Winter 2019 Class Schedule
Course | Title | Instructor | Lecture | Discussion |
---|---|---|---|---|
202 | Introduction to Statistics | Kuyper | MWF 9:00-9:50am | |
202 Introduction to StatisticsData 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. | ||||
Arend KuyperAssistant 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. | ||||
210 | Introductory Statistics for the Social Sciences | Lewis | MWF 12:00-12:50pm | T 5pm or Th 4pm |
210 Introductory Statistics for the Social SciencesA 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). | ||||
| ||||
232 | Applied Statistics | Tanner | TTh 12:30-1:50pm | |
232 Applied StatisticsBasic 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. | ||||
Martin TannerProfessor 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. | ||||
301-2 | Data Science 2 | Kuyper | MW 4:00-5:20pm | |
301-2 Data Science 2Data Science 2 focuses on foundational analytic methods such as linear regression, resampling, and tree-based methods. Prerequisite: STAT 301-1 or consent of instructor. | ||||
Arend KuyperAssistant 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. | ||||
320-2 | Statistical Theory and Methods 2 | W. Jiang | TTh 11:00-12:20pm | |
320-2 Statistical Theory and Methods 2Sampling, parameter estimation, confidence intervals, hypothesis tests. Prerequisite: STAT 320-1 or MATH 310-1. | ||||
Wenxin JiangProfessor 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. | ||||
320-2 | Statistical Theory and Methods 2 | Andrews | MWF 2:00-2:50pm | |
320-2 Statistical Theory and Methods 2Sampling, parameter estimation, confidence intervals, hypothesis tests. Prerequisite: STAT 320-1 or MATH 310-1. | ||||
Beth AndrewsAssociate 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. | ||||
350 | Regression Analysis | H. Jiang | TTh 12:30-1:50pm | |
350 Regression AnalysisSimple 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 | ||||
Hongmei JiangAssociate 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. | ||||
350 | Regression Analysis | H. Jiang | TTh 2:00-3:20pm | |
350 Regression AnalysisSimple 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 | ||||
Hongmei JiangAssociate 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. | ||||
359 | Topics in Statistics | Tanner | TTh 9:30-10:50am | |
359 Topics in StatisticsTopics 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. | ||||
Martin TannerProfessor 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. | ||||
365 | Introduction to Financial Statistics | Severini | MWF 11:00-11:50am | |
365 Introduction to Financial StatisticsStatistical methods for analyzing financial data. Models for asset returns, portfolio theory, parameter estimation. Prerequisites: STAT 320-3, MATH 240-0. | ||||
Thomas SeveriniProfessor 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. | ||||
383 | Probability and Statistics for ISP | Severini | MWF 1:00-1:50pm | T 1:00-1:50pm |
383 Probability and Statistics for ISPProbability 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 | ||||
Thomas SeveriniProfessor 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. | ||||
420-2 | Stat Theory/Meth 2 | Wang | TTh 11:00-12:20pm | |
420-2 Stat Theory/Meth 2Methods of estimation, hypothesis tests, confidence intervals, least squares, likelihood methods, and large-sample methods. | ||||
Ji-Ping WangProfessor of Statistics, Adjunct Professor of Molecular BioSciences Research interests include: statistical applications in bioinformatics and computational biology | ||||
461 | Advanced Topics in Statistics | W. Jiang | TTh 12:30-1:50pm | |
461 Advanced Topics in StatisticsNo description available. | ||||
Wenxin JiangProfessor 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. | ||||
465 | Statistical Methods for Bioinformatics and Computational Biology | Wang | TTh 9:30-10:50am | |
465 Statistical Methods for Bioinformatics and Computational BiologyAn introduction of statistical methodologies in cutting-edge fields of computational biology and bioinformatics topics including microarray gene expression data analysis; biological sequence analysis; EST and SAGE data analysis. | ||||
Ji-Ping WangProfessor of Statistics, Adjunct Professor of Molecular BioSciences Research interests include: statistical applications in bioinformatics and computational biology |