This course is intended to provide students with the skills necessary to digest, critique, and express every-day statistics and to use statistical thinking to answer questions in their own lives. Students will be exposed to and produce descriptive statistics, including measures of central tendency & spread, as well as common visual representations of data. The bulk of the class will be devoted to giving students the tools needed to analyze and critique statistical claims, including an understanding of the dangers of confounding variables and bias, the advantages and limitations of various study designs and statistical inference, and how to carefully read and parse claims which attempt to use numbers to sway their audience. The class will examine this material in authentic contexts such as political polling, medical decision making, online dating, and personal finance. This course is primarily aimed at students whose majors do not require mathematics or statistics.

An introduction to the fundamental ideas and methods of statistics for analyzing data. Topics include descriptive statistics, inference, and hypothesis testing. This course introduces statistical concepts from the perspective of statisticians and mathematicians, with concepts illustrated by simulation. Students will engage with statistics using the data analysis software R. Designed for students who plan to continue to study statistics and/or apply statistical methods to future work in the sciences or other fields. The course is accessible to those who have not yet had calculus.

Probability is the mathematics of uncertainty. We begin by developing the basic tools of probability theory, including counting techniques, conditional probability, and Bayes's Theorem. We then survey several of the most common discrete and continuous probability distributions (binomial, Poisson, uniform, normal, and exponential, among others) and discuss mathematical modeling using these distributions. Often we cannot calculate probabilities exactly, and we need to approximate them. A powerful tool here is the Central Limit Theorem, which provides the link between probability and statistics. Another strategy when exact results are unavailable is simulation. We examine Markov chain Monte Carlo methods, which offer a means of simulating from complicated distributions.  

This is an applied regression analysis course that involves hands-on data analysis. Topics covered during the semester include simple and multiple linear regression models, model diagnostics and remedial measures, matrix representation of linear regression models, model comparison and selection, generalized linear regression models (e.g. binary logistic regression, multinomial logistic regression, ordinal logistic regression, and Poisson regression). Statistical language R will be used throughout the course to realize fitting linear (or generalized linear) regressions models, model diagnostics, model comparison and selection, and simulations.