Douglas C. Montgomery. Design and Analysis of Experiment. 5-edition

Chapter 1.
Introduction.
Strategy of Experimentation.
Some Typical Applications of Experimental Design.
Basic Principles.
Guidelines for Designing Experiments.
A Brief History of Statistical Design.
Summary: Using Statistical Techniques in Experimentation.
Chapter 2.
Simple Comparative Experiments.
ntroduction.
Basic Statistical Concepts.
Sampling and Sampling Distributions.
nferences About the Differences in Means, Randomized Designs.
Hypothesis Testing.
Choice of Sample Size.
Confidence Intervals.
The Case Where a: f a$.
The Case Where a: and a; Are Known.
Comparing a Single Mean to a Specified Value.
Summary.
nferences About the Differences in Means, Paired Comparison.
Designs.
The Paired Comparison Problem.
Advantages of the Paired Comparison Design.
nferences About the Variances of Normal Distributions.
Problems.
Chapter 3.
Experiments with a Single Factor: The Analysis of Variance.
An Example.
The Analysis of Variance.
Analysis of the Fixed Effects Model.
Decomposition of the Total Sum of Squares.
Statistical Analysis.
Estimation of the Model Parameters.
Unbalanced Data.
Model Adequacy Checking.
The Normality Assumption.
Plot of Residuals in Time Sequence.
Plot of Residuals Versus Fitted Values.
Plots of Residuals Versus Other Variables.
Practical Interpretation of Results.
A Regression Model.
Comparisons Among Treatment Means.
Graphical Comparisons of Means.
Contrasts.
Orthogonal Contrasts.
Scheffk's Method for Comparing All Contrasts.
Comparing Pairs of Treatment Means.
Comparing Treatment Means with a Control.
Sample Computer Output.
Determining Sample Size.
Operating Characteristic Curves.
Specifying a Standard Deviation Increase.
Confidence Interval Estimation Method.
Discovering Dispersion Effects.
The Regression Approach to the Analysis of Variance.
Least Squares Estimation of the Model Parameters.
The General Regression Significance Test.
Nonparaetric Methods in the Analysis of Variance.
The Kruskal-Wallis Test.
General Comments on the Rank Transformation.
Problems.
Chapter 4.
Randomized Blocks, Latin Squares, and Related Designs.
The Randomized Complete Block Design.
Statistical Analysis of the RCBD.
Model Adequacy Checking.
Some Other Aspects of the Randomized Complete Block Design.
Estimating Model Parameters and the General Regression Significance Test.
The Latin Square Design.
The Graeco-Latin Square Design.
Balanced Incomplete Block Designs.
Statistical Analysis of the BIBD.
Least Squares Estimation of the Parameters.
Recovery of Interblock Information in the BIBD.
Problems.
Chapter 5.
ntroduction to Factorial Designs.
Basic Definitions and Principles.
The Advantage of Factorials.
The Two-Factor Factorial Design.
An Example.
Statistical Analysis of the Fixed Effects Model.
Model Adequacy Checking.
Estimating the Model Parameters.
Choice of Sample Size.
The Assumption of No Interaction in a Two-Factor Model.
One Observation per Cell.
The General Factorial Design.
Fitting Response Curves and Surfaces.
Blocking in a Factorial Design.
Problems.
Chapter 6.
The 2k Factorial Design.
ntroduction.
The 2^2 Design.
The 2^3 Design.
The General 2k Design.
A Single Replicate of the 2k Design.
The Addition of Center Points to the 2k Design.
Problems.
Chapter 7.
Blocking and Confounding in the 2k Factorial Design.
ntroduction.
Blocking a Replicated 2k Factorial Design.
Confounding in the 2k Factorial Design.
Confounding the 2k Factorial Design in Two Blocks.
Confounding the 2k Factorial Design in Four Blocks.
Confounding the 2k Factorial Design in 2P Blocks.
Partial Confounding.
Problems.
Chapter 8.
Two-Level Fractional Factorial Designs.
ntroduction.
The One-Half Fraction of the 2k design.
The One-Quarter Fraction of the 2k Design.
The General 2k-p Fractional Factorial Design.
Resolution I11 Designs.
Resolution IV and V Designs.
Summary.
Problems.
Chapter 9.
Three-Level and Mixed-Level Factorial and Fractional.
Factorial Designs.
The 3k Factorial Design.
Notation and Motivation for the 3k Design.
The 32 Design.
The 33 Design.
The General 3k design.
Confounding in the 3k Factorial Design.
The 3k Factorial Design in Three Blocks.
The 3k Factorial Design in Nine Blocks.
The 3k Factorial Design in 3P Blocks.
Fractional Replication of the 3k Factorial Design.
The One-Third Fraction of the 3k Factorial Design.
Other 3k-p Fractional Factorial Designs.
Factorials with Mixed Levels.
Factors at Two and Three Levels.
Factors at Two and Four Levels.
Problems.
Chapter 10.
Fitting Regression Models.
ntroduction.
Linear Regression Models.
Estimation of the Parameters in Linear Regression Models.
Hypothesis Testing in Multiple Regression.
Test for Significance of Regression.
Tests on Individual Regression Coefficients and Groups of Coefficients Confidence Intervals in Multiple Regression.
Confidence Intervals on the Individual Regression Coefficients.
Confidence Interval on the Mean Response.
Prediction of New Response Observations.
Regression Model Diagnostics.
Scaled Residuals and PRESS.
nfluence Diagnostics.
Testing for Lack of Fit.
Problems.
Chapter 11.
Response Surface Methods and Other Approaches.
to Process Optimization.
ntroduction to Response Surface Methodology.
The Method of Steepest Ascent.
Analysis of a Second-Order Response Surface.
Location of the Stationary Point.
Characterizing the Response Surface.
Ridge Systems.
Multiple Responses.
Experimental Designs for Fitting Response Surfaces.
Designs for Fitting the First-Order Model.
Designs for Fitting the Second-Order Model.
Blocking in Response Surface Designs.
Computer-Generated (Optimal) Designs.
Mixture Experiments.
Evolutionary Operation.
Robust Design.
Background.
The Response Surface Approach to Robust Design.
Problems.
Chapter 12.
Experiments with Random Factors.
The Random Effects Model.
The Two-Factor Factorial with Random Factors.
The Two-Factor Mixed Model.
Sample Size Determination with Random Effects.
Rules for Expected Mean Squares.
Approximate F Tests.
Some Additional Topics on Estimation of Variance Components.
Approximate Confidence Intervals on Variance Components.
The Modified Large-Sample Method.
Maximum Likelihood Estimation of Variance Components.
Problems.
Chapter 13.
Nested and Split-Plot Designs.
The Two-Stage Nested Design.
Statistical Analysis.
Diagnostic Checking.
ariance Components.
Staggered Nested Designs.
The General m-Stage Nested Design.
Designs with Both Nested and Factorial Factors.
The Split-Plot Design.
Other Variations of the Split-Plot Design.
Split-Plot Designs with More Than Two Factors.
The Split-Split-Plot Design.
The Strip-Split-Plot Design.
Problems.
Chapter 14.
Other Design and Analysis Topics.
Nonnormal Responses and Transformations.
Selecting a Transformation: The Box-Cox Method.
The Generalized Linear Model.
Unbalanced Data in a Factorial Design.
Proportional Data: An Easy Case.
Approximate Methods.
The Exact Method.
The Analysis of Covariance.
Description of the Procedure.
Computer Solution.
Development by the General Regression Significance Test.
Factorial Experiments with Covariates.
Repeated Measures.
Problems.