STA 505 Mathematical Statistics I (3)
A rigorous mathematical treatment of the underlying theory of probability and statistical inference. Probability spaces, discrete and continuous distribution theory, functions of random variables, Central Limit Theorem, and other topics.
STA 506 Mathematical Statistics II (3):
Continuation of STA 505. Point estimation, hypothesis tests, confidence intervals, asymptotic properties of estimators, and other topics. PREREQ: STA505 or instructor consent.
STA 507 Intro. Categorical Data Analysis (3):
Data-driven introduction to statistical techniques for analysis of categorical data arising from a variety of studies. Contingency tables, logistic regression survival models, nonparametric methods, and other topics.
STA 510 Statistical Methods for Research (3):
This course provides the tools and methods for designing a research project, conducting the research, managing and manipulating a dataset, and analyzing data. This course is for students not enrolled in the applied statistics graduate degree program. It requires no prior course in statistics or computer science. Topics include research design, basic statistics, introductory statistical programming using SAS and Excel, statistical analysis (including t-tests, linear regression, ANOVA, and chi-squared tests), and writing a final report, including graphics, summarizing the results.
STA 511 Intro. Statistical Computing (3):
This course will give students the ability to effectively manage and manipulate data, conduct statistical analysis, and generate reports and graphics, primarily using the SAS Statistical Software Package.
STA 512 Principles of Experimental Analysis (4):
This Course provides technology-driven introduction to regression and other common statistical multivariable modeling techniques. Emphasis on interdisciplinary applications. PREREQ: STA511 or instructor consent.
STA 513 Intermediate Linear Models (4):
A Rigorous mathematical and computational treatment of linear models. PREREQ: STA 505, 506, 511, and 512 or instructor consent.
STA 514 Modern Experimental Design (3):
Focusing on recent journal articles, this course will investigate issues associated with design of various studies and experiments. Pharmaceutical clinical trials, case-control studies, cohort studies, survey design, bias, causality, and other topics. PREREQ: STA 511 and 512 or instructor consent.
STA 531 Topics in Applied Statistics (3):
Topics of current interest in research and industry announced at time of offering.
STA 532 Survival Analysis (3):
This course provides students with the knowledge and tools to conduct a complete statistical analysis of time-to-event data. Students will get experience using common methods for survival analysis, including Kaplan-Meier Methods, Life Table Analysis, parametric regression methods, and Cox Proportional Hazard Regression. Additional Topics include discrete time data, competing risks, and sensitivity analysis.
STA 533 Longitudinal Data Analysis (3):
Introduction to the application and theory for clustered and longitudinal data models. Course addresses the analysis for both continuous and categorical response data. Course will be held in the statistics lab and use the statistical software package SAS. Other software such as R, HLM, SPSS, MIXOR MIXREG may be introduced. PREREQ: STA 507, 511, 512, and 513 or permission of director.
STA 534 Time Series (3):
Time series analysis deals with the statistical study of random events ordered through time. This class focuses on the characteristics inherent in processes such as repetitive cycles and deteriorating dependence. Topics include seasonal decomposition, exponential smoothing, and ARIMA models. Emphasis will be placed on real-life data analysis and statistical communication. Data analysis will be done with a variety of programs such as SAS, R, and Excel. PREREQ: STA 511 and 512.
STA 535 Multivariate Data Analysis (3):
Multivariate data typically consist of many records, each with readings on two or more variables, with or without an "outcome" variable of interest. Procedures covered in this course include multivariate analysis of variance (MANOVA), principal component analysis, factor analysis and classification techniques. This course will require that students are comfortable with matrix algebra. PREREQ: STA505, STA506, STA511, STA512 and background in Linear (Matrix) Algebra.
STA599 Independent Study (1-3):
Individual exploration of nine topics in statistics.
STA601 Internship in Applied Statistics (3-6):
In cooperation with a regional industrial company student will perform an internship in applied statistics.
STA609 Thesis I (3-6):
Preliminary research under the guidance of a mathematics faculty member. Students must present oral preliminary findings before proceeding to STA 610.
STA610 Thesis II (3-6):
Research project under the guidance of the mathematics faculty.