Courses
Statistics (FSBM)
Previous numbers appear in parentheses.
5002 (0403). Introduction to Biostatistics (3 s.h.)
Topics cover statistical methods and concepts with special emphasis on applications in health and biological sciences.
5001 (0500). Quantitative Methods for Business (3 s.h.)
This course is designed to introduce you to contemporary elementary applied statistics and to provide you with an appreciation for the uses of statistics in business, economics, everyday life, as well as hands-on capabilities needed in your later coursework and
professional employment.
8001 (0501). Probability and Statistics Theory I. (3 s.h.)
Prerequisite: calculus.
Topics include basic probability theory and combinatorial problems, generating functions, random variables, probability distributions, law of large numbers, and limit theorems.
8002 (0502). Probability and Statistics Theory II (3 s.h.)
Prerequisite: Stat. 8001 (0501)
A comprehensive development of the theory of statistics, including standard distributions, sampling distributions, general theory of estimation, testing of hypotheses, statistical decision theory, order statistics, linear statistical estimation.
8003 (0503). Statistical Methods I. (3 s.h.)
Prerequisite: previous coursework in statistical methodology or permission of instructor.
Introduction to applied statistics. Topics include data management, probability distributions, parameter estimation, hypothesis testing, sampling methodologies, graphical display, analysis of variance, andsimple and multiple regression. Use of R, S-Plus and SAS statistical software.
8004 (0504). Statistical Methods II. (3 s.h.)
Prerequisite: Stat 8003 or permission of instructor
Design of experiments, analysis of discrete data, introduction to nonparametric methods, logistic regression, ARIMA time series analysis, bootstrapping, jackknife, robustness, and selected topics in multivariate analysis. Use of R, S-Plus and SAS statistical software.
8101 (0509). Stochastic Processes (3 s.h.)Prerequisite: calculus
This is a first course in stochastic processes, with an emphasis on continuous-time models that support applications in financial mathematics and derivative evaluation. The course covers: fundamentals of probability, limit theorems, conditional expectation, change of measures, Markov chains, random walks, martingales, Brownian motion, the Ito integral, stochastic differential equations, the Black-Scholes model and its use in evaluating a variety of financial derivatives.
8103 (0511). Sampling Theory (3 s.h.)
Prerequisite: Stat. 503 or permission of instructor.
Theory and application of sampling from finite populations. Topics include random, stratified, cluster, and systematic sampling; estimation of means and variances; optimal allocation of resources; problems of nonsampling errors; and ratio and regression estimation.
8104 (0515). Matrix Theory for Statistics (3 s.h.)
Prerequisite: undergraduate linear algebra or permission of instructor.
Vector spaces; linear independence of vectors and basis; matrices and algebraic operations on matrices; determinants; rank of a matrix; inverse of nonsingular matrices; linear equations and their solutions; generalized inverse of a matrix; eigen values and vectors of matrices; diagonalization theorems; quadratic forms and their reduction to sum of squares; Jacobians.
8105 (0518). Time Series Analysis I. (3 s.h.)
Prerequisite: Stat. 8002 or permission of instructor.
Theory and application of univariate time series analysis. Includes both time domain and frequency domain methods. Considers stationary and nonstationary linear processes, time series model building, forecasting, unit root test, intervention models and outlier detection, spectral theory of stationary processes, spectral windows, and estimation of spectrum.
8106 (0521). Linear Models I. (3 s.h.)
Prerequisite: Stat. 8002, Stat 8004 and Stat. 8104 or permission of instructor.
Covers the basic theory and practice of generalized linear models (GLM), such as the logistic, Poisson and gamma regression, as well as models for multilevel or longitudinal Gaussian responses, such as the hierarchical linear model and linear mixed model. The students will need to work with R and SAS throughout the semester.
8107 (0522). Design of Experiments I. (3 s.h.)
Prerequisite: Stat. 504 or permission of instructor.
Principles of experimental designs, completely randomized designs, multiple comparisons, randomized block design, latin square design, missing value problems, analysis of covariance, and factorial experiments.
8108 (0533). Applied Multivariate Analysis I. (3 s.h.)
Prerequisite: Stat. 504 and 515, or permission of instructor.
Multivariate normal distribution; marginal and conditional distributions; estimation of population mean vector anddispersion matrix; correlation, partial correlation, and multiple correlation coefficients; Hotelling's T2; MANOVA; discriminant function; repeated measurements analysis; principal components and canonical correlation; factor analysis; and multidimensional scaling.
8109 (0551). Regression, Time Series, and Forecasting for Business Applications (3 s.h.)
Prerequisite: Stat. 500 or statistics 212 or permission of instructor.
Intermediate level course that covers regression analysis, time series analysis, and forecasting. The course is application oriented and standard statistical packages such as MINITAB are introduced and extensively used.
8111 (0554). Survey Techniques for Business Applications (3 s.h.)
Prerequisite: Stat. 500 or permission of instructor.
Application oriented. A course dealing with statistical and nonstatistical aspects of organizing a sample survey. Included are discussions of objectives, measurement, sample selection, pilot testing, data collection, data editing, summarization and interpretation of results in addition to describing the various sampling schemes. Students may be required to plan and execute a survey.
8112 (0555). Statistical Methods for Business Research I (3 s.h.)
Prerequisite: One-year undergraduate statistics courses, old CIS 401 or equivalent.
Part I of a doctoral level, one-year sequence of courses for the PhD students in Business Administration program. The course covers a variety of statistical methods useful in business research, such as: multiple regression analysis, ANOVA, linear models, analysis of covariance, logistic regression, principal component analysis, exploratory factor analysis and canonical correlation analysis. Emphases are placed on rationales, assumptions, techniques, and interpretation of results from computer packages. Relevant mathematical results will be presented, but proofs or abstract arguments shall be avoided. The lectures cover computer usages, such as R and/or SAS, and the students are expected to work with SAS (or equivalent packages) throughout the semester.
8113 (0556). Statistical Methods for Business Research II. (3 s.h.)
Prerequisite: Statistics 8112 or equivalent.
Part II of a doctoral level, one-year sequence of courses for the PhD students in Business Administration program. Topics covered in this course are: discriminant analysis, confirmatory factor analysis and structural equations modeling, time-series intervention analysis, survival (event history) analysis, MANOVA, multivariate profile analysis, hierarchical linear models (HLM), linear mixed models (LMM) for multilevel data.
8115 (0571). Nonparametric Methods (3 s.h.)
Prerequisite: Stat. 502 or permission of instructor.
A thorough course in nonparametric statistics. Estimation and testing of hypothesis when the function form of the population distribution function is not completely specified.
8116 (0572). Categorical Data Analysis (3 s.h.)
Prerequisite: Stat. 8002 or permission of instructor.
Sampling models and analyses for discrete data: Fisher's exact test; Logistic regression; ROC analysis; Log-linear models and Poisson regression; Conditional logistic regression; Cochran-Mantel-Haenszel test; Measures of agreement between observers; Quasi-independence; Multinomial logit models; Proportional odds model; Association models; generalized estimating equations (GEE); generalized linear mixed model (GLIMMIX); GSK models; Composite link functions. The students will need to work with R and SAS throughout the semester.
8117 (0575). Clinical Trials (3 s.h.)
Prerequisite: Stat. 502 or 504 or permission of instructor.
Introduction to the special problems associated with medical trials on humans. Topics include randomization, sample-size determination, methods for early trial termination, and tests for superiority, equivalence, and non-inferiority. Also discussed are choice of endpoints, control, side effects, use of historical data, meta-analysis and ethics of experimentation on humans.
8121 (0581). Statistical Computing (3 s.h.)
Prerequisites: Stat. 504 and CIS 401 or permission of instructor.
Use of computers in the solution of statistical problems. Topics include: floating point architecture, random number generation, design of statistical software, computational linear algebra, numerical integration, optimization methods.
8122 (0590). Advanced SAS Programming (3 s.h.)
5182 (0598). Independent Study (1-6 s.h.)
Prerequisite: approval of the department.
Special study in a particular aspect of statistics under thedirect supervision of an appropriate graduate faculty member. No more than six semester hours of independent study may be counted toward degree requirements.
9001 (0601). Advanced Statistical Inference I. (3 s.h.)
Prerequisite: Advanced Calculus, Stat 8001-8002 or equivalents.
Background: Matrix Theory Estimation: Sufficiency, Completeness, UMVU Estimation, Information Inequality, Invariance Principle, Bayes Estimation, Admissibility, Maximum Likelihood Estimation, Large Sample Properties of Estimators.
9002 (0602). Advanced Statistical Inference II (3 s.h.)
Prerequisite: Stat. 9001.
Testing of Hypotheses: Neyman-Pearson Fundamental Lemma; Uniformly Most Powerful Tests, Confidence Intervals, Likelihood Ratio Tests; Asymptotic Tests, Multiple Hypotheses Testing .
9101 (0618). Time Series Analysis II. (3 s.h.)
Prerequisite: Stat. 8105 or permission of instructor.
Theory and application of multiple time series analysis and special topics. Covers transfer function models, time series regression with autocorrelated errors, ARCH and GARCH models, vector time series models, cointegration, state space models, long memory processes and nonlinear processes, time series aggregation and disaggregation.
9106 (0621). Linear Models II. (3 s.h.)
Prerequisite: Stat. 8106 or permission of instructor.
Continuation of Stat 8106, covers the theory and practice of analyzing multivariate repeated/correlated non-Gaussian responses, with or without missing observations. Missing at random (MAR) models; informative missingness; EM algorithm; multiple imputations; quasi-likelihood estimation; generalized estimating equations (GEE); transition models; Gibbs sampling; Markov Chain Monte-Carlo (MCMC) technique. The students will need to work with R, SAS and WinBugs throughout the semester.
9107 (0622). Design of Experiments II. (3 s.h.)
Prerequisite: Stat. 522 or permission of instructor.
Covers symmetric and asymmetrical factorial experiments, fractional replication, split plot design, balanced and partially balanced incomplete block designs without and with recovery of interblock information and lattice designs.
9108 (0633). Multivariate Analysis II (3 s.h.)
Prerequisite: Stat. 502 and 533 or permission of instructor.
A study of specialized topics in multivariate analysis.
9180 (0701). Seminar in New Topics in Statistics (3 s.h.)
Prerequisite: permission of instructor.
Special topics in Statistics
9190 (0702). Seminar in New Topics in Statistics (3 s.h.)
Prerequisite: permission of instructor.
Special topics in Statistics
9994 (0799). Directed Study in Statistics (variable credit)
Prerequisite: departmental permission.
Preparation for preliminary examinations.
5801 (0800). Statistical Analysis for Management (3 s.h.)
In this course, you'll learn how to use statistics to help solve business problems throughout an enterprise. You'll examine case examples of statistical analysis in areas such as marketing, finance and management. You'll learn descriptive and inferential techniques such as regression analysis and how to analyze data and reach decisions, using statistical computer software and Excel.
5802 (0802). Quantitative Techniques for Management (3 s.h.)
Prerequisite: Limited to students matriculated in the Executive M.B.A. program.
In this course you'll apply advanced quantitative techniques for managerial decision-making such as forecasting, linear programming, simulation, decision analysis, Markov chains and game theory. You'll use customized software and Excel to analyze these models extensively and apply them to decisions regarding resource allocation and other managerial problems.
9183 (0896). Directed Study in Statistics (variable credit)
Prerequisite: departmental permission.
9998 (0899). Directed Study in Statistics (variable credit)
Prerequisite: departmental permission.
9999 (0999). Dissertation Research (1-12 s.h.)
Prerequisite: departmental approval and elevation to candidacy.