Graduate STAT Courses
STAT 5301 Statistical Data Analysis
STAT 5301 provides students with practical experience in various statistical methods useful in modern biological data analysis including Linear and Logistic Regressions, Bootstrap Methods; Multivariate Data Reduction Techniques (Factor Analysis, Principal Component Analysis); Canonical Correspondence and Multidimensional scaling (MDS) analyses; Data Mining techniques including regression trees with Random Forest, K-means Clustering and Resampling Methods; and Introduction to Bayesian Analysis.
STAT 6336 Advanced Sampling
This course will focus on planning, the execution and analysis of sampling from finite populations; simple, stratified, and multistate and systematic sampling; ratio estimates.
Prerequisite: Departmental approval.
STAT 6379 Stochastic Processes
Course topics include discrete and continuous-time Markov processes, Poisson processes, renewal processes, diffusion processes, Brownian motion.
Prerequisite: Grade of C or better in MATH 6365.
STAT 6380 Time Series Analysis
This course is an introduction to statistical time series analysis. Topics include ARIMA and other time series models, forecasting, spectral analysis, time-domain regression, model identification, estimation of parameters, and diagnostic checking.
Prerequisite: Grade of C or better in STAT 6379.
STAT 6381 Mathematical Statistics
This course in Mathematical Statistics includes the theory of estimation and hypothesis testing; point estimation, interval estimation, sufficient statistics, decision theory, most powerful tests, likelihood ratio tests, chi-square tests, minimum variance estimation, Neyman-Pearson theory of testing hypotheses, and elements of decision theory.
Prerequisite: Grade of C or better in MATH 6365.
STAT 6382 Statistical Computing
This is a course in modern computationally-intensive statistical methods including simulation, optimization methods, Monte Carlo integration, maximum likelihood/EM parameter estimation, Markov chain Monte Carlo methods, resampling methods, and non-parametric density estimation.
Prerequisite: Consent of instructor.
STAT 6383 Experimental Design and Categorical Data
Course topics include design and analysis of experiments, including one-way and two-way layouts; factorial experiments; balanced incomplete block designs; crossed and nested classifications; fixed, random, and mixed models; split-plot designs, inference for categorical data, contingency tables, generalized linear models, logistic regression, and logit and log linear models.
Prerequisite: Grade of C or better in MATH 6364.
STAT 6384 Biostatistics
This course is a survey of crucial topics in biostatistics; application of regression in biostatistics; analysis of correlated data; logistic and Poisson regression for binary or count data; survival analysis for censored outcomes; design and analysis of clinical trials; sample size calculation by simulation; bootstrap techniques for assessing statistical significance; data analysis using R.
Prerequisite: Consent of instructor.
STAT 6386 Statistical Data Mining
This course will provide the necessary statistical knowledge required for the effective handling of data mining techniques. The topics include an introduction to statistical learning, probability distributions, linear models for regression, linear models for classifications (linear discriminant analysis, logistic regression), artificial neural networks, support vector machine, resampling and regularization methods, variable selection, dimension reduction, and clustering. Students will be given the opportunity to apply data mining techniques to real-world applications from various interdisciplinary fields to discover hidden patterns and to make efficient predictions. R software will be used for the computational analysis.
Prerequisite: Grade of C or better in MATH 6330 and MATH 6364.
STAT 6389 Actuarial Statistics
This course will provide the necessary probability and statistical knowledge for loss models. Topics include modeling, random variables, basic distributional quantities, actuarial models, continuous models, discrete distributions and processes, frequency and severity with coverage modifications, and aggregate loss models. Students will be given the opportunity to apply loss data in actuarial science to establish and estimate actuarial models and to make efficient predictions. R software and Excel will be used for the computational analysis.
Prerequisite: Grade of C or better in MATH 6365 or consent of instructor.
STAT 6390 Internship
In this course, students will seek to apply the knowledge they acquired in the program to an internship in the private or government sector. They will have the opportunity to gain insight and experience in applying statistics and data science principles and concepts in an actual work-related environment. Students will perform the internship under the supervision of both a statistics faculty member and a collaborating member of the participating internship site.
Prerequisite: Consent of graduate program director.