Opportunities

The program has prepared or helped prepare past graduates for the following specific positions (with SOC information)

Statistical Assistant (43-9111)
Social Science Research Assistant (19-4061)
Biological Technician (19-4021)
Mathematical Science Teacher, Postsecondary (25-1022)
Secondary School Teachers (25-2031)
Mathematical Sciences, Other (15-2099)

Costs

General Information

Certificate Program Curriculum


The program requires 17 or 18 credit hours for completion. Students must complete a minimum of fifteen hours from graduate courses containing a substantial degree of statistical theory or application, at least twelve of which are from courses offered in the Department of Mathematics and Statistics. The remaining hours may include another three hour graduate course or a two hour research project STT 596. The course options selected are subject to approval by the program coordinator.

Course descriptions for statistics courses and other potential electives.

The following are courses that may be applied to elective coursework. This is by no means a comprehensive list and will be updated frequently. Coming soon: sample plans of study tailored to various professions.

STT 501. Applied Statistical Methods (3)
Prerequisite: Any elementary statistics course. A survey of statistical methods for scientists. Topics include: data description, probability, estimation and hypothesis testing, ANOVA, linear regression and contingency tables. This course does not count toward the masters degree in mathematics. No credit granted after successful completion of STT 411/511 or STT 412/512.

STT 505. Data Analysis (3)
Prerequisite: Any statistics course. Introduction to exploratory data analysis. Use of stem and leaf plots, boxplots. Transformations of data, resistant lines, analysis of two-way tables, residual analysis. Comparison of robust/resistant methods with standard statistical techniques.

STT 511. Design of Experiments and Analysis of Variance (3)
Prerequisite: Any elementary statistics course. Review of elementary statistics; design of experiments including completely randomized, randomized block, factorial, split-plot, and repeated measures designs; analysis of variance; non-parametric alternative methods of analysis. Statistical software packages will be used as appropriate in problem solving.

STT 512. Applied Regression and Correlation (3)
Prerequisite: Any elementary statistics course. Review of elementary statistics; linear and multiple regression; correlation. Statistical software packages will be used as appropriate in problem solving.

STT 520. Biostatistical Analysis (3)
Prerequisite: Statistical programming and consent of instructor. Statistical methods used in epidemiologic studies and clinical trials. Topics include measures of association, logistic regression, covariates, life tables and Cox regression; statistical analysis using SAS.

STT 525. Categorical Data Analysis (3)
Prerequisite: Statistical programming and consent of instructor. Introduction to the analysis of qualitative data. Basic methods of summary and inference for two and three way contingency tables; introduction to the generalized linear model for binary and Poisson data; focus on multinomial responses (nominal and ordinal) and matched pairs data; statistical analysis using SAS.

STT 530. Introduction to Non-parametric Statistics (3)
Prerequisite: A calculus-based statistics course. Theory and methods of non-parametric statistics in the one- and two-sample problems and their comparisons with standard parametric procedures. Non-parametric tests for comparing more than two samples; tests of randomness and independence.

STT 535. Applied Multivariate Analysis (3)
Prerequisite: STT 511, 512. Matrix manipulations; multivariate normal distribution; inference for mean vector and covariance matrix; multivariate analysis of variance; principal components; canonical correlations; discriminant analysis; factor analysis; cluster analysis; statistical analysis using SAS.

STT 540. Linear Models and Regression Analysis (3)
Prerequisite: A calculus-based statistics course. Theoretical introduction to the general linear model and its application to simple linear regression and multiple regression. Estimation and hypothesis testing of model coefficients; residual analysis; analysis of covariance.

STT 565. (MAT 565/465) Applied Probability (3)
Prerequisite: A calculus-based statistics course. The formulation, analysis and interpretation of probabilistic models. Selected topics in probability theory. Conditioning, Markov chains, and Poisson processes. Additional topics chosen from renewal theory, queueing theory, Gaussian processes, Brownian motion, and elementary stochastic differential equations.

STT 566-567. Mathematical Statistics (3-3)
Prerequisite: A calculus-based statistics course. A rigorous introduction to mathematical statistics. Univariate and multivariate probability distributions; conditional and marginal distributions; theory of estimation and hypothesis testing; limiting distributions and the central limit theorem; sufficient statistics and the exponential class of probability density functions.

STT 569. (MAT 569) Stochastic Processes in Operations Research (3)
Prerequisite: MAT/STT 565. Probabilistic models with applications in operations research. Queueing theory, birth-death processes, embedded Markov chains, finite and infinite waiting-room systems, single and multi-server queues, general service distributions; Markov decision processes; reliability.

STT 590. Case Studies in Statistical Consultation (3)
Prerequisite: Consent of instructor. Review of case studies involving consulting with clients on statistical design of experiments and analysis of experimental and observational data; consulting on statistical issues with clients on campus through the departmental consulting center; presentation of oral report on consulting experience.

STT 592. Topics in Statistics (3)
Prerequisite: Consent of instructor. Topics in statistics of current interest not covered in existing courses.

STT 596. Research Project (2)
Prerequisite: Consent of instructor. Design of an experiment and/or survey approved by the program coordinator. Collection and analysis of data to be detailed in an oral and written report.

EDN 525. Tests, Measures, and Measurement in Education (3)
Prerequisite: EDN 301, EDN 520, or approval of instructor. Designed to develop a conceptual framework for obtaining and interpreting data about behavioral and psychological traits of persons that may be needed for a variety of purposes. Particular attention will be given to developing understanding of validity of measures for the intended purposes and for assessing the trait that is intended to be measured. Students will learn to make judgments of validity of testing systems and to develop valid tests and testing systems. Mathematical and statistical tools will be studied for analyzing items, tests, and scores and students will practice their use. Students will learn to use computers for test development, and test administration, and to analyze records of performance on tests.

PLS 506. Research Methods and Program Evaluation (3)
Covers research methods and basic statistics including hypothesis testing and examines the theory and practice of program evaluation including the ethical issues related to the practice of program evaluation.

PSY 555. Psychological Research Methods I (3)
Prerequisite: Course in research methods and permission of instructor. Advanced study of research design and statistical analysis applicable to research in psychology. Topics in basic psychological statistics are taught from an advanced perspective and include analysis of variance, correlational and nonparametric techniques.

PSY 589. Psychological Research Methods II (3)
Prerequisite: PSY 555. Overview of the various research strategies and designs used in psychology. Application and extension of methods learned in Psychological Research Methods I to contemporary research problems in psychology.

GGY 524. Geographic Information Systems (3)
Permission of instructor. Introduction to the science and technology of Geographic Information Systems (GIS) including data collection, spatial data structures, spatial analysis theory and techniques, and end-user map products. Instruction will be provided through lectures, demonstrations, and lab exercises. Two lecture and three laboratory hours each week.

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