Michael W. Trosset
The Department of Mathematics offers the following courses in probability and statistics:
Math 106: Elementary Probability and Statistics
This course introduces some basic ways of thinking about uncertain
phenomena. Quantitative reasoning skills and statistical literacy are
emphasized. An important theme is the role of probability in statistical
inference. Math 106 has no prerequisites and fulfills GER 1.
Math 351: Applied Statistics
This course introduces the basic concepts of statistical inference through
a careful study of several important procedures. Topics include 1-sample
and 2-sample location problems, the analysis of variance, and linear
regression. Most assignments involve applying probability models and
statistical methods to practical situations and/or actual data sets. No
previous knowledge of probability is assumed; Math 351 is recommended for
students who wish to take a single, self-contained semester of statistics
that emphasizes analyzing experimental data. Math concentrators with an
interest in applications are also encouraged to take this course, followed
by the more theoretical Math 401 and Math 452. Prerequisite: Math 112 or
permission of the instructor.
Math 352: Data Analysis
This course is a sequel to Math 351 (or its equivalent; in particular, a
modest degree of familiarity with the statistical computing language R
will be helpful). It continues the study of applied statistics,
introducing statistical methods in the context of case studies. Thus,
instead of organizing the material by method and illustrating the method
with simple examples (as in 351), the material will be organized by
application. Each case study will be holistic: we will discuss
experimental design, data collection, data management, exploratory and
inferential analyses, and presentation of results. This course was
especially designed for students who are involved in research projects
that entail collecting and analyzing experimental data.
When appropriate, some of the case studies may be derived from such
projects. Prerequisite: permission of the instructor.
Math 401: Probability
This course is devoted to mathematical probability.
Topics include the Kolmogorov probability axioms, conditioning and
independence, random variables, various discrete and continuous
probability distributions, expectation and various limit theorems.
Most assignments involve solving problems and/or deriving elementary
propositions. Prerequisites: Math 211, 212, 214. Suggested: Math 311.
Math 401 does not involve measure theory.
Math 452: Mathematical Statistics
This course is devoted to the mathematical theory of statistical
inference. Possible topics include the method of maximum likelihood, the
method of least squares and the theory of linear models, and various
decision-theoretic methods for set estimation and hypothesis testing.
Most assignments involve solving problems and/or deriving elementary
propositions. Prerequisite: Math 401. Suggested: Math 351.
Here are links to documents related to some of my current and/or recent courses:
Math 106: Elementary Probability and Statistics
Math 150: The History of Chance
Math 452: Mathematical Statistics
Math 459: Knowledge Discovery in
Bioinformatics