4+1 B.S./M.S., Professional M.S. and Elective Courses
These courses may be taken for the Professional M.S. in Statistics or for the graduate Minor in Statistics. They may not be taken for credit for the M.S. in Statistics or the Ph.D. in Statistics.
5300 Statistical Methods
Introduction to descriptive and inferential statistics. Topics may be selected from the following: descriptive statistics and graphs, probability, regression, correlation, tests of hypotheses, interval estimation, measurement, reliability, experimental design, analysis of variance, nonparametric methods, and multivariate methods.
5301 Introduction to Experimental Design
Prerequisite(s): Graduate standing.
Simple and complex analysis of variance and analysis of covariance designs. The general linear model approach, including full-rank and less than full-rank models, will be emphasized.
5303 Applied Regression Analysis
Pre-requisite(s): STA 5300 or equivalent
Regression modeling, estimation, and diagnostics with emphasis on applications. Topics include simple linear regression, multiple regression, logistic regression, and Poisson regression. The statistical programming language R is used.
5320 Predictive Analytics
Pre-requisite(s): STA 5303 Concepts, methods, and tools used for predictive modeling and data analytics with applications are considered
The focus of this course is on advanced tools using various multivariate regression techniques, statistical modeling, machine learning, and simulation for forecasting. Practical applications are emphasized.
5350 Statistical Machine Learning
Pre-requisite(s): STA 5303
Fundamental topics of machine learning including supervised/unsupervised learning, cost function optimization, feature selection and engineering, and bias/variance trade-off. Learning algorithms including classification methods, support vector machines, decision trees, neural networks, and deep learning are covered.
5360 Introduction to Bayesian Data Analysis
Prerequisite(s): STA 3381 or consent of instructor
An overview of analytic and computational methods in Bayesian inference beginning with two-sample t-inference procedures and extending through regression, focusing on state-of-the art software for Bayesian computation.
5361 Applied Time Series Analysis
Prerequisite(s): STA 3386 or STA 5303 or equivalent or concurrent enrollment or consent of instructor
Statistical methods of analyzing time series. Model identification, estimation, forecasting, and spectral analysis will be discussed. Applications in a variety of areas including economics and environmental science will be considered. The R statistical programming language will be used.
5370 Applied Sampling Techniques
Pre-requisite(s): Grade of C or better in one of STA 2381 or STA 5300 or equivalent course in statistical methods
Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, subsampling, regression estimates and multi-frame techniques are covered. Using computer software for analyzing data collected from designs covered in class.
5371 Methods in Data Mining and Management
Pre-requisite(s): STA 3386, STA 5303 or equivalent course, or consent of instructor
This course introduces the methods and practice of data mining.
5372 Statistical Process Control
Pre-requisite(s): STA 3381 or equivalent; STA 2381 or equivalent
Development of statistical concepts and theory underlying procedures used in statistical process control applications. Topics include sampling inspection procedures, continuous sampling procedures, theory of process control procedures, and experimental design and response surface analysis to design and analyze process experiments.
5373 Computational Statistical Methods
Pre-requisite(s): STA 2381 or STA 3381 or consent of the instructor.
Computational methods using statistical packages and programming.
5374 Applied Sampling Techniques
Pre-requisite(s): A grade of C or better in any one of STA 2381 or STA 5300 or an equivalent course in statistical methods
Planning, execution, and analysis of sampling from finite populations. Simple random, stratified random, ratio, systematic, cluster, subsampling, regression estimates, and multi-frame techniques are covered. Use of computer software for analyzing data collected from designs covered in class.
5376 Methods in Biostatistics
Pre-requisite(s): STA 2381 or STA 5300 or an equivalent course in statistical methods
A survey of methods of data analysis for biostatisticians in the biomedical and pharmaceutical fields. Regression analysis, experimental design, categorical data analysis, clinical trials, longitudinal data, and survival analysis.
5384 Multivariate Statistical Methods
Discriminant analysis, canonical correlation analysis, and multivariate analysis of variance.