Design and Analysis of Experiments

Design and Analysis of Experiments 6Ed
Douglas C. Montgomery
Contents
Chapter 1
S-1.1 More About Planning Experiments
S-1.2 Blank Guide Sheets from Coleman and Montgomery (1993)
S-1.3 Montgomery’s Theorems on Designed Experiments
Chapter 2
S-2.1 Models for the Data and the t-Test
S-2.2 Estimating the Model Parameters
S-2.3 A Regression Model Approach to the t-Test
S-2.4 Constructing Normal Probability Plots
S-2.5 More About Checking Assumptions in the t-Test
S-2.6 Some More Information About the Paired t-Test
Chapter 3
S-3.1 The Definition of Factor Effects
S-3.2 Expected Mean SquaresS-3.3 Confidence Interval for σ2
S-3.4 Simultaneous Confidence Intervals on Treatment Means
S-3.5 Regression Models for a Quantitative Factor
S-3.6 More about Estimable Functions
S-3.7 Relationship between Regression and Analysis of Variance
Chapter 4
S4-1 Relative Efficiency of the RCBD
S4-2 Partially Balanced Incomplete Block Designs
S4-3 Youden Squares
S4-4 Lattice Designs
Chapter 5
S5-1 Expected Mean Squares in the Two-factor Factorial
S5-2 The Definition of Interaction
S5-3 Estimable Functions in the Two-factor Factorial Model
S5-4 Regression Model Formulation of the Two-factor Factorial
S5-5 Model Hierarchy
Chapter 6
S6-1 Factor Effect Estimates are Least Squares Estimates
S6-2 Yates’s Method for Calculating Factor Effects
S6-3 A Note on the Variance of a Contrast
S6-4 The Variance of the Predicted Response
S6-5 Using Residuals to Identify Dispersion Effects
S6-6 Center Points versus Replication of Factorial Points
S6-7 Testing for “Pure Quadratic” Curvature using a t-Test
Chapter 7
S7-1 The Error Term in a Blocked design
S7-2 The Prediction Equation for a Blocked Design
S7-3 Run Order is Important
Chapter 8
S8-1 Yates’ Method for the Analysis of Fractional Factorials
S8-2 Alias Structures in Fractional Factorials and Other Designs
S8-3 More About Fold Over and Partial Fold Over of Fractional Factorials
Chapter 9
S9-1 Yates’ Algorithm for the 3k Design
S9-2 Aliasing in Three-Level and Mixed-Level Designs
Chapter 10
S10-1 The Covariance Matrix of the Regression Coefficients
S10-2 Regression Models and Designed Experiments
S10-3 Adjusted R2S10-4 Stepwise and Other Variable Selection Methods in Regression
S10-5 The Variance of the Predicted Response
S10-6 The Variance of Prediction Error
S10-7 Leverage in a Regression Model
Chapter 11
S11-1 The Method of Steepest Ascent
S11-2 The Canonical Form of the Second-Order Response Surface Model
S11-3 Center Points in the Central Composite Design
S11-4 Center Runs in the Face-Centered Cube
S11-5 A Note on Rotatability
Chapter 12
S12-1 The Taguchi Approach to Robust Parameter Design
S12-2 Taguchi’s Technical Methods
Chapter 13
S13-1 Expected Mean Squares for the Random Model
S13-2 Expected Mean Squares for the Mixed Model
S13-3 Restricted versus Unrestricted Mixed Models
S13-4 Random and Mixed Models with Unequal Sample Size
S13-5 Some Background Concerning the Modified Large Sample Method
S13-6 A Confidence Interval on a Ratio of Variance Components using the Modified
Large Sample Method
Chapter 14
S14-1 The Staggered, Nested Design
S14-2 Inadvertent Split-Plots
Chapter 15
S15-1 The Form of a Transformation
S15-2 Selecting λ in the Box-Cox Method
S15-3 Generalized Linear Models
S15-3.1. Models with a Binary Response Variable
S15-3.2. Estimating the Parameters in a Logistic Regression Model
S15-3.3. Interpreting the Parameters in a Logistic Regression Model
S15-3.4. Hypothesis Tests on Model Parameters
S15-3.5. Poisson Regression
S15-3.6. The Generalized Linear Model
S15-3.7. Link Functions and Linear Predictors
S15-3.8. Parameter Estimation in the Generalized Linear Model
S15-3.9. Prediction and Estimation with the Generalized Linear Model
S15-3.10. Residual Analysis in the Generalized Linear Model
S15-4 Unbalanced Data in a Factorial Design
S15-4.1. The Regression Model Approach
S15-4.2.The Type 3 AnalysisS15-4.3 Type 1, Type 2, Type 3 and Type 4 Sums of Squares
S15-4.4 Analysis of Unbalanced Data using the Means Model
S15-5 Computer Experiments
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