Design and Analysis of Experiments, EMEA Edition

Höfundur: Douglas C. Montgomery (Útgáfa: 10)
Design and Analysis of Experiments, EMEA Edition

Kaup valmöguleikar

Design and Analysis of Experiments provides a rigorous introduction to product and process design improvement through quality and performance optimization. Clear demonstration of widely practiced techniques and procedures allows readers to master fundamental concepts, develop design and analysis skills, and use experimental models and results in real-world applications. Detailed coverage of factorial and fractional factorial design, response surface techniques, regression analysis, biochemistry and biotechnology, single factor experiments, and other critical topics offer highly-relevant guidance through the complexities of the field.

Stressing the importance of both conceptual knowledge and practical skills, this text adopts a balanced approach to theory and application. Extensive discussion of modern software tools integrate data from real-world studies, while examples illustrate the efficacy of designed experiments across industry lines, from service and transactional organizations to heavy industry and biotechnology. Broad in scope yet deep in detail, this text is both an essential student resource and an invaluable reference for professionals in engineering, science, manufacturing, statistics, and business management.

Nánar um bókina

Útgefandi
Wiley Global Education UK
ISBN
9781119816966
Print ISBN
9781119816959
Format
ePub
Útgáfa
10
Höfundar
Douglas C. Montgomery
Tungumál
English
Útgefið
202308
Prent takmörkun á líftíma
100
Prent takmörkun
10
Afritunar takmörkun
2

Kaflar

  • Cover
  • Preface
  • Audience
  • About the Book
  • Course Suggestions
  • The Supplemental Text Material
  • Student and Instructor Supplements
  • Acknowledgments
  • Chapter 1: Introduction
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 2: Simple Comparative Experiments
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 3: Experiments with a Single Factor: The Analysis of Variance
  • Problems
  • Study Guide
  • Readings
  • 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 Other Examples of Single‐Factor Experiments
  • 3.9 The Random Effects Model
  • 3.10 The Regression Approach to the Analysis of Variance
  • 3.11 Nonparametric Methods in the Analysis of Variance
  • Chapter 4: Randomized Blocks, Latin Squares, and Related Designs
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 5: Introduction to Factorial Designs
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 6: The 2k Factorial Design
  • Problems
  • Study Guide
  • Readings
  • 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 Designs
  • 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
  • Chapter 7: Blocking and Confounding in the 2k Factorial Design
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 8: Two‐Level Fractional Factorial Designs
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 9: Additional Design and Analysis Topics for Factorial and Fractional Factorial Designs
  • Problems
  • Study Guide
  • Readings
  • 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
  • 9.5 Nonregular Fractional Factorial Designs
  • 9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool
  • Chapter 10: Fitting Regression Models
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 11: Response Surface Methods and Designs
  • Problems
  • Study Guide
  • Readings
  • 11.1 Introduction to Response Surface Methodology
  • 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
  • Chapter 12: Robust Parameter Design and Process Robustness Studies
  • Problems
  • Study Guide
  • Readings
  • 12.1 Introduction
  • 12.2 Crossed Array Designs
  • 12.3 Analysis of the Crossed Array Design
  • 12.4 Combined Array Designs and the Response Model Approach
  • 12.5 Choice of Designs
  • Chapter 13: Experiments with Random Factors
  • Problems
  • Study Guide
  • Readings
  • 13.1 Random Effects Models
  • 13.2 The Two‐Factor Factorial with Random Factors
  • 13.3 The Two‐Factor Mixed Model
  • 13.4 Rules for Expected Mean Squares
  • 13.5 Approximate F‐Tests
  • 13.6 Some Additional Topics on Estimation of Variance Components
  • Chapter 14: Nested and Split‐Plot Designs
  • Problems
  • Study Guide
  • Readings
  • 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
  • Chapter 15: Other Design and Analysis Topics
  • Problems
  • Study Guide
  • Readings
  • 15.1 Nonnormal Responses and Transformations
  • 15.2 Unbalanced Data in a Factorial Design
  • 15.3 The Analysis of Covariance
  • 15.4 Repeated Measures
  • Appendix
  • Bibliography
  • Index
  • End User License Agreement