- Statistical Modeling

Catalog description

This course is a survey of advanced statistical modeling techniques, building off the models studied in Applied Statistics, and introducing models based on more sophisticated mathematical structures such as matrices. The course emphasizes how to create, fit, test, and use statistical models in a variety of situations, and how to verify the conditions required for each model introduced. Course topics will be demonstrated via real-world applications, using the statistical computing language R to carry out the necessary computations. The selection of statistical models will depend on the instructor, but the course will contain elementary time series analysis regardless of who is teaching it. Other possible models include hierarchical linear models, longitudinal models, mixed models, Bayesian models, non-parametric models, statistical learning and data mining models, and principal component analysis.