Design of Experiments - US Army Conference on Applied Statistics

More documents

Recommendations

Info

Module 2 - Summary 1. Main message is that standard designs are optimal designs. 2. Optimal design generators can reproduce standard designs for routine problems.
Module 3 – Modern Screening Methods There is substantial new research in both design and analysis <strong>of</strong> screening experiments in the last 15 years. Much <strong>of</strong> this new research calls into question the conventional strategy <strong>of</strong> the standard use <strong>of</strong> regular fractional factorial designs for screening. We will introduce some <strong>of</strong> these new methods in this section. Many <strong>of</strong> the new designs are orthogonal but have more desirable aliasing properties than the regular fractional factorial designs previously shown.
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 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 94 and 95: Embarrassing Problem Case Suppose w
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

Create successful ePaper yourself

Delete template?

Save as template?