Design and Analysis of Experiments / Edition 7

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"Across a wide range of fields - from industrial engineering to business and statistics - Douglas C. Montgomery's Design and Analysis of Experiments has been a foundational work for students and professionals needing to design, conduct, and analyze experiments for optimizing performance in products and processes." Placing a strong focus on the use of the computer, the new edition makes much more use of optimal designs and includes even more software examples than before, taken from the three most dominant programs in the field: Minitab, Design-Expert V7, and JMP. There is additional material discussing the latest developments in robust design and factorial designs along with fresh examples and exercises that illustrate the use of designed experiments in service and transactional organizations.
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Editorial Reviews

An introductory textbook dealing with the design and analysis of experiments for engineering and science students who have completed a course in statistics. The fifth edition encourages incorporation of the computer during both design and analysis, and adds material on alphabetically optimal designs, experiments with mixtures, and the robust parameter design problem. Annotation c. Book News, Inc., Portland, OR
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Product Details

  • ISBN-13: 9780470128664
  • Publisher: Wiley
  • Publication date: 7/28/2008
  • Edition description: New
  • Edition number: 7
  • Pages: 680
  • Product dimensions: 8.10 (w) x 10.00 (h) x 1.10 (d)

Meet the Author

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.

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Table of Contents


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 2k Factorial Design.

6.1 Introduction.

6.2 The 22 Design.

6.3 The 23 Design.

6.4 The General 2k Design.

6.5 A Single Replicate of the 2k Design.

6.6 Additional Examples of Unreplicated 2k Design.

6.7 2k Designs are Optimal Designs.

6.8 The Addition of Center Points to the 2k Design.

6.9 Why We Work with Coded Design Variables.

6.10 Problems.

7. Blocking and Confounding in the 2k Factorial Design.

7.1 Introduction.

7.2 Blocking a Replicated 2k Factorial Design.

7.3 Confounding in the 2k Factorial Design.

7.4 Confounding the 2k Factorial Design in Two Blocks.

7.5 Another Illustration of Why Blocking Is Important.

7.6 Confounding the 2k Factorial Design in Four Blocks.

7.7 Confounding the 2k Factorial Design in 2p 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 2k Design.

8.3 The One-Quarter Fraction of the 2k Design.

8.4 The General 2k–p 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 3k Factorial Design.

9.2 Confounding in the 3k Factorial Design.

9.3 Fractional Replication of the 3k Factorial Design.

9.4 Factorials with Mixed Levels.

10. Fitting Regression Models.

10.1 Introduction.

10.2Linear 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.


Table I. Cumulative Standard Normal Distribution.

Table II. Percentage Points of the t Distribution.

Table III. Percentage Points of the X2 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 Dunnett’s Test for Comparing Treatments with a Control.

Table IX. Coefficients of Orthogonal Polynomials.

Table X. Alias Relationships for 2k–p Fractional Factorial Designs with k <15 and n < 64.



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