Design of Experiments - US Army Conference on Applied Statistics

More documents

Recommendations

Info

Embarrassing Problem Case Suppose we have 4 factors and want to generate an 8 run experiment. The classical design everyone would use is the resolution IV design that confounds factor 4 with the 123 interaction. Yet, any orthogonal 2-level design is optimal for the main effect model. Demonstration in JMP
A New Optimality Criterion Recently Jones and Nachtsheim proposed a new criterion that addresses this concern. Their designs minimize the squared norm <strong>of</strong> the alias matrix subject to a lower bound constraint on the D-efficiency <strong>of</strong> the design. Their results are impressive and provide a safer approach to screening design for novice investigators.
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 38 and 39:
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
Page 69 and 70: Recommended Nonregular 7 Factor <st
Page 71 and 72: Color Plot for the Standard Minimum
Page 73 and 74: Recommended Nonregular 8 Factor <st
Page 75 and 76: Alternatives to Resolution III <str
Page 77 and 78: Recommended 10 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 92 and 93: The data for the example came from
Page 96 and 97: Reactor Case Study Box, Hunter and
Page 98 and 99: Robust Screening Design</st
Page 100 and 101: Robust Screening Design</st
Page 102 and 103: The Case for Non-orthogonal <strong
Page 104 and 105: References Hall, M. Jr. (1961). Had
Page 106 and 107: Module 4 - Blocking • Many experi
Page 108 and 109: Blocking • Blocking is a techniqu
Page 110 and 111: The randomized complete block desig
Page 112 and 113: Cars The Latin Square Desig
Page 114 and 115: JMP Demo
Page 116 and 117: Another Example of
Page 118 and 119: In general, if there are p treatmen
Page 120 and 121: Back to tire testing • Suppose th
Page 122 and 123: Module 4 - Summary • Most design
Page 124 and 125: 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 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 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
show all

Design of Experiments - US Army Conference on Applied Statistics

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?