M.S. Statistics - Graduate Courses

OPR/MAT 561 Deterministic Models in Operations Research
OPR/STA 562 Stochastic Modeling in Operations Research
OPR/STA/MAT 563 System Modeling and Simulation
OPR/STA 564 Queueing Networks
MAT 570 Theory of Matrices
MAT 571 Graph Theory
MAT 572 Numerical Analysis
STA 574 Statistical Computer Packages
STA 575 Graduate Internship
STA 580 Statistical Inference
STA 581 Statistical Quality Control
STA 582 Time Series Modeling
STA 583 Sampling Methods
STA 584 Advanced Design and Analysis of Experiments
STA 585 Regression Analysis
STA 586 Non-parametric Methods
STA 587 Categorical Data Analysis
STA 588 Introduction to Biostatistics
STA 589 Survival Analysis
STA/OPR/MAT 575 Graduate Internship
STA/OPR/MAT 590 Master's Project/Thesis
STA/OPR/MAT 599 Independent Study
COS 562 Performance Analysis (Department of Computer Science)
AMS 535 Introduction to Epidemiological Research (Department of Applied Medical Science)
AMS 545 Applied Biostatistical Analysis (Department of Applied Aedical Science)
AMS 677 Regression Models in the Health Sciences (Department of Applied Aedical Science)


OPR/MAT 561 Deterministic Models in Operations Research
Formulation and analysis of deterministic models in operations research, linear programming, integer programming, project management, network flows, dynamicprogramming, nonlinear programming, game theory, and group projects on practical problems from business and industry. Prerequisite: MAT 152D or MAT 295 or permission of instructor. Cr 3.

OPR/STA 562 Stochastic Modeling in Operations Research
Formulation and analysis of stochastic models in operations research, Markov chains, birth-death models, Markov decision models, reliability models, inventory models, applications to real world problems, and group projects on practical problems from business and industry. Prerequisite: MAT 281 or MAT 380 or permission of instructor. Cr 3.

OPR/STA/MAT 563 System Modeling and Simulation
Basic simulation methodology, general principles of model building, model validation and verification, random number generation, input and output analysis, simulation languages, applications to computer and communication networks, manufacturing, business, and engineering will be considered, and group projects on practical problems from business and industry. Prerequisite: MAT 281 or MAT 380 or permission of instructor. Cr 3.

OPR/STA 564 Queueing Networks
Queueing and stochastic service systems, birth-death processes, Markovian queues, open and closed Jackson networks, priority queues, imbedded Markov chain models, optimal control and design, stochastic scheduling, applications to computer and communication networks, manufacturing, business, and engineering will be considered, and projects on practical problems from business and industry. Prerequisite: MAT 281 or MAT 380 or permission of instructor. Cr 3.

MAT 570 Theory of Matrices
The course will begin with a brief review of vector spaces and matrices and some related concepts such as determinants, rank, nonsingularity, change of bases, and applications. It will also cover elementary cannonical forms, Hermitian and symmetric matrices, and norms for vectors and matrices. Prerequisite: MAT 295. Cr 3.

MAT 571 Graph Theory
This course considers various properties of graphs and diagraphs and includes applications to optimization questions and networks. Prerequisite: MAT 290 or permission of instructor. Cr 3.

MAT 572 Numerical Analysis
The course examines numerical solutions of linear systems, eigenvalue location, roots for systems of equations, polynomial interpolation, numerical integration, and numerical solution of differential equations. Prerequisites: MAT 152D , MAT 153 , and MAT 295 . Cr 3.

STA 574 Statistical Computer Packages
The course will introduce two of the commonly used statistical packages such as SAS, MINITAB, SPSS, and S-Plus. Prerequisite: MAT 212 or MAT 282 or permission of instructor. Cr 3.

STA 575 Graduate Internship
The course is ideal for students who have had no work experience with statistical data analysis or mathematical modeling. Such students can try to locate paid or unpaid internship opportunities that might be available on-campus or off-campus. The students will submit to the graduate committee a formal written report on the internship experience. Prerequisite: graduate standing. Cr var.

STA 580 Statistical Inference
Sampling distributions such as Chi-square, t and F, order statistics, parametric point estimation covering methods of moments, maximum likelihood, and Bayesian techniques, concept of sufficiency and completeness, parametric interval estimation covering pivotal quantity method, parametric hypothesis testing covering GLR and UMP tests, and analysis of real and simulated data. Prerequisites: MAT 153 and MAT 282. Cr 3.

STA 581 Statistical Quality Control
Methods and philosophy of statistical process control, control charts for variables, control charts for attributes, CUSUM and EWMA control charts, some other statistical process control techniques, process capability analysis, and certain process design and improvements with experimental design. Prerequisite: MAT 282 . Cr 3.

STA 582 Time Series Modeling
Overview of the basic concepts of trend and seasonality, ARMA and ARIMA models, parameter estimation with asymptotic properties, forecasting techniques, spectral analysis, bivariate time series, and some special topics. Prerequisite: MAT 282. Cr 3.

STA 583 Sampling Methods
Simple random, stratified, systematic, cluster, and multi-stage sampling, PPS sampling, optimum sample size, use of auxiliary variables in sample surveys, ratio and regression estimates, double sampling, sources of error in surveys and ways of removing them, and methods of collecting data. Prerequisite: MAT 282 . Cr 3.

STA 584 Advanced Design and Analysis of Experiments
Factorial experiments, fractional replications in factorial experiments, BIB and PBIB designs, and response surface methodology. Prerequisite: MAT 484 or equivalent. Cr 3.

STA 585 Regression Analysis
Certain concepts of data reduction, simple linear regression using matrices, residual analysis, certain techniques to select a best regression equation, multiple regression, analysis of variance and covariance, and data analysis and computation using statistical package programs. Prerequisite: MAT 282. Cr 3.

STA 586 Nonparametric Methods
Empirical distribution functions and their properties, certain goodness-of-fit techniques, inference concerning quantiles, comparison of two and more treatments, rank tests in randomized complete designs, and some special topics. Prerequisite: MAT 282 Cr 3.

STA 587 Categorical Data Analysis
Two-way tables; generalized linear models; logistic and conditional logistic models; loglinear models; fitting strategies; model selection; residual analysis. Prerequisite: MAT 282 . Cr 3.

STA 588 Introduction to Biostatistics
Basic concepts of estimation and hypothesis testing, standardization of rates, life tables, analysis of categorical data, multiple regression including binary response regression models. Prerequisite: MAT 282 . Cr 3.

STA 589 Survival Analysis
Survival and reliability concepts, mathematics of survival models, parametric and non-parametric estimates from complete and censored data, Kaplan-Meier estimators, regression models including Poisson regression and Cox's proportional hazards model, time-dependent covariates, and analysis of rates. Prerequisite: MAT 282 . Cr 3.

AMS 535 Introduction to Epidemiologic Research
This course is intended to give students a basic foundation in principles for the conduction and interpretation of population-based studies of the distribution, etiology, and control of disease. Topics will include randomized experiments, non-randomized cohort studies, case-control studies, cross-sectional and ecological studies, causal inference, source of bias, and measures of effect. Recent publications from the epidemiologic and general medical literature will be used to illustrate the application of the concepts to specific epidemiologic issues. Cr 3.

AMS 545 Applied Biostatistical Analysis
This course is intended to give students a working understanding of the major types of biostatistical analysis used in laboratory sciences, clinical research, and public health. Topics will include estimation, descriptive statistics, crosstabulations and stratified analysis, life tables, multiple regression. The course is designed primarily for students with little formal training in biostatistics, but may also prove valuable to other students who desire a course providing an integrated approach to diverse biostatistical techniques within an applied framework. Students will learn to manipulate datasets, analyze them, and interpret the results using the SAS software package. Cr 3.

AMS 638 Practicum in Epidemiologic Research
This course is designed to provide students with direct experience in the formulation of epidemiologic hypotheses and the analysis and interpretation of data. Each student will frame a research question that can be addressed using a dataset available on campus or elsewhere in Maine. With guidance from faculty, each student will conduct data analyses and will write a report in the format of a journal article. Prerequisites: AMS 535 and AMS 545 or equivalent. Cr 4.

AMS 677 Regression Models in the Health Sciences
This course will familiarize students with the use of regression models for the analysis of epidemiologic and other biomedical data. Topics will include multiple linear regression, logistic regression, log-linear models, proportional hazard models, Poisson regression, generalized linear models, goodness of fit, and analysis of residuals and other diagnostics. Students will work on individual projects and will learn to use the SAS software package for conducting the analyses. Prerequisite: AMS 545 or equivalent. Cr 3.

COS 562 Performance Analysis
The course integrates system measurement, analytic modeling, and simulation modeling to develop computer system performance evaluation techniques. The approach will be problem-oriented with emphasis on benchmarking, simulation modeling and queueing models. Subjects covered will include system measurement, operational analysis, simulation modeling, analysis of simulation results, and mean value analysis. Prerequisites: MAT 380 or equivalent and some experience with an operating system. Cr 3.