ECE 901 - Electromagnetic Wave Theory I - - MWF Kingsbury S320, Chamberlin
Credits: 3.00; Maxwell's equations; plane wave propagation; reflection and refraction; guided wave propagation; waveguides; simple resonators; elements of microwave circuits, linear and aperture antennas, arrays of dipoles; receiving antennas. (Synchronous: archiving available; on-campus visits will be required approximately once per month)
ECE 992:02 - Advanced Topics: Wireless Communications Systems -- TR (6:10-7:30 PM), Kings S320, Kirsch
Credits: 3.00. Wireless Communication Systems is a graduate level course covering many topics related to the wireless transmission of signals and systems that use them. A detailed analysis of the wireless channel will be provided to understand the physical medium of wireless communications. This analysis will include modeling of large and small scale fading and Doppler effects. Cellular communications will be covered to show how a complete system operates within the limitations of the wireless channel. Several multi-access schemes which enable communication systems to use resources more efficiently will be detailed. Finally, advanced topics in communication systems such as OFDM and multiple antenna communications will be included. This course will also include a project for participants to learn and share recent research in wireless communications. (synchronous: (4) required campus meetings over the course of the semester)
MATH 739\839 Applied Regression Analysis - - MW (8-10:00 AM) MUB DL Classroom, Li
Statistical methods for the analysis of relationships between response and input variables: simple linear regression, residual analysis and model selection, multicollinearity, nonlinear curve fitting, categorical predictors, introduction to analysis of variance, examination of validity of underlying assumptions. Emphasizes real applications with use of statistical software. Prereq: MATH 539 (or 644); or permission. Writing intensive.
MATH 740\840 Design of Experiments I - - MW (4-6;00 PM) MUB DL Classroom, Ramsey
Credits: 4.00. Course in design of experiments with applications to quality improvement in industrial manufacturing, engineering research and development, or research in physical and biological sciences. Experimental factor identification, statistical analysis and modeling of experimental results, randomization and blocking, full factorial designs, random and mixed effects models, replication and sub-sampling strategies, fractional factorial designs, response surface methods, mixture designs, and screening designs. Focuses on various treatment structures for designed experimentation and the associated statistical analyses. Use of statistical software. Prereq: MATH 539 (or 644); or permission. (Synchronous: does not require campus visits)
MATH 755\855 Probability & Stochastic Proc - - MW (10-12:00) MUB DL Classroom, Li
Credits: 4.00. Introduces the theory, methods, and applications of randomness and random processes. Probability concepts, random variable, expectation, discrete and continuous distributions, stochastic processes, Markov chains, Poisson processes, moment-generating functions, convergence of random variables. Prereq: MATH 528 and 639 (or 644); or permission.
MATH 796/896.02 Introduction to the R Statistical Software -- W (6:40-8:00 pm) MUB DL Classroom, Linder
Credits: 1.00. First half of the semester, basic introduction to the open source R statistical software system. (Synchronous: does not require campus visits)
MATH 796/896.03 Introduction to the SAS Statistical Software -- W (6:40-8:00 pm) MUB DL Classroom, Ramsey
Credits: 1.00. Second half of the semester, basic introduction to the SAS statistical software system. (Synchronous: does not require campus visits)
MATH 835 Stat Methods for Researchers - - MW (2-4:00 PM) MUB DL Classroom, Ramsey
Credits: 3.00; This course provides a solid grounding in modern applications of statistics to a wide range of disciplines by providing an overview of the fundamental concepts of statistical inference and analysis, including t-tests and confidence intervals. Additional topics include: ANOVA, multiple linear regression, analysis of cross classified categorical data, logistic regression, nonparameteric statistics and data mining using CART. The use of statistical software, such as JMP. S PLUS, or R, is fully integrated into the course. (Synchronous: does not require campus visits)
MATH 944 Spatial Statistics- - MW (12:40 2:00 PM) MUB DL Classroom, Linder
Credits: 3.00; Frequentist and Bayesian methods for estimation of characteristics measured in space (usually 2-dimensional Euclidean space). Spatial averaging. Spatial point processes: models for clustering and inhibition. Cluster detection. Point referenced data: varigram estimation, Kriging, spatial regression. Lattice based data: spatial auto-regression, Markov random field models. Spatial regression models. Non-Gaussian response variables. Hierarchical Bayesian spatial models and Markov chain Monte Carlo methods. Multivariable spatial models. Prereq: Intermediate statistics including basics of maximum likelihood estimation; linear regression modeling including familiarity with matrix notation, basic concepts of calculus including partial derivatives. (Synchronous: does not require campus visits)
MATH 736/836 Advanced Statistical Methods for Research - - MW (2-4:00 PM) MUB DL Classroom, Ramsey
Credits: 4.00; An introduction to multivariate statistical methods, including principal components, discriminant analysis, cluster analysis, factor analysis, multidimensional scaling, and MANOVA. Additional topics include generalized linear models, general additive models, depending on the interests of class participants. The use of statistical software, such as JMP, S PLUS, or R, is fully integrated into the course. Prereq: MATH 739.
MATH 741/841 Survival Analysis - - MWF (9:40-11:00 AM) Kingsbury Hall N328, Li
Credits: 4.00; Explorations of models and data-analytic methods used in medical, biological, and reliability studies. Event-time data, censored data, reliability models and methods, Kaplan-Meier estimator, proportional hazards, Poisson models, loglinear models. Suitable statistical software, such as SAS, JMP, S-Plus, or R, are used. Prereq: MATH 539 (or MATH 644). (Offered in alternate years.)
MATH 744/844 Design Experiments II - - MW (4-6:00 PM) MUB DL Classroom, Ramsey
Credits: 4.00; A second course in design of experiments, with applications in quality improvement and industrial manufacturing, engineering research and development, research in physical and biological sciences. Covers experimental design strategies and issues that are often encountered in practice: complete and incomplete blocking, partially balanced incomplete blocking (PBIB), partial confounding, intra and inter block information, split plotting and strip plotting, repeated measures, crossover designs, Latin squares and rectangles, Youden squares, crossed and nested treatment structures, variance components, mixed effects models, analysis of covariance, optimizations, space filling designs, and modern screening design strategies. Prereq: MATH 740; or permission.
MATH 756/856 Principles of Statistical Inference -- MWF (12:40-2:00 PM) DL Classroom, Li
Credits: 4.00; Introduces the basic principles and methods of statistical estimation and model fitting. One- and two-sample procedures, consistency and efficiency, likelihood methods, confidence regions, significance testing, Bayesian inference, nonparametric and resampling methods, decision theory. Prereq: MATH 755; or permission.
MATH 979 Topics - - MW (11:10-12:30 PM) HEW 301, Linder
An exploration of the main statistical issues and computational methods associated with research problems from such areas as survival analysis, reliability, latitudinal data, categorical data, spatio-temporal data, and industrial processes. Student term projects require: literature searches, presentation, use of modern statistical software, and written reports. Prereq: permission. May be repeated.