Statistics 415 - Advanced Statistical Methods for Research Workers

Class: Monday, Friday 2:10-3:00, Pearson 1106
Laboratory: Wednesday 2:10-4:00, Pearson 1106 (or Snedecor Lab).

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,,

Dr. Dianne Cook, 325 Snedecor Hall, 4-8865,,

Dr. Bob Stephenson, 327 Snedecor Hall, 4-7805,,

Dr. Phil Dixon, 125 Snedecor Hall, 4-2142,,

Dr. Bill Duckworth, 326 Snedecor Hall, 4-7766,,

Dr. Mark Kaiser, 102E Snedecor Hall, 4-8871,,

Dr. Ken Koehler, 120 Snedecor Hall, 4-4181,,

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):

The links to the "chapters" below contain some of these materials from the Spring 2003 Stat 415 course. A number of links are not complete because the professors are constructing materials that will be presented for the first time this year or because they are revising materials from last year's class. Materials will be posted here as they become available.

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) 

Last revision: Fri Dec 3 17:08:46 CST 2004