Design of Experiments - US Army Conference on Applied Statistics

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208 Solution: Bayesian D-Optimality Consider two kinds <strong>of</strong> effects: Primary effects are ones you are sure you want to estimate. There are p 1 <strong>of</strong> these. Potential effects are ones you are afraid to ignore. There are p 2 <strong>of</strong> these. For sample size, n p 1 < n onal Factorial Resolution IV design intercept and main effects are primary 2-factor interactions are potential p 1 < n < p 1 + p 2 p 1 = 7 p 2 = 15 n = 16 (7 < 16 < 22)
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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
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INPUTS (Factors) 40 SPEAR AGM Tests
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Suppose that we want to focus on ma
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Alias Matrix Effect Intercept A B C
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Module 2 - Summary 1. Main message
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Regular Designs ma
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Define Nonisomorphic Two designs ar
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Screening for Shrinkage Contrasts T
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Where Did the Data in this Experime
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Stepwise Fit Response: Shrinkage St
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No-Confounding Design</stro
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Hall II 15 Factor Design</s
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Hall IV 15 Factor Design</s
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Constructing the Recommended 6 Fact
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Color Plot for the Standard Minimum
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Recommended Nonregular 7 Factor <st
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Color Plot for the Standard Minimum
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Recommended Nonregular 8 Factor <st
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Alternatives to Resolution III <str
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Recommended 10 Factor Desig
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Note that both main effects and two
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Main effects are not aliased. One t
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Plackett-Burman Design</str
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A 12-Factor Example 1 1 1 1 1 1 1 1
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The data for the example came from
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Embarrassing Problem Case Suppose w
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Reactor Case Study Box, Hunter and
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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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References Hall, M. Jr. (1961). Had
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Module 4 - Blocking • Many experi
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Blocking • Blocking is a techniqu
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The randomized complete block desig
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Cars The Latin Square Desig
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JMP Demo
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Another Example of
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In general, if there are p treatmen
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Back to tire testing • Suppose th
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Module 4 - Summary • Most design
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Split-plot Graphic Definition
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Model for Split-plot Experi
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General procedure Split-plot <stron
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Scenario 1. Four factors Split-Plot
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Ad hoc Design #1
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I-optimal Split-Plot Design
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OLS vs GLS Data Analysis OLS Analys
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Module 6 - Introduction to RSM •
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E(y) = f( ) The response surface Th
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Response Surface Methodology • Wh
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CI on the mean response at a point
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Three Typical Applications
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Useful References on RSM • Box, G
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Categorical and Continuous Variable
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Goals Module 7 - RSM with Factor Co
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A problem with a constrained design
Page 157 and 158: JMP Default RSM Design</str
Page 159 and 160: Design Comparison
Page 161 and 162: Module 7 - Summary • Constraints
Page 163 and 164: Robust Parameter Design</st
Page 165 and 166: The Response Surface Approach Secti
Page 167 and 168: Filtration Robust Processing Exampl
Page 169 and 170: JMP Demo
Page 171 and 172: • Goals Module 9 Mixture
Page 173 and 174: Mixtures occur in lots of</
Page 175 and 176: Simplex Designs Si
Page 177 and 178: An experiment involving shampoo for
Page 179 and 180: Makeup of an Aircr
Page 181 and 182: Mixtures in JMP Ternary Plot .1 X1
Page 183 and 184: • Goals Module 10 - Covering Arra
Page 185 and 186: Covering Array Definition A coverin
Page 187 and 188: Covering arrays 1 1 1 1 1 2 2 2 2 2
Page 189 and 190: Covering arrays and sof</st
Page 191 and 192: Example - Air to ground missile sys
Page 193 and 194: CA(N:2,k,2) Results k 2-3 4 5-10 11
Page 195 and 196: References • R. Brownlie, J. Prow
Page 197 and 198: Module 10 - Summary • Covering ar
Page 199 and 200: 199 What is a supersaturated design
Page 201 and 202: 201 A more general definition… Su
Page 203 and 204: 203 Classical “supersaturated”
Page 205 and 206: 205 Case 2 - Fractional Factorial <
Page 207: 207 Problem D-Optimal designs depen
Page 211 and 212: 211 Bayesian D-Optimal designs Find
Page 213 and 214: 213 Question Why should only higher
Page 215 and 216: Card Trick in JMP
Page 217: DOX Course - Final Thoughts 1. Opti
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