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COURSE DESCRIPTIONS STATISTICS (STA)

5073 Methods of Statistics I
(3-0)3 hours credit. Prerequisite: STA 1053. Emphasis on methods and applications of statistics. Measure of location, variability, and association. Interpretation of categorical data, hypothesis testing, and use of SAS programs and applications. Cannot be applied to a Master of Science degree in Mathematics or Statistics.

5083 Methods of Statistics II
(3-0)3 hours credit. Prerequisite: STA 5073. A continuation of STA 5073, with emphasis on linear statistical models. Use of SAS programs and applications. Topics in applied statistics may include maximum likelihood estimation and its properties, and likelihood ratio tests. Procedures in regression and model fitting, transformations of data, analysis of variance, and others. Cannot be applied to a Master of Science degree in Mathematics or Statistics.

5103 Regression Analysis
(3-0)3 hours credit. Prerequisites: MAT 2233 and STA 3523, or their equivalents. Topics covered include simple linear regression, ordinary least squares and weighted least squares, analysis of residuals and variable selection methods. Nonlinear and logistic regression, and some topics in robust regression. Use of statistical software will be emphasized.

5133 Data Analysis with Statistical Software
(3-0)3 hours credit. Prerequisites: CS 1713 and STA 3523, or their equivalents. Statistical analysis of data sets using SAS, JMP, S-Plus, and other popular statistical software. Manipulation of data sets and production of reports and graphs. Emphasis is on linear models and basic multivariate procedures. Introduction to programming in the S-Plus language.

5213 Advanced Statistical Quality Control
(3-0)3 hours credit. Prerequisite: EGR 5103 or consent of instructor. Methods and techniques for process control, process and gage capability analyses, inspection plans, American National Standards, and recent advanced techniques. Use of statistical software including JMP. Tour of manufacturing industry. Case studies in process control outgoing quality and costs. A required project, assigned by a manufacturing company, must be presented. This course is designed for technology managers and engineers and cannot be applied to a Master of Science degree in Mathematics or Statistics.

5233 Product and Manufacturing Reliability
(3-0)3 hours credit. Prerequisite: EGR 5103 or consent of instructor. Topics include product and manufacturing reliability from managerial, engineering, and statistical perspectives. Emphasis on component and system reliability estimation, testing, and demonstration. Advanced topics such as accelerated life tests, Bayesian procedures, system availability, system maintainability, and compliance with international standards are addressed. Methods and theory are supported through data analytic packages such as JMP, SAS, and S-Plus. This course is designed for technology managers and engineers and cannot be applied to a Master of Science degree in Mathematics or Statistics.

5253 Applied Time Series Analysis
(3-0)3 hours credit. Prerequisite: STA 3523 or consent of instructor. Modern techniques for time series analysis and their applications. Principles of model building. Regression methods, moving averages, and autoregressive integrated moving average models. Practical examples drawn from various application environments. Use of software such as MINITAB, SAS, and S-Plus in time series analysis.

5313 Theory of Sample Surveys with Applications
(3-0)3 hours credit. Prerequisite: STA 3523. Basic sampling techniques and their comparisons for finite populations. Topics include simple random sampling, stratified sampling, ratio and regression estimates, systematic sampling, cluster sampling, multistage and double sampling, and bootstrap and other sampling plans.

5413 Nonparametric Statistics
(3-0)3 hours credit. Prerequisite: STA 3523 or consent of instructor. Order statistics, test of goodness of fit, rank-order statistics, linear rank statistics for problems involving location and scale, association in multiple classifications, and asymptotic relative efficiency.

5503 Mathematical Statistics I
(3-0)3 hours credit. Prerequisites: MAT 4213 and STA 3513. Axioms of probability, random variables and probability distributions, sampling distributions, and stochastic convergence.

5513 Mathematical Statistics II
(3-0)3 hours credit. Prerequisite: STA 5503. Sufficient statistics, unbiased estimation, likelihood ratio test, sequential probability ratio test, and decision theory.

5643 Stochastic Processes
(3-0)3 hours credit. Prerequisite: STA 5503 or consent of instructor. Poisson processes, renewal theory, Markov chains, and Markov processes, including branching processes, ruin problems, birth and death processes, and Brownian motion. Applications in queueing theory, analysis of algorithms, and molecular genetics may be discussed.

5713 Foundation of Linear Models
(3-0)3 hours credit. Prerequisites: MAT 2233 and either STA 5103 or consent of instructor. G-inverses, multivariate normal, and distribution of quadratic forms, least squares estimation and the Gauss-Markov theorem, likelihood ratio tests for full-rank and less-than-full-rank models, including regression and analysis of variance models.

5723 Theory and Application of Linear Models
(3-0)3 hours credit. Prerequisite: STA 5713. Analysis of covariance, random effects, and mixed effects models; analysis of repeated measures. Emphasis on applications and use of statistical packages.

5803 Process Control and Acceptance Sampling
(3-0)3 hours credit. Prerequisite: STA 3523 or consent of instructor. Introduction to statistical process control and product inspection plans. Topics include control charts by attributes and variables, special control charts, specification limits, process capability, and acceptance sampling plans by attributes and variables. Use of statistical software.

5813 Applied Multivariate Statistics
(3-0)3 hours credit. Prerequisites: MAT 2233 and either STA 3523 or consent of instructor. Principal components, factor analysis, cluster analysis, multidimensional scaling, discriminant analysis, multivariate normal distribution, estimation of mean vector and covariance matrix, Hotelling’s T, and tests concerning covariance matrices.

5833 Design and Analysis of Experiments
(3-0)3 hours credit. Prerequisite: STA 3523, STA 5513, or consent of instructor. Introduction to experimental design and data analysis in scientific and engineering settings. Topics include one-factor experiments, randomized block designs, factorials, two- and three-level factorial and fractional factorial designs, nested and split-plot designs, response surface methods, and Taguchi methods. Use of statistical software.

5853 Analysis of Categorical Data
(3-0)3 hours credit. Prerequisite: STA 5503 or consent of instructor. Analysis of multifactor contingency tables, linear and log-linear models, inference in complete and incomplete tables, model selection and assessing goodness of fit, other methods of estimation such as information theoretic approach, minimum chi-square and logit chi-square, and measures of association. Models of discrete data.

5903 Survival Analysis
(3-0)3 hours credit. Prerequisite: STA 5513 or consent of instructor. This course covers topics in survival measures and lifetime distributions. A primary approach focuses on estimation and hypothesis testing regarding the parameters in these models. Advanced topics, such as Cox regression models and competing risk models, are presented from epidemiological and biomedical databases. Methods and theory are supported through analytic software such as SAS and S-Plus.

5913 Statistical Methods in Bioinformatics and Data Mining I
(3-0)3 hours credit. Prerequisite: STA 3523, STA 4713, or consent of instructor. This course provides a detailed overview of statistical models and data analysis tools to analyze vast amounts of data found in biology (genomics), and other high tech industries with an emphasis on the softwares used in bioinformatics and data mining. Topics covered include S-plus programming, Bootstrap, Smoothing and Generalized Additive Models, Classification and Regression Trees (CART), Neural Networks and Applications, Mutivariate Adaptive Regression Splines, Clustering, and Introductory Microarray data analysis.

5923 Statistical Methods in Bioinformatics and Data Mining II
(3-0)3 hours credit. Prerequisite: STA 5913 or consent of instructor. Topics covered include advanced S-plus programming, Software R and the Bioconductor project, microarray data analysis, Boosting and Bagging technique in data mining, TREE NET (MART), introduction to genome biology, basic laboratory techniques, DNA microarray technologies, pre-processing (normalization), microarray experimental design and analysis, multiple testing in DNA microarray experiments (SAM, ANOVA,), distances and expression measures, cluster analysis in microarray experiments, classification in microarray experiments, and dimension reduction in microarray data.

5973 Directed Research
3 hours credit. Prerequisites: Graduate standing and permission in writing (form available) of the instructor and the student’s Graduate Advisor of Record. The directed research course may involve either a laboratory or a theoretical problem. May be repeated for credit, but not more than 6 hours, regardless of discipline, will apply to the Master’s degree.

6953 Independent Study
3 hours credit. Prerequisites: Graduate standing and permission in writing (form available) of the instructor and the student’s Graduate Advisor of Record. Independent reading, research, discussion, and/or writing under the direction of a faculty member. For students needing specialized work not normally or not often available as part of the regular course offerings. May be repeated for credit, but not more than 6 hours, regardless of discipline, will apply to the Master’s degree.

6961 Comprehensive Examination
1 hour credit. Prerequisite: Approval of the appropriate graduate program committee to take the Comprehensive Examination. Independent study course for the purpose of taking the Comprehensive Examination. May be repeated as many times as approved by the Graduate Program Committee. Enrollment is required each term in which the Comprehensive Examination is taken if no other courses are being taken that term. The grade report for the course is either “CR” (satisfactory performance on the Comprehensive Examination) or “NC” (unsatisfactory performance on the Comprehensive Examination).

6971-3 Special Problems
(1-0, 2-0, 3-0) 1 to 3 hours credit. Prerequisite: Consent of instructor. An organized course offering the opportunity for specialized study not normally or not often available as part of the regular course offerings. Special Problems courses may be repeated for credit when topics vary, but not more than 6 hours, regardless of discipline, will apply to the Master’s degree.

6983 Master’s Thesis
3 hours credit. Prerequisite: Permission of the Graduate Advisor of Record and thesis director. Thesis research and preparation. May be repeated for credit, but not more than 6 hours will apply to the Master’s degree. Credit will be awarded upon completion of the thesis. Enrollment is required each term in which the thesis is in progress.

7013 Advanced Applied Business Statistical Methods
(3-0)3 hours credit. Prerequisite: Consent of instructor. Methods and applications of statistics. Topics include basic probability theory, probability distributions of both discrete and continuous random variables, expectations, moments, distributions of functions of random variables, sampling distributions, estimations of population parameters, and hypothesis testing. Nonparametric statistical techniques and their applications to business research will also be covered in the course. Statistical computer software such as SAS or SPSS will be used in the course for data analysis. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

7023 Applied Linear Statistical Models
(3-0)3 hours credit. Prerequisite: Consent of instructor. An in-depth study of regression and analysis of variance models. Topics include multiple regression and model building, multiple and partial correlation, analysis of residuals, analysis of variance, multivariate analysis of variance, analysis of variance as regression analysis, generalized linear model, and applications of statistical models to problems in business. Computer software packages such as SAS or SPSS will be used for data analysis. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

7033 Multivariate Statistical Analysis
(3-0)3 hours credit. Prerequisite: Consent of instructor. An advanced treatment of multivariate statistical techniques. Topics include multivariate normal distribution, multivariate tests of hypotheses, confidence regions, principal component analysis, factor analysis, discrimination and classification analysis, and clustering. Computer software packages such as SAS or SPSS will be used for data analysis. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

7043 Time Series Analysis
(3-0)3 hours credit. Prerequisite: Consent of instructor. Univariate and multivariate time series analysis of economic and financial data, autoregressive integrated moving average (ARIMA) models and vector autoregression, out-of-sample forecasting using computer software. Unit roots, conintegration and error correction, and ARCH models. This course is designed for doctoral students in Business and cannot be applied to a Master of Science degree in Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

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