Case studies, and analysis of individual data sets. Knowledge of regression is helpful. Extensions of exploratory data analysis and modeling to multivariate response observations and to non-Gaussian data. Introduction to generalized linear modeling. Case studies of complex data sets with multiple objectives for analysis.
Graduate students give both written and oral presentations of data analyses.
Mathematica notebooks and simulations will be used. Monte Carlo methods. Graduate students are required to do an extra project. Introduction to stochastic modeling of data. Markovian and semi-Markovian models, point processes, point cluster models, queuing model s, likelihood methods, estimating equations.