STAT 607 – Planning, execution, and analysis of sampling from finite populations; simple, stratified, multistage, and systematic sampling; ratio estimates.
STAT 636 – Multivariate extensions of the chi-square and t-tests, discrimination and classification procedures. Applications to diagnostic problems in biological, medical, anthropological, and social research; multivariate analysis of variance, principal component and factor analysis, canonical correlations.
STAT 638 – Students learn how uncertainty regarding parameters can be explicitly described as a posterior distribution which blends information from a sampling model and prior distribution. Students are exposed to foundational principles, but the course emphasizes modeling and computations under the Bayesian paradigm.
STAT 647 – Spatial correlation and its effects; spatial prediction (kriging); spatial regression; analysis of point patterns (tests for randomness and modelling patterns); subsampling methods for spatial data.
Stats in Research III
STAT 653 – The analysis of messy and complex data sets using analysis of variance, analysis of covariance and regression analysis. Transformations; regression diagnostics; nonlinear, robust, logistic and principal components regression; structural equations.
STAT 656 – Applied Analytics Using SAS Enterprise Miner: Introduction to data mining and will demonstrate the procedures; Optimal prediction decisions; comparing and deploying predictive models; neural networks; constructing and adjusting tree models; the construction and evaluation of multi-stage models. NOTE: For this course, you will be required to have a computer with a Windows OS and good processing power.
Advanced SAS Programming
STAT 657 – Programming with SAS/IML, programming in SAS Data Step, advanced use of various SAS procedures.
Categorical Data Analysis
Introduction to analysis and interpretation of categorical data using ANOVA/regression analogs; includes contingency tables, loglinear models, logistic regression; use of computer software such as SAS, GLIM, SPSSX.