3 credit hours
Prerequisites: Stat 401, or Stat 328 (basic concepts of statistical analysis through multiple linear regression).
Description: Advanced modern methods for analyzing experimental and observational data. Methods to be covered include graphical methods for high-dimensional data, maximum likelihood estimation, analysis of censored time-to-event data, nonlinear regression with random parameters, logistic regression, bootstrap and other simulation-based inference methods. The course will focus on data analysis, modeling, and interpretation, using examples from a variety of scientific and engineering disciplines.
Click here to see the course syllabus.
Spring 2005 Instructors and contact information:
Dr. William Q. Meeker, 304C Snedecor Hall, 4-5336, email@example.com, www.public.iastate.edu/~wqmeeker
Dr. Dianne Cook, 325 Snedecor Hall, 4-8865, firstname.lastname@example.org, www.public.iastate.edu/~dicook
Dr. Bob Stephenson, 327 Snedecor Hall, 4-7805, email@example.com, www.public.iastate.edu/~wrstephe
Dr. Phil Dixon, 125 Snedecor Hall, 4-2142, firstname.lastname@example.org, www.public.iastate.edu/~pdixon
Dr. Bill Duckworth, 326 Snedecor Hall, 4-7766, email@example.com, www.public.iastate.edu/~wmd
Dr. Mark Kaiser, 102E Snedecor Hall, 4-8871, firstname.lastname@example.org, www.public.iastate.edu/~mskaiser
Dr. Ken Koehler, 120 Snedecor Hall, 4-4181, email@example.com, www.public.iastate.edu/~kkoehler
Files are given in pdf format. Although it seems that they can be viewed on-line without any problems using Adobe's (free) Acrobat Reader program, we have had some problems in printing pdfs with certain file/platform/printer combinations (especially with Adobe Reader 3.0 on Vincent in documents with math symbols). Experiment with a single page to protect against making mistakes.
|Chapter 1||Principles of graphical methods for high-dimensional data (Cook)|
|Chapter 2||Principles of Maximum likelihood estimation and the analysis of censored data (Meeker)|
|Chapter 3||Binary response and logistic regression analysis (Stephenson)|
|Chapter 4||Resampling and other simulation-based inference methods (Duckworth)|
|Chapter 5||Linear mixed effect models (Dixon)|
|Chapter 6||Repeated measures data and random parameters models (Meeker)|
|Chapter 7||Modeling biological and physical mechanisms with random-parameter models (Kaiser)|
|Chapter 8||Model free curve fitting (Koehler)|