The aim of this course is to enable the student to use the R language and environment for effective graphical presentation of data, for creating and presenting simulation results, for fitting statistical models to data and for making inferences through maximum likelihood or by Bayesian methods using Markov Chain Monte Carlo (MCMC). The homework assignments and suggested exercises will provide the opportunity to implement many of these methods using real or simulated data. See the syllabus for more information.

Linear algebra, Introductory Statistics and Probability.

All of the books listed below are on reserve at Wendt library. These are suggested references, not required texts.

• R Programming for Bioinformatics Gentleman (CRC Press, 2008).
• Software for Data Analysis: Programming in R Chambers (Springer, 2008).
• Lattice: Multivariate Data Visualization with R Sarkar (Springer, 2008).
• ggplot2: Elegant Graphics for Data Analysis Wickham (Springer, 2009).