9. Applications to Tests on Proportions
2.10. Analysis of Experiments with SAS
2.11. Further Reading
Problems
3. Single-Factor Experiments with No Restrictions on Randomization
3.1. Introduction
3.2. Analysis of Variance Rationale
3.3. After ANOVA--What?
3.4. Tests on Means
3.5. Confidence Limits on Means
3.6. Components of Variance
3.7. Checking the Model
3.8. SAS Programs for ANOVA and Tests after ANOVA
3.9. Summary
3.10. Further Reading
Problems
4. Single-Factor Experiments: Randomized Block and Latin Square Designs
4.1. Introduction
4.2. Randomized Complete Block Design
4.3. ANOVA Rationale
4.4. Missing Values
4.5. Latin Squares
4.6. Interpretations
4.7. Assessing the Model
4.8. Graeco-Latin Squares
4.9. Extensions
4.10. SAS Programs for Randomized Blocks and Latin Squares
4.11. Summary
4.12. Further Reading
Problems
5. Factorial Experiments
5.1. Introduction
5.2. Factorial Experiments: An Example
5.3. Interpretations
5.4. The Model and Its Assessment
5.5. ANOVA Rationale
5.6. One Observation Per Treatment
5.7. SAS Programs for Factorial Experiments
5.8. Summary
5.9. Further Reading
Problems
6. Fixed, Random, and Mixed Models
6.1. Introduction
6.2. Single-Factor Models
6.3. Two-Factor Models
6.4. EMS Rules
6.5. EMS Derivations
6.6. The Pseudo-F Test
6.7. Expected Mean Squares Via Statistical Computing Packages
6.8. Remarks
6.9. Repeatability and Reproducibility for a Measurement System
6.10. SAS Problems for Random and Mixed Models
6.11. Further Reading
Problems
7. Nested and Nested-Factorial Experiments
7.1. Introduction
7.2. Nested Experiments
7.3. ANOVA Rationale
7.4. Nested-Factorial Experiments
7.5. Repeated-Measures Design and Nested-Factorial Experiments
7.6. SAS Programs for Nested and Nested-Factorial Experiments
7.7. Summary
Further Reading
Problems
8. Experiments of Two or More Factors: Restrictions on Randomization
8.1. Introduction
8.2. Factorial Experiment in a Randomized Block Design
8.3. Factorial Experiment in a Latin Square Design
8.4. Remarks
8.5. SAS Programs
8.6. Summary
Problems
9. 2f Factorial Experiments.
9.1. Introduction
9.2. 2 Squared Factorial
9.3. 2 Cubed Factorial
9.4. 2f Remarks
9.5. The Yates Method
9.6. Analysis of 2f Factorials When n=1
9.7 Some Commments about Computer Use.
9.8. Summary
9.9. Further Reading
Problems
10. 3f Factorial Experiments
10.1. Introduction
10.2. 3 Squared Factorial
10.3. 3 Cubed Factorial
10.4. Computer Programs
10.5. Summary
Problems
11. Factorial Experiment: Split-Plot Design
11.1. Introduction
11.2. A Split-Plot Design
11.3. A Split-Split-Plot Design
11.4. Using SAS to Analyze a Split-Plot Experiment
11.5. Summary
11.6. Further Reading
Problems
12. Factorial Experiment: Confounding in Blocks
12.1. Introduction
12.2. Confounding Systems
12.3. Block Confounding, No Replication
12.4. Block Confounding with Replication
12.5. Confounding in 3F Factorials
12.6. SAS Progrms
12.7. Summary
12.8. Further Reading
Problems
13. Fractional Replication
13.1. Introduction
13.2. Aliases
13.3. 2f Fractional Replications
13.4. Plackett-Burman Designs
13.5. Design Resolution
13.6. 3f-k Fractional Factorials
13.7. SAS Programs
13.8. Summary
13.9. Further Reading
Problems
14. The Taguchi Approach to the Design of Experiments
14.1. Introduction
14.2. The L4 (2 Cubed) Orthogonal Array
14.3. Outer Arrays
14.4. Signal-To-Noise Ratio
14.5. The L8 (2 7) Orthogonal Array
14.6. The L16 (2 15) Orthogonal Array
14.7. The L9 (3 4) Orthogonal Array
14.8. Some Other Taguchi Designs
14.9. Summary
14.10. Further Reading
Problems
15. Regression
15.1. Introduction
15.2. Linear Regression
15.3. Curvilinear Regression
15.4. Orthogonal Polynomials
15.5. Multiple Regression
15.6. Summary
15.7. Further Reading
Problems
16. Miscellaneous Topics
16.1. Introduction
16.2. Covariance Analysis
16.3. Response Surface Experimentation
16.4. Evolutionary Operation (EVOP)
16.5. Analysis of Attribute Data
16.6. Randomized Incomplete Blocks: Restriction On Experimentation
16.7. Youden Squares
16.8. Further Reading
Problems
Summary and Special Problems
Glossary of Terms
References
Statistical Tables
Table A. Areas Under the Normal Curve
Table B. Student's t Distribution
Table C. Cumulative Chi-Square Distribution
Table D. Cumulative F Distribution
Table E.1. Upper 5% of Studentized Range q
Table E.2. Upper 1% of Studentized Range q
Table F. Coefficients of Orthogonal Polynomials
Answers to Selected Problems
Index

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