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.

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.

Basic concepts of using statistical models to draw conclusions from experimental and survey data. Topics include simple linear regression, multiple regression, analysis of variance, and analysis of covariance. Practical application of the methods and the interpretation of the results will be emphasized. Prerequisites: STAT 202-0, STAT 210-0, or equivalent; MATH 220-0.

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.

Sampling, parameter estimation, confidence intervals, hypothesis tests. Prerequisite: STAT 320-1 or MATH 310-1.

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.

Simple linear regression and correlation, multiple regression, residual analysis, selection of subsets of variables, multi-collinearity and shrinkage estimation, nonlinear regression. Prerequisite or corequisite: STAT 320-2

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: 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.

Probability and statistics. Ordinarily taken only by students in ISP; permission required otherwise. May not receive credit for both STAT 383-0 and any of STAT 320-1; MATH 310-1, MATH 311-1, MATH 314-0, MATH 385-0; EECS 302-0; or IEMS 202-0. Prerequisites: MATH 281-1,MATH 281-2, MATH 281-3; PHYSICS 125-1, PHYSICS 125-2, PHYSICS 125-3.

*NOTE: This course does not count toward a statistics major or minor

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.

No description available.

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.