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ME 350 – Lecture 14 – DOE Part 2 Design of Experiments - at Grainger in reference section - covering chapters 17 2 k Factorial Design Determination of Statistically Significant Effects

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2 k Factorial Design - Review “k” refers to the number of variables being tested Assumes a linear, or near linear output response when changing an input variable (very typical) Tests points only at the extremes (more efficient)
2 3 Factorial Design Example Study on the alertness of students in the morning: Variables Design Matrix Variables low high 1. Hours of Sleep 4 8 2. Ounces of Coffee 6 12 3. Number of Donuts 1 2 Test x1 x2 x3 Alertness (%) 1 -1 -1 -1 56 2 -1 -1 1 43 3 -1 1 -1 68 4 -1 1 1 59 5 1 -1 -1 72 6 1 -1 1 62 7 1 1 -1 89 8 1 1 1 75

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Effect of Variables? Graphically, the “effect” of variable 1 is the difference between the average results of the planes food sleep 62 (+,-,+) coffee 75 (+,+,+) 59 (-,+,+) 43 (-,-,+) 56 (-,-,-) 72 (+,-,-) 89 (+,+,-)
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