Fundamental concepts in the design of experiments /

Fundamental concepts in the design of experiments /

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Bibliographic Details
Author / Creator:Hicks, Charles Robert, 1920-
Edition:5th ed.
Imprint:New York : Oxford University Press, 1999.
Description:x, 565 p. : ill. ; 25 cm.
Language:English
Subject:
Experimental design.
Experimental design.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/3964483
Hidden Bibliographic Details
Other authors / contributors:Turner, Kenneth V.
ISBN:0195122739 (cloth : acid-free paper)
Notes:Includes bibliographical references (p. 505-506) and index.
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 ReadingProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 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. SummaryFurther ReadingProblems
  • 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. SummaryProblems
  • 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 ReadingProblems
  • 10. 3f Factorial Experiments
  • 10.1. Introduction
  • 10.2. 3 Squared Factorial
  • 10.3. 3 Cubed Factorial
  • 10.4. Computer Programs
  • 10.5. SummaryProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 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 ReadingProblems
  • 15. Regression
  • 15.1. Introduction
  • 15.2. Linear Regression
  • 15.3. Curvilinear Regression
  • 15.4. Orthogonal Polynomials
  • 15.5. Multiple Regression
  • 15.6. Summa
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