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.)
ADMISSION REQUIREMENTS
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.
Other Scholarly Requirements
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 faculty.
Degree Plans
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 results to the department, and a written report on the results. |
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.
Mathematical Statistics Minor Program. The required courses are STA 533, 561, 562, and 564 with STA 560 strongly recommended as well.
M.A. in Mathematics Program. The required coursework is STA 471, 472 or STA
561,562, and two additional courses selected from STA 533, 560, 566, 567, 568, 569,
and 570.
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.
STATISTICS COURSE DESCRIPTIONS
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.
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