Course Description: This course will cover the major topics in the design of experiments, with particular emphasis on classical as well as recently developed methods for spatial sampling design. The mathematical level is mostly elementary, but the students should be at least somewhat familiar with linear model, at the level of Appendix A through A.2.5 in Cox and Reid (2000). The rest of Appendix A will be covered in the course. The focus of this course will be on understanding key concepts involved in the design of experiments, and we will not cover any of the abstract algebra behind experimental design. The plan is to cover the first six chapters of Cox and Reid, selected chapters of Muller, and additional topics as time and interest allow. Covered topics include: randomization, blocking, Latin squares, balanced incomplete block designs, factorial experiments, Taguchi methods, optimal design, space filling designs, designs for variogram estimation, designs for spatial prediction, and Bayesian design.
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