| Description |
1 online resource |
| Contents |
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 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 -- 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 |
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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 |
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9 Additional Design and Analysis Topics for 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 -- 9.5 Nonregular Fractional Factorial Designs -- 9.6 Constructing Factorial and Fractional Factorial Designs Using an Optimal Design Tool -- 9.7 Problems -- 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 -- 11.1 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 -- 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 Designs and the Response Model Approach -- 12.5 Choice of Designs -- 12.6 Problems -- 13 Experiments with Random Factors |
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13.1 Random Effects Models -- 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 |
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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 _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 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 -- Bibliography -- Index |
| Summary |
"The eighth edition of Design and Analysis of Experiments continues to provide extensive and in-depth information on engineering, business, and statistics-as well as informative ways to help readers design and analyze experiments for improving the quality, efficiency and performance of working systems. Furthermore, the text maintains its comprehensive coverage by including: new examples, exercises, and problems (including in the areas of biochemistry and biotechnology); new topics and problems in the area of response surface; new topics in nested and split-plot design; and the residual maximum likelihood method is now emphasized throughout the book"-- Provided by publisher |
| Bibliography |
Includes bibliographical references and index |
| Notes |
Machine generated contents note: Chapter 1: Introduction Chapter 2: Simple Comparative Experiments Chapter 3: Experiments with a Single Factor: The Analysis of Variance Chapter 4: Randomized Blocks, Latin Squares, and Related Designs Chapter 5: Introduction to Factorial Designs Chapter 6: The 2k Factorial Design Chapter 7: Blocking and Confounding in the 2k Factorial Design Chapter 8: Two-Level Fractional Factorial Designs Chapter 9: Additional Design and Analysis for Factorial and Fractional Factorial Designs Chapter 10: Fitting Regression Models Chapter 11: Response Surface Methods and Designs Chapter 12: Robust Parameter Design and Process Robustness Studies Chapter 13: Experiments with Random Factors Chapter 14: Nested and Split-Plot Designs Chapter 15: Other Design and Analysis Topics Appendix |
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English |
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Print version record |
| Subject |
Experimental design.
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TECHNOLOGY & ENGINEERING -- Industrial Engineering.
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Experimental design
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Mathematics.
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Physical Sciences & Mathematics.
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Mathematical Statistics.
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| Form |
Electronic book
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| LC no. |
2012005389 |
| ISBN |
9781621982272 |
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1621982270 |
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