DEPARTMENT OF STATISTICS AND DATA SCIENCE

A mathematical introduction to probability theory and statistical methods, including properties of probability distributions, sampling distributions, estimation, confidence intervals, and hypothesis testing. STAT 210-0 is primarily intended for economics majors. May not receive credit for both STAT 202-0 and STAT 210-0. Prerequisite: strong background in high school algebra (calculus is not required).

Introduction to the knowledge, skills, and tools required to visualize data of various formats across statistical domains and to create quality visualizations for both data exploration and presentation. 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.

Comparison of parameters, goodness-of-fit tests, regression analysis, analysis of variance, and nonparametric methods. Prerequisites: STAT 320-2, MATH 240-0.

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.

Methods of designing experiments and analyzing data obtained from them: one-way and two-way layouts, incomplete block designs, factorial designs, random effects, split-plot and nested designs. Prerequisite: STAT 320-1 or equivalent.

Associate Professor of Statistics

Jiang focuses on developing statistical and computational methodologies to analyze and understand the massive amount of data generated by high throughput biological technologies, especially microarray and next generation sequencing based genomics, methylation, and metagenomics data analysis.

Topics in theoretical and applied statistics, to be chosen by the instructor. This course may be taken more than once for credit. Prerequisite: consent of instructor.

Professor of Statistics

Research interests include: statistical theory, data sciences, biostatistics, econometrics, and applications in social sciences. He has published papers on performance measurements in data mining, Bayesian statistics based on assumptions of moments, model selection and combination, and partial identification.

Theories of inference, multivariate methods, and contingency tables.

Professor of Statistics

Research interests include: statistical theory, data sciences, biostatistics, econometrics, and applications in social sciences. He has published papers on performance measurements in data mining, Bayesian statistics based on assumptions of moments, model selection and combination, and partial identification.