Christopher T. Franck

Regression Analysis

• Taught as a comparative assessment between classical statistical regression approaches and (mostly tree-based) machine learning methods. A contemporary exploration of Leo Breimen’s classic paper Statistical Modeling: The Two Cultures.

Probability and Distributions

• Probability theory, including set theoretic and combinatorial concepts; random experiments; discrete and continuous random variables and their probability mass and density functions; expectation and variance; conditional probability; maximum likelihood estimation.

Scientific Writing for Statistics

• Training in scientific writing with a focus on statistical writing including structure, effectively motivating research, reviewing scientific literature, and providing and responding to feedback

Intro to Categorical Data Analysis

• Statistical techniques for frequency data including goodness-of-fit, tests and measures of association for two-way tables, log-linear models for multidimensional tables, parameter estimation, model selection, incomplete tables, ordinal categories, and logistic regression.

Inference Fundamentals

• Classical and resampling-based approaches for point estimation, interval estimation, and hypothesis testing with emphasis on using R, visualizing data, computing and interpreting effect sizes, and conducting Monte Carlo simulation.

Biostatistics

• Responsible for developing and teaching biostatistics curriculum to first-year medical students at Virginia Tech Carilion School of Medicine.

Introduction to Statistical Program Packages

• Taught data management, visualization, analysis, modeling, computation, hypothesis testing, simulation, and the Bayesian paradigm to first-year statistics graduate students using R, LaTeX, and SAS