Constructing and analyzing statistical experimental designs;
blocking, randomization, replication, and interaction; complete
and incomplete block designs; factorial experiments; repeated
measures; confounding effects; orthogonal arrays; computer
experiments and simulations; design and analysis for generalized
linear models.
Athena Title
Applied Experimental Designs
Prerequisite
STAT 4230/6230
Semester Course Offered
Offered fall, spring and summer
Grading System
A - F (Traditional)
Student Learning Outcomes
Students will choose sound and suitable experimental designs for a given research objective. This includes evaluating the choice of response variable, identifying and deciding how to handle extraneous sources of variability, and evaluating the pros and cons of various design strategies, such as blocking.
Students will explore real data sets using a variety of graphical and numerical methods.
Students will analyze data using R and/or Python, and write reports using professional typesetting system (e.g. LaTeX).
Students will decompose the variability in any experimentally-generated data set into components corresponding to the factors of the design and find the parallel decompositions of the sums of squares and degrees of freedom. Conduct formal inference from experimental data by constructing the appropriate estimates and F-tests.
Students will design an efficient experiment, collect data (if applicable), analyze it and draw meaningful conclusions.
