Design of Experiments - US Army Conference on Applied Statistics

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• Aliases: The 2 5-1 – All main effects are clear <strong>of</strong> the two-factor interactions – All two-factor interactions are clear <strong>of</strong> each other • Orthogonal • Makes interpretation easy • Minimizes the variance <strong>of</strong> the model coefficients • Minimizes the average prediction variance • Minimizes the maximum prediction variance • Once again, you can’t do any better than this!
Page 1 and 2: Design of<
Page 3 and 4: What Is a Designed
Page 5 and 6: What Is a Response? A response is a
Page 7 and 8: What is a model? a simplified mathe
Page 9 and 10: Important Points from the Fathers <
Page 11 and 12: ANOVA Model for Mileage Study Note
Page 13 and 14: Orthogonal Coding and Orthogonal <s
Page 15 and 16: ANOVA/Regression Model - Matrix Not
Page 17 and 18: Categorical Factor Coding - 2 level
Page 19 and 20: Continuous Factor Coding MR - midra
Page 21 and 22: The Model/Design R
Page 23 and 24: The Model/Design R
Page 25 and 26: Design Optimality
Page 27 and 28: Simple experiment for three factors
Page 29 and 30: Standard designs using an optimal d
Page 31 and 32: INPUTS (Factors) Airspeed Turn Rate
Page 33 and 34: Fractional Factorial designs are D-
Page 35: Consider the 2 5-1 - Again Created
Page 39 and 40: Designs are optima
Page 41 and 42: • Aliases: The 2 4-1 - All main e
Page 43 and 44: This is a 2 6-3 fractional factoria
Page 45 and 46: • Aliases: The 2 6-3 - All main e
Page 47 and 48: Module 3 - Modern Screening Methods
Page 49 and 50: Number of Orthogon
Page 51: A Six-Factor Example • Based on E
Page 54 and 55: The No-Confounding Design</
Page 56 and 57: Color Plot for the No-Confounding <
Page 58 and 59: Stepwise Fit Response: Shrinkage St
Page 60 and 61: Hall I 15 Factor Design</st
Page 62 and 63: Hall III 15 Factor Design</
Page 64 and 65: Hall V 15 Factor Design</st
Page 66 and 67: Recommended Nonregular 6 Factor <st
Page 68 and 69: Constructing the Recommended 7 Fact
Page 70 and 71: Comparison of Colo
Page 72 and 73: Constructing the Recommended 8 Fact
Page 74 and 75: Comparison of Colo
Page 76 and 77: Recommended 9 Factor Design
Page 78 and 79: Recommended 11 Factor Desig
Page 80 and 81: Recommended 13 Factor Desig
Page 82 and 83: Nine Factor Example from a Consumer
Page 84 and 85: Nonregular Alternative Data was con
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Plackett-Burman Design</str
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The Alias Matrix for the 12-run Pla
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Stepwise Regression Analysis 91
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Alias Optimal Design</stron
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A New Optimality Criterion Recently
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Robust Screening Design</st
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Robust Screening Design</st
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The Case for Non-orthogonal <strong
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JMP Demo
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Module 3 - Conclusions • The trad
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Tire Wear Study • We have 4 brand
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• The tire mileage experiment An
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Variance 0.4375 0.8 0.6 0.4 0.2 0 J
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The Latin Square Design</st
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Another Example of
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Treatments for the 6 x 6 Latin squa
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JMP Demo
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Prediction Variance 0.7 0.6 0.5 0.4
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DOE Course - Module 5 Desig
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Split-plot Definition A split-plot
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Split-plot versus Random Blocks 1.
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Split-plot Design
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Factor Table
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Ad hoc Design #2
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Comparison of Coef
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Module 5 - Summary 1. Split-plot de
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The Response Surface Framework for
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Response Surface Methodology • Th
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Confidence Intervals (Page 36) CI o
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The Sequential Nature of</s
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• “Classical” RSM problem RSM
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Designs for the Se
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Module 6 - Summary • RSM is all a
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Situations where Standard D
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How would we design an experiment f
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Design Comparison
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Infeasible Factor Combinations Espe
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• Goals Module 8 - Robust <strong
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Example of Noise F
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A Modeling Approach that Includes b
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• Factors 1. Filter Type 2. Groun
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Module 8 - Summary • By running a
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Experiments with M
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Mixture experiments involve a const
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Constraints on the mixture componen
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An example: formulating the optimum
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Mixture Constraints: X attack, X 25
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Module 9 - Summary • Mixture expe
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Scenario Suppose we are testing a s
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Minimum Covering Array The size <st
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Covering arrays and sof</st
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Example - Air to ground missile sys
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Example - Air to ground missile sys
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JMP Card Trick #1
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References • A. Hartman & L. Rask
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Goals Module 11 - Supersaturated <s
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200
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Supersaturated Design</stro
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204 Case 1 - Center points. 2x2 fac
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206 D-Optimal Design</stron
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208 Solution: Bayesian D-Optimality
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210 K 0 0 Defining the K matrix p p
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212 Comparison Five Run D-Optimal F
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214 Benefits of Ba
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Module 11 - Summary Supersaturated
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Design of Experiments - US Army Conference on Applied Statistics

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