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Course Criteria
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3.00 Credits
Credits: 3 Design and implementation of sample surveys. Covers components of a survey; probability sampling designs to include simple random, systematic, Bernoulli, proportional to size, stratified, cluster and two-stage sampling; and ratio and regression estimators. Discusses practical problems in conducting a survey. Methods applied to case studies of actual surveys. Class project required. Prerequisites STAT 354 or 554.
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3.00 Credits
Credits: 3 Examination of a wide variety of case studies illustrating data-driven model building and statistical analysis. With each case study, various methods of data management, data presentation, statistical analysis, and report writing are compared. Prerequisites STAT 554 and working knowledge of SAS, 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 Cross-Listed with OR 645 Selected applied probability models, including Poisson processes, discrete- and continuous-time Markov chains, renewal and regenerative processes, semi-Markov processes, queuing and inventory systems, reliability theory, and stochastic networks. Emphasis on applications in practice, as well as analytical models. Prerequisites OR 542 or STAT 544, 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 CSI 672 Fundamental principles of estimation and hypothesis testing. Topics include limiting distributions and stochastic convergence, sufficient statistics, exponential families, statistical decision theory and optimality for point estimation, Bayesian methods, maximum likelihood, asymptotic results, interval estimation, optimal tests of statistical hypotheses, and likelihood ratio tests. Prerequisites STAT 544, ECE 528, 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 Single and multifactor analysis of variance, planning sample sizes, introduction to the design of experiments, random block and Latin square designs, and analysis of covariance. Prerequisites STAT 554 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 Simple and multiple linear regression, polynomial regression, general linear models, subset selection, step-wise regression, and model selection. Also covered are multicollinearity, diagnostics, and model building. Both the theory and practice of regression analysis are covered. Prerequisites STAT 554, matrix algebra, and working knowledge of SAS. 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 Distribution-free procedures for making inferences about one or more samples. Tests for lack of independence, association or trend, and monotone alternatives are included. Measures of association in bivariate samples and multiple classifications are discussed. Both theory and applications are covered. Students are introduced to appropriate statistical software. Prerequisites STAT 544 and 554. 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 Cross-Listed with CSI 678 Modeling stationary and nonstationary processes, autoregressive, moving average and mixed model processes, autocovariance functions, autocorrelation functions, partial autocorrelation functions, spectral density functions, identification of models, estimation of model parameters, and forecasting techniques. Prerequisites STAT 544 or equivalent. 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 Focuses on biostatistical aspects of design and analysis of biomedical studies, including epidemiologic observational studies and randomized clinical trials. Topics include randomization principle, confounding, ethics in human experimentation, methods of randomization, stratification, primary outcome analyses, covariate-adjusted analyses, epidemiologic measures, and sample size and power computation. Prerequisites STAT 554 or STAT 535 and a working knowledge of a statistical software package, such as SAS or SPSS. 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 Standard techniques of applied multivariate analysis. Topics include review of matrices, T square tests, principle components, multiple regression and general linear models, analysis of variance and covariance, multivariate ANOVA, canonical correlation, discriminant analysis, classification, factor analysis, clustering, and multidimensional scaling. Computer implementation via a statistical package is an integral part of the course. Prerequisites STAT 554 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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