R. Dennis Cook received his Ph.D. in Statistics from Kansas State University in 1971. He is the author of the new Wiley book Regression Graphics: Ideas for Studying Regressions Through Graphics (published in Sep 1998) and numerous articles in statistics and applications journals. He is a Fellow of the ASA, IMS and a member of the International Statistical Institute.
Sanford Weisberg received his Ph.D. in Statistics from Harvard University in 1973. He is the author of AppliedLinear Regression, Second Edition, and numerous articles in statistics and applications journals. He is a Fellow of the ASA and a member of the International Statistical Institute.
Cook, R. Dennis (1998): Regression Graphics: Ideas for Studying Regressions Through Graphics. Wiley, New York.
Furthermore, there is a new Wiley-book entitled Applied Regression Including Computing and Graphics that will correspond exactly to the short course. It is scheduled to be published sometime between August 1st and September 10th.
The methodology described is very general, and can be used in almost any regression problem. In the course of the workshop, we will work examples with both continuous and binary responses.
All the methodology discussed will be illustrated with a computer package called R-code that can be used for all the new methods described, and many standard methods for linear regression, nonlinear regression and generalized linear models. A copy of the most recent version of R-code, which runs on the Mac, PC or Unix, will be made available to all workshop participants for use in their own work.
Much of the material in this workshop will be drawn from An Introduction to Regression Graphics, published by Wiley in 1994. Prerequisite for this course is familiarity with standard regression methodology at the level of one of the major textbooks in this area.
Regression graphics has developed rapidly over the past six years, and many new developments are in progress. The topics for this course were selected to be immediately useful in applications, to set the stage for future study in the area and to show its promise. The present literature contains much more on regression than will be presented in this course.
[2] Foundations. In this second hour, we will provide the fundamental ideas for re-inventing regression thru graphics. This includes the structural dimension of the problem, defining and finding sufficient summary plots, the role of the distribution of the predictors, and using graphs for inference.
[3] Finding summary plots and exploring the importance of linear predictors.
[4] Graphics for regressions with a binary response.
[5] Graphical Regression. Graphical regression provides methodology for finding structural dimension, and then building models, in problems with many predictors. The innovation here is that the methodology is graphical, not numeric, so the analysis can actually see the models that are suggested.
[6] Special-purpose graphics. In this last hour, we will discuss two special types of graphs, net-effects plots, and marginal model plots. Net-effects plots provide an accurate graphical summary of the effect of a predictor in a regression problem. They can be particularly useful in visualizing the effect of a treatment after adjusting for covariates. We conclude with marginal model plots which provide a way of deciding if a model is appropriate or not without using residuals.
| before June 1st 1999: | FIM 300 = ca. USD 60 | Students: FIM 30 |
| after June 1st 1999: | FIM 400 | Students: FIM 200 |
All participants are required to register on arrival. The registration
desk will be at the Pinni Building of the University of Tampere on Kalevantie,
and will be open on Thursday, August 19th, from 09:00 onwards.
All lectures will be held in Paavo Koli Auditorium of the Pinni Building.
[Updated: 7 April 1999]