Experiments

Preface to the Second Edition. First Suggestions of Topics for Instructors. List Experiments and Data Sets. 1 Basic Concepts Experimental Design Introductory Regression Analysis. 1.1 Introduction Historical Perspective. 1.2 A Systematic Approach Planning Implementation Experiments. 1.3 Fundamental Principles: Replication, Randomization, Blocking. 1.4 Simple Linear Regression. 1.5 Testing Hypothesis Interval Estimation. 1.6 Multiple 1.7 Variable Selection in 1.8 Analysis Air Pollution Data. 1.9 Practical Summary. 2 with a Single Factor. 2.1 One-Way Layout. 2.2 Comparisons. 2.3 Quantitative Factors Orthogonal Polynomials. 2.4 Expected Mean Squares Sample Size Determination. 2.5 Random Effects Model. 2.6 Residual Analysis: Assessment Model Assumptions. 2.7 3 More Than One 3.1 Paired Comparison Designs. 3.2 Randomized Block 3.3 Two-Way Layout: With Fixed Levels. 3.4 3.5 Multi-Way Layouts. 3.6 Latin Square Designs: Two Blocking Variables. 3.7 Graeco-Latin 3.8 Balanced Incomplete 3.9 Split-Plot 3.10 Covariance: Incorporating Auxiliary Information. 3.11 Transformation Response. 3.12 4 Full Factorial at 4.1 An Epitaxial Layer Growth Experiment. 4.2 Designs Levels: General Discussion. 4.3 Plots. 4.4 Using Compute Effects. 4.5 ANOVA Treatment 4.6 Principles Effects: Effect Hierarchy, Sparsity, Heredity. 4.7 Comparisons One-Factor-at-a-Time Approach. 4.8 Normal Half-Normal Plots Judging Significance. 4.9 Lenth's Method: Significance Without Variance Estimates. 4.10 Nominal-the-Best Problem Quadratic Loss Function. 4.11 Use Log Dispersion 4.12 Location Dispersion: Revisiting 4.13 Test Homogeneity Pooled Estimate Variance. 4.14 Studentized Maximum Modulus Test: 4.15 Optimal Arrangement k q Blocks. 4.16 5 Fractional 5.1 Leaf Spring 5.2 Aliasing Criteria Of Resolution Minimum Aberration. 5.3 5.4 Techniques Resolving Ambiguities Aliased 5.5 k-p Aberration Related Criteria. 5.6 5.7 6 Three 6.1 Seat-Belt 6.2 Larger-the-Better Smaller-the-Better Problems. 6.3 6.4 6.5 Methods: 6.6 Alternative Method. 6.7 Strategies Responses I: Out-of-Spec Probabilities. 6.8 6.9 7 Other 7.1 Router Bit Experiment Based on Mixed Two-Level Four-Level Design. 7.2 Method Replacement Construction m n 7.3 = 1, 2. 7.4 Strategy 7.5 7.6 Paint Three-Level 7.7 36-Run And 7.8 r any Prime Number . 7.9 Factors: Sliding Levels, Nested Analysis, Response Surface Modeling. 7.10 8 Nonregular Properties. 8.1 Experiments: Weld-Repaired Castings Blood Glucose Testing. 8.2 Some Advantages Over Series 8.3 Lemma Arrays. 8.4 Plackett-Burman Hall's 8.5 Collection Useful Mixed-Level 8.6 Arrays Difference Matrices. 8.7 Through Replacement. 8.8 Main-Effect Plans Collapsing Factors. 8.9 9 Complex Aliasing. 9.1 Partial Alias Matrix. 9.2 Traditional Strategy: Screening Main 9.3 Simplification via Sparsity. 9.4 9.5 Bayesian 9.6 Supersaturated 9.7 10 Methodology. 10.1 Ranitidine Separation 10.2 Sequential Nature 10.3 From First-Order Second-Order Steepest Ascent Search Rectangular Grid Search. 10.4 Surfaces. 10.5 10.6 II: Contour Desirability Functions. 10.7 Central Composite 10.8 Box-Behnken Uniform Shell 10.9 11 Robust Parameter 11.1 Perspective 11.2 Reducing Variation. 11.3 Noise (Hard-to-Control) 11.4 Variation Reduction 11.5 Experimentation Modeling Cross Array. 11.6 Array 11.7 Arrays: Estimation Capacity Selection. 11.8 Choosing Between 11.9 Signal-to-Noise Ratio Its Limitations Optimization. 11.10 Further Topics. 11.11 12 Signal-Response Systems. 12.1 Injection Molding 12.2 Systems their Classification. 12.3 Performance Measures 12.4 Strategies. 12.5 12.6 Choice Plans. 12.7 13 Improving Reliability. 13.1 Failure Time 13.2 13.3 Likelihood Handling Censoring. 13.4 Design-Dependent 13.5 13.6 Reliability 13.7 Types 13.8 14 Nonnormal 14.1 Wave Soldering Count 14.2 Generalized Models. 14.3 Likelihood-Based 14.4 14.5 14.6 14.7 Uses Extensions Models 14.8 Ordinal 14.9 Foam 14.10 Scoring: Analyzing 14.11 Appendix Upper Tail Probabilities Standard Distribution. B Percentiles t C chi D F E Range G Coefficients Contrast Vectors. H Critical Values Author Index. Subject