Undergraduate STAT courses


STAT 2334 - Applied Stats for Health (previously MATH 2334)

Topics to be covered are: descriptive statistics (including data summarization and simple linear regression), basics of probability, inferential statistics (including hypothesis testing, confidence intervals, one-way analysis of variance, introduction to nonparametric statistics), and the basics of experimental design in the context of health-related sciences and disciplines. Computer laboratory experience will be an important part of this course. 

Prerequisites: MATH 1314, MATH 1414, MATH 1324, or MATH 1342 with a grade of 'C' or better.

STAT 2336 - Statistical Computing and Data Management

Topics include Statistical programming in R, SPSS and/or SAS; random number generation; design of simulation studies; interactive and dynamic statistical graphics; data processing and management.

Prerequisites: CSCI 1380 and STAT 2331, both with a grade of 'C' or better.

STAT 3301 - Applied Statistics for SEMS (previously MATH 3331; may be taken in lieu of STAT 2334)

This course concerns itself with probabilistic models, regression analysis, nonparametric statistics, and the basics of experimental design. Computer laboratory experience will be an important part of the class.

Prerequisites: MATH 2413 (or MATH 2487) with a grade of 'C' or better.

STAT 3335 - Applied Regression Analysis (previously MATH 3335)

This course discusses applications of regression in various areas of study. Topics include simple and multiple linear regression, ordinary and weighted least square techniques, detection of outliers, multicollinearity, variable selection, dummy variables, and logistic regression.

Prerequisites: MATH 1342 (or MATH 1387), MATH 1343, or STAT 3301, with a grade of 'C' or better.

STAT 3336 – Sampling (previously MATH 3334)

This course surveys the basic elements of sampling including concept of population and sample, the organization of a sample survey, coverage content error, questionnaire design, basic survey designs, and computation of estimates and variances.

Prerequisites: MATH 1342 (or MATH 1387), MATH 1343, or STAT 3301, with a grade of 'C' or better.

STAT 3337 - Probability & Statistics (previously MATH 4337)

Topics include probability, random variables, discrete, and continuous probability distributions, expectations, moments, and moment generating functions, functions of random variables and limiting distributions.

Prerequisites: MATH 2414 (or MATH 2488) with a grade of 'C' or better.

STAT 3338 – Mathematical Statistics (previously MATH 4338)

Topics include sampling distributions and data descriptions, estimation problems, test of hypothesis, regression models, analysis of variance, nonparametric statistics, statistical quality control, and Bayesian statistics.

Prerequisites: STAT 3337 with a grade of 'C' or better.

STAT 3351 - Multivariate Analysis

The course covers the theoretical foundations of the topic. 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 the course include multivariate analysis of variance (MANOVA), principal components, factor analysis and classification.

Prerequisites: MATH 2318 and STAT 2331/STAT 3301, both with a grade of ‘C’ or better.

STAT 3352 - Introduction to Linear Models

The course covers the theory of univariate linear models, including multiple regression and analysis of variance models. Treatment of other normal models, including generalized linear, repeated measures, random effects, mixed, correlation, and some multivariate models.

Prerequisites: MATH 2318 and STAT 3335, both with a grade of ‘C’ or better.

STAT 4332 - Experimental Design

Topics to be covered are: Design fundamentals; completely randomized designs; blocking; factorial, nested, nested-factorial designs; incomplete designs; split pilot; crossover designs; power analysis, sample size determination.

Prerequisites: STAT 2336 with a grade of 'C' or better.

STAT 4341 - Intro to Stochastic Processes

This course is particularly well-suited for those wanting to see how probability theory can be applied to the study of random phenomena in fields such as engineering, management science, the physical and social sciences, and operations research. Topics include Conditional Expectation, Markov chains, Poisson processes, and queuing theory. Additional applications chosen from such topics as reliability theory, Brownian motion, finance and asset pricing, inventory theory, dynamic programming, and simulation.

Prerequisites: MATH 2318 and STAT 3337, both with a grade of ‘C’ or better.

STAT 4342 - Time Series Analysis

Topics include: identification of models for empirical data collected over time; use of models in forecasting. Students should understand the differences between cross-sections and time series, and those specific problems, which occur while working with data of these types. In this course, students should master traditional methods of time series analysis of univariate time series data including: autoregressive and moving average models (denoted as ARIMA models); smoothing methods and trend/seasonal decomposition methods; longitudinal analysis and repeated measures models for comparing treatments when the response is a time series; and intervention analysis (before/after analysis of a time series to assess effect of a new policy, treatment, etc).

Prerequisites: MATH 2318 and STAT 3335 both with grade of 'C' or better.

STAT 4345 - Introduction to Simulation

The course covers random number generation; generating discrete and continuous random variables; generating multivariate normally distributed vectors; Monte Carlo simulation experiments; Monte Carlo integration and variance reduction; Monte Carlo methods in Statistical Inference; Resampling Techniques such as Jack-Knife and Boot Strap; Simulation of Stochastic Processes. Statistical software like R and Python will be used during classes.

Prerequisites: STAT 2336 and STAT 3338 both with a grade of 'C' or better.

STAT 4346 - Introduction to Bayesian Inference

This course is designed to introduce the students to Bayesian data analysis. The topics to be covered include. Examples of current application of the Bayesian inferential framework. The fundamentals: prior, likelihood, posterior. Exponential families and conjugate priors. Invariance, non-informative priors. How to explore the posterior: numerical integration? Examples of MCMC; empirical Bayes; applications.

Prerequisites STAT 3338 with a grade of ‘C’ or better.

STAT 4347 - Nonparametric Statistics

Topics include distribution free statistical procedures or methods valid under nonrestrictive assumptions: Goodness-of-fit test, test of homogeneity, order statistics, ranks, distribution free tests and associated interval and point estimators; sign test; signed rank tests; rank tests; Mann Whitney Wilcoxon procedures; Kolmogorov Smirnov tests; permutation methods; discussion and comparison with parametric methods.

Prerequisites: STAT 2331 and STAT 3335 both with a grade of 'C' or better.

STAT 4351 - Categorical Data Analysis

Techniques for the analysis of categorical data; contingency table analysis; logistic regression; Poisson regression; loglinear models; analysis of ordinal data.

Prerequisites: STAT 3338 and STAT 2336, both with a grade of 'C' or better.

STAT 4352 - Survival Analysis

Topics to be covered are: introduction to the principles and methods for the analysis of time-to-event data; the relationship between the survival function, distribution function, hazard function, relative hazard, and cumulative hazard; Kaplan-Meier estimator: two-sample analyses for survival data; nonparametric, semi-parametric, and parametric models for survival analysis.

Prerequisites: STAT 2336 and STAT 3338 both with a grade of 'C' or better.

STAT 4390 - Statistics Project (New Course: Joint with Math project course MATH 4390)

Students will complete a major statistical project and will communicate its results in oral and written form.

Prerequisites: STAT 2336 and STAT 3335, and 9 additional advanced hours of STAT, all with grades of 'C' or better.

STAT 4392 - Statistical Consulting

Topics include: integration of statistical models, design, sampling, graphics and computing for the analysis of real problems; planning, drafting, revising and editing reports; ethics; principles of collaboration and communication. Students will be required to hold regular statistical consulting help desk hours.

Prerequisites: STAT 2336, STAT 3335, and 9 advanced hours of STAT, all with grades of 'C' or better.

STAT 4399 - Special Topics in Statistics

Topic to be selected by instructor and should cover special undergraduate topics in statistics which are not taught elsewhere in other department. This course may be repeated for credit when topic is different.

Prerequisites: Consent of instructor and Departmental approval.