Program Description: Master of Science

The following is a summary of the Master of Science degree requirements in statistics. Course listings are consistent with the current University General Catalog. More information on specific Graduate School requirements referred to in the following sections can be found in the Graduate and Professional Bulletin.

ASSUMED BACKGROUND

The undergraduate major of a prospective student is not important. However, since some undergraduate level exposure to certain concepts from mathematics and statistics is a prerequisite for our master's program, it is desirable to have had at least three semesters of calculus and a course in linear algebra, plus a minimum of six credits of upper division statistics. Experience with at least one major computer programming language is also recommended.

MS Degree Options

Students may elect one of the following MS degree options:
Plan A (thesis option), or
Plan B (project option), or
Plan B (exam option)
The course requirements for all three options are as follows.

COURSE REQUIREMENTS

The courses leading to the M.S. degree are categorized into the three groups listed below. The course requirements are designed to cover the fundamental topics of probability, mathematical statistics, and statistical methodology (Group I), to provide an exposure to a range of areas in statistics (Group II), and to allow further specialization in a subject of the student's choosing (Group III).

GROUP I: Students will take all courses listed under Group I

ST 501 Statistical Science (one credit)
ST 520 Introduction to Probability Theory (four credits)
ST 530 Mathematical Statistics (three credits)
ST 540 Data Analysis and Regression (three credits)
ST 586 Practicum in Consulting Techniques (one credit)
ST 592 Seminar (one credit each semester)
ST 640 Design and Linear Modeling I (four credits)
ST 699 Thesis (variable credit/minimum of three credits)

GROUP II: Two courses from Group II

One of
ST 521 Stochastic Processes I (three credits)
or ST 525 Analysis of Time Series I (three credits)

One of
ST 605 Sampling Techniques (three credits)
or ST 650 Design and Linear Modeling II (three credits)

Electives: Three additional courses, which may consist of Group III courses below or any of ST521, ST525, ST605, ST650, not already used to meet Group II requirements. Selection of electives requires approval of adviser.

GROUP III

ST 522 Stochastic Processes II (three credits)
ST 523 Quantitative Spatial Analysis (three credits)
ST 526 Analysis of Time Series II (three credits)
ST 560 Applied Multivariate Analysis (three credits)
ST 570 Nonparametric Statistics (three credits)
ST 600 Statistical Computing (three credits)
ST 645 Categorical Data Analysis and GLIM (three credits)
ST 675 A-L Topics in Statistical Methods (three credits)
Other Interdisciplinary courses from an approved list (e.g., epidemiology, signal processing, biostatistical methods, various courses in mathematics, etc.)

In completing their coursework, students may select from pre-designed MS TRACKS, or put together a plan of study with the help of their graduate committee. In the latter case, the plan of study should be consistent with the course requirements stated above.

MASTER'S PROJECT (for Plan A or Plan B (project))

Plan B (project) candidates must complete an independent studies project and submit a written report on it to his/her graduate committee. Acceptable topics for a project range from a thorough literature search in a selected area of applied or theoretical statistics to original research on a statistical problem. In the latter case, the report may be used to satisfy the Graduate School's thesis option for the master's degree (Plan A). The student's project findings must be presented in a Department seminar.

MASTER'S EXAM (for Plan B (exam))

Plan B (exam) candidates must take and pass the MS comprehensive exams--one exam on Probability & Mathematical Statistics and another on Linear Models & Methods. The student is required to take the Probability/Math Stat exam at the end of summer following completion of the ST520, ST530 sequence and the Linear Models/Methods exam at the end of the summer following completion of the ST540, ST640 sequence. Students will be allowed a maximum of two attempts to pass each of these exams. In addition to passing these exams, the student must take an additional elective course from the Electives listed above, and pass an oral exam administered by the student's graduate committee.

(Note: A student who originally chose the Plan B (exam) option may request to be allowed to switch to the Plan B (project) option at a later time.)

CATALOG DESCRIPTIONS FOR 500/600 LEVEL COURSES

ST 500. 1(0-2-0). Statistical Computer Packages. S.

Prerequisites: ST 302, ST 304. Comparison, evaluation, and use of computer packages for univariate and multivariate statistical analyses.

ST 501. 1(1-0-0). Statistical Science. F. An overview of statistics: theory; use in agriculture, business, environment, engineering; modeling; computing; statisticians as researchers/consultants.

ST 511. 4(3-0-1). Design and Data Analysis for Researchers I. F. Prerequisite: ST/STCC301 or ST/STCC307 or EH/EHCC307 or STCC309 or ST 311 or written consent of instructor. Statistical methods for experimenters and researchers emphasizing design and analysis of experiments.

ST 512. 4(3-0-1). Design and Data Analysis for Researchers II. S. Prerequisite: ST 511. Model building and decision making; communication of statistical information.

ST 515. 3(2-1-0). Statistical Science and Process Improvement. S. Prerequisite: ST 511 or ST 540 or BQ 570; or written consent of instructor. Statistical methods in process design; statistical methods; measurement processes; customer evaluation.

ST 520. 4(4-0-0). Introduction to Probability Theory. F. Prerequisite: M 340. Probability, random variables, distributions, expectations, generating functions, limit theorems, convergence, random processes.

ST 521. 3(3-0-0). Stochastic Processes I. S. Prerequisite: ST 520. Characterization of stochastic processes, Markov chains in discrete and continuous time, branching processes, renewal theory, Brownian motion.

ST 522. 3(3-0-0). Stochastic Processes II. F, SS. Prerequisite: ST 521. Martingales and applications, random walks, fluctuation theory, diffusion processes, point processes, queueing theory.

ST 523. 3(3-0-0). Quantitative Spatial Analysis. S. Prerequisite: ST/STCC 301 or ST/STCC 307 or EH/EHCC 307. Credit not allowed for both ST 523 and NR 523. Techniques in spatial analysis; point pattern analysis, spatial autocorrelation, trend surface and spectral analysis.

ST 525. 3(3-0-0). Analysis of Time Series I. F. Prerequisite: ST 430. Trend and seasonality, stationary processes, Hilbert space techniques, the spectral distribution function, fitting ARIMA models, linear prediction.

ST 526. 3(3-0-0). Analysis of Time Series II. S, SS. Prerequisite: ST 525. Spectral analysis; the periodogram; spectral estimation techniques; multivariate time series; linear systems and optimal control; Kalman filtering and prediction.

ST 530. 3(3-0-0). Mathematical Statistics. S. Prerequisite: ST 520. Sampling distributions, estimation, testing, confidence intervals; exact and asymptotic theories of maximum likelihood and distribution-free methods.

ST 540. 3(3-0-0). Data Analysis and Regression. F. Prerequisite: Six credits of upper-division statistics courses or written consent of instructor. Introduction to multiple regression and data analysis with emphasis on graphics and computing.

ST 544. 3(3-0-0). Biostatistical Methods for Quantitative Data. S. Prerequisite: ST 307/EH 307 or STCC/EHCC 307 or ST/STCC 301. Credit not allowed for both ST 544 and EH 544. Regression and analysis of variance methods applied to both observational studies and designed experiments in the biological sciences.

ST 547. 3(3-0-0). Statistics for Environmental Monitoring. S. Prerequisite: ST/STCC 301. Credit not allowed for both ST 547 and CB 547. Applications of statistics in environmental pollution studies involving air, water, or soil monitoring; sampling designs trend analysis; censored data.

ST 560. 3(3-0-0). Applied Multivariate Analysis. F, S. Prerequisite: ST 520, ST 540. Multivariate analysis of variance; principal components; factor analysis; discriminant analysis; cluster analysis.

ST 570. 3(3-0-0). Nonparametric Statistics. S, SS. Prerequisite: ST 430 or written consent of instructor. Distribution and uses of order statistics; nonparametric inferential techniques, their uses and mathematical properties.

ST 586. 1(0-2-0). Practicum in Consulting Techniques. Attend consulting sessions of faculty. Computing on elementary research problems for beginning students through planning and designing experiments. ST 592. 1(0-0-1). Seminar.

ST 600. 3(3-0-0). Statistical Computing. F, S. Prerequisite: ST 520, ST 540. Statistical packages; graphical data presentation; model fitting and diagnostics; random numbers; simulation; numerical methods in statistics.

ST 605. 3(3-0-0). Theory of Sampling Techniques. F. Prerequisites: ST/STCC301 or ST/EH 307 or STCC/EHCC 307 or ST/STCC 309 or ST 311, ST 430. Survey designs; simple random, stratified, cluster samples; theory of estimation; optimization techniques for minimum variance or costs.

ST 640. 4(4-0-0). Design and Linear Modeling I. S. Prerequisites: ST 540 or written consent of instructor. Introduction to linear models; experimental design; fixed, random, and mixed models.

ST 645. 3(3-0-0). Categorical Data Analysis and GLIM. S. Prerequisite: Concurrent registration in ST 640. Generalized linear models, binary and polytomous data, log linear models, quasilikelihood models, survival data models.

ST 650. 3(3-0-0). Design and Linear Modeling II. F. Prerequisite: ST 640 or written consent of instructor. Mixed factorials; response surface methodology; Taguchi methods; variance components.

ST 675. A-L. 3(3-0-0). Topics in Statistical Methods. F, S, SS. Prerequisite: ST 430 or written consent of instructor. A. Sampling; B. Design; C. Multivariate and regression analysis; D. Computer intensive methods; F. Robustness and nonparametric methods; I. Industrial statistical methods; J. Reliability; K. Bayesian statistics; L. Medical and pharmaceutical statistical methods.

ST 684. 1-3 credits. Supervised College Teaching. F, S, SS. Prerequisite: Enrollment in M.S./Ph.D. program in statistics. Guidance and instruction in effective teaching of college courses in statistics.

ST 695. Variable Credit. Independent Study.

ST 699. Variable credit. Thesis.