Syllabus Information |
Fall 2024
Apr 26, 2025 |
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| DEF 5007P - Dsgn Exp (DOE) II Advanced |
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Associated Term:
Fall 2024
Learning Objectives: Using a Socratic teaching style alongside class examples, case studies, manipulatives, and software practice, students who take this class will learn how to: Size experiments with binary responses Identify outliers quickly and learn techniques to recover from them Design an experiment that cannot be fully randomized (split-plot designs) Find the best possible solution for multiple responses with competing interests (desirability) Analyze an experiment with missing data Nest designs to control against multiple nuisance factors Learn about other very popular fractional designs to further increase resource efficiency (think 11 factors in 12 runs!) Holistically evaluate multiple possible experimental designs Use random and non-random factors together Properly review all the numbers in an ANOVA table (not just p-values!) Use computer generated factorial designs to optimize the size of an experiment Capitalize on the intersection between model and simulation (M&S) with factorial experimentation (DOE) Required Materials: Required Prerequisite: DEF 5003P - Design of Experiments (DOE) I: Introduction to DOE Technical Requirements: Laptop with access to internet and spreadsheet software for calculations |
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