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.

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

Pre-requisite(s) or Co-requisite(s): STA 4385 and STA 3386

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.

5373 Computational Statistical Methods

Pre-requisite(s):  STA 2381 or STA 3381 or consent of the instructor.

Computational methods using statistical packages and programming.

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.