Department of Management Science and Statistics

Mission Statement

The mission of the Department of Management Science and Statistics is to offer both undergraduate and graduate educational programs that are of high quality and meet the changing needs of the global community; to provide a supportive learning environment for students; to foster the success of our students in their professional careers; and to create an academic environment that stresses excellence in teaching, intellectual contributions, and service. The Department contributes to the field of knowledge through research and education in the quantitative sciences. Theory and analysis are applied to a variety of interdisciplinary problems to discover new approaches for meeting the challenges of decision making in a global arena of expanding technology and information.

Department Information

The disciplines of Management Science and Statistics are integral to modern decision-making processes. These interdisciplinary fields emphasize the use of quantitative methods and computers for analyzing, understanding, visualizing, and interpreting data. Management Science seeks to provide a rational basis for decision analysis across a broad spectrum of business functions such as production/operations, marketing, finance, human resources, project management, logistics, and supply chain management. Statistical methods provide analytical tools for research in high-technology and biomedical industries, insurance, and government agencies. For students choosing to obtain a Master of Business Administration degree, the Department of Management Science and Statistics offers a concentration in Management Science. The Department also offers a Master of Science degree in Applied Statistics and a Doctor of Philosophy degree in Applied Statistics.

Master of Business Administration Degree – Management Science Concentration

The Master of Business Administration (M.B.A.) degree with a concentration in Management Science is accredited by AACSB International—The Association to Advance Collegiate Schools of Business—and conforms to recommended guidelines.

This concentration is designed to offer the opportunity for qualified graduate students to develop expertise in the field of management science while studying business administration. Students are provided the opportunity to learn quantitative methods and to apply these methods to organizational processes to improve the quality of managerial decision making, to improve operational efficiencies, to increase productivity, and to facilitate the timely flow of goods, services, and information. To achieve this end, students can focus their elective courses on the use of modern methodologies and techniques in the analysis and support of managerial decision-making activities, including the application of computer hardware and software.

Students choosing to concentrate in management science must complete the 24 semester credit hours of courses containing the M.B.A. Core and 12 semester credit hours of electives from the following:

Decision Support Systems for Building Business Intelligence
Statistical Methods for Business Analytics
Introduction to Business Analytics
Logistics Systems Management
Demand and Forecasting Management
Simulation Analysis of Business Systems
Topics in Production/Operations Management
Integrated Global Supply Chain Management
Service Management and Operations
Effective Project Management
Management and Control of Quality
Lean Operations and Six Sigma
Logistics System Analysis
Procurement and Inventory Management
Management Science Internship
Independent Study
Special Problems

Additionally, a student may request the management science coordinator or chair to substitute other appropriate College of Business graduate electives for one or two of the above courses.

Master of Science Degree in Applied Statistics

Today more professions are depending on data analysis to assist in making informed decisions. Organizations need individuals with knowledge in statistics and methods to collect, analyze, interpret data, and communicate the results. There is a growing demand for individuals who are well trained in designing experiments, statistical modeling, making predictions and forecasts, and analyzing large complex data sets commonly encountered in various areas of scientific study. For example, statisticians are needed in such areas as biomedical fields and bioinformatics to address drug development and health related issues, in environmental studies to address pollution and contamination. They are also needed to analyze big data encountered in internet traffic, fraud detection, cyber security and national defense. Statisticians are employed by such industries as insurance, health, finance, manufacturing and service. The Master of Science degree in Applied Statistics at UTSA is designed to meet these demands. It includes instruction in a broad range of applied statistical methods and computational tools to prepare students for careers as government, industrial, or academic statisticians, or to pursue doctoral studies in statistics.

Program Admission Requirements

All application materials must be submitted using the University's online application system and received by the program-specific Fall deadline. Degree-seeking students normally are not admitted for the Spring or Summer semesters due to course-sequence requirements in the program.

In addition to satisfying the University-wide graduate admission requirements, a B.A. or B.S. in statistics, mathematics, engineering, business, or a closely related field is highly recommended as preparation. In particular, three semesters of calculus and a course in matrix theory/linear algebra or their equivalents are required for unconditional admission. A course in probability and/or statistics is preferred but not required. Those students who do not qualify for unconditional admission should anticipate that additional undergraduate and/or graduate coursework may be required to complete the degree. All applicants are required to submit recent scores from the Graduate Record Examination (GRE) aptitude test.

Degree Requirements

Candidates for this degree are required to successfully complete 33 semester credit hours as specified below:

A. All candidates for the Master of Science in Applied Statistics must complete the following 18 semester credit hours of coursework:
STA 5093Introduction to Statistical Inference3
STA 5103Applied Statistics3
STA 5503Mathematical Statistics I3
STA 5513Mathematical Statistics II3
STA 6033Advanced Programming and Data Management in SAS3
STA 6233Advanced Statistical Programming Using SAS Software3
B. A candidate for the Master of Science degree in Applied Statistics must complete 9 semester credit hours of coursework chosen from one or a combination of the following focus areas:9
Biostatistics:
Simulation and Statistical Computing
Nonparametric Statistics
Multivariate Analysis
Design and Analysis of Experiments
Categorical Data Analysis
Spatial Statistics
Survival Analysis
Bioinformatics: Microarray and Proteomics Data Analysis
Advanced Statistical Learning/Data Mining
Industrial Statistics:
Process Control and Acceptance Sampling
Regression Analysis
Applied Bayesian Statistics
Simulation and Statistical Computing
Design and Analysis of Experiments
Response Surface Methodology
Management Science:
Decision Analysis and Production Management
Management and Control of Quality
Lean Operations and Six Sigma
Regression Analysis
Simulation and Statistical Computing
Financial Modeling:
Econometrics and Business Forecasting
Modeling of Financial Decision Making
Regression Analysis
Applied Bayesian Statistics
Simulation and Statistical Computing
Time Series Analysis and Applications
Big Data and Analytics
Introduction to Business Analytics
Statistical Methods for Business Analytics
Demand and Forecasting Management
Regression Analysis
Time Series Analysis and Applications
Multivariate Analysis
Advanced Statistical Learning/Data Mining
General Applied Statistics
Any 9 hours of 5000/6000-level courses in Statistics or other disciplines as approved by the Graduate Advisor.
C. A candidate for the Master of Science degree in Applied Statistics must complete 6 semester credit hours of graduate-level courses in Statistics, Engineering, Biology, or other disciplines as approved by the Graduate Advisor.6
D. Each candidate for the degree is required to pass a comprehensive examination in Statistics that will cover material in the following courses: STA 5093, STA 5103, STA 5503, and STA 5513.
Total Credit Hours33

Doctor of Philosophy Degree in Applied Statistics

In this age of advanced technology, there is an increasing demand for individuals with expertise in designing experiments and analyzing large complex data sets via the latest advances in computing technology. In particular, there is a real need for professionals with a Ph.D. in Applied Statistics. Statisticians are in high demand in various areas of scientific study. For example, in biomedical field, they are needed to develop methods for evaluating the efficacy and safety of new medications/drugs, surgeries, and other treatments. In the Bioinformatics area they address topics such as gene therapy, genomic research, and disease mapping. In environmental studies, statisticians are needed to detect exposure of human population to particulate matter based on air quality, to identify polluted areas based on soil samples, and to model areal data. Statisticians are also needed to analyze big data, especially in areas of fraud detection, cyber security, and defense related issues. Statisticians are being recruited in a variety of industries, including insurance and finance institutions, manufacturing and service businesses. Thus, the Ph.D. in Applied Statistics combines theory with applications to prepare students to pursue careers in academia, research organizations, government, and private industry.

Program Admission Requirements

In addition to satisfying the University-wide graduate admission requirements, a B.A., B.S., M.A. or M.S. in mathematics, statistics, or a closely related field is required. Students who have not taken mathematical statistics courses at the undergraduate level may be required to complete the equivalent courses in the appropriate background areas before taking graduate courses. The admission requirements consist of:

  • A cumulative grade point average of 3.3 or higher in the last 60 hours of coursework.
  • A Graduate Record Examination (GRE) score from a recent (no more than five years prior to the application date) administration of the exam.
  • Official transcripts of all undergraduate and graduate coursework completed.
  • Three letters of recommendation from academic or professional sources familiar with the applicant’s background.
  • A curriculum vita and a statement of experiences, interests, and goals.
  • International students from non-English speaking countries must also submit a score of at least 550 on the Test of English as a Foreign Language (TOEFL). TOEFL scores may not be more than two years old.

  • Applicants may be asked to appear before the admissions committee for a personal interview.

Degree Requirements

Candidates for this degree are required to successfully complete a minimum of 87 semester credit hours of graduate coursework as specified below:

A. Foundation Courses
All candidates entering the program with only a bachelor’s degree or with a non-quantitative masters’ degree must complete the following 18 semester credit hours of coursework:
STA 5093Introduction to Statistical Inference3
STA 5103Applied Statistics3
STA 5503Mathematical Statistics I3
STA 5513Mathematical Statistics II3
STA 6033Advanced Programming and Data Management in SAS3
STA 6233Advanced Statistical Programming Using SAS Software3
B. All candidates entering the program with a bachelor’s degree must complete 12 semester credit hours of 5000/6000-level Statistics courses approved by the Graduate Advisor.12
C. All candidates must complete the following 15 semester credit hours of advanced coursework:
STA 6133Simulation and Statistical Computing3
STA 6713Linear Models3
STA 6993Statistical Consulting3
STA 7503Advanced Inference I3
STA 7513Advanced Inference II3
D. All candidates for the Ph.D. degree in Applied Statistics must complete 6 semester credit hours of approved graduate courses with numbers 6000 or higher within the Department of Management Science and Statistics.6
E. All candidates for the Ph.D. degree in Applied Statistics must complete at least 6 semester credit hours of approved graduate elective courses.6
F. All candidates for the Ph.D. in Applied Statistics must complete a minimum of 15 semester credit hours of Doctoral Research.15
G. All candidates for the Ph.D. in Applied Statistics must complete a minimum of 15 semester credit hours of Doctoral Dissertation.15
Total Credit Hours87

All students in the program will be required to complete a degree plan specifying the courses they will complete. This degree plan must be approved by the Doctoral Studies Committee before the end of the second semester of enrollment.

Applicants with a master’s degree in statistics or a related field may apply up to 30 hours of previously earned graduate credits toward the doctoral degree. Each student’s transcript will be evaluated by the Doctoral Studies Committee and credit will be designated on a course-by-course basis to satisfy the foundation requirements of the degree.

Advancement to Candidacy

Advancement to candidacy requires a student to complete University and Applied Statistics program requirements. After completing the required coursework, all candidates for the Ph.D. degree must pass written qualifying examinations and oral defense of dissertation proposal before being admitted to candidacy for the degree. Students admitted with a bachelor’s degree must pass the Master’s comprehensive examination. However, those who do not pass the qualifying examination at the Ph.D. level may qualify for the M.S. degree. The written examination is administered by the graduate faculty in the specialization area. Written examinations are scheduled once a year, whereas the oral proposal defense is administered at the discretion of the student’s Dissertation Committee. The oral defense is for the purpose of eliminating any questions of competency related to substantive written exams and serves as a hearing for the student’s dissertation proposal. Students will be provided no more than two attempts to pass the written qualifying examination and two attempts to pass the oral proposal defense examination. Majority approval of the dissertation examination committee is required to pass the oral proposal defense. Results of the written and oral qualifying examinations must be reported to the Dean of the Graduate School.

Dissertation

Candidates must demonstrate the ability to conduct independent research by completing and defending an original dissertation. The research topic is determined by the student in consultation with his or her supervising professor. A Dissertation Committee selected by the student and supervising professor, guides and critiques the candidate’s research. The completed dissertation must be formally presented to and approved by the Dissertation Committee.

Following an open presentation of the dissertation findings, the Dissertation Committee conducts a closed meeting to determine the adequacy of the research and any further requirements for completion of the dissertation. Results of the meeting must be reported to the Dean of the College and to the Dean of the Graduate School.

Awarding of the degree is based on the approval of the Dissertation Committee, and the approval of the Dean of the College. The UTSA Dean of the Graduate School certifies the completion of all University-wide requirements.

Graduate Certificate in Operations and Supply Chain Management

The Graduate Certificate in Operations and Supply Chain Management is a 12-semester-credit-hour program offered by the Department of Management Science and Statistics. The Graduate Certificate in Operations and Supply Chain Management (OSCM) is designed to provide specialized training to help expand students' area of expertise, learn about new developments in their fields, augment their professional skills and provide credentials that help advance their careers. It certifies to employers that students awarded the certificate have completed coursework that help them understand a myriad of issues, challenges, problems, and decision tools that relate to the internal and external flow of materials and requisite knowledge. Production/operations management, logistics management, and procurement topics are included to resolve the myriad of complex problems. Moreover, this certificate program will help students discover cutting edge techniques and best practices to leverage their operations and supply chain complexities to achieve competitive advantage.

The operations and supply chain management certificate program provides specialized skills in supply chain management for

  • Students who seek foundational knowledge of supply chain complexities as well as a strong understanding of how companies leverage their supply chains to achieve competitive advantage
  • Experienced professionals who wish to update their knowledge of current thinking and best practices through interaction with faculty
  • Working professionals  who want to supplement their undergraduate or graduate degree with graduate courses in supply chain management.

Supply chain management is a broad career field where professionals are involved in every function of global commerce, including marketing, procurement, production and service operations, logistics, inventory management, etc. The certificate program provides students with a thorough understanding of integrated supply chain and operations activities while emphasizing skills in problem solving, communication, and teamwork.

To earn a Graduate Certificate in Operations and Supply Chain Management, students must complete 12 semester credit hours from the following courses, one of which is required:

A. Required course:3
Integrated Global Supply Chain Management
B. Select three courses from the following:9
Logistics Systems Management
Demand and Forecasting Management
Topics in Production/Operations Management
Service Management and Operations
Effective Project Management
Management and Control of Quality
Lean Operations and Six Sigma
Logistics System Analysis
Total Credit Hours12

Applicants for the Operations and Supply Chain Management certificate program who are currently enrolled in a graduate degree program at UTSA have already met University requirements for admission. Thus, no formal application process is necessary. The applicant should contact the Certificate Program Advisor and complete a form requesting permission to enter and complete the certificate program. If the request is approved, the form will be signed by the Certificate Program Advisor and the Dean of the College of Business.

Applicants who are not currently enrolled in a graduate degree program at UTSA will be required to apply for admission to UTSA (non-degree seeking) graduate student and to indicate their intent to seek admission into a certificate program. Applicants will be required to meet University admission requirements for special graduate students. If admitted as a special graduate student, the applicant should contact the Certificate Program Advisor and complete a form requesting permission to enter and complete the certificate program. The form will be signed by the Certificate Program Advisor and the Dean of the College of Business A copy of this form will be sent to the Graduate School.

If it is determined by the Certificate Program Advisor that an applicant requires prerequisite background courses to adequately prepare for the courses included in the certificate program, this will be noted in the applicant's file. The applicant will be notified that the prerequisite courses must be taken before enrolling in certificate program coursework.

Any applicant who is admitted into a certificate program without being currently enrolled in a graduate degree program is considered to be a special graduate student. If the applicant wishes to be admitted into a degree program, they will be required to apply to that program as a degree-seeking student. Admittance into or completion of a certificate program is not ·considered to be qualification for entry into a graduate degree program. Applicants who are admitted into a certificate program while also pursuing a graduate degree will be classified as degree-seeking students.

Management Science (MS) Courses

MS 5003. Quantitative Methods for Business Analysis. (3-0) 3 Credit Hours.

Prerequisites: MAT 1033 and MS 1023, their equivalents, or consent of instructor. Introduction to managerial decision analysis using quantitative and statistical tools. Course includes a general framework for structuring and analyzing decision problems. Some of the topics include decision theory, statistical techniques (such as analysis of variance, regression, nonparametric tests), introduction to linear programming, and introduction to time series. Uses applicable decision support software.

MS 5023. Decision Analysis and Production Management. (3-0) 3 Credit Hours.

Prerequisite: MS 5003 or an equivalent. Study of applications of quantitative approaches (such as mathematical programming, networks, stochastic processes, multicriteria analysis, and simulation) to business decision analysis. Emphasis is given to production management applications (such as resource allocation, scheduling, inventory control, capital budgeting) and the use of computerized decision support systems.

MS 5303. Decision Support Systems for Building Business Intelligence. (3-0) 3 Credit Hours.

Prerequisite: MS 5023. Study of systems for supporting managerial decision processes. Topics include review of decision support systems, methodologies for identifying decision needs, exploration of analysis tools and related computer technologies and software, survey of expert systems and artificial intelligence applications. (Formerly titled "Topics in Decision Support Systems.").

MS 5323. Statistical Methods for Business Analytics. (3-0) 3 Credit Hours.

Prerequisite: MS 5003 or an equivalent. Introduction to multivariate statistical analysis. Typical topics include multiple regression, multiple analysis of variance, logistic regression, discriminant analysis, conjoint analysis, cluster analysis, and factor analysis. Emphasizes the use of computer statistical packages.

MS 5333. Introduction to Business Analytics. (3-0) 3 Credit Hours.

This course introduces the basic concepts of business analytics, principles of data mining, Structured Query Language (SQL), and Big Data. It provides students an opportunity to understand how analytics can help improve decisions throughout an organization’s value chain. Presents the most prevalent methods for descriptive (e.g., cluster analysis, association analysis), predictive (e.g., multiple regression, logistic regression, decision tree methods), and prescriptive (e.g., optimization) analytics.

MS 5343. Logistics Systems Management. (3-0) 3 Credit Hours.

Study of business logistics: the process of planning, implementing, and controlling the flow and storage of goods or services and related information from point of origin to point of consumption to achieve customer satisfaction. Focuses on the cost and value added to products or services by making them available in the desired condition when and where they are needed.

MS 5353. Demand and Forecasting Management. (3-0) 3 Credit Hours.

This course provides an in-depth study of the processes that balance customer demands with production, procurement, and distribution capabilities. Accurate demand forecasting provides for added flexibility and visibility of inventory, and reduced variability in supply chain outcomes. Core conceptual areas include demand forecasting and management, synchronization of supply and demand, inventory capacity, balancing and positioning, inventory planning, sales and operations planning, and strategic order fulfillment issues. This course introduces modern and practical methods for operations planning and decision making. Short-term forecasting of demand, personnel requirements, costs and revenues, raw material needs, and desired inventory levels are some of the topics included. Other topics covered include technological and environmental forecasting, decomposition methods, and monitoring (automatic procedures such as tracking signals).

MS 5373. Simulation Analysis of Business Systems. (3-0) 3 Credit Hours.

Prerequisite: MS 5023. Study of computer simulation techniques in the analysis of business decision situations. Currently available tools, including general purpose simulation languages, spreadsheets, and graphics programs, are explored. Applications from a wide spectrum of areas are discussed.

MS 5393. Topics in Production/Operations Management. (3-0) 3 Credit Hours.

Prerequisite: MS 5023. Survey of the body of knowledge concerning the management of operations. Considers manufacturing and service principles. The course reviews a variety of topics necessary in the field of production and inventory management, including logistics and distribution processes.

MS 5413. Integrated Global Supply Chain Management. (3-0) 3 Credit Hours.

Focuses on effective supply chain strategies for organizations that operate globally with emphasis on how to plan and integrate supply chain components into a coordinated system. Specifically, the course seeks to integrate different perspectives from the practices of marketing, logistics, and operations management. The course will introduce key tactics such as risk pooling and inventory placement, integrated planning, and information sharing. One of the key objectives is to understand the relationship between a focal firm and its suppliers and customers.

MS 5423. Service Management and Operations. (3-0) 3 Credit Hours.

Focuses on understanding the variety of service industries (both profit and nonprofit) and the growing importance of the service industry to the economy. In addition to the traditional topics of quality, customer satisfaction and value creation, topics include service encounters, service design and development, service productivity, and globalization of services. Tools and techniques for management service operations are also emphasized.

MS 5433. Effective Project Management. (3-0) 3 Credit Hours.

Approaches project management from the perspective that the material is applicable to all disciplines and project types. It not only emphasizes individual project execution, but also provides a strategic perspective. It integrates the critical PMBoK elements in the context of cases and projects. The course examines the traditional concepts and techniques of project management for long-term development programs and short-term projects as well as introducing the innovative adaptive and extreme concepts.

MS 5453. Management and Control of Quality. (3-0) 3 Credit Hours.

Prerequisite: MS 5023. An examination of the fundamental nature of quality assurance, its strategic importance in business and industry, and the economic impact of quality. Theoretical and management issues relating to quality problem solving are emphasized. The contribution of the leaders in modern quality management are discussed.

MS 5463. Lean Operations and Six Sigma. (3-0) 3 Credit Hours.

Course provides an introduction to Six Sigma methodologies and is designed to present the fundamentals of Six Sigma and instill an understanding of what is required to build a sustainable Six Sigma structure. Lean tools, such as physical maps, time value, and Kanban are included as well as advanced Six Sigma statistical tools.

MS 5473. Logistics System Analysis. (3-0) 3 Credit Hours.

The design and management of logistics systems for firms of varying size and differing supply and market conditions. This course relies upon heavy use of computer-assisted cases and problems to illustrate and integrate issues found in materials management and distribution organizations.

MS 5493. Procurement and Inventory Management. (3-0) 3 Credit Hours.

A portion of this course focuses on the key issues related to the strategic implications of sourcing of products, the purchasing of goods and services, and the role of purchasing in a supply chain context. It provides students with an understanding of purchasing processes, issues, and best practices. Emphasis areas include supplier quality, relationship management, and global sourcing. Inventory control concepts, techniques, and strategies for effective integration with basic finance, marketing, and manufacturing objectives are topics covered in this course. Models for dependent and independent demand inventory systems, material requirements planning systems, distribution requirements, planning techniques, and the classical reorder point inventory model are also included.

MS 6943. Management Science Internship. (0-0) 3 Credit Hours.

Prerequisites: Graduate standing, 15 semester credit hours of graduate work, and consent of instructor. Internship must be approved in advance by the Internship Coordinator and the student’s Graduate Advisor of Record. Supervised full- or part-time off-campus work experience and training in management science. Individual conferences and written reports required.

MS 6953. Independent Study. (0-0) 3 Credit Hours.

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 degree.

MS 6971. Special Problems. (1-0) 1 Credit Hour.

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 degree.

MS 6973. Special Problems. (3-0) 3 Credit Hours.

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 degree.

MS 6983. Master’s Thesis. (0-0) 3 Credit Hours.

Prerequisites: 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.

MS 7033. Applications in Causal Structural Modeling. (3-0) 3 Credit Hours.

Prerequisite: Consent of instructor. The purpose of this course is to provide students with an overview of structural equation modeling (SEM) procedures, which includes, but not limited to, issues related to measurement evaluation, model selection and specification, model estimation, and model fit. An additional aim of this course is to provide students with the computer skills needed to analyze and interpret their data, especially as it related to factor analysis, path analysis, and SEM. This course also addresses supplemental topics commonly encouraged in SEM and applied research (sample size and power, missing data, non-normal data, order categorical data, etc.).

Statistics (STA) Courses

STA 5093. Introduction to Statistical Inference. (3-0) 3 Credit Hours.

Prerequisite: Admission to the M.S. program or consent of instructor. Introduction to experiments and sampling; probability, random variables, and distributions; standard discrete and continuous models; sampling distributions; maximum likelihood and moment estimation; confidence intervals and hypothesis tests for one- and two-sample means, proportions, and variances; large sample and bootstrap methods; goodness-of-fit and nonparametric tests. Use of R for simulation and inference.

STA 5103. Applied Statistics. (3-0) 3 Credit Hours.

Prerequisite: STA 5093 or consent of instructor. Simple linear regression, correlation, multiple regression, model selection, one-, and two-way analysis of variance, fixed-, random- and mixed-effects models, multiple comparisons, factorial experiments, and logistic regression. Use of statistical packages such as SAS or JMP for data analysis.

STA 5313. Theory of Sample Surveys with Applications. (3-0) 3 Credit Hours.

Prerequisite: STA 5093 or consent of instructor. 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.

STA 5503. Mathematical Statistics I. (3-0) 3 Credit Hours.

Prerequisite: Admission to the Statistics graduate program or consent of instructor. Axioms of probability, counting rules, univariate random variables, multivariate random variables, joint, marginal, and conditional probability distributions, mathematical expectation, variable transformation, moment generating function, commonly used probability distributions, sampling distributions, laws of large numbers and the central limit theorem.

STA 5513. Mathematical Statistics II. (3-0) 3 Credit Hours.

Prerequisite: STA 5503 or consent of instructor. Data reduction, sufficient and complete statistics, unbiased estimation, maximum likelihood estimation, method of moments, best unbiased estimator, Fisher information, Cramer-Rao lower bound, hypothesis testing, likelihood ratio test, Neyman-Pearson lemma and uniformly most powerful test, and interval estimation.

STA 5803. Process Control and Acceptance Sampling. (3-0) 3 Credit Hours.

Prerequisite: STA 5093 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.

STA 5973. Directed Research. (0-0) 3 Credit Hours.

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.

STA 6013. Regression Analysis. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or consent of instructor. Model selection methods, model validation, diagnostics, outlier detection, autocorrelated data, multicollinearility, cross validation, transformation of data, and generalized linear regression models.

STA 6033. Advanced Programming and Data Management in SAS. (3-0) 3 Credit Hours.

Prerequisite: An introductory course in computer programming or consent of instructor. Essential SAS programming concepts with a focus on data management and the preparation of data for statistical analysis: reading raw data from different sources, creating data files in various formats, creating and modifying SAS datasets, SAS libraries, formats, character and numeric functions, combining datasets, summarizing and displaying data, arrays and macros. Efficient programming techniques are stressed. (Formerly STA 5133. Credit cannot be earned for both STA 5133 and STA 6033.).

STA 6113. Applied Bayesian Statistics. (3-0) 3 Credit Hours.

Prerequisites: STA 5103 and STA 5513, or consent of instructor. Probability and uncertainty, conditional probability and Bayes’ Rule, single parameter and multiple parameter Bayesian analysis, posterior analysis for commonly used distributions, prior distribution elicitation, Bayesian methods in linear models, Bayesian computation including Markov chain Monte Carlo (MCMC) simulation, and applications.

STA 6133. Simulation and Statistical Computing. (3-0) 3 Credit Hours.

Prerequisite: STA 5513 or consent of instructor. Random variable generation, accept-reject methods, simulation from multivariate distributions, Markov chain Monte Carlo simulation, numerical quadrature, Monte Carlo integration, importance sampling, Laplace approximation, methods for variance reduction, bootstrap and jackknife, deterministic methods for function optimization, and EM algorithm.

STA 6233. Advanced Statistical Programming Using SAS Software. (3-0) 3 Credit Hours.

Prerequisites: STA 5093, STA 5103, and STA 6033. STA 5103 may be taken concurrently. Methods for analyzing continuous and categorical data, using Base SAS, SAS/Graph and SAS/STAT software modules. Applications are drawn from regression analysis, analysis of variance, categorical data analysis, survival analysis multivariate methods, simulation and resampling. Implementation of methods, efficient programming, and interpretation of results are the focus of a written project or oral presentation.

STA 6253. Time Series Analysis and Applications. (3-0) 3 Credit Hours.

Prerequisite: STA 5513 or consent of instructor. Examples and goals of time series analysis, autocovariance function, stationarity, linear processes, autoregressive and moving average (ARMA) processes, spectral analysis, the periodogram, linear filters, regression models with ARMA errors, forecasting in times series models, estimation by maximum likelihood and least squares, diagnostics, model selection, autoregressive integrated moving average (ARIMA) and other nonstationary processes. (Formerly STA 5253. Credit cannot be earned for both STA 6253 and STA 5253.).

STA 6413. Nonparametric Statistics. (3-0) 3 Credit Hours.

Prerequisite: STA 5093 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. (Formerly STA 5413. Credit cannot be earned for both STA 5413 and STA 6413.).

STA 6713. Linear Models. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or equivalent, or consent of instructor. Multivariate normal distribution; distribution of quadratic forms; Gauss Markov Theorem; theory for the full rank and less than full rank models; generalized least squares; estimability and testable hypotheses; general linear hypothesis; linear mixed models and variance components; generalized linear models. (Formerly STA 5713. Credit can be earned for only one of the following: STA 5713, STA 6713, or STA 7723.).

STA 6813. Multivariate Analysis. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or equivalent, or consent of instructor. Multivariate normal distribution; estimation of mean vector and covariance matrix; Hotelling’s T2; principal components, factor analysis, MANOVA, multivariate regression; cluster analysis, discriminant analysis; Wishart distribution; and tests concerning covariance matrices.

STA 6833. Design and Analysis of Experiments. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or equivalent or consent of instructor. Introduction to experimental design and data analysis in scientific and engineering settings. Topics include one- and two-factor experiments, randomized block designs, two- and three-level factorial and fractional factorial designs, nested and split-plot designs, and optimal designs. Use of statistical software such as SAS for data analysis. (Formerly STA 5833. Credit cannot be earned for both STA 6833 and STA 5833.).

STA 6843. Response Surface Methodology. (3-0) 3 Credit Hours.

Prerequisite: STA 6833 or equivalent, or consent of instructor. Factorial designs, first and second order models, process improvement with steepest ascent, experimental designs for fitting response surfaces, use of model diagnostics for finding optimum operating conditions, and robust parameter designs.

STA 6853. Categorical Data Analysis. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or equivalent, or consent of instructor. Types of categorical data, analysis of cross-classified tables, test of independence, measures of association, logit models and analogies with regression, multinomial logit models, log-linear models for two- and multi-dimensional tables, specialized methods for ordinal data, and models for matched pairs data, delta method and large sample tests. Use of statistical packages such as SAS for data analysis.

STA 6863. Spatial Statistics. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or consent of instructor. Problems dealing with spatial statistics, random fields, Gaussian random fields, covariograms and variograms, stationarity and isotropy, covariogram/variogram estimation, spatial prediction (kriging), statistical properties of kriging predictors, cross validation, simulation of random fields, models for lattice/areal data.

STA 6903. Survival Analysis. (3-0) 3 Credit Hours.

Prerequisite: STA 5093 or consent of instructor. This course introduces both parametric and nonparametric methods for analyzing survival data. Topics include Kaplan-Meier estimator, inference based on standard lifetime distributions, regression approach to survival analysis including the Cox proportional hazards model. Emphasis on application and data analysis using SAS and S-Plus. (Formerly STA 5903. Credit cannot be earned for both STA 6903 and STA 5903.).

STA 6913. Bioinformatics: Microarray and Proteomics Data Analysis. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or consent of instructor. This course provides a detailed overview of statistical methods used in microarray and proteomics data analysis and exploits the design of such experiments. The topics include introduction to genome biology and microarray technology, R programming and Bioconductor, pre-processing, normalization, microarray experimental design and analysis, multiple testing, LIMMA, dimension reduction in microarray, cluster analysis, and classification in microarray experiments. (Formerly STA 5913. Credit cannot be earned for both STA 6913 and STA 5913.) (Formerly titled “Bioinformatics and Data Mining I: Microarray Data Analysis.”).

STA 6923. Advanced Statistical Learning/Data Mining. (3-0) 3 Credit Hours.

Prerequisite: STA 5103 or consent of instructor. This course provides an overview of statistical learning and data mining tools in analyzing the vast amounts of data found in bioinformatics, business, and other high-tech industries. The topics include R programming language, data mining tools in R, data gathering and cleansing, linear models, generalized additive models, model assessment, Classification and Regression Trees (CART), bagging and boosting, random forest, neural networks, support vector machines, nearest-neighbor classification, combining classifiers, cluster analysis, association rules, visualization, Big Data Analytics, Hadoop, and Rhadoop, applications to microarray/proteomics data analysis. (Formerly STA 5923 and STA 7923. Credit can be earned for only one of the following: STA 5923, STA 6923, or STA 7923.).

STA 6943. Statistics Internship. (0-0) 3 Credit Hours.

Prerequisites: Graduate standing, 15 semester credit hours of graduate work, and consent of instructor. Internship must be approved in advance by the Internship Coordinator and the student’s Graduate Advisor of Record. Supervised full- or part-time off-campus work experience and training in statistics. Individual conferences and written reports required.

STA 6953. Independent Study. (0-0) 3 Credit Hours.

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 degree.

STA 6961. Comprehensive Examination. (0-0) 1 Credit Hour.

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).

STA 6972. Special Problems. (2-0) 2 Credit Hours.

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 degree.

STA 6973. Special Problems. (3-0) 3 Credit Hours.

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 degree.

STA 6983. Master’s Thesis. (0-0) 3 Credit Hours.

Prerequisites: 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.

STA 6991. Statistical Consulting. (1-0) 1 Credit Hour.

Prerequisites: STA 6033, STA 6233 or equivalents, and background in regression analysis and experimental design. Restricted to students who have completed two semesters in the Master’s or Doctoral programs. The principles dealing with the basic art and concepts of consulting in statistics. This course discusses the roles and responsibilities of applied statisticians, relationship between clients and consultants, effective information gathering and report writing. Each student is assigned at least one consulting problem and is required to submit a comprehensive final report.

STA 6992. Statistical Consulting. (2-0) 2 Credit Hours.

Prerequisites: STA 6033, STA 6233 or equivalents, and background in regression analysis and experimental design. Restricted to students who have completed two semesters in the Master’s or Doctoral programs. The principles dealing with the basic art and concepts of consulting in statistics. This course discusses the roles and responsibilities of applied statisticians, relationship between clients and consultants, effective information gathering and report writing. Each student is assigned at least one consulting problem and is required to submit a comprehensive final report.

STA 6993. Statistical Consulting. (3-0) 3 Credit Hours.

Prerequisites: STA 6033, STA 6233 or equivalents, and background in regression analysis and experimental design. Restricted to students who have completed two semesters in the Master’s or Doctoral programs. The principles dealing with the basic art and concepts of consulting in statistics. This course discusses the roles and responsibilities of applied statisticians, relationship between clients and consultants, effective information gathering and report writing. Each student is assigned at least one consulting problem and is required to submit a comprehensive final report.

STA 7013. Advanced Applied Business Statistical Methods. (3-0) 3 Credit Hours.

Prerequisite: Consent of instructor. The course will focus on the applications of statistical methods in business. Topics include basic probability theory, models for discrete and continuous data, sampling distributions, confidence intervals for means and proportions, hypothesis tests for means, proportions, and variances, goodness-of-fit tests, power of tests and sample size determination, and nonparametric statistical techniques. Emphasis will be placed on understanding the underlying assumptions and limitations of the different techniques. Statistical computer software such as SPSS or SAS 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 Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

STA 7023. Applied Linear Statistical Models. (3-0) 3 Credit Hours.

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 Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

STA 7033. Multivariate Statistical Analysis. (3-0) 3 Credit Hours.

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 Applied Statistics without consent of the instructor and prior approval from the Graduate Advisor of Record.

STA 7113. Bayesian Statistics. (3-0) 3 Credit Hours.

Prerequisite: STA 6113 or consent of instructor. Topics include multiple parameter Bayesian analysis, informative and objective Bayesian methods, Bayesian and frequentist interface, Bayesian variable selection and model averaging, Bayesian hierarchical models and empirical models, Bayesian model checking, Bayesian applications to generalized linear models, and Bayesian decision theory.

STA 7211. Doctoral Research. (0-0) 1 Credit Hour.

May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7212. Doctoral Research. (0-0) 2 Credit Hours.

May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7213. Doctoral Research. (0-0) 3 Credit Hours.

May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7214. Doctoral Research. (0-0) 4 Credit Hours.

May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7215. Doctoral Research. (0-0) 5 Credit Hours.

May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7216. Doctoral Research. (0-0) 6 Credit Hours.

May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7311. Doctoral Dissertation. (0-0) 1 Credit Hour.

Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics. May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7312. Doctoral Dissertation. (0-0) 2 Credit Hours.

Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics. May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7313. Doctoral Dissertation. (0-0) 3 Credit Hours.

Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics. May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7314. Doctoral Dissertation. (0-0) 4 Credit Hours.

Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics. May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7315. Doctoral Dissertation. (0-0) 5 Credit Hours.

Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics. May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7316. Doctoral Dissertation. (0-0) 6 Credit Hours.

Prerequisite: Admission to candidacy for Doctoral degree in Applied Statistics. May be repeated for credit, but not more than 15 hours may be applied toward the Doctoral degree.

STA 7503. Advanced Inference I. (3-0) 3 Credit Hours.

Prerequisites: STA 5503 and STA 5513 or equivalent and Doctoral standing. Brief introduction to measure and Lebesgue integration, location-scale families of distributions, exponential families of distributions, sufficiency, completeness, ancillarity, Fisher information, model identifiability, principles of estimation, best unbiased estimation, variance lower bounds, maximum likelihood estimation, and small sample properties of estimators.

STA 7513. Advanced Inference II. (3-0) 3 Credit Hours.

Prerequisite: STA 7503. Different forms of stochastic convergence, laws of large numbers, central limit theorems, multivariate delta method, asymptotic properties of maximum likelihood estimators, tests of hypotheses, Neyman-Pearson theory, uniformly most powerful tests, unbiased tests, monotone likelihood ratio families, likelihood ratio tests, Wald and Rao/Score tests, asymptotic properties of tests, tests of linear hypothesis, Bonferroni and Scheffe multiple tests, confidence regions, duality between confidence regions and tests of hypotheses.

STA 7903. Advanced Survival Analysis: Counting Process Approach. (3-0) 3 Credit Hours.

Prerequisite: Consent of instructor. STA 5903 recommended. This course introduces and extends the survival regression model to multiple event data using a counting process approach. The topics include counting processes, estimation of the survival and hazard functions, Cox model, residual and influence analysis, testing proportional hazard, multiple events model, frailty models, and R programming.