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Course Criteria
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3.00 Credits
Credits: 3 Cross-Listed with CSI 773 Exploratory data analysis provides a reliable alternative to classical statistical techniques that are designed to be the best possible when stringent assumptions apply. Topics include graphical techniques such as scatter plots, box plots, parallel coordinate plots and other graphical devices, re-expression and transformation of data, influence and leverage, and dimensionality reduction methods such as projection pursuit. Prerequisites A 300-level course in statistics (STAT 554 is strongly recommended). Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered F
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3.00 Credits
Credits: 3 Cross-Listed with SYST 664 Introduces decision theory and relationship to Bayesian statistical inference. Teaches commonalities, differences between Bayesian and frequentist approaches to statistical inference, how to approach a statistics problem from the Bayesian perspective and how to combine data with informed expert judgment in a sound way to derive useful and policy-relevant conclusions. Teaches necessary theory to develop firm understanding of when and how to apply Bayesian and frequentist methods, and practical procedures for inference, hypothesis testing, and developing statistical models for phenomena. Teaches fundamentals of Bayesian theory of inference, including probability as a representation for degrees of belief, likelihood principle, use of Bayes Rule to revise beliefs based on evidence, conjugate prior distributions for common statistical models, and methods for approximating the posterior distribution. Introduces graphical models for constructing complex probability and decision models from modular components. Prerequisites STAT 544 or 554, or equivalent. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered S
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3.00 Credits
Credits: 3 Analyzes cross-classified categorical data in two and higher dimensions. Familiarity with the basic test for two-way contingency tables and elementary regression and analysis of variance as presented in STAT 554 is presumed. Topics include association tests and measures of association in two- and three-dimensional contingency tables, logistic regression, and log linear models. Computer statistical package used extensively for data analysis. Prerequisites STAT 554, STAT 656, and working knowledge of SAS. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AS
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3.00 Credits
Credits: 3 Survival Analysis is a class of statistical methods for studying the occurrence and timing of events. In medical research, the events may be deaths, and the objective is to determine factors affecting survival times of patients following treatment, usually in the setting of clinical trials. Methods can also be applied to the social and natural sciences and engineering where they are known by other names (reliability, event history analysis). Concepts of censored data, time-dependent variables, and survivor and hazard functions are central. Nonparametric methods for comparing two or more groups of survival data are studied. The Cox regression model (proportional hazards model), Weibull model, and the accelerated failure time model are studied in detail. Concepts are applied to analysis of real data from major medical studies using SAS software. Prerequisites STAT 544, STAT 535 or 554, and working knowledge of SAS. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AF
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3.00 Credits
Credits: 3 Presents modern statistical approaches to the analysis of longitudinal data, i.e., data collected repeatedly on experimental units over time (or other conditions). Topics include linear mixed effects models, generalized linear models for correlated data (including generalized estimating equations), and computational issues and methods for fitting models. Prerequisites STAT 544, STAT 656, and working knowledge of a statistical software package. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AS
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3.00 Credits
Credits: 3 Continuation of STAT 574. Regression estimators for complex sampling designs, domain estimation, two-phase sampling, weighting adjustments for nonresponse, imputation, nonresponse models, measurement error models, introduction to variance estimation. Applications to case studies of actual surveys. Prerequisites STAT 554, STAT 574, and working knowledge of SAS. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered AS
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3.00 Credits
Credits: 3 Cross-Listed with OR 677/SYST 677 Introduces concepts of quality control and reliability. Acceptance sampling, control charts, and economic design of quality control systems are discussed, as are system reliability, fault-tree analysis, life testing, repairable systems, and the role of reliability, quality control, and maintainability in life-cycle costing. Role of MIL and ANSI standards in reliability and quality programs also considered. Prerequisites STAT 544 or 554, or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0
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3.00 Credits
Credits: 3 Cross-Listed with OR 675 Introduces component and system reliability, their relationship, and problems of inference. Topics include component lifetime distributions and hazard functions, parameter estimation and hypothesis testing, life testing, accelerated life testing, system structural functions, and system maintainability. Prerequisites STAT 544 or 554, or permission of instructor. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered IR
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3.00 Credits
Credits: 3 Multivariate analysis of variance (MANOVA, MANCOVA), multiple regression, and logistic regression. Students apply multivariate statistical methods using statistical software to analyze and interpret data in health care research. Prerequisites STAT 535 or HSCI 799. Notes Cannot be used to satisfy requirements for MS in statistical science. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered IR
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3.00 Credits
Credits: 3 Coverage of discriminate analysis, canonical correlation analysis, structural analysis (LISREL and path analysis), and factor analysis. Prerequisites STAT 700, HSCI 800, or equivalent. Notes Cannot be used to satisfy requirements for MS in statistical science. Hours of Lecture or Seminar per week 3 Hours of Lab or Studio per week 0 When Offered IR
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