Design and Analysis of Experiments

 Content: 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. 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 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. 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. 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.
 Abstract:             
              A comprehensive guide to understanding AC machines with exhaustive simulation models to practice design and control  Nearly seventy percent of the electricity generated worldwide is used by electrical motors.                 Read more...