MATH 373 - Experimental Design & Statistical Analysis

3 credits
An introduction to experimental and quasi-experimental designs intended for causal inference. In this course, students will learn how to effectively design an experiment in such a way that the statistical analysis objectively, substantively, and validly answers a given research question (given the contextual constraints of the physical or social environment). They will also learn the statistical tools necessary to analyze the resulting data to reach their conclusions. Statistical analysis techniques for balanced and unbalanced designs are introduced, including 1-way ANOVA, 2- and 3-way ANOVA with fixed, random, & mixed effects, hierarchically nested designs, repeated measures ANOVA, ANCOVA, and a brief introduction to general linear models and multilevel modeling. Additional topics may include sample size estimation, power, multiple comparison protocols, MANOVA, non-parametric analyses, and response surface methods.

Prerequisite(s): MATH 301 with grade of “C-” or better
Corequisite(s): None