Enhanced descriptions from Syndetics:

This brief, yet comprehensive text 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 analyzing 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. The use of the computer is promoted and SAS (Statistical Analysis System) computer programs are incorporated to facilitate analysis. Coverage of the analysis of residuals, the concepts of resolution in fractional replications, the Plackett-Burman designs, and Taguchi techniques is new to this edition.

Table of contents provided by Syndetics

• 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

Author notes provided by Syndetics