ANSC 617 Quantative Techniques in Physiology and Nutrition
(3 credits)
Prerequitsite: MATH 220 or permission of department.
Development and evaluation of quantative techniques to explore mechanisims of physiological and nutritional regulation. Kinetic and dynamic models will be emphasized.
ANSC 627 Molecular and Quantitative Genetics
(3 credits)
Classical, molecular, and population genetics with specific emphasis on animal systems will be covered. Also, disseminate information on molecular approaches for manipulating genetics at the whole animal level (transgenic and cloning). Other model organisms will be discussed to provide a conceptual framework.
AREC Agricultural and Resource Economics
AREC 623 Applied Econometrics I
(4 credits)
Prerequitsite
Fundamentals of mathematical statistics for applications in econometrics. Development of the standard linear model and computer applications in applied econometric problems.
Keywords: Analytic Software, Subject-specific Statistical Techniques
AREC 624 Applied Econometrics II
(4 credits)
Prerequitsite
Variations of the standard linear model and simultaneous equations estimation. Application of econometric tools including nonlinear regression, nonlinear simultaneous equations estimation, qualitative econometric models including logit, probit, and tobit models, varying parameters models, unobserved variables, time series models, and model selection procedures. This course is being co-taught by Richard Just, Anna Alberini, and Barrett Kirwan.
Keywords: Model Estimation, Regression, Survival Analysis, Time Series
AREC 829 Topics in Applied Econometrics
(3 credits)
Prerequitsite: AREC 623 and AREC 624 or permission of instructor.
Topics in applied econometrics. Topics vary from year to year.
BIOM Biometrics
BIOM 405 Computer Applications in Biometrics
(1 credits)
Prerequitsite: BIOM402 or equivalent.
An introduction to computer applications for data analysis. This is equivalent to the computer lab of 601 and is required for students that have taken BIOM 301 and BIOM402 and wish to go directly into BIOM602.
Keywords: Analytic Software.
BIOM 601 Biostatistics I
(4 credits)
Prerequitsite: BIOM 301, STAT 464 or equivalent. Not open to students who have completed BIOM 402. Credit will be granted for only one of the following: BIOM 401 or BIOM 601.
Estimation and hypothesis testing, t tests, one and two way analysis of variance, regression, analysis of frequency data. Lecture will emphasize uses and limitations of these methods in biology, while the laboratory will emphasize the use of statistical analysis software for the analysis of biological data.
Keywords: Analysis of Variance, Analytic Software, Hypothesis Testing, Regression.
BIOM 602 Biostatistics II
(4 credits)
Prerequitsite: BIOM 601 or (BIOM 402 and BIOM 405). Also offered as AGRO 804.
The principles of experimental design and analysis of variance and covariance.
Keywords: Analysis of Variance, Experimental Design/Statistics.
BIOM 603 Biostatistics III
(4 credits)
Prerequitsite: BIOM 602; or equivalent.
Applications and implementation of linear model analysis to biological data, including logistic and Poisson regression models for correlated data.
Keywords: Regression.
BIOM 621 Applied Multivariate Statistics
(3 credits)
Prerequisite: BIOM 602. Recommended: BIOM 603. Not open to students who have completed BIOM 688B.
Brief review of matrix algebra, means, covariance matrices, multivariate normal, multivariate confidence ellipses, MANOVA, Discriminant Methods, Principal Component Analysis, Factor Analysis, Multidimensional Scaling, Cluster Analyses, and other topics, depending on student interest.
Keywords: Analysis of Variance, Discriminant Analysis, Factor Analysis / LC, Multivariate Estimation, Principal Component Analysis.
BIOM 688A Topics in Biometrics: Consulting Experience in Biometrics
(1-3 credits)
Individual Instruction course: contact department or instructor to obtain section number.
Keywords: Analytic Software, Model Estimation, Spatial Statistics, Time Series.
BIOM 698 Special Problems in Biometrics
(1-3 credits)
Individual Instruction course: contact department or instructor to obtain section number.
Keywords: Analytic Software, Model Estimation, Spatial Statistics, Time Series.
ENBE Biological Resources Engineering
ENBE 462 Nonpoint Source Pollution Assessment Techniques
(3 credits)
Prerequisite: one course in hydrology or permission of department.
Various techniques to identify and measure nonpoint source pollution. Primary focus is on agriculture and water.
NFSC Nutrition and Food Science
EFSC 431 Food Quality Control
(3 credits)
Definition and organization of the quality control function in the food industry; preparation of specifications; statistical methods for acceptance sampling; in-plant and processed product inspection. Instrumental and sensory methods for evaluating sensory quality, identity and wholesomeness and their integration into grades and standards of quality. Statistical Process Control (SPC).
Keywords: Other Sampling Methods, Subject-specific Statistical Techniques.
EFSC 660 Research Methods
(3 credits)
Prerequisite: a statistics course. Formerly NUTR 660.
A study of appropriate research methodology and theories including experimental design. Each student is required to develop a specimen research proposal.
Keywords: Experimental Design/Statistics, Research Methods.
NRSC Natural Resource Sciences
NRSC 415 GIS Application in Soil Science
(4 credits)
Prerequitsite: NRSC200 (formerly AGRO202). Credit will be granted for only one of the following: AGRO415 or NRSC415. Formerly AGRO 415.
Introduction to geospatial analysis of soil and related resources. Topics will include understanding the nature and portrayal of digital soils data in soil surveys, the use, analysis, and application of soil survey and other spatial data types (topography, hydrography, etc.), uncertainty and validation of spatial data, and methods in geospatial analysis such as mapping, landscape analysis, and spatial statistics. Analyses will be performed primarily with ESRI ArcGIS software.
Keywords: Analytic Software, Other Sampling Methods, Spatial Statistics.
Arts and Humanities
LING Linguistics
LING 645 Computational Linguistics II
Prerequitsite: LING 645 or permission of instructor.
(3 credits)
Further exploration of statistical and symbolic techniques in computational linguistics.
LING 647 Introduction to Computational Linguistics
Prerequitsite: permission of instructor. Also offered as CMSC 723.
(3 credits)
Introduction to statistical and symbolic approaches to Computational Linguistics. Automatic methods for tasks involving human language understanding, production or learning.
LING 723 Computational Linguistics I
Prerequitsite: CMSC421 or equivalent; or permission of instructor. Also offered as CMSC723. Not open to students who have completed LING645. Formerly LING645.
(3 credits)
Fundamental methods in natural language processing. Topics include: finite-state methods, context-free and extended context-free models of syntax; parsing and semantic interpretation; n-gram and Hidden Markov models, part-of-speech tagging; natural language applications such as machine translation, automatic summarization, and question answering.
Behavioral and Social Sciences
ANTH Anthropology
ANTH 630 Quantification and Statistics in Applied Anthropology
(3 credits)
An intensive overview of key quantitative and statistical approaches used by social scientists in applied ad policy research. This includes nonparametric and parametric statistical approaches. Students utilize statistical software and analyze existing and student-created databases. Anthropological case studies are emphasized.
Keywords: Analytic Software, Subject-specific Statistical Techniques.
CCJS Criminology and Criminal Justice
CCJS 611 Statistical Tools for Criminal Justice
(3 credits)
An introduction to essential statistical concepts for analyzing crime and evaluating criminal justice policies. Interpreting crime trends and correlations, risk and conditional probability analysis for repeat offenders and hot spots of crime, time series analysis, experimental statistics, effect sizes, statistical power and significance.
Keywords: Correlation, Experimental Design/Statistics, Hypothesis Testing, Probability/Stat Theory, Risk Analyses, Time Series.
CCJS 612 Applied Data Analysis in Criminal Justice
(3 credits)
Requires students to analyze such data as patterns and distributions of criminal careers, temporal and spatial data on reported crimes, recidivism rates after correctional programs, and statistical profiles of offender M.O. patterns. Data base management, computerized crime mapping, graphical and tabular methods for displaying data.
Keywords: Graphical Techniques.
CCJS 621 General Linear Models in Criminal Justice Research
(3 credits)
Prerequitsite: CCJS620. Credit will be granted for only one of the following: CCJS498F or CCJS621. Formerly CCJS498F.
An in-depth exploration of applied linear regression analysis. Covers characteristics of estimates, such as unbiasedness and efficiency. Encourages fluency with the theoretical issues involved in the basic linear regression using simple algebra, familiarity with the general model using matrix algebra, and fluency with the computer application of multivariate regressions and the probit/logit models.
CCJS 710 Advanced Research Methods in Criminology
(3 credits)
Prerequisite: approved doctoral level statistics course. Formerly CRIM 710.
Application of advanced research methods and data analysis strategies to criminological and criminal justice problems.
Keywords: Research Methods, Subject-specific Statistical Techniques.
CCJS 711 Randomized Experiments in Criminology and Criminal Justice
(3 credits)
Constrast randomized designs with other approaches, examining both statistical, methodological, ethical and practical concerns. What are the statistical advantages of randomized experimental designs? Why do some researchers believe that randomized studies violate ethical standards in criminal justice? Why are experiments considered to have higher internal validity than non-randomized designs and how do different types of designs compare in terms of external validity? Focus on how experiments can be developed and how they are analyzed. What are the practical barriers to experimentation and how can they be overcome? What statistical methods are most appropriate for experimental analysis? How can block randomization or hierarchical modeling be used to develop more powerful or more practical research approaches?
CCJS 712 Longitudinal Data Analysis with Latent Variables
(3 credits)
Credit will be granted for only one of the following: CCJS699F or CCJS712. Formerly CCJS699F.
This course is designed for graduate students with an interest in the use of latent variables in longitudinal data analysis as it is conceptualized in the Mplus framework. This course explores more general features of latent variable analyses as they are related to longitudinal modeling. Topics to be covered include latent growth analysis with a combination of continuous and categorical latent variables as well as the inclusion of continuous and categorical variables as predictors and outcomes.
Keywords: Longitudinal, Latent Variable, Categorical Analysis.
ECON Economics
ECON 422 Econometrics I
(3 credits)
Prerequitsite: ECON321 (or STAT400) with a grade of 'C' (2.0) or better. For ECON majors only.
Emphasizes the interaction between economic problems and the assumptions employed in statistical theory. Formulation, estimation, and testing of economic models, including single variable and multiple variable regression techniques, theory of identification, and issues relating to inference.
Keywords: Regression.
ECON 423 Econometrics II
(3 credits)
Prerequitsite: ECON422. For ECON majors only.
Interaction between economic problems and specification and estimation of econometric models. Topics include issues of autocorrelation, heteroscedasticity, functional form, simultaneous equation models, and qualitative choice models.
Keywords: Regression.
ECON 424 Computer Methods in Economics
(3 credits)
Prerequitsite: ECON325 and ECON326 (or ECON305 and ECON306 by permission of department) and ECON321 with a grade of 'C' (2.0) or better. For ECON majors only.
Database development from Internet and other sources, research methods, and statistical analysis in economics using EXCEL and SAS.
Keywords: Analytic Software, Research Methods.
ECON 425 Mathematical Economics
(3 credits)
Prerequitsite: ECON325 and ECON326 with a grade 'C' (2.0) or better (or ECON305 and ECON306 by permission of department). For ECON and MATH majors only.
Mathematical developments of theory of household and firm, general equilibrium and welfare economics, market imperfections, and role of information.
Keywords: Analytic Software, Research Methods.
ECON 621 Quantitative Methods I
(3 credits)
Prerequitsite: ECON 600 or permission of department.
An introduction to econometrics, and a development of the mathematical background concepts needed. Background materials relate to various topics in linear algebra, and in distribution theory. Focus on estimation, hypothesis testing, and prediction in the classical linear regression model. Corresponding large sample issues are considered. Special topics such as non-nested models, hypotheses relating to nonlinear functions of parameters, and specification analysis, including tests for the dynamic stability of a model.
Keywords: Correlation, Hypothesis Testing, Large Sample Theory, Model Estimation, Multivariate Estimation, Regression, Structural Equation Models.
ECON 622 Quantitative Methods II
(3 credits)
Prerequitsite: ECON 621 or permission of department.
A continuation of ECON 621. Topics relate to the generalized least squares model, to dynamic single equation and simultaneous equation models, and to qualitative dependent variable models. Among the topics discussed are various tests for heteroskedasticity and autocorrelation, prediction issues, time series models such as ARCH and GARCH models, tests for unit roots, panel data models, and systems estimation including the GMM procedure. Both linear and nonlinear models are considered. General testing principles, such as likelihood ratio, Wald, and Hausman-type test are also discussed.
Keywords: Correlation, Hypothesis Testing, Large Sample Theory, Model Estimation, Multivariate Estimation, Panel Data Models, Qualitative & Limited Dependent Variable Models, Regression, Spatial Statistics, Structural Equation Models, Time Series.
ECON 623 Advanced Econometrics I
(3 credits)
Prerequitsite: advanced undergraduate course in probability and statistics with permission of department.
Specification, estimation, hypothesis testing and prediction in the classical and generalized linear regression model. Small and large sample properties of estimators. Instrumental variables estimation and quantile regression methods.
Keywords: Correlation, Hypothesis Testing, Probability/Stat Theory.
ECON 624 Econometrics II
(3 credits)
Prerequitsite: ECON 623 or permission of department.
Estimation, hypothesis testing and prediction in the classical and generalized linear regression model. Topics include: ordinary least squares and generalized least squares, including a discussion of their algebraic, small and large sample properties, prediction and parameter restriction; specification tests; large sample distribution theory.
Keywords: Correlation, Hypothesis Testing, Large Sample Theory, Model Estimation, Multivariate Estimation, Regression.
ECON 625 Computational Economics
(3 credits)
Prerequitsite: ECON 604 and ECON 622; or ECON 721. Credit will be granted for only one of the following: ECON 625 or ECON 698R. Formerly ECON 698R.
A one-semester course designed to give students tools for numerical dynamic programming and computation of related general equilibrium and game-theoretic problems.
Keywords: Game Theory, Statistical Programming.
ECON 626 Empirical Microeconomics
(3 credits)
Prerequitsite: ECON 622 or ECON 721 or permission of instructor. For ECON majors only.
Empirical techniques that are particularly valuable in the analysis of microeconomic data. Topics include panel data, nonlinear optimization, limited dependent variables, truncated, censored, and selected samples, the analysis of natural experiments, and quantile regressions.
Keywords: Panel Data Models, Qualitative & Limited Dependent Variable Models, Regression, Subject-specific Statistical Techniques.
ECON 627 Empirical Macroeconomics
(3 credits)
Prerequitsite: ECON 622 or ECON 721 or permission of instructor.
Introduction to the solution, identification, estimation, and evaluation of macroeconomic models under rational expectations. Emphasis is on those tools that allow researchers to tightly link economic theory with econometric methods. Hands-on application of these techniques to empirical macroeconomic problems (business cycles, growth, consumption/ saving, investment), using time-series and panel data.
Keywords: Regression, Subject-specific Statistical Techniques, Time Series.
ECON 721 Econometrics III
(3 credits)
Prerequitsite: ECON 624 or permission of instructor.
A continuation of ECON 624. Estimation hypothesis testing and prediction in various generalized linear regression models, and in dynamic and simultaneous equation models. Topics include: autocorrelation, heteroskedasticity, seemingly unrelated regressions, cross section and time-series models, and general testing principles for significance.
Keywords: Correlation, Hypothesis Testing, Large Sample Theory, Model Estimation, Multivariate Estimation, Panel Data Models, Regression, Stochastic Process, Structural Equation Models, Time Series.
ECON 722 Econometrics IV
(3 credits)
Prerequitsite: ECON 721 or permission of instructor.
A continuation of ECON 721. A "topics course." The topics considered are a large subset of the following: inference in parametric and semi-parametric nonlinear econometric models (least mean distance and GMM estimation); pretest estimation issues; rational expectations models; further issues in specification testing; qualitative and limited dependent variable models (binary and polychotomous choice models, truncated and censored samples, etc.); causality and exogeneity; time series models with unit roots; cointegration; spatial models; ARCH and GARCH models; the Kalman filter; cross section time series models (random parameters); and optimal control.
Keywords: Analytic Software, Bayesian, Correlation, Hypothesis Testing, Large Sample Theory, Model Estimation, Multivariate Estimation, Nonparametric Methods, Qualitative & Limited Dependent Variable Models, Regression, Spatial Statistics, Stochastic Process, Structural Equation Models, Time Series.
ECON 723 Time Series Econometrics
(3 credits)
Prerequitsite: ECON 622 or ECON 722 or permission of instructor.
Provides a broad survey of the models and methods commonly used in the analysis of time series data. Emphasis on analyzing the statistical properties of the methods being discussed. Particular attention to recent developments in time series econometrics.
Keywords: Bayesian, Hypothesis Testing, Large Sample Theory, Model Estimation, Multivariate Estimation, Regression, Time Series.
ECON 725 Empirical Economic Modeling I
(3 credits)
Prerequitsite: ECON 622 or ECON 721. Credit will be granted for only one of the following: ECON 725 or ECON 625.
The experience of building a structural macroeconomic model. Computer techniques for creating models and writing model-building software. Basics of input-output economics.
Keywords: Analytic Software, Statistical Programming.
ECON 726 Empirical Economic Modeling II
(3 credits)
Prerequitsite: ECON 725.
Modeling of interindustry flows, personal consumption and saving, investment, exports and imports, wages, employment, profits, prices, interest and income distribution. Analyzing a model's simulation properties. Applications of general models to specific questions.
Keywords: Regression, Simulation.
GEOG Geography
GEOG 506 Introduction to Quantitative Methods for the Geographic Environmental Sciences
(3 credits)
Prerequitsite: Admission to MPS GIS program.
Essentials in the quantitative analysis of spatial and other data, with a particular emphasis on statistics and programming. Topics include data display, data description and summary, statistical inference and significance tests, analysis of variance, correlation, regression, and spatial statistics. Students will develop expertise in data analysis using advanced statistical software.
GEOG 606 Quantitative Spatial Analysis
(3 credits)
Prerequitsite: GEOG 305; or permission of department. Credit will be granted for only one of the following: GEOG 605 or GEOG 606. Formerly GEOG605.
Multivariate statistical method applications to spatial problems. Linear and non-linear correlation and regression, factor analysis, cluster analysis. Spatial statistics including: trend surfaces, sequences, point distributions. Applications orientation. The course has a $40 lab fee.
Keywords: Factor Analysis / LC, Regression, Spatial Statistics.
GVPT Government and Politics
GVPT 622 Quantitative Methods For Political Science
(3 credits)
Introduction to quantitiative methods of data analysis, with emphasis on statistical methods and computer usage. Measures of association, probability, correlation, linear regression estimation techniques, introductory analysis of variance, and use of package computer programs. Course will meet in the LeFrak OASIS lab.
Keywords: Analysis of Variance, Analytic Software, Correlation, Probability/Stat Theory, Regression.
GVPT 729A Special Topics in Quantitative Political Analysis: Advanced MLE
(3 credits)
Prerequitsite: GVPT622 and GVPT722.
Keywords: Research Methods.
HESP Hearing and Speech Sciences
HESP 724 Research Design
(3 credits)
Prerequitsite: a course in basic statistics.
Evaluations of research designs, critique of published articles and student involvement in designing experiments on assigned topics.
Keywords: Experimental Design/Statistics.
PSYC Psychology
PSYC 601 Quantitative Methods I
(4 credits)
Prerequitsite: PSYC 200 or equivalent.
A basic course in quantitative/mathematical analysis and statistical methods in psychology with an emphasis on conceptual understanding. Topics include issues in measurement, probability theory, statistical inference and hypothesis testing, parameter estimation, bivariate regression, and correlation. For PSYC majors only. For all non-psychology graduate students, written permission of the instructor and department is required.
Keywords: Experimental Design/Statistics, Hypothesis Testing, Regression.
PSYC 602 Quantitative Methods II
(4 credits)
Prerequitsite: PSYC 601.
A continuation of PSYC 601. Topics include experimental design, analysis of variance, analysis of covariance, multiple regression, and general linear models. For all non-psychology graduate students, written permission of the instructor is required.
Keywords: Analysis of Variance, Experimental Design/Statistics, Regression.
PSYC 701 Multivariate Analysis I
(3 credits)
Prerequitsite: PSYC 602 or permission of instructor.
Fundamentals of maxtrix algebra, multivariate distributions, multivariate estimation problems and test of hypotheses, general linear model.
Keywords: Hypothesis Testing, Multivariate Estimation, Regression.
PSYC 702 Multivariate Analysis II
(3 credits)
Prerequitsite: PSYC 701 or permission of instructor.
Component and factor analysis with emphasis on the appropriateness of the models to psychological data. Both theoretical issues and research implications will be discussed. The course will treat the factor analytic model, the three indeterminant problems of communalities, factor loadings, and factor scores, extraction algorithms, rotational algorithms, and the principal component model. For all non-psychology graduate students, written permission of the instructor is required.
Keywords: Factor Analysis / LC, Multivariate Estimation.
SOCY Sociology
SOCY 601 Statistics For Sociological Research I
(3 credits)
Prerequitsite: SOCY 201 or equivalent, and permission of instructor or graduate director. Credit will be granted for only one of the following: SOCY 601 and SURV 601.
Introductory statistical concepts are covered including descriptive statistics, probability, sampling distributions, expected values, hypothesis testing, tests of significance, measures of association, and if time permits, introduction to regression analysis. Statistical programming software may be used. Also offered as SURV 601.
Keywords: Analytic Software, Correlation, Hypothesis Testing, Probability/Stat Theory, Regression, Statistical Programming.
SOCY 602 Statistics For Sociological Research II
(3 credits)
Prerequitsite: SOCY 601 or equivalent, and permission of instructor or graduate director. Credit will be granted for only one of the following: SOCY 602 or SURV 602.
This course introduces regression analysis using matrix algebra. Topics include bivariate regression, multivariate regression, tests of significance, regression diagnostics, indicator variables, interaction terms, extra sum of squares, and the general linear model. Other topics may be addressed such as logistic regression and path analysis. Statistical programming software may be used.
Keywords: Analytic Software, Factor Analysis / LC, Hypothesis Testing, Regression, Statistical Programming.
SOCY 604 Survey Research Methods
(3 credits)
The design, collection, and analysis of data using the method of the social survey. Comparison of the advantages and disadvantages of the survey method with those of other methods of social inquiry. Control over the major sources of survey variation: survey mode, sampling, questionnaire format, question wording, interviewing and coding. Measurement and multivariate analysis alternatives.
Keywords: Research Methods, Survey Sampling.
SOCY 605 Methods of Program Evaluation
(3 credits)
Prerequitsite: SOCY 202 or equivalent or permission of instructor.
Survey of research methods used to evaluate social programs. Conceptualization and measurement of program inputs and outcomes; experimental, quasi-experimental and time-series designs for determining causal influence of program; strategies of data analysis.
Keywords: Experimental Design/Statistics, Research Methods, Time Series.
SOCY 609 Practicum in Social Research
(3 credits)
Keywords: Research Methods, Statistical Programming.
SOCY 609 Practicum in Social Research
(3 credits)
Keywords: Research Methods, Statistical Programming.
SOCY 618 Computer Methods for Sociologists
(3 credits)
Prerequitsite: SOCY 400 or SOCY 401 or equivalent and elementary knowledge of a programming language, CMSC 120, CMSC 220 or equivalent and permission of instructor.
Designed to present the potential of the computer as a tool in sociological research. Projects involving programming and running of data manipulation techniques, statistical techniques, and simple simulations.
Keywords: Experimental Design/Statistics, Research Methods, Time Series.
SOCY 709 Advanced Special Topics in Data Analysis: Categorical Analysis
(3 credits)
Prerequitsite: permission of instructor. May be repeated for credit with permission of instructor.
An intensive examination of an area of interest in data analysis, including such topics as log linear analysis; discriminant function analysis; canonical correlation; factor analysis; analysis of qualitative data; content analysis; mathematical models.
Keywords: Content Analysis, Factor Analysis / LC, Regression.
SURV Survey Methodology
SURV 400 Fundamentals of Survey Methodology
(3 credits)
Prerequitsite: STAT100 or permission of department. Credit will be granted for only one of the following: SURV699M or SURV400. Formerly SURV699M.
Introduces the student to a set of principles of survey design that are the basis of standard practices in the field. The course exposes the student to both observational and experimental methods to test key hypotheses about the nature of human behavior that affect the quality of survey data. It will also present important statistical concepts and techniques in simple design, execution, and estimation, as well as models of behavior describing errors in responding to survey questions. Not acceptable to graduate degrees in SURV.
Keywords: Experimental Design/Statistics, Hypothesis Testing, Survey Sampling.
SURV 410 Introduction to Probability Theory
(3 credits)
Prerequitsite: MATH240; and MATH241 or permission of department. Also offered as STAT410. Credit will be granted for only one of the following: SURV410 or STAT410.
Probability and its properties. Random variables and distribution functions in one and several dimensions. Moments, characteristic functions, and limit theorems.
Keywords: Probability/Stat Theory.
SURV 420 Introduction to Statistics
(3 credits)
Prerequitsite: SURV410 or STAT410. Also offered as STAT420. Credit will be granted for only one of the following: STAT420 or SURV420.
Mathematical statistics, presenting point estimation, sufficiency, completeness, Cramer-Rao inequality, maximum likelihood, confidence intervals for parameters of normal distributions, chi-square tests, analysis of variance, regression, correlation, and nonparametric methods. Course is offered in the spring semester only.
Keywords: Analysis of Variance, Correlation, Nonparametric Methods, Probability/Stat Theory, Regression.
SURV 440 Sampling Theory
(3 credits)
Prerequitsite: STAT401 or STAT420. Not open to students who have completed STAT440.
Simple random sampling, sampling for proportions, estimation of sample size, sampling with varying probabilities of selection, stratification, systematic selection, cluster sampling, double sampling, and sequential sampling. Also offered as STAT 440.
Keywords: Correlation, Probability/Stat Theory, Survey Sampling.
SURV 601 Social Statistics I
(3 credits)
Prerequitsite: SOCY 401 or permission of instructor. Not open to students who have completed SOCY 601.
Probability, hypothesis testing, the normal, chi-square and t-distributions, correlation, and simple analysis of variance. Emphasis is on applications of statistics. Students complete data analytic exercises using real data. Also offered as SOCY 601.
Keywords: Analysis of Variance, Correlation, Hypothesis Testing.
SURV 602 Social Statistics II
(3 credits)
Prerequitsite: SURV 601 or permission of department. Not open to SOCY students who have completed SOCY 602. Credit will be granted for only one of the following: SURV 602 or SOCY 602.
Statistical analyses based on the general linear model. Topics include simple regression, multiple regression, with an emphasis on diagnostic procedures checking model assumptions; elementary structural equation models; and logistic regression. Emphasis on applications of these analytic procedures to real data.
Keywords: Regression.
SURV 615 Statistical Methods I
(3 credits)
Prerequitsite: two course sequence in probability and statistics or equivalent.
First course in a two term sequence in applied statistical methods covering topics such as regression, analysis of variance, categorical data, and survival analysis. This course begins on 09/06/06. It runs concurrently with the University of Michigan course.
Keywords: Analysis of Variance, Hypothesis Testing, Regression.
SURV 616 Statistical Methods II
(3 credits)
Prerequitsite: SURV 615
Builds on the introduction to linear models and data analysis provided in Statistical Methods I. Topics include analysis of longitudinal data and time series, categorical data analysis and contingency tables, logistic regression, log-linear models for counts, statistical methods in epidemiology, and introductory life testing.
Keywords: Factor Analysis / LC, Regression.
SURV 620 (PermReq) Survey Practicum I
(3 credits)
Prerequitsite: degree seeking student in JPSM or permission of instructor.
First part of an applied workshop in sample survey design, implementation, and analysis. Problems of moving from substantive concepts to questions on a survey questionnaire, designing a sample, pretesting the questionnaire, administering the questionnaire to a sample, processing and editing the data, and analyzing the results.
Keywords: Statistical Programming, Survey Sampling.
SURV 621 Survey Practicum II
(3 credits)
Prerequitsite: SURV 620.
Second part of an applied workshop in sample survey design, implementation, and analysis. Problems of moving from substantive concepts to questions on a survey questionnaire, designing a sample, pretesting the questionnaire, administering the questionnaire to a sample, processing and editing the data, and analyzing the results.
Keywords: Statistical Programming, Survey Sampling.
SURV 623 Data Collection Methods in Survey Research
(3 credits)
Review of alternative data collection methods used in surveys, concentrating on the impact these techniques have on the quality of survey data, including measurement error properties, levels of nonresponse and coverage error. Reviews of the literature on major mode comparisons (face-to-face interviewing, telephone survey and self-administered questionnaires), and alternative collection methods (diaries, administrative records, direct observation, etc.). The statistical and social science literatures on interviewer effects and nonresponse, and current advances in computer-assisted telephone interviewing (CATI), computer-assisted personal interviewing (CAPI), and other methods such as touchtone data entry (TDE) and voice recognition (VRE). This course begins on 09/11/06. It runs concurrently with the University of Michigan course.
SURV 625 Applied Sampling
(3 credits)
Prerequitsite: statistics course approved by the department.
Practical aspects of sample design. Topics include: probability sampling (including simple random, systematic, stratified, clustered, multistage and two-phase sampling methods), sampling with probabilities proportional to size, area sampling, telephone sampling, ratio estimation, sampling error estimation, frame problems, nonresponse, and cost factors. This coure will be held at BLS Conference Center, Meeting Room 9, Washington DC.
Keywords: Survey Sampling.
SURV 699 Special Topics in Survey Methodology: Readings in Survey Methodology
(1-4 credits)
Prerequitsite: statistics course approved by the department.
Keywords: Analysis of Variance, Analytic Software, Bayesian, Experimental Design/Statistics, Hypothesis Testing, Large Sample Theory, Model Estimation, Nonparametric Methods, Probability/Stat Theory, Regression, Research Methods, Statistical Programming, Subject-specific Statistical Techniques, Survey Sampling.
SURV 699A Special Topics in Survey Methodology: Categorical Data Analysis
(3 credits)
Keywords: Correlation, Hypothesis Testing, Probability/Stat Theory, Survey Sampling.
SURV 699C Special Topics in Survey Methodology: Introduction to Questionnaire Design
(1 credits)
Keywords: Bayesian, Hypothesis Testing, Regression.
SURV 699K Special Topics in Survey Methodology: Multi-Level Analysis of Survey Data
(3 credits)
Keywords: Analytic Software.
SURV 699K Special Topics in Survey Methodology: Measurement Error Methods
(1-4 credits)
Keywords: Analytic Software, Large Sample Theory, Regression.
SURV 699N Special Topics in Survey Methodology: Introduction to Survey Statistics Using Compusters
(3 credits)
Keywords: Analytic Software, Statistical Programming.
SURV 699S Special Topics in Survey Methodology: Prediction Approach to Sampling Theory
(3 credits)
Prerequitsite: STAT 420, SURV 440.
Keywords: Analytic Software, Statistical Programming.
SURV 701 Analysis of Complex Sample Data
(3 credits)
Prerequitsite: SURV 625.
Analysis of data from complex sample designs covers: the development and handling of selection and other compensatory weights; methods for handling missing data; the effect of stratification and clustering on estimation and inference; alternative variance estimation procedures; methods for incorporating weights, stratification and clustering, and imputed values in estimation and inference procedures for complex sample survey data; and generalized design effects and variance functions. Computer software that takes account of complex sample design in estimation. This course begins on 09/07/06. It runs concurrently with the University of Michigan course.
Keywords: Analytic Software, Survey Sampling.
SURV 722 Randomized/Nonrandomized Design
(3 credits)
Research designs from which causal inferences are sought. Classical experimental design will be contrasted with quasi-experiments, evaluation studies, and other observational study designs. Emphasis placed on how design features impact the nature of statistical estimation and inference from the designs. Issues of blocking, balancing, repeated measures, control strategies, etc.
Keywords: Experimental Design/Statistics.
SURV 723 (PermReq) Total Survey Error
(3 credits)
Prerequitsite: SURV 625.
Total error structure of sample survey data, reviewing current research findings on the magnitudes of different error sources, design features that affect their magnitudes, and interrelationships among the errors. Coverage, nonresponse, sampling, measurement, and postsurvey processing errors. For each error source reviewed, social science theories about its causes and statistical models estimating the error source are described. Empirical studies from the survey methodological literature are reviewed to illustrate the relative magnitudes of error in different designs. Emphasis on aspects of the survey design necessary to estimate different error sources. Relationships to show how attempts to control one error source may increase another source. Attempts to model total survey error will be presented.
Keywords: Survey Sampling.
SURV 742 Inference from Complex Surveys
(3 credits)
Prerequitsite: STAT 440.
Inference from complex sample survey data covering the theoretical and empirical properties of various variance estimation strategies (e.g., Taylor series approximation, replicated methods, and bootstrap methods for complex sample designs). Incorporation of those methods into inference for complex sample survey data. Variance estimation procedures applied to descriptive estimators and to analysis of categorical data. Generalized variances and design effects presented. Methods of model-based inference for complex sample surveys examined, and results contrasted to the design-based type of inference used as the standard in the course. Real survey data illustrating the methods discussed. Students will learn the use of computer software that takes account of the sample design in estimation.
Keywords: Analysis of Variance, Analytic Software, Regression, Survey Sampling.
SURV 744 Topics in Sampling
(3 credits)
Prerequitsite: STAT 440. Advanced course in survey sampling theory.
Keywords: Small Area Estimation, Survey Sampling.
SURV 798B Advanced Topics in Survey Methodology: Small Area Estimation
(3 credits)
Prerequitsite: Also offered as STAT798B.
Keywords: Analytic Software, Regression, Small Area Estimation.
Chemical and Life Sciences
BIOL Biology
BIOL 708L Advanced Topics in Biology: Quantitative Analysis of Biological Data
(1-4 credits)
Keywords: Other Sampling Methods, Signal Processing / Detection, Time Series.
MICB688N Special Topics: Advanced Comparative Bioinformatics
(3 credits)
Prerequitsite: An advanced course in Molecular Genetics and an advanced course in Evolution, or the permission of the instructor.
Keywords: Analytic Software, Model Estimation, Other Sampling Methods, Statistical Programming, Subject-specific Statistical Techniques.
PBIO Plant Biology
PBIO699K Special Problems in Plant Biology: Molecular Systematics
(3 credits)
Keywords: Experimental Design/Statistics, Model Estimation, Multivariate Estimation, Nonparametric Methods, Regression, Research Methods, Subject-specific Statistical Techniques.
Public Policy
PUAF Public Affairs
PUAF610 Quantitative Aspects of Public Policy
(3 credits)
Prerequitsite: For PUAF majors only or permission of department.
Introduces statistical methods needed for evaluating and choosing among policy options. Topics include probability; decision-making under uncertainty; the organization, interpretation, and visual display of complex data; prediction and inferences about causality; hypothesis testing; and linear and multiple regression. Develops analytical skills and the ability to apply theory to complex, real-world problems.
Keywords: Analytic Software, Regression, Statistical Programming, Stochastic Process.
Public Health
EPIB Epidemiology
EPIB 650 Biostatistics I
(3 credits)
Formerly: HLTH651 and HLTH688B. Not open to students who have completed HLTH651 or HLTH688B. Credit will be granted for only one of the following: EPIB650, HLTH651, or HLTH688B.
Basic statistical concepts and procedures for Public Health. Focuses on applications, hands-on-experience, and interpretations of statistical findings.
EPIB 651 Biostatistics II
(3 credits)
Prerequisite: EPIB650.
Introduction to a variety of stattistical tools with applictions in public health, including simple and mutiple regression, experimental design, categorical data analysis, logistic regression, and survival analysis.
EPIB 652 Categorical Data Analysis
(3 credits)
Prerequisite: EPIB650 and EPIB651.
Methods for the analysis of categorical data as applied to public health research, including variables with two or more categories, analysis of data structures that are counted, ordered, censored, or subjecto to selection.
EPIB 653 Survival Data Analysis
(3 credits)
Prerequisite: EPIB650 and EPIB651.
Overview of statistical methods for anlayzing censored survival data, including the Kaplan-Meier estimator and the log-rank test.
EPIB 654 Clinical Trial Analysis
(3 credits)
Prerequisite: EPIB650 and EPIB651.
Principles of clinical trial design, including randomization strategies, design and analytic issues to minimize threats to validity, sample size and power calculations, intention to treat analyses.
EPIB 655 Longitudinal Data Analysis
(3 credits)
Prerequisite: EPIB650 and EPIB651.
Statistical models for drawing scientific inferences from longitudinal data, longitudinal study design, repeated measures and random effects to account for experimental designs that involve correlated responses, handling of missing data.
HLTH Health
HLTH652 Quantitative Research Methods I in Public health
(3 credits)
Prerequitsite: HLTH 651, HLTH 688B or equivalent. For CHED and PCHL majors only. Not open to students who have completed HLTH 688R. Credit will be granted for only one of the following: HLTH 652 or HLTH 688R. Formerly HLTH688R.
Intermediate statistics and procedures in public health-related research for doctoral students. Focuses on applied statistics rather than theoretical, with emphasis on 1) how to apply statistical models, 2) how to perform the analysis with avialable software, and 3) how to interpret findings.
Keywords: Analytic Software, Hypothesis Testing, Model Estimation, Regression, Research Methods.
KNES Kinesiology
KNES612 Qualitative Research
(3 credits)
Theoretical frameworks and methodologies necessary to conduct qualitative research, including research designs, observation and interview methods, data analysis, and development of grounded theory.
Keywords: Analytic Software, Experimental Design/Statistics, Hypothesis Testing, Regression, Research Methods.
Computer, Mathematical and Physical Sciences
CMSC Computer Science
CMSC 421 Introduction to Artificial Intelligence
(3 credits)
Prerequitsite: A grade of C or better in CMSC330 and in CMSC351; and permission of the department or CMSC graduate student.
Areas and issues in artificial intelligence, including search, inference, knowledge representation, learning, vision, natural languages, expert systems, robotics. Implementation and application of programming languages (e.g. LISP, PROLOG, SMALLTALK), programming techniques (e.g. pattern matching, discrimination networks) and control structures (e.g. agendas, data dependencies).
Keywords: Machine Learning, Model Estimation.
CMSC 710 Performance Evaluation of Computer Systems
(3 credits)
Prerequitsite: CMSC 412, MATH 141, and STAT 400 or equivalent.
Performance evaluation methodologies. Methods for evaluating computer/communication systems. Analytical modeling using queueing theoretic approach. Simulation for performance evaluation. Applying theoretical methods by modeling computer system components. Case studies using analytical and simulation techniques.
Keywords: Simulation, Statistical Programming.
CMSC 726 Machine Learning
(3 credits)
Prerequitsite: CMSC 421 or equivalent or permission of instructor.
Reviews and analyzes both traditional symbol-processing methods and genetic algorithms as approaches to machine learning. (Neural network learning methods are primarily covered in CMSC 727.) Topics include induction of decision trees and rules, version spaces, candidate elimination algorithm, exemplar-based learning, genetic algorithms, evolution under artificial selection of problem-solving algorithms, system assessment, comparative studies, and related topics.
Keywords: Machine Learning, Statistical Programming.
CMSC 723 Computational Linguistics I
(3 credits)
Prerequitsite: CMSC421 or equivalent; or permission of instructor. PhD Comp credit for CMSC723 or CMSC823, not both. Also offered as LING723. Not open to students who have completed LING645.
Fundamental methods in natural language processing. Topics include: finite-state methods, context-free and extended context-free models of syntax; parsing and semantics interpretation; n-gram and Hidden Markov models, part-of-speech tagging; natural language applications such as machine translation, automatic summarization, and question answering.
CMSC 727 Neural Modeling
(3 credits)
Prerequitsite: CMSC 421 or equivalent; or permission of instructor. Undergraduate calculus, linear algebra, and elementary probability and statistics are assumed.
Fundamental methods of neural modeling. Surveys historical development and recent research results from both the computational and dynamical systems perspective. Logical neurons, perceptrons, linear adaptive networks, attractor neural networks, competitive activation methods, error back-propagation, self-organizing maps, and related topics. Applications in artificial intelligence, cognitive science, and neuroscience.
Keywords: Model Estimation.
CMSC 735 A Quantitative Approach to Software Management and Engineering
(3 credits)
Prerequitsite: CMSC 435; and STAT 400 or permission of instructor.
Introduction to the fundamental ideas for measuring and evaluating the software development process and product. Types of models and metrics currently in use. Paradigms for using practical measurement for managing and engineering the software development and maintenance process; evaluating software methods and tools; and improving productivity, quality and the effective use of methodology.
Keywords: Analytic Software.
CMSC 753 Mathematical Linguistics
(3 credits)
Prerequitsite: CMSC 650 and STAT 400.
Introductory course on applications of mathematics to linguistics. Elementary ideas in phonology, grammar and semantics. Automata, formal grammars and languages. Chomsky's theory of transformational grammars, Yngve's depth hypothesis and syntactic complexity. Markov-chain models of word and sentence generation, Shannon's information theory Carnap and Bar-Hillel's semantic theory, lexicostatistics and stylostatistics, Zipf's law of frequency and Mandelbrot's rank hypothesis. Mathematical models as theoretical foundation for computational linguistics.
CMSC 773 Computational Linguistics II
(3 credits)
Prerequitsite: CMSC723 or LING723; or permission of instructor. May only receive PhD Comp. credit for CMSC723 or CMSC823, not both. Also offered as LING773. Not open to students who have completed LING647. Formerly CMSC828R.
Natural language processing with a focus on corpus-based statistical techniques. Topics inlcude: stochastic language modeling, smoothing, noisy channel models, probabilistic grammars and parsing; lexical acquisition, similarity-based methods, word sense disambiguation, statistical methods in NLP applications; system evaluation.
CMSC 828G Advanced Topics in Information Processing: Link Mining
(3 credits)
Keywords: Probability/Stat Theory.
CMSC 858S Advanced Topics in Theory of Computing: Randomness and Computation
(3 credits)
Keywords: Probability/Stat Theory, Survey Sampling.
MAIT Masters in the Mathematics of Advanced Industrial Tech
MAIT 626 Statistical Pattern Recognition and Classification
(3 credits)
Mathematical and statistical tools for decision making based on categorization of patterns present in data. Topics include regression, feature extraction, dimensionality reduction, parametric and non- parametric approaches to decision, estimation, and classification problems.
Keywords: Decision making, Parametric, Estimation, Classification.
MATH Mathematics
MATH 420 Mathematical Modeling
(3 credits)
Prerequitsite: MATH241, MATH246, STAT400, MATH240 or MATH461; and permission of department. Also offered as AMSC420. Credit will be granted for only one of the following: AMSC420, MAPL420, or MATH420.
The course will develop skills in mathematical modeling through practical experience. Students will work in groups on specific projects involving real-life problems that are accessible to their existing mathematical backgrounds. In addition to the development of mathematical models, emphasis will be placed on the use of computational methods to investigate these models, and effective oral and written presentation of the results.
Keywords: Modeling.
MATH687 Minicourse Series in the Mathematical Sciences
(1 credits)
Also offered as AMSC687 and STAT687. Credit will be granted for only one of the following: AMSC687, MATH687 or STAT687.
This series will consist of up to sixteen 3-lecture presentations covering a broad range of topics in the mathematical sciences. Each minicourse is intended to be self-contained and accessible to first year graduate students and advanced undergraduates. The goal of each minicourse is to present an active research area or significant result and the necessary vocabulary and perspective for students to appreciate it. The goal of the Minicourse Series is to broaden a student's awareness of the mathematical sciences and to inform them of research directions.
Keywords: Model Estimation.
METO Meteorology
METO601 Synoptic Meteorology II
(3 credits)
Prerequitsite: METO 600.
Weather forecasting using numerical and statistical models. Prediction on the global, synoptic, meso, and local scales.
Keywords: Model Estimation.
METO630 Statistical Methods in Meteorology and Oceanography
(3 credits)
Prerequitsite: STAT 400 or equivalent introductory statistics course.
Parametric and non-parametric tests; time series analysis and filtering; wavelets. Multiple regression and screening; neural networks. Empirical orthogonal functions and teleconnections. Statistical weather and climate prediction, including MOS, constructed analogs. Ensemble forecasting and verification.
Keywords: Model Estimation, Regression, Time Series.
METO634 Air Sampling and Analysis
(3 credits)
Prerequitsite: METO 434 or METO 637 or permission of department.
Theory and application of analytical techniques for the analysis of atmospheric gases and particles including priority pollutants. Combined chemical and meteorological considerations in designing field experiments.
Keywords: Experimental Design/Statistics, Subject-specific Statistical Techniques.
PHYS Physics
PHYS851 Advanced Quantum Field Theory
(3 credits)
Prerequitsite: PHYS 624.
Renormalizations of Lagrangian field theories, Lamb shift, positronium fine structure, T. C. P. Invariance, connection between spin and statistics, broken symmetries in many body problems, soluble models, analyticity in perturbation theory, simple applications of dispersion relations.
Keywords: Model Estimation.
STAT Statistics and Probability
STAT405 Stochastic Models for Queues and Networks
(3 credits)
Prerequitsite: STAT 400 or ENEE 324. Credit will be granted for only one of the following: BMGT 435 or STAT 405.
Review of probability and random variables. Generating functions. Poisson and renewal processes. Single server queues with random customer arrivals. Markov models, balance equations. Examples of queuing networks. Applications to computer and communications networks.
Keywords: Analytic Software, Model Estimation, Probability/Stat Theory, Regression.
STAT440 Sampling Theory
(3 credits)
Prerequitsite: STAT401 or STAT420. Also offered as SURV440. Credit will be granted for only one of the following: STAT440 or SURV440.
Simple random sampling. Sampling for proportions. Estimation of sample size. Sampling with varying probabilities. Sampling: stratified, systematic, cluster, double, sequential, incomplete.
Keywords: Correlation, Probability/Stat Theory, Survey Sampling.
STAT464 Introduction to Biostatistics
(3 credits)
Prerequitsite: One semester of calculus. Not acceptable for credit towards degrees in mathematics or statistics. Junior standing.
Probabilistic models. Sampling. Some applications of probability in genetics. Experimental designs. Estimation of effects of treatments. Comparative experiments. Fisher-Irwin test. Wilcoxon tests for paired comparisons.
Keywords: Experimental Design/Statistics, Survey Sampling.
STAT470 Actuarial Mathematics
(3 credits)
Prerequitsite: Calculus through MATH240 and MATH241. Recommended: STAT400.
Major mathematical ideas involved in calculation of life insurance premiums, including compound interest and present valuation of future income streams; probability distribution and expected values derived from life tables; the interpolation of probability distributions from values estimated at one-year multiples; the 'Law of Large Numbers' describing the regular probabilistic behavior of large populations of independent individuals; and the detailed calculation of expected present values arising in insurance problems.
Keywords: Large Sample Theory, Probability/Stat Theory.
STAT498A (PermReq) Selected Topics in Statistics
(1-6 credits)
Individual Instruction course: contact department or instructor to obtain section number.
Keywords: Probability/Stat Theory.
STAT600 Probability Theory I
(3 credits)
Prerequitsite: STAT 410.
Probability space, classes of events, construction of probability measures. Random variables, convergence theorems, images of measures. Independence. Expectation and moments, Lebesque integration, spaces, Radon-Nikodym and LP theorem, singular and absolutely continuous measures. Conditional expectations, existence of regular distributions, applications. Probabilities on product spaces, Fubini theorem, Kolmogorov extension theorem, Tulcea product theorem.
Keywords: Probability/Stat Theory.
STAT601 Probability Theory II
(3 credits)
Prerequitsite: STAT 600.
Characteristic functions. Bochner's representation theorem. Helly's theorems and Levy's inversion formula. Applications of residue theorem. Infinitely divisible distributions. Kolmogorov's three-series theorem. Law of the iterated logarithm. Arc sine Law. Central limit theorems (Lindeberg-Feller theorem). Weak and strong laws of large numbers. Martingale convergence theorems (for sequences). Course is offered in the spring semester only.
Keywords: Probability/Stat Theory.
STAT650 Applied Stochastic Processes
(3 credits)
Prerequitsite: STAT 410 or MATH 410 with one semester of probability.
Basic concepts of stochastic processes. Renewal processes and random walks, fluctuation theory. Stationary processes, spectral analysis. Markov chains and processes (discrete and continuous parameters.) Birth and death processes, diffusion processes. Applications from theories of queuing, storage, inventory, epidemics, noise, prediction and others. Course is offered in the spring semester only.
Keywords: Probability/Stat Theory, Stochastic Process.
STAT687 Minicourse Series in the Mathematical Sciences
(1 credits)
Also offered as AMSC687 and MATH687. Credit will be granted for only one of the following: AMSC687, MATH687 or STAT687.
This series will consist of up to sixteen 3-lecture presentations covering a broad range of topics in the mathematical sciences. Each minicourse is intended to be self-contained and accessible to first year graduate students and advanced undergraduates. The goal of each minicourse is to present an active research area or significant result and the necessary vocabulary and perspective for students to appreciate it. The goal of the Minicourse Series is to broaden a student's awareness of the mathematical sciences and to inform them of research directions.
STAT689 (PermReq) Research Interactions in Statistics
(1-3 credits)
Individual Instruction course: contact department or instructor to obtain section number.
STAT698A Selected Topics in Probability
(1-4 credits)
Individual Instruction course: contact department or instructor to obtain section number.
Keywords: Probability/Stat Theory.
STAT710 Advanced Statistics I
(3 credits)
Prerequitsite: STAT 421. Recommended corequisite: STAT 600.
Statistical decision theory. Neyman-Pearson lemma and its extensions. Uniformly most powerful test. Monotone likelihood ratio. Exponential families of distributions, concepts of similiarity, and tests with Neyman structure. Unbiased tests and applications to normal families.
Keywords: Large Sample Theory, Probability/Stat Theory.
STAT711 Advanced Statistics II
(3 credits)
Prerequitsite: STAT 710.
Invariance, almost invariance, and applications to rank tests. Invariant set estimation. Linear models with applications to analysis of variance and regression. Elements of asymptotic theory. Minimax principle and Hunt-Stein theorem.
Keywords: Analysis of Variance, Large Sample Theory, Nonparametric Methods, Regression.
STAT730 Time Series Analysis
(3 credits)
Prerequitsite: STAT 700 plus a graduate course in analysis, or permission of instructor. Recommended: STAT 701, STAT 650.
The methodology of probabilistic description and statistical analysis of (primarily stationary) random sequences and processes. Correlation functions, Gaussian processes, Hilbert-space methods including Wold decomposition and spectral representation, periodogram and estimation of spectral densities, parameter estimation and model identification for ARMA processes, linear filtering, Kalman-Bucy filtering, sampling theorems for continuous-time series, multivariate time series.
Keywords: Stochastic Process, Time Series.
STAT770 Analysis of Categorical Data
(3 credits)
Prerequitsite: STAT 420 and STAT 430 or permission of department.
Loglinear and logistic models. Single classification, two-way classification; contingency tables; tests of homogeneity and independence models, measures of association, distribution theory. Bayesian methods. Incomplete contingency tables. Square contingency tables - symmetry. Extensions to higher dimensional contingency tables.
Keywords: Analytic Software, Bayesian, Probability/Stat Theory, Regression, Statistical Programming.
STAT798C Selected Topics in Statistics: Computational Methods in Statistics
(1-4 credits)
Course is offered in the spring semester only.
Keywords: Analytic Software, Simulation, Statistical Programming, Time Series.
STAT798S Selected Topics in Statistics: Survival Analysis
(1-4 credits)
Course is offered in the spring semester only.
Keywords: Analytic Software, Bayesian, Hypothesis Testing, Regression, Stochastic Process, Survival Analysis.
Education
EDMS Measurement, Statistics, and Evaluation
EDMS 451 Introduction to Educational Statistics
(3 credits)
Prerequitsite: Junior standing.
Introduction to statistical reasoning; location and dispersion measures; computer applications; regression and correlation; formation of hypotheses tests; t-test; one-way analysis of variance; analysis of contingency tables.
Keywords: Analysis of Variance, Analytic Software, Correlation, Experimental Design/Statistics, Hypothesis Testing, Regression, Research Methods.
EDMS 645 Quantitative Research Methods I
(3 credits)
Prerequitsite: Junior standing.
Research design and statistical applications in educational research: data representation; descriptive statistics; estimation and hypothesis testing. Application of statistical computer packages is emphasized.
Keywords: Analysis of Variance, Analytic Software, Correlation, Experimental Design/Statistics, Hypothesis Testing, Regression, Research Methods.
EDMS 646 Quantitative Research Methods II
(3 credits)
Prerequitsite: EDMS 645.
A second-level inferential statistics course with emphasis on analysis of variance procedures and designs. Assignments include student analysis of survey data. Application of statistical computer packages is emphasized.
Keywords: Analysis of Variance, Analytic Software, Experimental Design/Statistics, Model Estimation, Research Methods.
EDMS 651 Applied Multiple Regression Analysis
(3 credits)
Prerequitsite: EDMS 646 or equivalent.
Multiple regression and correlation analysis; trend analysis; hierarchical and stepwise procedures; logistic regression; computer programs for regression analysis.
Keywords: Correlation, Discriminant Analysis, Model Estimation, Multivariate Estimation, Regression.
EDMS 653 Correlation and Regression Analysis
(3 credits)
Prerequitsite: EDMS 651 and permission of department.
Systematic development of multiple regression, non-linear regression and other regression-based methods. Emphasis is on underlying theory of procedures and on analytical approaches.
Keywords: Analysis of Variance, Analytic Software, Correlation, Experimental Design/Statistics, Hypothesis Testing, Model Estimation, Multivariate Estimation, Regression, Simulation, Statistical Programming.
EDMS 657 Factor Analysis
(3 credits)
Prerequitsite: EDMS 651.
Development of models for factor analysis and their practical applications. Treatment of factor extraction, rotation, second-order factor analysis, and factor scores. Introduction to linear structural relations models.
Keywords: Factor Analysis / LC, Model Estimation.
EDMS 722 Structural Modeling
(3 credits)
Prerequitsite: EDMS 657.
Statistical theory and methods of estimation used in structural modeling; computer program applications; multisample models; mean structture models; structural models with multilevel data (e.g., sampling weights, growth models, multilevel latent variable models).
Keywords: Analytic Software, Factor Analysis / LC, Model Estimation, Multivariate Estimation, Regression, Research Methods, Survey Sampling.
EDMS 723 Latent Structure Models
(3 credits)
Prerequitsite: EDMS 623 and EDMS 651.
Theoretical development and application of latent class models.
Keywords: Factor Analysis / LC, Model Estimation, Multivariate Estimation, Qualitative & Limited Dependent Variable Models.
EDMS 738B Seminar in Special Problems in Measurement: Foundations of Assessments
(3 credits)
Keyword: Analytic Software, Bayesian, Factor Analysis / LC, Model Estimation, Simulation.
EDMS 771 Multivariate Data Analysis
(3 credits)
Prerequitsite: EDMS 651.
Principal components, canonical correlation, discriminant functions, multivariate analysis of variance/covariance and other multivariate techniques.
Keywords: Analysis of Variance, Discriminant Analysis, Principal Component Analysis.
LBSC802 (PermReq) Seminar in Research Methods and Data Analysis
(3 credits)
Prerequitsite: permission of department; and coursework in statistics and introduction to research methods.
Topics and issues in information studies research. Design and conduct of research project.
Keywords: Analytic Software, Research Methods, Subject-specific Statistical Techniques.
Engineering
BIOE Bioengineering
BIOE 450 Quantitative Cell Physiology
(3 credits)
Recommended: MATH141, MATH241, MATH246 or equivalent.
Introduction to quantitative aspects of enuronal, skeletal muscle and cardiac physiological systems, with an emphasis on cellular function and plasticity.
ENCE Engineering, Civil
ENCE402 (PermReq) Simulation and Design of Experiments for Engineers
(3 credits)
Prerequitsite: ENCE302 and permission of department..
Review of statistics and hypothesis testing, sample design and design of experiments, generation of discrete and continuous distributions and their applications. Introduction of simulation languages and simulation of discrete and continuous engineering systems. Output analysis, model validation and sensitivity and reliability analysis.
Keywords: Experimental Design/Statistics, Hypothesis Testing, Model Estimation, Reliability Analysis, Research Methods, Simulation, Statistical Programming.
ENCE615 Structural Reliability
(3 credits)
Prerequitsite: ENCE302 and permission of department.
Probability and statistics. Fundamentals of uncertainty analysis. Fundamentals of structural reliability. Reliability-based design. Simulation and variance reduction techniques. Fuzzy sets and applications.
Keywords: Probability/Stat Theory, Reliability Analysis, Simulation, Statistical Programming.
ENCE620 Risk Analysis for Engineering
(3 credits)
Sources of hazards, definition of risk, system analysis, functional modeling and analysis techniques, probabilistic risk assessment procedure, risk methods, risk acceptance, assessment of failure likelihood, consequence assessment, risk benefit assessment, uncertainty surces and types, modeling uncertainty, risk analysis and decision making under uncertainty, collection of data, expert-opinion elicitation, human-machine interface and human factors engineering.
Keywords: Machine Learning, Model Estimation, Risk Analyses.
ENCE621 Uncertainty Modeling and Analysis
(3 credits)
Prerequitsite: ENCE 620 or equivalent.
Definition of engineering systems, knowledge levels using information science concepts as applied to engineering systems, sources and types of knowledge and ignorance, uncertainty sources and types for engineering systems, probability models, statistical models, fuzziness, fuzzy sets, fuzzy logic, fuzzy arithemetic, imprecise probabilities, evidence methods, uncertainty measures, uncertainty management, uncertainty reduction, applications of these analytical methods to engineering systems and in decision making.
Keywords: Model Estimation, Probability/Stat Theory.
ENCE676 Highway Traffic Flow Theory
(3 credits)
Prerequitsite: ENCE 461 and ENCE 462; or permission of instructor.
An examination of physical and statistical laws that are used to represent traffic flow phenomena. Deterministic models including heat flow, fluid flow, and energy-momentum analogies, car following models, and acceleration noise. Stochastic approaches using independent and Markov processes, Queuing models, and probability distributions.
Keywords: Experimental Design/Statistics, Probability/Stat Theory, Stochastic Process.
ENCE 725 Probabilistic Optimization in Project Management
(3 credits)
Introduction to optimiztion under uncertainty. Includes: chance-constrained programming, reliability programming, value of information, decomposition methods, nonlinear and linear programming theory, and probability theory.
ENCH Engineering, Chemical
ENCH476 Statistics and Experiment Design
(3 credits)
Credit will be granted for only one of the following: ENCH468G or ENCH476. Formerly ENCH468G.
Intelligent design of experiments and statistical analysis of data. Probability, probability distribution, error analysis; data collection, sampling, graphing; variance, significant tests. Cluster analysis and pattern recognition. Factorial design, combinatorial methods.
Keywords: Analysis of Variance, Correlation, Discriminant Analysis, Experimental Design/Statistics, Large Sample Theory, Probability/Stat Theory, Regression, Survey Sampling.
ENEE Electrical & Computer Engineering
ENEE 425 (PermReq) Digital Signal Processing
(3 credits)
Prerequitsite: ENEE322 and completion of all lower-division technical courses in the EE curriculum. See above note.
Sampling as a modulation process; aliasing; the sampling theorem; the Z-transform and discrete-time system analysis; direct and computer-aided design of recursive and nonrecursive digital filters; the Discrete Fourier Transform (DFT) and Fast Fourier Transform (FFT); digital filtering using the FFT; analog-to-digital and digital-to analog conversion; effects of quantization and finite-word-length arithmetic. ENEE majors (09090) only.
Keywords: Other Sampling Methods, Signal Processing / Detection.
ENEE 620 Random Processes in Communication and Control
(3 credits)
Prerequitsite: ENEE 324 or equivalent.
Introduction to random processes: characterization, classification, representation; Gaussian and other examples. Linear operations on random processes, stationary processes: covariance function and spectral density. Linear least square waveform estimating Wiener-Kolmogroff filtering, Kalman-Bucy recursive filtering: function space characterization, non-linear operations on random processes.
Keywords: Model Estimation.
ENEE 621 Estimation and Detection Theory
(3 credits)
Prerequitsite: ENEE 620 or equivalent. Also offered as MAPL 644.
Estimation of unknown parameters, Cramer-Rao lower bound; optimum (map) demodulation; filtering, amplitude and angle modulation, comparison with conventional systems; statistical decision theory Bayes, minimax, Neyman/Pearson, Criteria-68 simple and composite hypotheses; application to coherent and incoherent signal detection; M-ary hypotheses; application to uncoded and coded digital communication systems.
Keywords: Bayesian, Hypothesis Testing, Large Sample Theory, Probability/Stat Theory, Signal Processing / Detection.
ENEE 630 Advanced Digital Signal Processing
(3 credits)
Prerequitsite: ENEE 425. Corequisite: ENEE 620. Credit will be granted for only one of the following: ENEE 624 or ENEE 630. Formerly ENEE624.
This is the first-year graduate course in signal processing. The objective is to establish fundamental concepts of signal processing on multirate processing, parametric modeling, linear prediction theory, modern spectral estimation, and high-resolution techniques.
Keywords: Model Estimation, Signal Processing / Detection.
ENEE 633 Statistical and Neural Pattern Recognition
(3 credits)
Prerequitsite: ENEE 620. Credit will be granted for only one of the following: ENEE633 or ENEE739Q. Formerly ENEE 739Q.
Classical and modern approaches to statistical and neural pattern recognition are covered. Topics include Bayes decision theory, discriminant functions for the Normal density, error probabilities, integrals and bounds; non-parametric techniques: density estimation, Parzen windows, nearest neighborhood rule and error bounds; linear discriminant functions; linear separability, perceptrons, minimum squared-error procedure, Ho-Kashyap procedure; Multi-layer neural networks: backpropagation algorithm, error surfaces, radial basis functions, convolutional networks, recurrent networks; stochastic methods: stochastic search, Boltzmann learning, Boltzmann networks and graphical models, evolutionary methods; Nonparametric methods: CART and other trees; algorithm independent machine learning: no free lunch theorem, MDL, Occam's razor, resampling for estimating statistics, resampling for classifier design, estimating and comparing classifiers; unsupervised learning and clustering.
Keywords: Bayesian, Discriminant Analysis, Graphical Techniques, Machine Learning, Nonparametric Methods, Regression, Stochastic Process.
ENEE 721 Information Theory
(3 credits)
Prerequitsite: ENEE 620. Prerequisite: STAT 400 or equivalent. Also offered as MAPL 731.
Information measure, entropy, mutual information; source encoding; noiseless coding theorem, noisy coding theorem; exponential error bounds; introduction to probabilistic error correcting codes, block and convolutional codes and error bounds; channels with memory; continuous channels; rate distortion function.
ENEE724 Statistical and Adaptive Signal Processing
(3 credits)
Prerequitsite: ENEE 624.
Review of parametric modeling, Wold decomposition, eigenanalysis; matrix computations: orthogonal decompositions and algorithms, singular value decomposition; super-resolution algorithms: MUSIC and ESPRIT; least mean square algorithm: steepest descent, error behavior, convergence analysis; recursive least-squares algorithms: standard RLS, QRD-RLS systolic array. RLS lattice filters, blind deconvolution. Advanced topics from emerging research areas will also be covered at the instructor's discretion.
Keywords: Model Estimation, Signal Processing / Detection.
ENFP Engineering, Fire Protection
ENFP622 (PermReq) Advanced Fire Protection Risk Assessment
(3 credits)
Prerequitsite: permission of department.
Definition, evaluation of the fire risk to a process, facility or area. Prevention, intervention, control, suppression strategies. Resource allocation, queing theory, decision priority, cost analysis.
Keywords: Risk Analyses.
ENME Engineering, Mechanical
ENME695 Failure Mechanisms and Reliability
(3 credits)
This course will present classical reliability concepts and definitions based on statistical analysis of observed failure distributions. Techniques to improve reliability, based on the study of root-cause failure mechanisms, will be presented; based on knowledge of the life-cycle loadprofile, product architecture and material properties. Techniques toprev ent operational failures through robust design and manufacturing practices will be discussed. Students will gain the fundamentals and skills in the field of reliability as it directly pertains to the designand the manufacture of electrical, mechanical, andelectomechanical products. Also offered as ENRE648D.
Keywords: Model Estimation, Statistical Mechanics.
ENNU Engineering, Nuclear
ENNU620 Mathematical Techniques for Engineering Analysis and Modeling
(3 credits)
Also offered as ENRE 620 and ENMA 698M.
Probability and probability distributions; statistics; ordinary differential equations; linear algebra and vectors; Laplace transform; Fourier analysis; boundary value problems; series solutions to differential equations; partial differential equations; numerical methods.
Keywords: Probability/Stat Theory.
ENPM Engineering, Professional Masters
ENPM600 Probability and Stochastic Processes for Engineers
(3 credits)
Prerequitsite: undergraduate introduction to discrete and continuous probability.
Axioms of probability; conditional probability and Bayes' rule; random variables, probability distributions and densities; functions of random variables; definition of stochastic process; stationary processes, correlation functions, and power spectral densities; stochastic processes and linear systems; estimation and optimum filtering. Applications in communication and control systems, signal processing, and detection and estimation.
Keywords: Bayesian, Probability/Stat Theory, Signal Processing / Detection, Stochastic Process.
ENPM620 (PermReq) Computer Aided Engineering Analysis
(3 credits)
Prerequitsite: permission of department.
Computer assisted approach to the solution of engineering problems. Review and extension of undergraduate material in applied mathematics including linear algebra, vector calculus, differential equations, and probability and statistics.
Keywords: Analytic Software, Hypothesis Testing, Probability/Stat Theory.
ENPM647 (PermReq) Quality Management in Systems
(3 credits)
Prerequitsite: permission of department. Also offered as ENSE627.
Introduction to the roles of management, marketing, accounting, finance and engineering, and the synergy which must be present among these functions of an organization, to provide products and services which satisfy customer demands for quality. Introduction to the important statistical tools which are the foundation of any successful quality effort.
ENPM808B Advanced Topics in Engineering: Chemical and Biological Detection
(3 credits)
Prerequitsite: permission of department. Also offered as ENSE627.
Keywords: Analytic Software, Model Estimation, Regression, Statistical Programming.
ENRE Reliability Engineering
ENRE445 Applied Reliability Engineering I
(3 credits)
Prerequitsite: MATH246, PHYS270 and 271 (Formerly: PHYS263), or permission of instructor. Credit will be granted for only one of the following: ENRE445 or ENRE489C. Formerly ENRE 489C.
Topics covered include: fundamental understanding of how things fail, probabilistic models to represent failure phenomena, life-models for non-repairable items, reliability data collection and analysis and applicable quality techniques. Distribution functions such as the normal, Weibull, exponential, binomial, and gamma are explored.
Keywords: Model Estimation, Probability/Stat Theory, Reliability Analysis.
ENRE446 Applied Reliability Engineering II
(3 credits)
Prerequitsite: MATH246, PHYS270 and 271 {Formerly: PHYS263}, or permission of instructor. Credit will be granted for only one of the following: ENRE446 or ENRE489D. Formerly ENRE489D.
Topics covered include: System modeling and analysis, designing for reliability, reliability testing, reliability in manufacturing, and reliability management. Fault tree analysis, RBD, and cut sets are covered along with sneak circuits, time-on-test plots and acceptance testing.
Keywords: Model Estimation, Probability/Stat Theory, Reliability Analysis.
ENRE620 Mathematical Techniques of Reliability Engineering
(3 credits)
Also offered as ENNU 620.
Basic probability and statistics (required for ENRE 600 and ENRE 602). Application of selected mathematical techniques to the analysis and solution of reliability engineering problems. Applications of matrices, vectors, tensors, differential equations, integral transforms, and probability methods to a wide range of reliability related problems. Those sections that begin with a letter are taught via ITV and are not intended for College Park campus students. Also offered as ENMA 698M.
Keywords: Probability/Stat Theory, Reliability Analysis.
ENRE 640 Collection and Analysis of Reliability Data
(3 credits)
Prerequisites: ENRE 620 and ENRE 602.
Basic life model concepts. Probabilistic life models, for components with both time independent and time dependent loads. Data analysis, parametric and nonparametric estimation of basic time-to-failure distributions. Data analysis for systems. Accelerated life models. Repairable systems modeling.
Keywords: Nonparametric Methods, Regression, Reliability Analysis.
ENRE 641 Accelerated Testing
(3 credits)
Prerequisite: ENRE 663 or permission of department. Credit will be granted for only one of the following: ENRE 641 or ENRE 650. Formerly ENRE650.
Models for life testing at constant stress. Graphical and analytical methods. Test plans for accelerated testing. Competing failure modes and size effects. Models and data analyses for step and time varying stresses. Optimizing of test plans.
ENRE 644 Bayesian Reliability Analysis
(1-4 credits)
Prerequisite: ENRE 602 and ENRE 655 or permission of department. Credit will be granted for only one of the following: ENRE 644 or ENRE 730. Formerly ENRE730.
Foundations of Bayesian statistical inference, Bayesian inference in reliability, performing a Bayesian reliability analysis, Bayesian decision and estimation theory, prior distribution such as non-informative, conjugate, beta, gamma, and negative log gamma, estimation methods basedon attribute life test data for estimating failure rates and survival probabilities. System reliability assessment and methods of assigning priordistribution. Empirical Bayes reliability estimates (implicitly or explicitly estimated priors).
ENRE674 (PermReq) Failure Mechanisms and Effects Laboratory
(3 credits)
Prerequisites: ENRE 600 or permission of instructor.
Techniques for studying failure analysis, corrosion and corrosion protection, statistical process control, mechanical failure mode analysis, failure reporting and corrective action systems, and environmental stress screening.
Keywords: Reliability Analysis, Statistical Mechanics.
ENTS Telecommunications
ENTS635 Decision Support Methods for Telecommunication Managers
(3 credits)
Prerequitsite: MATH 241 and ENEE 324 or equivalent.
The aim of this course is to introduce management science techniques for informed decision making. Topics covered will include data analysis and regression, optimization models and applications (workforce scheduling, manufacturing, network design, facility location), sensitivity analysis, decision trees, risk analysis and business simulation models. Emphasis will be on telecommunications managerial problems, model development and the use of software packages for decision support. Restricted to ENTS majors. All non-majors will need to obtain department permission.
Keywords: Analytic Software, Regression, Risk Analyses, Simulation, Statistical Programming, Subject-specific Statistical Techniques.
Business
BMGT Business and Management
BMGT430 Linear Statistical Models in Business
(3 credits)
Prerequitsite: BMGT230 or BMGT231 or permission of department.
Model building involving an intensive study of the general linear stochastic model and the applications of this model to business problems. The model is derived in matrix form and this form is used to analyze both the regression and ANOVA formulations of the general linear model. This course is restricted to BMGT majors with 84 credit hours completed.
Keywords: Analysis of Variance, Analytic Software, Model Estimation, Regression.
BMGT434 Introduction to Optimization
(3 credits)
Prerequitsite: MATH220 or MATH140; or equivalent. Recommended: MATH221 or MATH141. For BMGT majors only.
Introduces concepts and techniques of operations research to model and solve business decision problems, focusing on optimization and commercially available software tools. Models include linear programming, the transportation and assignment problems, network flow models, and non-linear programming. Emphasis is placed on analyzing business scenarios and formulating associated decision models. This course is restricted to BMGT majors and Quest program students with 72 credit hours completed.
Keywords: Statistical Programming.
BMGT435 Business Process Simulation
(3 credits)
Prerequitsite: BMGT230 or BMGT231 or equivalent. For BMGT majors only.
Develop and plan simulation studies, build simulation models with special purpose software, analyze and interpret the results. Extensive use of applications and real-world examples. The emphasis is on model formulation and the interpretation of results, rather than mathematical theory.
Keywords: Probability/Stat Theory, Simulation, Statistical Programming.
BMGT486 Total Quality Management
(3 credits)
Prerequitsite: BMGT230 or equivalent.
Total Quality Management and the synergy required between functions to obtain the customer's quality demands. Statistical tools which are mandatory in any successful quality effort. This course is restricted to BMGT majorsand Quest program students with 72 credit hours completed.
Keywords: Analytic Software.
BMGT487 Six Sigma Innovation
(3 credits)
Prerequitsite: BMGT230, BMGT231, STAT400 or ENME392.
Enhances the overall understanding of Six Sigma Strategy, Tools and Methods to positively influence the performance of a business process, a product or service. Highlights the application of Define-Measure-Analyze-Improve-Control (DMAIC),Design For Six Sigma (DFSS), and the pursuit of Critical to Quality criteria (CTQ's) in a collaborative perspective, one that recognizes a balance between efficiency, and effectiveness and between statistical analysis and statistical thinking. This course is restricted to BMGT majors and Quest program students with 60 credit hours completed.
Keywords: Analytic Software.
BMGT830 Operations Research: Linear Programming
(3 credits)
Prerequitsite: MATH 240 or equivalent; or permission of department.
Concepts and applications of linear programming models, theoretical development of the simplex algorithm, and primal-dual problems and theory.
Keywords: Analytic Software, Statistical Programming.
BMGT831 Operations Research: Extension of Linear Programming and Network Analysis
(3 credits)
Prerequitsite: BMGT 830 or equivalent; or permission of department.
Concepts and applications of network and graph theory in linear and combinatorial models with emphasis on computational algorithms.
Keywords: Model Estimation, Statistical Programming.
BMGT832 Operations Research: Optimization and Nonlinear Programming
(3 credits)
Prerequitsite: {BMGT 830; and MATH 241; or equivalent}; or permission of department.
Theory and applications of algorithmic approaches to solving unconstrained and constrained non-linear optimization problems. The Kuhn Tucker conditions, Lagrangian and Duality Theory, types of convexity, and convergence criteria. Feasible direction procedures, penalty and barrier techniques, and cutting plane procedures. Also offered as ENCE724.
Keywords: Analytic Software, Statistical Programming.
BMGT833 Operations Research: Integer Programming
(3 credits)
Prerequitsite: {BMGT 830; and MATH 241 or equivalent}; or permission of department.
Theory, applications, and computational methods of integer optimization. Zero-one implicit enumeration, branch and bound methods, and cutting plane methods.
Keywords: Statistical Programming.
BMGT834 Operations Research: Probabilistic Models
(3 credits)
Prerequitsite: {MATH 241; and STAT 400 or equivalent} or permission of department.
Theoretical foundations for the construction, optimization, and applications of probabilistic models. Queuing theory, inventory theory, Markov processes, renewal theory, and stochastic linear programming.
Keywords: Analytic Software, Model Estimation, Probability/Stat Theory.
BMGT835 Simulation of Discrete-Event Systems
(3 credits)
Prerequitsite: Knowledge of Fortran, Basic, C, or Pascal; and BMGT 630 or equivalent.
Simulation modeling and analysis of stochastic discrete-event systems such as manufacturing systems, inventory control systems, and computer/ communications networks.
Keywords: Model Estimation, Other Sampling Methods, Simulation, Statistical Programming.
BMGT882 Applied Multivariate Analysis I
(3 credits)
Prerequitsite: ECON 621, ECON 624, EDMS 651, STAT 450 or permission of department.
Multivariate statistical methods and their use in empirical research. Topics include summarization and visualization of multivariate data, principal components, metric multidimensional scaling, canonical correlation, multivariate paired comparisons and repeated-measures designs, multivariate analysis of variance, and discriminant analysis. The maximum likelihood and likelihood ratio principles are also discussed. An important component of the course is analysis of business data using contemporary software.
Keywords: Analysis of Variance, Analytic Software, Discriminant Analysis, Multivariate Estimation, Principal Component Analysis.
BMGT 883 Applied Multivariate Analysis II
(3 credits)
Prerequitsite: BMGT 882.
A continuation of BMGT 882. Topics include generalized least squares, seemingly unrelated regressions, simultaneous-equations models, factor analysis, structural equations models with latent variables (covariance structure analysis), and specification testing.
Keywords: Analytic Software, Factor Analysis / LC, Model Estimation, Regression.
BMGT 887 Bayesian Inference and Decision Theory
(3 credits)
Prerequitsite: BMGT 733 or equivalent.
Bayesian Methodologies in statistical inference and decision theory. Includes discussion of subjective probability and coherence, elicitation of distributions conjugate distributions, estimation, testing, preposterior analysis and regression analysis. Applications are drawn from the functional business areas.
BUDT Decision and Information Technologies
BUDT733 Data Mining for Business
(3 credits)
Prerequitsite: BUSI 630. Credit will be granted for only one of the following: BMGT 733 or BUDT 733. Formerly BMGT733.
Data mining techniques and their use in strategic business decision making. A hands-on course that provides an understanding of the key methods of data visualization, exploration, classification, prediction, time series forecasting, and clustering. Non-majors should review their registration eligibility in the statement preceding the BUDT courses.
Keywords: Analytic Software, Discriminant Analysis, Graphical Techniques, Model Estimation, Principal Component Analysis, Qualitative & Limited Dependent Variable Models, Regression, Survey Sampling, Time Series.
BUDT736 Data Mining
(3 credits)
Prerequitsite: BUSI 630. Recommended: BUDT 704.
Contemporary methods and processes for extracting information from large databases in support of tactical and strategic business decisions. Applications in areas such as customer relationship management, direct marketing, e-commerce, financial services and retailing. Non-majors should review their registration eligibility in the statement preceding the BUDT courses.
Keywords: Model Estimation.
BUDT737 Management Simulation
(3 credits)
Prerequitsite: BUSI 630. Credit will be granted for only one of the following: BMGT 737 or BUDT 737. Formerly BMGT 737.
Methodology of systems simulation, Monte Carlo simulation, and discrete simulation. Verification and validation of simulation models with computer applications. Non-majors should review their registration eligibility in the statement preceding the BUDT courses.
Keywords: Analytic Software, Model Estimation, Simulation, Statistical Programming.
BUSI MBA Core and Cross-Functional
BUSI630 (PermReq) Data Models and Decisions
(3 credits)
For BMGT majors only. Credit will be granted for only one of the following: BMGT 630 or BUSI 630. Formerly BMGT630.
To develop probabilistic and statistical concepts, methods and models through examples motivated by real-life data from business and to stress the role that statistics plays in the managerial decision making process. Non-majors should review their registration eligibility in the statement preceding the BUSI courses.
Keywords: Analytic Software, Correlation, Graphical Techniques, Hypothesis Testing, Model Estimation, Probability/Stat Theory, Regression.
BUFN Finance
BUFN 735 Computational Finance
(3 credits)
Prerequisites: BUSI 630 and BUSI 640.
Introduces and applies various computational techniques useful in management of equities and fixed income portfolios, valuation of financial derivatives, such as stock options, valuation of fixed income securities and their derivatives. Techniques include portfolio Monte Carlo Simulation, binomial and Black-Scholes option pricing models, value at risk and stochastic processes.
Keywords: Computation, Simulation, Stochastic Processes
