This pages linked below provide some of the
instructional materials that have been developed for this course. Each
section of the course has (or will have):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, wqmeeker@iastate.edu, www.public.iastate.edu/~wqmeeker
Dr. Dianne Cook, 325 Snedecor Hall, 4-8865, dicook@iastate.edu, www.public.iastate.edu/~dicook
Dr. Bob Stephenson, 327 Snedecor Hall, 4-7805, wrstephe@iastate.edu, www.public.iastate.edu/~wrstephe
Dr. Phil Dixon, 125 Snedecor Hall, 4-2142, pdixon@iastate.edu, www.public.iastate.edu/~pdixon
Dr. Bill Duckworth, 326 Snedecor Hall, 4-7766, wmd@iastate.edu, www.public.iastate.edu/~wmd
Dr. Mark Kaiser, 102E Snedecor Hall, 4-8871, mskaiser@iastate.edu, www.public.iastate.edu/~mskaiser
Dr. Ken Koehler, 120 Snedecor Hall, 4-4181, kkoehler@iastate.edu, www.public.iastate.edu/~kkoehler
This pages linked below provide some of the
instructional materials that have been developed for this course. Each
section of the course has (or will have):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.
Course Material
| 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) |