8/7/2019 Desingn Of Experiments

1/4

About the Supplemental Text Material

I have prepared supplemental text material for each chapter of the 6 th edition ofDesign

and Analysis of Experiments. This material consists of (1) some extensions of andelaboration on topics introduced in the text and (2) some new topics that I could not

easily find a home for in the text without disrupting the flow of the coverage withineach chapter, or making the book ridiculously long.

Some of this material is in partial response to the many suggestions that have been madeover the years by textbook users, who have always been gracious in their requests and

very often extremely helpful. However, sometimes there just wasnt any way to easily

accommodate their suggestions directly in the book. Some of the supplemental materialis in direct response to FAQs or frequently asked questions from students. It also

reflects topics that I have found helpful in consulting on experimental design and analysis

problems, but again, there wasnt any easy way to incorporate it in the text. Obviously,

there is also quite a bit of personal bias in my selection of topics for the supplementalmaterial. The coverage is far from comprehensive.

I have not felt as constrained about mathematical level or statistical background of the

readers in the supplemental material as I have tried to be in writing the textbook. There

are sections of the supplemental material that will require considerably more backgroundin statistics than is required to read the text material. However, I think that many

instructors will be able to use this supplement material in their courses quite effectively,

depending on the maturity and background of the students. Hopefully, it will alsoprovide useful additional information for readers who wish to see more in-depth

discussion of some aspects of design, or who are attracted to the eclectic variety of

topics that I have included.

Contents

Chapter 1

S-1.1 More About Planning ExperimentsS-1.2 Blank Guide Sheets from Coleman and Montgomery (1993)

S-1.3 Montgomerys 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-TestS-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 EffectsS-3.2 Expected Mean Squares

8/7/2019 Desingn Of Experiments

2/4

S-3.3 Confidence Interval for 2

S-3.4 Simultaneous Confidence Intervals on Treatment Means

S-3.5 Regression Models for a Quantitative FactorS-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 DesignsS4-3 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 FactorialS5-5 Model Hierarchy

Chapter 6

S6-1 Factor Effect Estimates are Least Squares Estimates

S6-2 Yatess Method for Calculating Factor Effects

S6-3 A Note on the Variance of a ContrastS6-4 The Variance of the Predicted Response

S6-5 Using Residuals to Identify Dispersion Effects

S6-6 Center Points versus Replication of Factorial PointsS6-7 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 DesignS7-3 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 3kDesign

S9-2 Aliasing in Three-Level and Mixed-Level Designs

Chapter 10

S10-1 The Covariance Matrix of the Regression CoefficientsS10-2 Regression Models and Designed Experiments

S10-3 AdjustedR2

8/7/2019 Desingn Of Experiments

3/4

S10-4 Stepwise and Other Variable Selection Methods in RegressionS10-5 The Variance of the Predicted Response

S10-6 The Variance of Prediction Error

S10-7 Leverage in a Regression Model

Chapter 11S11-1 The Method of Steepest AscentS11-2 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 DesignS12-2 Taguchis Technical Methods

Chapter 13S13-1 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 SizeS13-5 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 in the Box-Cox Method

S15-3 Generalized Linear Models

S15-3.1. Models with a Binary Response VariableS15-3.2. 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 RegressionS15-3.6. The Generalized Linear Model

S15-3.7. Link Functions and Linear Predictors

S15-3.8. Parameter Estimation in the Generalized Linear ModelS15-3.9. 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 DesignS15-4.1. The Regression Model Approach

S15-4.2.The Type 3 Analysis

8/7/2019 Desingn Of Experiments

4/4

S15-4.3 Type 1, Type 2, Type 3 and Type 4 Sums of SquaresS15-4.4 Analysis of Unbalanced Data using the Means Model

S15-5 Computer Experiments