Design and analysis of experiments / Douglas C. Montgomery.

By: Montgomery, Douglas C .

Publisher: Hoboken, N.J. : Chichester : Wiley ; John Wiley [distributor], 2008Edition: 7th ed.Description: xvii, 656 p. : ill. ; 26 cm. + pbk.ISBN: 9780470128664; 0470128666.Subject(s): Experimental design

DDC classification: 001.434

Contents:

Introduction to designed experiments -- Basic statistical methods -- Analysis of variance -- Experiments with blocking factors -- Factorial experiments -- Two-level factorial designs -- Blocking and confounding systems for two-level factorials -- Two level fractional factorial designs -- Other topics on factorial and fractional factorial design -- Regression modeling -- Response surface methodology -- Robust design -- Random effects models -- Experiments with nested factors and hard-to-change factors -- Other topics.

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Enhanced descriptions from Syndetics:

This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.

Previous ed.: 2005.

Bibliography: (pages 647-651) and index.

CIT Module STAT 8002 - Core reading

Table of contents provided by Syndetics

Preface
1 Introduction.
1.1 Strategy of Experimentation
1.2 Some Typical Applications of Experimental Design
1.3 Basic Principles
1.4 Guidelines for Designing Experiments
1.5 A Brief History of Statistical Design
1.6 Summary: Using Statistical Techniques in Experimentation
1.7 Problems
2 Simple Comparative Experiments.
2.1 Introduction
2.2 Basic Statistical Concepts
2.3 Sampling and Sampling Distributions
2.4 Inferences About the Differences in Means, Randomized Designs
2.5 Inferences About the Differences in Means, Paired Comparison Designs
2.6 Inferences About the Variances of Normal Distributions
2.7 Problems
3 Experiments with a Single Factor: The Analysis of Variance.
3.1 An Example
3.2 The Analysis of Variance
3.3 Analysis of the Fixed Effects Model
3.4 Model Adequacy Checking
3.5 Practical Interpretation of Results
3.6 Sample Computer Output
3.7 Determining Sample Size
3.8 A Real Economy Application of a Designed Experiment
3.9 Discovering Dispersion Effects
3.10 The Regression Approach to the Analysis of Variance
3.11 Nonparametric Methods in the Analysis of Variance
3.12 Problems
4 Randomized Blocks, Latin Squares, and Related Designs.
4.1 The Randomized Complete Block Design
4.2 The Latin Square Design
4.3 The Graeco-Latin Square Design
4.4 Balanced Incomplete Block Designs
4.5 Problems
5 Introduction to Factorial Designs.
5.1 Basic Definitions and Principles
5.2 The Advantage of Factorials
5.3 The Two-Factor Factorial Design
5.4 The General Factorial Design
5.5 Fitting Response Curves and Surfaces
5.6 Blocking in a Factorial Design
5.7 Problems
6 The 2 k Factorial Design.
6.1 Introduction
6.2 The 2 2 Design
6.3 The 2 3 Design
6.4 The General 2 k Design
6.5 A Single Replicate of the 2 k Design
6.6 Additional Examples of Unreplicated 2 k Design
6.7 2 k Designs are Optimal Designs
6.8 The Addition of Center Points to the 2 k Design
6.9 Why We Work with Coded Design Variables
6.10 Problems
7 Blocking and Confounding in the 2 k Factorial Design.
7.1 Introduction
7.2 Blocking a Replicated 2 k Factorial Design
7.3 Confounding in the 2 k Factorial Design
7.4 Confounding the 2 k Factorial Design in Two Blocks
7.5 Another Illustration of Why Blocking Is Important
7.6 Confounding the 2 k Factorial Design in Four Blocks
7.7 Confounding the 2 k Factorial Design in 2 p Blocks
7.8 Partial Confounding
7.9 Problems
8 Two-Level Fractional Factorial Designs.
8.1 Introduction
8.2 The One-Half Fraction of the 2 k Design
8.3 The One-Quarter Fraction of the 2 k Design
8.4 The General 2 kûp Fractional Factorial Design
8.5 Alias Structures in Fractional Factorials and other Designs
8.6 Resolution III Designs
8.7 Resolution IV and V Designs
8.8 Supersaturated Designs
8.9 Summary
8.10 Problems
9 Three-Level and Mixed-Level Factorial and Fractional Factorial Designs.
9.1 The 3 k Factorial Design
9.2 Confounding in the 3 k Factorial Design
9.3 Fractional Replication of the 3 k Factorial Design
9.4 Factorials with Mixed Levels
10 Fitting Regression Models.
10.1 Introduction
10.2 Linear Regression Models
10.3 Estimation of the Parameters in Linear Regression Models
10.4 Hypothesis Testing in Multiple Regression
10.5 Confidence Intervals in Multiple Regression
10.6 Prediction of New Response Observations
10.7 Regression Model Diagnostics
10.8 Testing for Lack of Fit
10.9 Problems
11 Response Surface Methods and Designs.
11.2 The Method of Steepest Ascent
11.3 Analysis of a Second-Order Response Surface
11.4 Experimental Designs for Fitting Response Surfaces
11.5 Experiments with Computer Models
11.6 Mixture Experiments
11.7 Evolutionary Operation
11.8 Problems
12 Robust Parameter Design and Process Robustness Studies.
12.1 Introduction
12.2 Crossed Array Designs
12.3 Analysis of the Crossed Array Design
12.4 Combined Array Design and the Response Model Approach
12.5 Choice of Designs
12.6 Problems
13 Experiments with Random Factors.
13.1 The Random Effects Model
13.2 The Two-Factor Factorial with Random Factors
13.3 The Two-Factor Mixed Model
13.4 Sample Size Determination with Random Effects
13.5 Rules for Expected Mean Squares
13.6 Approximate F Tests
13.7 Some Additional Topics on Estimation of Variance Components
13.8 Problems
14 Nested and Split-Plot Designs.
14.1 The Two-Stage Nested Design
14.2 The General m-Stage Nested Design
14.3 Designs with Both Nested and Factorial Factors
14.4 The Split-Plot Design
14.5 Other Variations of the Split-Plot Design
14.6 Problems
15 Other Design and Analysis Topics.
15.1 Nonnormal Responses and Transformations
15.2 Unbalanced Data in a Factorial Design
15.3 The Analysis of Covariance
15.4 Repeated Measures
15.5 Problems
Appendix
Table I Cumulative Standard Normal Distribution
Table II Percentage Points of the t Distribution
Table III Percentage Points of the X 2 Distribution
Table IV Percentage Points of the F Distribution
Table V Operating Characteristic Curves for the Fixed Effects Model Analysis of Variance
Table VI Operating Characteristic Curves for the Random Effects Model Analysis of Variance
Table VII Percentage Points of the Studentized Range Statistic
Table VIII Critical Values for DunnettÆs Test for Comparing Treatments with a Control
Table IX Coefficients of Orthogonal Polynomials
Table X Alias Relationships for 2 kûp Fractional Factorial Designs with k
Bibliography.
Index.

Author notes provided by Syndetics

Douglas C. Montgomery , Regents' Professor of Industrial Engineering and Statistics at Arizona State University, received his B.S., M.S., and Ph.D. degrees from Virginia Polytechnic Institute, all in engineering. From 1969 to 1984, he was a faculty member of the School of Industrial & Systems Engineering at the Georgia Institute of Technology; from 1984 to 1988, he was at the University of Washington, where he held the John M. Fluke Distinguished chair of Manufacturing Engineering, was Professor of Mechanical Engineering, and Director of the Program in industrial Engineering. He has authored and coauthored many technical papers as well as twelve other books. Dr. Montgomery is a Stewart Medalist of the American Society for Quality, and has also received the Brumbaugh Award, the William G. Hunter Award, and the Shewell Award(twice) from the ASQ.