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Design and Analysis of Experiments, 9th Edition

Name: Design and Analysis of Experiments, 9th Edition
Author: Douglas C. Montgomery
ISBN: 9781119320937

by Douglas C. Montgomery

May 2017

Intermediate to advanced

630 pages

27h 27m

English

Wiley

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Cover
Title Page
Preface
AudienceAbout the BookCourse SuggestionsThe Supplemental Text MaterialWebsiteAcknowledgments
CHAPTER 1: Introduction
1.1 Strategy of Experimentation1.2 Some Typical Applications of Experimental Design1.3 Basic Principles1.4 Guidelines for Designing Experiments1.5 A Brief History of Statistical Design1.6 Summary: Using Statistical Techniques in Experimentation1.7 Problems
CHAPTER 2: Simple Comparative Experiments
2.1 Introduction2.2 Basic Statistical Concepts2.3 Sampling and Sampling Distributions2.4 Inferences About the Differences in Means, Randomized Designs2.5 Inferences About the Differences in Means, Paired Comparison Designs2.6 Inferences About the Variances of Normal Distributions2.7 Problems
CHAPTER 3: Experiments with a Single Factor: The Analysis of Variance
3.1 An Example3.2 The Analysis of Variance3.3 Analysis of the Fixed Effects Model3.4 Model Adequacy Checking3.5 Practical Interpretation of Results3.6 Sample Computer Output3.7 Determining Sample Size3.8 Other Examples of Single-Factor Experiments3.9 The Random Effects Model3.10 The Regression Approach to the Analysis of Variance3.11 Nonparametric Methods in the Analysis of Variance3.12 Problems
CHAPTER 4: Randomized Blocks, Latin Squares, and Related Designs
4.1 The Randomized Complete Block Design4.2 The Latin Square Design4.3 The Graeco-Latin Square Design4.4 Balanced Incomplete Block Designs4.5 Problems
CHAPTER 5: Introduction to Factorial Designs
5.1 Basic Definitions and Principles5.2 The Advantage of Factorials5.3 The Two-Factor Factorial Design5.4 The General Factorial Design5.5 Fitting Response Curves and Surfaces5.6 Blocking in a Factorial Design5.7 Problems
CHAPTER 6: The 2k Factorial Design
6.1 Introduction6.2 The 22 Design6.3 The 23 Design6.4 The General 2k Design6.5 A Single Replicate of the 2k Design6.6 Additional Examples of Unreplicated 2k Designs6.7 2k Designs are Optimal Designs6.8 The Addition of Center Points to the 2k Design6.9 Why We Work with Coded Design Variables6.10 Problems
CHAPTER 7: Blocking and Confounding in the 2k Factorial Design
7.1 Introduction7.2 Blocking a Replicated 2k Factorial Design7.3 Confounding in the 2k Factorial Design7.4 Confounding the 2k Factorial Design in Two Blocks7.5 Another Illustration of Why Blocking Is Important7.6 Confounding the 2k Factorial Design in Four Blocks7.7 Confounding the 2k Factorial Design in 2p Blocks7.8 Partial Confounding7.9 Problems

CHAPTER 8: Two‐Level Fractional Factorial Designs
8.1 Introduction8.2 The One‐Half Fraction of the 2k Design8.3 The One‐Quarter Fraction of the 2k Design8.4 The General 2k–p Fractional Factorial Design8.5 Alias Structures in Fractional Factorials and Other Designs8.6 Resolution III Designs8.7 Resolution IV and V Designs8.8 Supersaturated Designs8.9 Summary8.10 Problems
CHAPTER 9: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
9.1 The 3k Factorial Design9.2 Confounding in the 3k Factorial Design9.3 Fractional Replication of the 3k Factorial Design9.4 Factorials with Mixed Levels9.5 Nonregular Fractional Factorial Designs9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool9.7 Problems
CHAPTER 10: Fitting Regression Models
10.1 Introduction10.2 Linear Regression Models10.3 Estimation of the Parameters in Linear Regression Models10.4 Hypothesis Testing in Multiple Regression10.5 Confidence Intervals in Multiple Regression10.6 Prediction of New Response Observations10.7 Regression Model Diagnostics10.8 Testing for Lack of Fit10.9 Problems
CHAPTER 11: Response Surface Methods and Designs
11.1 Introduction to Response Surface Methodology11.2 The Method of Steepest Ascent11.3 Analysis of a Second‐Order Response Surface11.4 Experimental Designs for Fitting Response Surfaces11.5 Experiments with Computer Models11.6 Mixture Experiments11.7 Evolutionary Operation11.8 Problems
CHAPTER 12: Robust Parameter Design and Process Robustness Studies
12.1 Introduction12.2 Crossed Array Designs12.3 Analysis of the Crossed Array Design12.4 Combined Array Designs and the Response Model Approach12.5 Choice of Designs12.6 Problems
CHAPTER 13: Experiments with Random Factors
13.1 Random Effects Models13.2 The Two‐Factor Factorial with Random Factors13.3 The Two‐Factor Mixed Model13.4 Rules for Expected Mean Squares13.5 Approximate F‐Tests13.6 Some Additional Topics on Estimation of Variance Components13.7 Problems
CHAPTER 14: Nested and Split‐Plot Designs
14.1 The Two‐Stage Nested Design14.2 The General m‐Stage Nested Design14.3 Designs with Both Nested and Factorial Factors14.4 The Split‐Plot Design14.5 Other Variations of the Split‐Plot Design14.6 Problems
CHAPTER 15: Other Design and Analysis Topics
15.1 Nonnormal Responses and Transformations15.2 Unbalanced Data in a Factorial Design15.3 The Analysis of Covariance15.4 Repeated Measures15.5 Problems
Appendix
Bibliography
Index
End User License Agreement

Content preview from Design and Analysis of Experiments, 9th Edition

CHAPTER 12Robust Parameter Design and Process Robustness Studies

The supplemental material is on the textbook website www.wiley.com/college/montgomery.

12.1 Introduction

Robust parameter design (RPD) is an approach to product realization activities that focuses on choosing the levels of controllable factors (or parameters) in a process or a product to achieve two objectives: (1) to ensure that the mean of the output response is at a desired level or target and (2) to ensure that the variability around this target value is as small as possible. When an RPD study is conducted on a process, it is usually called a process robustness study. The general RPD problem was developed by a Japanese ...

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Design and Analysis of Experiments, 9th Edition

by Douglas C. Montgomery

CHAPTER 12Robust Parameter Design and Process Robustness Studies

12.1 Introduction

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