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.

This is a calculus-based introductory statistics course. Topics covered include data collection, data visualization, descriptive statistics, linear regression, sampling schemes, design of experiment, probability, random variables (both discrete and continuous cases), Normal model, statistical tests and inference (e.g. one-sample and two-sample z-tests and t-tests, chi-square test, etc). Statistical language R will be used throughout the course to realize data visualization, linear regression, simulations, and statistical tests and inference.

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), and basic time-series autoregressive AR(p) models. 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.

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.

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.

This is a calculus-based introductory statistics course. Topics covered include data collection, data visualization, descriptive statistics, linear regression, sampling schemes, design of experiment, probability, random variables (both discrete and continuous cases), Normal model, statistical tests and inference (e.g. one-sample and two-sample z-tests and t-tests, chi-square test, etc). Statistical language R will be used throughout the course to realize data visualization, linear regression, simulations, and statistical tests and inference.

This course focuses on statistical methods for causal inference, with an emphasis on how to frame a causal (rather than associative) research question and design a study to address that question. What implicit assumptions underlie claims of discrimination? Why do we believe that smoking causes lung cancer? We will cover both randomized experiments – the history of randomization, principles for experimental design, and the non-parametric foundations of randomization-based inference – and methods for drawing causal conclusions from non-randomized studies, such as propensity score matching. Students will develop the expertise necessary to assess the credibility of causal claims and master the conceptual and computational tools needed to design and analyze studies that lead to causal inferences. Examples will come from economics, psychology, sociology, political science, medicine, and beyond. Previous exposure to the statistical software R is expected; students who have not previously coded in R may enroll but should expect to put in additional effort to learn this skill.