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Design of<
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What Is a Designed
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What Is a Response? A response is a
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What is a model? a simplified mathe
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Important Points from the Fathers <
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ANOVA Model for Mileage Study Note
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Orthogonal Coding and Orthogonal <s
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ANOVA/Regression Model - Matrix Not
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Categorical Factor Coding - 2 level
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Continuous Factor Coding MR - midra
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The Model/Design R
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The Model/Design R
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Design Optimality
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Simple experiment for three factors
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Standard designs using an optimal d
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INPUTS (Factors) Airspeed Turn Rate
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Fractional Factorial designs are D-
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Consider the 2 5-1 - Again Created
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JMP Demo Relative Variance
- Page 40 and 41: INPUTS (Factors) 40 SPEAR AGM Tests
- Page 42 and 43: Suppose that we want to focus on ma
- Page 44 and 45: Alias Matrix Effect Intercept A B C
- Page 46 and 47: Module 2 - Summary 1. Main message
- Page 48 and 49: Regular Designs ma
- Page 50 and 51: Define Nonisomorphic Two designs ar
- Page 53 and 54: Screening for Shrinkage Contrasts T
- Page 55 and 56: Where Did the Data in this Experime
- Page 57 and 58: Stepwise Fit Response: Shrinkage St
- Page 59 and 60: No-Confounding Design</stro
- Page 61 and 62: Hall II 15 Factor Design</s
- Page 63 and 64: Hall IV 15 Factor Design</s
- Page 65 and 66: Constructing the Recommended 6 Fact
- Page 67 and 68: Color Plot for the Standard Minimum
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- Page 71 and 72: Color Plot for the Standard Minimum
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- Page 75 and 76: Alternatives to Resolution III <str
- Page 77 and 78: Recommended 10 Factor Desig
- Page 79 and 80: Recommended 12 Factor Desig
- Page 81 and 82: Recommended 14 Factor Desig
- Page 83 and 84: Note that both main effects and two
- Page 85 and 86: Main effects are not aliased. One t
- Page 87 and 88: Plackett-Burman Design</str
- Page 89: A 12-Factor Example 1 1 1 1 1 1 1 1
- Page 93 and 94: Alias Optimal Design</stron
- Page 95 and 96: A New Optimality Criterion Recently
- Page 97 and 98: Robust Screening Design</st
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- Page 101 and 102: The Case for Non-orthogonal <strong
- Page 103 and 104: JMP Demo
- Page 105 and 106: Module 3 - Conclusions • The trad
- Page 107 and 108: Tire Wear Study • We have 4 brand
- Page 109 and 110: • The tire mileage experiment An
- Page 111 and 112: Variance 0.4375 0.8 0.6 0.4 0.2 0 J
- Page 113 and 114: The Latin Square Design</st
- Page 115 and 116: Another Example of
- Page 117 and 118: Treatments for the 6 x 6 Latin squa
- Page 119 and 120: JMP Demo
- Page 121 and 122: Prediction Variance 0.7 0.6 0.5 0.4
- Page 123 and 124: DOE Course - Module 5 Desig
- Page 125 and 126: Split-plot Definition A split-plot
- Page 127 and 128: Split-plot versus Random Blocks 1.
- Page 129 and 130: Split-plot Design
- Page 131 and 132: Factor Table
- Page 133 and 134: Ad hoc Design #2
- Page 135 and 136: Comparison of Coef
- Page 137 and 138: Module 5 - Summary 1. Split-plot de
- Page 139 and 140: 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