STAT/FNR 598 Modern Applied Statistics
T,TR 10:30:00-11:45, Recitation 309
Instructor: Professor Hao ZhangEmail: zhanghao@purdue.edu
Office Hours: Tuesday, 1:30-2:30, MATH 536 and by appointment.
Description: This course covers a wide range of topics that are most useful in agricultural, ecological, environmental, and natural resources sciences. Some topics are: analysis of categorical data, linear mixed effects models, spatial experiments and spatial data analysis, resampling methods (bootstrap), Markov chain Monte Carlo, state-space models, Bayesian analysis, and spline-smoothing.
This course exploits the power of the computing language R, and BUGS for the Bayesian analysis of complex statistical models using Markov chain Monte Carlo (MCMC) methods, both of which are free downloadable at www.r-project.org and www.mrc-bsu.cam.ac.uk/bugs/welcome.shtml. The objective of the course is for the students to understand and apply these statistical methods that a student generally is not exposed in a single course.
This course is designed for graduate students of both statistics and non-statistics majors, and approaches statistics in a novel and creative way. Emphases are given to not only the understanding of the models and methods but also the use of computer to solve complex real problems.
Suggested Textbooks:
- John Maindonald and W. John Braun (2012) Data Analysis and Graphics Using R: An Example-Based Approach (3rd ed)
- W. N. Venables and B. D. Ripley (2002) Modern Applied Statistics with S (4th ed.). This book has a wealth of examples of applying various statistics models using R or S-Plus.
