Graduate Courses

6301 (old number 5371): Experimental Statistics I. Non-calculus development of fundamental statistical techniques, including hypothesis testing for population means and proportions, analysis of variance, factorial designs, and linear regression. Obtaining sample sizes during the planning stages of research studies. Emphasizes interpretation of results from analysis with SAS Statistical Software.

6302 (old number 5372): Experimental Statistics II. Extension of techniques in STAT 6301 to multivariate data. Multiple linear regression, multivariate analysis of variance, canonical regression and principal components analysis. Emphasizes interpretation of results from analysis with SAS. Prerequisite: STAT 6301 (5371).

6306: Introduction to Data Science. An introduction to methods, concepts, and current practice in the growing field of data science, including statistical inference, algorithms, financial modeling, data visualization, social networks, and data engineering. Prerequisite: Enrollment in the applied statistics and data analytics program or the data science program, or permission of instructor.

6307 (old number 5304): Introduction to Statistical Computing. This course introduces students to statistical computing with an emphasis on SAS programming. Students will learn how to read, write, and import data; prepare data for analysis; use SAS procedures; and create graphs.

6308: SAS II and Databases: This second semester course in SAS covers topics in data management and statistical analysis techniques including data cleaning and verification; reading, writing, and manipulating data using DDE and SQL techniques; programming macros; combining, interleaving, stacking, and transposing data sets; customizing output using ODS; and advanced data analysis techniques. Prerequisite: STAT 6307 (5304) or permission of instructor.

6311 (old number 5373): Introduction to Mathematical Statistics I. Topics include: probability; probability distributions; mathematical expectation; discrete and continuous random variables and their distributions; sampling distributions; moment generating function; functions of random variables; confidence intervals. Prerequisite: MATH 2339

6312 (old number 5374): Introduction to Mathematical Statistics II: Second course in mathematical statistics. Topics include: order statistics; limiting distributions; central limit theorem; point estimation; testing statistical hypotheses; Bayesian procedures; nonparametric methods. Prerequisite: STAT 6311 (5373).

6315 (old number 5370): Survey Sampling. Principles of planning and conducting surveys. Simple random sampling; stratified, systematic, subsampling; means, variances, confidence limits; finite population correction; sampling from binomial populations; margin of error and sample-size determination. Prerequisites: Co-requisite: STAT 6307 (5304).

6316 (old number 5380) (ECO 6380) : Predictive Analytics/Data Mining. A study of data mining techniques in fields of applied economics, marketing, finance, and statistics. Techniques include classification methods (logistic models, classification trees, neural networks), affinity analysis (association rules), and data reduction and exploration methods (principal components and k-means clustering).


6324 (old number 6304): Computational Statistics. Designed to introduce students to the fundamentals of statistical computing. Introduces computational methods in statistics with emphasis on the use of statistical software packages, statistical simulation, numerical methods, and related topics. Topics include introduction to R and other statistical software for statistical analysis and graphics; generating random deviates from various distributions; and the use of Monte Carlo methods for solving optimization problems. Prerequisite: STAT 6311 (5373) or STAT 6327 or concurrent enrollment in these courses.

6360: Statistical Methods in Epidemiology. This course presents an introduction to epidemiologic principles and statistical methods used in biomedical research. Topics involve the design, analysis, and interpretation of biomedical study results. Prerequisites: 6302 (5372), 6312 (5374), 6307 (5304) or permission of instructor.

6363: Time Series Analysis. Statistical methods of analyzing time series. Autocorrelation function and spectrum. Autoregressive and moving average processes. More general models, forecasting, stochastic model building. Prerequisite: Permission of instructor.

6366: Statistical Consulting. Apprenticeship under an experienced consultant, with exposure to real problems handled by the Center for Statistical Consulting and Research. Between four to six hours per week will be spent in consultation sessions and seminars. In addition to a variety of technical statistical issues the class will study the existing literature on the non-statistical aspects of the consulting endeavor.

6395: Special Topics in Statistics. NOTE: Some courses listed under this number are for MASDA, some are Ph.D. and some for both, check with Instructor.