This course will teach you how to extend the Bayesian modeling framework to cover hierarchical models and to add flexibility to standard Bayesian modeling problems.
This course will teach you the basic ideas of Bayesian Statistics: how to perform Bayesian analysis for a binomial proportion, a normal mean, the difference between normal means, the difference between proportions, and for a simple linear regression model.
In this course, students learn how to apply Markov Chain Monte Carlo techniques (MCMC) to Bayesian statistical modeling using R and rstan.
This course will teach you the fundamental concepts and theory of Structural Equation Modeling, including model specification, model identification, model estimation, model testing, and model modification.
This course will teach you how multiple linear regression models are derived, the use software to implement them, what assumptions underlie the models, how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.
This course will teach you spatial statistical analysis methods to address problems in which spatial location. This course will explain and give examples of the analysis that can be conducted in a geographic information system such as ArcGIS or Mapinfo.
This course will teach you how to use various cluster analysis methods to identify possible clusters in multivariate data. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables.