Course Description
Theory and methods for constructing and analyzing designed experiments are considered. Basic concepts in the design of experiments, ANOVA, completely randomized designs, complete and incomplete block designs, cross-over designs, factorial designs, split-plot experiments, non-regular designs, designs for generalized linear models, online experiments, global optimization, computer experiments, and space-filling designs will be covered.
Athena Title
Design Analysis Experiments
Prerequisite
STAT 6420 or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Course Objectives
An in-depth discussion of the concepts and systematic development of the theory of experimental design will be emphasized. The students will obtain a thorough understanding of the basic concepts in design of experiments, including experimental and observational units, randomization, blocking and replication. They will be exposed to different kinds of experimental designs along with their strengths and weaknesses. They will learn how to search for, or construct efficient experiments, analyze and interpret the data collected from those experiments with the help of advanced statistical software packages.
Topical Outline
The course will cover the following topics: analysis of variance and covariance; completely randomized designs; randomized complete block and incomplete block designs; multi-way layout; row-column designs; repeated measures designs; cross-over designs; non-regular designs; full factorial and fractional factorial designs; designs involving both quantitative and qualitative factors at different levels; split-plot experiments; non-regular designs; designs for generalized linear models; approximate design theory and algorithms; online experiments; global optimization; and computer experiments with space-filling designs. At the discretion of the instructor, additional topics may be covered.
Syllabus
Public CV