Previous Courses Taught

1. Elementary Statistics (Whitman College, Math 128).

This course introduces students to basic tools for describing and summarizing data as well as methods of statistical inference such as confidence intervals and hypothesis tests. The randomization approach used in the course allows students to develop a deeper understanding of the fundamental idea of statistical inference. A web-based statistical applet is used throughout the course.

2. Introduction to Biostatistics (Colorado State University, Stat 307).

Biostatistical methods; confidence intervals, hypothesis tests, simple correlation and regression, one-way analysis of variance.

3. Statistics with Applications (Whitman College, Math 247).

An introduction to statistics for students who have taken at least one course in calculus. This course focuses on introducing statistical concepts and inference through active learning assignments. Students learn about the process of statistical investigations. This includes data collection and exploration, methods of statistical inference, and the ability to draw appropriate conclusions. The widely-used statistical software R will be used in addition to web-based applets.

4. Linear Algebra (Whitman College, Math 240).

This course first considers the solution set of a system of linear equations. The ideas generated from systems of equations are then generalized and studied in a more abstract setting, which considers topics such as matrices, determinants, vector spaces, inner products, linear transformations, and eigenvalues.

5. Statistical Modeling (Whitman College, Math 248).

This course follows introductory statistics by investigating more complex statistical models and their application to real data. The topics may include simple linear regression, multiple regression, non-parametric methods, and logistic regression. A statistical software package will be used.

6. Probability Theory (Whitman Collge, Math 349).

A formal introduction to probability and randomness. The topics of the course include but are not limited to conditional probability, Bayes’ Theorem, random variables, the Central Limit Theorem, expectation and variance. Both discrete and continuous probability distribution functions and cumulative distribution functions are studied.

7. Statistical Theory (Whitman Collge, Math 438).

This course studies the mathematical theory of statistics with a focus on the theory of estimation and hypothesis tests. Topics may include properties of estimators, maximum likelihood estimation, convergence in probability, the central limit theorem, order statistics, moment generating functions, and likelihood ratio tests. A statistical software package will be used.