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Lecture 12. Today: 4.2, 4.3-4.6 Next day: more 4.3-4.6 Assignment #4: Chapter 4 - 13 (a,b), 14, 15, 23, additional question at end of these notes Due in 2 weeks. Example. Speedometer cables can be noisy because of shrinkage in the plastic casing material
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Lecture 12 • Today: 4.2, 4.3-4.6 • Next day: more 4.3-4.6 • Assignment #4: Chapter 4 - 13 (a,b), 14, 15, 23, additional question at end of these notes • Due in 2 weeks
Example • Speedometer cables can be noisy because of shrinkage in the plastic casing material • An experiment was conducted to find out what caused shrinkage • Engineers started with 6 different factors: • A braiding tension • B wire diameter • C liner tension • D liner temperature • E coating material • F melt temperature
Example • Response is percentage shrinkage per specimen • There were two levels of each factor • A 26-2 fractional factorial • The purpose of such an experiment is to determine which factors impact the response
Example • Constructing the design • Write down the 16 run full factorial • Use interaction columns to set levels of the other 2 factors • Which interaction columns do we use? • Table 4A.2 gives 16 run minimum aberration (MA) designs • E=ABC; F=ABD
Example • Results
Example • Which effects can we estimate? • Defining Contrast Sub-Group: I=ABCE=ABDF=CDEF • Word-Length Patter: • Resolution:
Example • Effect Estimates and QQ-Plot:
Comments • Use defining contrast subgroup to determine which effects to estimate • Can use qq-plot or Lenth’s method to evaluate the significance of the effects • Fractional factorial designs allow you to explore many factors in relatively few trials • Trade-off run-size for information about interactions
Techniques for Resolving Ambiguities • Suppose the experiment in the previous example was performed and the AC=BE interaction was identified as significant (in addition to the A and E main effects) • Which is the important interaction AC or BE or both? • Prior knowledge may indicate that one of the effects is not important • Can conduct a follow-up experiment
Optimal Design Approach (4.4.2) • Can perform a follow-up experiment to “de-alias” the AC and BE interaction, but what treatments should be run? • Would like to estimate the model with all potentially significant effects • A, E, AC, BE • The experiment is not completely randomized since the follow-up runs are performed only after original experiment • Include a block effect • Model:
Optimal Design Approach (4.4.2) • The best set of new trials should optimize some design criterion • Should estimate the model of interest in best possible manner • Already have initial (say 16) trials, so design criterion is driven by original experiment and the model • D-optimality: • Motivation:
Optimal Design Approach (4.4.2) • Ds-optimality:
Optimal Design Approach (4.4.2) • Algorithm:
Assignment Question • Suppose in the cable shrinkage example, effects A, E and AC=BE are identified as signifincat • To resolve the aliasing of the interaction effects, a follow-up experiment with 4 trials is to be performed • What 4 trails should be performed? • Use the D-optimality criterion and report the value of Dmax