DEPARTMENT OF STATISTICS AND DATA SCIENCE

Data collection, summarization, correlation, regression, probability, sampling, estimation, tests of significance. Does not require calculus and makes minimal use of mathematics. May not receive credit for both STAT 202-0 and STAT 210-0.

Elizabeth Tipton
Associate Professor of Statistics

Research interests include: social statistics, education statistics, randomized experiments, meta-analysis.

Data Science 1 focuses on data management, manipulation, and visualization skills and techniques for exploratory data analysis. Prerequisite: STAT 202-0 or equivalent

Assistant Professor of Instruction

Kuyper’s work is dedicated to the development and implementation of methods, techniques, and strategies for teaching statistics. His current focus is on incorporating data science methods and topics into the introductory statistics course and developing data science courses with a focus on application.

Sample spaces, computing probabilities, random variables, distribution functions, expected values, variance, correlation, limit theory. May not receive credit for both STAT 320-1 and any of STAT 383-0, MATH 310-1, MATH 311-1, MATH 314-0, MATH 385-0, EECS 302-0, or IEMS 202-0. Corequisites: STAT 202-0 or STAT 210-0, MATH 234-0.

Associate Professor of Statistics

Research interests include: time series analysis, spatial statistics, stochastic processes and their applications, robust statistics, extreme value theory, and financial mathematics. The focus of her recent research is model fitting and prediction for nonlinear, non-Gaussian processes observed over space and time. This work has applications in the areas of economics and finance, the geosciences, and signal processing.

First Quarter: Design of experiments and surveys, numerical summaries of data, graphical summaries of data, correlation and regression, probability, sample mean, sample proportion, confidence intervals and tests of significance, one and two sample problems, ANOVA. Second Quarter: Simple linear regression, inference, diagnostics, multiple regression diagnostics, autocorrelation, 1-way ANOVA, power and sample size determination, 2-way ANOVA, ANCOVA, randomized block designs.

Professor of Statistics

Research interests include: Markov chain Monte Carlo methods for Bayesian and frequentist inference, nonparametric estimation of the hazard function for right-censored and interval-censored data, methodology for ecological Inference, applications of multiple imputation to censored regression data, as well as models and measures of interrater agreement/disagreement.

Statistical methods for describing and analyzing multivariate data. Principal component analysis, factor analysis, canonical correlation, clustering. Emphasis on statistical and geometric motivation, practical application, and interpretation of results. Prerequisites: STAT 320-2, MATH 240-0.

Professor of Statistics

Research interests include: likelihood-based statistical methods such as maximum likelihood estimation and tests and confidence regions based on the likelihood ratio statistic, the application of statistical methods to the analysis of sports data.

Development, analysis, interpretation, use, and misuse of statistical data and methods for description, evaluation, and political action regarding war, disappearances, justice, violence against women, trafficking, profiling, elections, hunger, refugees, discrimination, etc. Prerequisites: Two of STAT 325-0, STAT 350-0, STAT 320-2,STAT 302-3; or ECON 381-1, ECON 381-2; or MMSS 386-1, MMSS 386-2; or IEMS 303-0, IEMS 304-0.

Professor of Statistics

Spencer works in the interface of statistics and public policy with a special focus on the design and evaluation of large-scale statistical data programs. He is currently conducting a cost-benefit analysis to see how much accuracy is needed for the next census (in 2020), and at what cost. With Prof. Seth Stein (Earth & Planetary Sciences), he is jointly studying accuracy of seismic hazard forecasts. Spencer has developed statistical methods for assessing the accuracy of verdicts in criminal cases when the truth is unknown.

Inference and fitting of generalized linear models with application to classical linear models, binomial and multinomial logit models, log-linear models, Cox's proportional hazards model and GEE's for longitudinal data. Prerequisites: STAT 350-0 and STAT 420-3

Associate Professor of Statistics

Research interests include: time-series analysis, nonlinear time-series modeling with emphasis to threshold models, statistical inference for infectious diseases and epidemiology, statistical ecology, statistical applications to biomedical research.