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Courses

Statistics (FSBM)

5001. Quantitative Methods for Business   (3 s.h.)

This course is designed to introduce you to contemporary elementary applied statistics so you can appreciate the uses of statistics in business, economics, everyday life.  It also develops hands-on capabilities needed in your later coursework and professional employment.

5002. Introduction to Biostatistics   (3 s.h.)

Topics cover statistical methods and concepts with special emphasis on applications in health and biological sciences.

5182.  Independent Study   (1-6 s.h.)
Prerequisite: Approval of the department.

Special study in a particular aspect of statistics under the direct supervision of an appropriate graduate faculty member. No more than six credits of independent study may be counted toward degree requirements.

5801. Statistical Analysis for Management   (3 s.h.)

In this course, you will learn how to use statistics to help solve business problems throughout an enterprise. You will examine case examples of statistical analysis in areas such as marketing, finance, and management. You will learn descriptive and inferential techniques such as regression analysis and how to analyze data and reach decisions, using statistical computer software and Excel.

5802. Quantitative Techniques for Management   (3 s.h.)
Prerequisite: Limited to students matriculated in the Executive M.B.A. program.

In this course, you will apply advanced quantitative techniques for managerial decision making such as forecasting, linear programming, simulation, decision analysis, Markov chains, and game theory. You will use customized software and Excel to analyze these models extensively and apply them to decisions regarding resource allocation and other managerial problems.

8001. 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. Probability and Statistics Theory II   (3 s.h.)

Prerequisite: STAT 8001.

This course comprehensively develops the theory of statistics, including standard distributions, sampling distributions, general theory of estimation, testing of hypotheses, statistical decision theory, order statistics, and linear statistical estimation.

8003. Statistical Methods I   (3 s.h.)

Prerequisite: Previous coursework in statistical methodology or permission of instructor

For this introduction to applied statistics, topics include data management, probability distributions, parameter estimation, hypothesis testing, sampling methodologies, graphical display, analysis of variance, and simple and multiple regression.  R, S-Plus, and SAS statistical software are used.

8004. Statistical Methods II   (3 s.h.)

Prerequisite: STAT 8003 or permission of instructor.

                            

This course includes 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.  R, S-Plus and SAS statistical software are used. 

8101. 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, and the Black-Scholes model and its use in evaluating a variety of financial derivatives.

8103. Sampling Theory   (3 s.h.)

Prerequisite: STAT 8003 or permission of instructor.

In presenting theory and application of sampling from finite populations, topics in this course 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. Matrix Theory for Statistics   (3 s.h.)

Prerequisite: Undergraduate linear algebra or permission of instructor.

This course covers 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, and Jacobians.

8105. Time Series Analysis I   (3 s.h.)

Prerequisite: STAT 8002 or permission of instructor.

This course covers theory and application of univariate time series analysis, including both time domain and frequency domain methods.  It 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. Linear Models I   (3 s.h.)

Prerequisite: STAT 8002, STAT 8004, and STAT 8104 or permission of instructor.

This course 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. Students work with R and SAS throughout the semester.  

8107. Design of Experiments I   (3 s.h.)

Prerequisite: STAT 8004 or permission of instructor.

The course covers 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. Applied Multivariate Analysis I   (3 s.h.)

Prerequisite: STAT 8004 and STAT 8104 or permission of instructor.

The course discusses multivariate normal distribution; marginal and conditional distributions; estimation of population mean vector and dispersion 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. Regression, Time Series, and Forecasting for Business Applications   (3 s.h.)

Prerequisite: STAT 5001 or permission of instructor.

This intermediate-level course 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. Survey Techniques for Business Applications   (3 s.h.)

Prerequisite: STAT 5001 or permission of instructor.

This application-oriented course deals 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, and descriptions of various sampling schemes. Students may be required to plan and execute a survey.

8112. Statistical Methods for Business Research I   (3 s.h.)

Prerequisite: One-year undergraduate statistics courses. 

Part I of a doctoral level, one-year sequence of courses for Ph.D. students in the 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 are presented, but proofs or abstract arguments are 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. Statistical Methods for Business Research II   (3 s.h.)

Prerequisite: STAT 8112 or equivalent.

Part II of a doctoral level, one-year sequence of courses for Ph.D. students in the Business Administration program.  Topics covered in this course include 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), and linear mixed models (LMM)  for multilevel data. 

8115. Nonparametric Methods   (3 s.h.)

Prerequisite: STAT 8002 or permission of instructor.

This thorough course in nonparametric statistics covers estimation and testing of hypotheses when the function form of the population distribution function is not completely specified.

8116. Categorical Data Analysis   (3 s.h.)

Prerequisite: STAT 8002 or permission of instructor.

This course covers 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, and composite link functions.  Students work with R and SAS throughout the semester.

8117. Clinical Trials   (3 s.h.)

Prerequisite: STAT 8002 or STAT 8004 or permission of instructor.

This course provides an 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 the ethics of experimentation on humans.

8121. Statistical Computing   (3 s.h.)

Prerequisites: STAT 8004 or permission of instructor.

This course deals with the use of computers in solving statistical problems. Topics include floating point architecture, random number generation, design of statistical software, computational linear algebra, numerical integration, and optimization methods.

8122. Advanced SAS Programming   (3 s.h.)

 

8123. Time Series Analysis and Forecasting   (3 s.h.)

Prerequisite: ECON 8009, STAT 8002, STAT 8101, or an advanced undergraduate statistics and probability course equivalent to MATH 3031 or MATH 3032.

This time series analysis course with financial and business applications covers important univariate and multivariate time series methods, including ARIMA models, intervention analysis and outlier detection, time series regression, volatility and GARCH models, vector time series, and cointegration.

9001. Advanced Statistical Inference I   (3 s.h.)

Prerequisite: Advanced calculus, STAT 8001, and STAT 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. Advanced Statistical Inference II   (3 s.h.)

Prerequisite: STAT 9001.

Testing of Hypotheses: Neyman-Pearson Fundamental Lemma; Uniformly Most Powerful Tests, Confidence Intervals, and Likelihood Ratio Tests; Asymptotic Tests; Multiple Hypotheses Testing.

9101. Time Series Analysis II   (3 s.h.)

Prerequisite: STAT 8105 or permission of instructor.

This course covers the theory and application of multiple time series analysis and special topics. It deals with 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, and time series aggregation and disaggregation.

9106. Linear Models II   (3 s.h.)

Prerequisite: STAT 8106 or permission of instructor.

As a continuation of STAT 8106, this course covers the theory and practice of analyzing multivariate repeated/correlated non-Gaussian responses, with or without missing observations.  It includes missing at random (MAR) models, informative missingness, EM algorithm, multiple imputations, quasi-likelihood estimation, generalized estimating equations (GEE), transition models, Gibbs sampling, and Markov Chain Monte-Carlo (MCMC) technique.  Students work with R, SAS, and WinBugs throughout the semester. 

9107. Design of Experiments II   (3 s.h.)

Prerequisite: STAT 8107 or permission of instructor.

This course covers symmetrical and asymmetrical factorial experiments, fractional replication, split plot design, balanced and partially balanced incomplete block designs with and without recovery of interblock information, and lattice designs.

9108. Multivariate Analysis II   (3 s.h.)

Prerequisite: STAT 8002 and STAT 8108 or permission of instructor.

This course is a study of specialized topics in multivariate analysis.

9180. Seminar in New Topics in Statistics   (3 s.h.)

Prerequisite: Permission of instructor.

Special topics in Statistics.

9183. Directed Study in Statistics   (variable credit)

Prerequisite: Approval of the department.


9190. Seminar in New Topics in Statistics   (3 s.h.)

Prerequisite: Permission of instructor.

Special topics in Statistics.

9994. Directed Study in Statistics   (variable credit)

Prerequisite: Approval of the department.

Preparation for preliminary examinations.

9998. Directed Study in Statistics   (variable credit)

Prerequisite: Approval of the department.

9999. Dissertation Research   (1-12 s.h.)

Prerequisite: Approval of the department and elevation to candidacy.

 

Updated 10.26.09