Students will learn basic concepts and methods for designing an
experiment and analyzing data from the experiment. They will 
also learn strengths and weaknesses of these methods, enabling 
them to assess which method is preferable for a particular 
problem. They will obtain a thorough understanding of the basic 
concepts in design of experiments, including experimental and 
observational units, randomization, blocking and replication. 
Students will also learn how to deal with special treatment 
structures, including the factorial treatment structure. 
Inference methods for assessing differences among treatments or 
for specified comparisons of interest are discussed for each of 
the designs. In addition, students will learn how to select 
appropriate replication of treatments to meet objectives of the 
experiment. They will learn how to perform inferences based on 
the methods in this course by using a statistical software 
package.

The topics covered in this course focus on different plans for
collecting data in designed experiments and on the analysis of
such data. The concepts of randomization, blocking and
replication will be covered. Designs considered include
completely randomized designs, randomized complete and 
incomplete block designs, Latin square designs and other row-
column designs, and split-plot designs. Special treatment 
structures will be considered, such as comparisons to a control 
and factorial treatment structure. Construction of fractional 
factorials will also be considered. Analysis of variance, 
treatment contrasts, and sums of squares play important roles 
in the analyses. Both fixed and mixed effects models are 
considered.