The educational objectives of the Master of Science degree in Statistics are threefold: to provide professionally competent statisticians equipped to accept responsibilities in business, industry and public service positions, to provide the academic foundation needed to pursue the study of statistics at the doctoral level, and to provide opportunity for study of statistics at the graduate level by students whose primary area of specialization is a field in which applications of statistics are appropriate. (Such areas include social sciences, education, criminal justice and the physical sciences.)
Students seeking admission to the graduate program in Statistics must submit the Graduate Studies Application for Admission with the one-time application fee to the Office of Graduate Studies, official transcripts of all college-level work (including the transcript that shows the date the undergraduate degree was conferred), and official GRE scores. Two letters of recommendation from the Mathematics or Statistics faculty at the student’s undergraduate degree-granting institution are required with the application for admission. A 3.0 overall undergraduate GPA is recommended for admission into the Mathematics program. For a final admissions decision, GRE scores do not constitute the sole criterion for consideration of the applicant, nor do GRE scores and undergraduate GPA constitute the primary criteria to end consideration of an applicant. Based on review of a student’s undergraduate transcript, the Department of Mathematics and Statistics may require completion of undergraduate stem courses as a condition for admission.
An oral examination is administered by the advisory committee for each Master of Science degree candidate. [NOTE: The oral examination must be scheduled with the Graduate Advisor at least three weeks in advance. Request forms are available in the department office. Students must be enrolled the semester in which they take comprehensive examinations.
Requirements specified in the degree programs that follow are
subject to minor modification by the department. Also, to ensure
a balanced program, all electives must be approved by the department
chair or an authorized representative of the graduate Statistics
Master of Science Degree, 37 Semester Hours, Thesis or Non-thesis.
Prerequisites: STA 471, 472.
Required Core: STA 511, 533, 561, 562, 564, 568, MTH 668.
Electives: (Four courses chosen from STA 560, 565, 566, 567, 568, 569, 570, MTH 570, 673, 694, CS 593).
Research/Thesis: STA 698:699 or 3 semester hour Practicum (STA 560) and an additional
3 semester hour graduate statistics elective. The Practicum must include an oral presentation of the
Graduate Minor in Statistics. Three specific plans are available for the graduate minor in Statistics with each plan requiring a minimum of 12 semester hours of statistics.
Statistical Methods Minor Program. This minor is particularly appropriate for graduate students in the social or natural sciences, education and criminal justice. The required courses are STA 533, 568, and 2 additional courses selected from STA 560, 566, 567, 569, and 570.
STA 511 Software for Statistical Sciences. Topics include MINITAB, SAS, Maple and Scientific Workplace (or equivalents). This one-hour course is available for graduate students in all disciplines. Prerequisites: STA 380 (or equivalent), graduate standing and consent of instructor. Credit 1.
STA 533 Design and Analysis of Experiments. Topics include the design, analysis and interpretation of results from standard experimental design models including the completely randomized design, the randomized complete block, the incomplete block, factorial models, Latin squares, Greco-Latin squares, screening designs, fractional factorials, and general fixed, mixed and random effects ANOVA models. Prerequisites: STA 472 (or equivalent). Credit 3.
STA 560 Special Topics in Statistics. Topics and courses are selected to suit individual student needs. Methods of independent study and research are stressed. Such topics as stochastic processes, Markov chain models, game theory, remote sensing, statistical decision theory, time series analysis and pattern recognition may be included. Also listed as MTH 560. Prerequisites: Consent of instructor. Credit 3.
STA 561 Theory and Applications of Probability. Topics include probability axioms and properties, conditional probability, random variables, probability distributions, moment generating functions, laws of large numbers and the Central Limit Theorem. Also listed as MTH 561. Prerequisites: STA 472 (or equivalent) or consent of instructor. Credit 3.
STA 562 Theory and Applications of Statistics. Topics include point estimation, hypothesis testing, interval estimation, nonparametric statistics, regression, correlation, analysis of variance, robustness and model fitting. Prerequisites: STA 561 (or equivalent). Credit 3.
STA 564 Applied Multivariate Statistical Analysis. Topics include the multivariate normal distribution, inferences about a mean vector, comparisons of several multivariate means, principal components analysis, clustering, discriminant and classification analysis. Prerequisites: STA 472 and MTH 668, or consent of instructor. Credit 3.
STA 565 Linear Statistical Models. Topics include the statistical properties of quadratic forms, the full-rank general linear statistical model, the less-than-full-rank model, the linear model structure of regression models, ANOVA models, ANCOVA models, the general characteristics of the fixed, mixed and random effects models and model diagnostics considerations. Prerequisites: STA 472 or STA 562 (or equivalents). Credit 3.
STA 566 Sampling Methods. Topics include the theory and applications of standard methods for performing scientific-based sampling. Among these are simple random sampling, cluster sampling, stratified random sampling, systematic sampling, probability proportional to size (pps) sampling, sampling from finite populations and ratio regression estimation. Prerequisite: STA 472, STA 562, or consent of instructor. Credit 3.
STA 567 Reliability Analysis and Quality Control. Topics include measures of failure, reliability functions, failure models, life testing and censoring, system reliability, parameter estimation and testing, control charting, acceptance sampling plans, software reliability and process control. Prerequisites: STA 472, STA 562, or consent of instructor. Credit 3.
STA 568 Regression Modeling and Analysis. Topics include model estimation and testing, simple and multiple regression models, residual analysis, variables selection, polynomial regression, multicollinearity, ridge regression, logistic regression and real data analysis and applications. Prerequisites: STA 472, STA 562, or consent of instructor. Credit 3.
STA 569 Statistical Computing and Consulting. This course consists of a detailed study of the SAS package including SAS/BASICS, SAS/STAT, SAS/GRAPH and SAS/IML with emphasis on applying these tools in a consulting environment. Techniques and principles important in working with representatives of user disciplines are included. Prerequisites: STA 380 and graduate standing. Credit 3.
STA 570 Nonparametric Statistics. Topics include order statistics, contingency analysis, rank tests (Wilcoxin signed-rank test, Mann-Whitney U test and others), distribution- free tests of location and scale, Kendall’s tau and related areas. Prerequisites: STA 472, STA 562, or consent of instructor. Credit 3.
STA 698 Research and Thesis. This course includes a study of research methods in statistics, identification of an appropriate thesis problem and the preparatory work leading to a plan for its solution. Study must be supervised by a member of the graduate statistics faculty. Prerequisite: STA 562. Credit 3.
STA 699 Research and Thesis. This course continues the thesis research and concludes with a carefully written solution of the thesis problem and a satisfactory oral presentation of the results. Study must be supervised by a member of the graduate statistics faculty. Prerequisite: STA 698. Credit 3.
STA 765 Statistical Methods for Decision Making. Topics covered are oriented toward statistical methods supporting the decision environment. Topics include estimation, hypothesis testing, statistical modeling and decision methods. Prerequisite: 3 credit hour of graduate-level, introductory probability and statistics or the equivalent. Credit 3.