Design of Experiments - US Army Conference on Applied Statistics

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Constraints on the mixture components are common, <strong>of</strong>ten in the form <strong>of</strong> lower and upper bounds on component proportions The effect <strong>of</strong> these constraints is to alter the shape <strong>of</strong> the original simplex region If there are only lower bounds, the simplex designs shown previously will still work If there are both lower and upper bounds, simplex designs will not work for these types <strong>of</strong> problems
An experiment involving shampoo formulation There are upper and lower bounds on each component proportion The response variable is foam height The experiment involves a constrained design region An optimal design constructed by computer is a good choice
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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
Page 126 and 127: Model for Split-plot Experi
Page 128 and 129: General procedure Split-plot <stron
Page 130 and 131: Scenario 1. Four factors Split-Plot
Page 132 and 133: Ad hoc Design #1
Page 134 and 135: I-optimal Split-Plot Design
Page 136 and 137: OLS vs GLS Data Analysis OLS Analys
Page 138 and 139: Module 6 - Introduction to RSM •
Page 140 and 141: E(y) = f( ) The response surface Th
Page 142 and 143: Response Surface Methodology • Wh
Page 144 and 145: CI on the mean response at a point
Page 147 and 148: Three Typical Applications
Page 149 and 150: Useful References on RSM • Box, G
Page 151 and 152: Categorical and Continuous Variable
Page 153 and 154: Goals Module 7 - RSM with Factor Co
Page 155 and 156: 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: Simplex Designs Si
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 and 208: 207 Problem D-Optimal designs depen
Page 209 and 210: 209 Example 2 6-2 Fractional Factor
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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Design of Experiments - US Army Conference on Applied Statistics

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