Course Number
Primary Program
Applied and Computational Mathematics
Mode of Study
Virtual Live

Statistically designed experiments are plans for the efficient allocation of resources to maximize the amount of empirical information supporting objective decisions. Design of experiments is widely applicable to physical, health, and social sciences, business, and government. This course covers the principles and concepts of experimental design and analysis of the general linear model. Design building elements of blocking, randomization, and replication within the context of basic comparative experimentation are extended to concepts of nested and crossed effects, fixed and random effects, aliasing and confounding, and power and sample size. Specific design structures include completely random, randomized block, Latin squares and hypercubes, factorial, fractional factorial, hierarchical/nested, response surface, and space-filling designs. Developing problem solving skills for constructing a variety of designs and making inference on parameters for the associated general linear models are main objectives for the course. Assignments focusing on statistical computation will require suitable statistical software (e.g., RStudio). Assignments focusing on extensive analysis and interpretation will employ JMP.

Course Prerequisite(s)

Multivariate calculus, linear algebra, and one semester of graduate probability and statistics (e.g., EN.625.603 Statistical Methods and Data Analysis). Some computer-based homework assignments will be given.