5. Process Improvement
1. Introduction Definition of experimental design Uses Steps	2. Assumptions Measurement system capable Process stable Simple model Residuals well-behaved
3. Choosing an Experimental Design Set objectives Select process variables and levels Select experimental design Completely randomized designs Randomized block designs Full factorial designs Fractional factorial designs Plackett-Burman designs Response surface designs Adding center point runs Improving fractional design resolution Three-level full factorial designs Three-level, mixed-level and fractional factorial designs	4. Analysis of DOE Data DOE analysis steps Plotting DOE data Modeling DOE data Testing and revising DOE models Interpreting DOE results Confirming DOE results DOE examples Full factorial example Fractional factorial example  Response surface example
5. Advanced Topics When classical designs don't work Computer-aided designs D-Optimal designs Repairing a design Optimizing a process Single response case Multiple response case Mixture designs Mixture screening designs Simplex-lattice designs Simplex-centroid designs Constrained mixture designs Treating mixture and process variables together Nested variation Taguchi designs John's 3/4 fractional factorial designs Small composite designs An EDA approach to experiment design	6. Case Studies Eddy current probe sensitivity study Sonoluminescent light intensity study
7. A Glossary of DOE Terminology	8. References
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