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. Students are required to have had at least three semesters of calculus, a course in linear algebra, and at least one proof-based mathematics course such as real analysis.   Additional background that is useful but not required for admission: Upper division statistics courses and experience in at least one computer programming language.

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

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

GROUP II: Two courses from Group II

One of

One of

Electives: Three additional courses, which may consist of Group III courses below or any of STAT521, STAT525, STAT605, STAT650, not already used to meet Group II requirements. Selection of electives requires approval of adviser.

GROUP III

STAT 522 Stochastic Processes II (three credits)
STAT 523 Quantitative Spatial Analysis (three credits)
STAT 526 Analysis of Time Series II (three credits)
STAT 560 Applied Multivariate Analysis (three credits)
STAT 570 Nonparametric Statistics (three credits)
STAT 600 Statistical Computing (three credits)
STAT 645 Categorical Data Analysis and GLIM (three credits)
STAT 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



STAT 500: Statistical Computer Packages 01 (0-2-0) S

Prerequisite: ST302, ST304

Comparison, evaluation, and use of computer packages for univariate and multivariate statistical analyses.



STAT 501: Statistical Science 01 (1-0-0) F

Prerequisite: None

An overview of statistics: theory; use in agriculture, business, environment, engineering; modeling; computing; statisticians as researchers/consultants.



STAT 511: Design and Data Analysis for Researchers I 04 (3-0-1) 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.



STAT 512: Design and Data Analysis for Researchers II 04 (3-0-1) S

Prerequisite: STAT 511

Model building and decision making; communication of statistical information.



STAT 515: Statistical Science and Process Improvement 03 (2-1-0) 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.



STAT 520: Introduction to Probability Theory 04 (4-0-0) F

Prerequisite: M 340

Probability, random variables, distributions, expectations, generating functions, limit theorems, convergence, random processes.



STAT 521: Stochastic Processes I 03 (3-0-0) F

Prerequisite: ST 520

Characterization of stochastic processes, Markov chains in discrete and continuous time, branching processes, renewal theory, Brownian motion.



STAT 522: Stochastic Processes II 03 (3-0-0) S

Prerequisite: ST 521.

Martingales and applications, random walks, fluctuation theory, diffusion processes, point processes, queueing theory.



STAT 523: Quantitative Spatial Analysis 03 (3-0-0) 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.



STAT 525: Analysis of Time Series I 03 (3-0-0) F

Prerequisite: ST 430.

Trend and seasonality, stationary processes, Hilbert space techniques, the spectral distribution function, fitting ARIMA models, linear prediction.



STAT 526: Analysis of Time Series II 03 (3-0-0) S,SS

Prerequisite: ST 525

Spectral analysis; the periodogram; spectral estimation techniques; multivariate time series; linear systems and optimal control; Kalman filtering and prediction.



STAT 530: Mathematical Statistics 03 (3-0-0) S

Prerequisite: ST 520

Sampling distributions, estimation, testing, confidence intervals; exact and asymptotic theories of maximum likelihood and distribution-free methods.



STAT 540: Data Analysis and Regression 03 (3-0-0) 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



STAT 544: Biostatistical Methods for Quantitative Data. 03 (3-0-0) 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.



STAT 547: Statistics for Environmental Monitoring 03 (3-0-0) 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.



STAT 620: Introduction to Measure Theoretic Probability 03 (3-0-0) F, S

Prerequisite: ST 520, ST 540

Introduction to rigorous probability theory in real Euclidean spaces based on a foundation of measure theory.


STAT 560: Applied Multivariate Analysis 03 (3-0-0) F, S

Prerequisite: ST 520, ST 540

Multivariate analysis of variance; principal components; factor analysis; discriminant analysis; cluster analysis



STAT 570: Nonparametric Statistics. 03 (3-0-0) S, SS

Prerequisite: ST 430 or written consent of instructor

Distribution and uses of order statistics; nonparametric inferential techniques, their uses and mathematical properties.



STAT 586: Practicum in Consulting Techniques 01 (0-2-0) S

Prerequisite: ST/STCC 301

Attend consulting sessions of faculty. Computing on elementary research problems for beginning students through planning and designing experiments.



STAT 592: Seminar 01 (0-0-1) S

Prerequisite: None

Seminar



STAT 600: Statistical Computing 03 (3-0-0) F,S

Prerequisite: ST 520, ST 540

Statistical packages; graphical data presentation; model fitting and diagnostics; random numbers; simulation; numerical methods in statistics.



STAT 605: Theory of Sampling Techniques 03 (3-0-0) F

Prerequisite: 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.



STAT 640: Design and Linear Modeling I 04 (4-0-0) S

Prerequisite: ST 540 or written consent of instructor

Introduction to linear models; experimental design; fixed, random, and mixed models.



STAT 645: Categorical Data Analysis and GLIM. 03 (3-0-0) S

Prerequisite: Concurrent registration in ST 640

Generalized linear models, binary and polytomous data, log linear models, quasilikelihood models, survival data models.



STAT 650: Design and Linear Modeling II 03 (3-0-0) F

Prerequisite: ST 640 or written consent of instructor

Mixed factorials; response surface methodology; Taguchi methods; variance components.



STAT 675. A-L: Topics in Statistical Methods 03 (3-0-0) 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.



STAT 684: Supervised College Teaching. 1-3 (3-0-0) F,S,SS

Prerequisite: Enrollment in M.S./Ph.D. program in statistics

Guidance and instruction in effective teaching of college courses in statistics.



STAT 695: Independent Study Variable credit F,S,SS

Independent Study



STAT 699: Thesis Variable credit F,S,SS

Thesis