Spring 2005
Statistics 415
Advanced Statistical Methods for
Research Workers
Time: Class: MF 2:10 - 3 (MOL BIO 1428) Lab: W 2:10 - 4 (McKay 119 or Snedecor 321)
Credit-hours: 3 (Non-major graduate credit)
Prerequisite: Statistics 401 of 328
This course will present advanced modern methods for analyzing
experimental and observational data. The course will focus on data
analysis, modeling, and interpretation, using examples from a
variety of scientific and engineering disciplines. Topics to be
covered include:
Principles of maximum likelihood estimation and the analysis of
censored time-to-event data (Professor Meeker)
Logistic and Poisson regression analysis (Professor Stephenson)
Resampling and other simulation-based inference methods (Professor
Duckworth)
Principles of graphical methods for high-dimensional data (Professor Cook)
Linear mixed effect models: incorporating multiple sources of
variation into an analysis (Professor Dixon)
Modeling biological and physical mechanisms with random-parameter
models (Professor Kaiser)
Model free curve fitting (Professor Koehler)