Workshop on Regression Graphics
R. Dennis Cook and Sanford Weisberg, University of Minnesota
This course will provide an overview of the ideas of regression
graphics, which is a collection of methodological tools that are used to
discover and understand dependence of a response on predictors primarily
through the use of simple graphs in one, two and three dimensions. It will
mostly follow the outline of the book
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. The course will also introduce the
R-code, which is Xlisp-Stat computer code that accompanies the book and
allows the user to use all the methods described in the book and in the
course. The program is very easy to use, and requires no knowledge of
The objectives of this course is to help university-level instructors
gain the confidence to use regression graphics in their own teaching, and
to provide training in the methodology for
practicing statisticians for use in their own work.
Particpants will also be given access to the R-code2, the lastest
version of the
Outline of the course
Throughout the course, we will include hints for teaching, and demonstrate the
use of the
R-code for doing the analyses suggested.
- Introduction illustrating graphical issues.
sufficient summary plots,
first graphical inference
- Finding summary plots and exploring the importance of linear
- Illustrations, 3D plots, residual plots
- Graphics for regressions with a binary response
- Graphics for model assessment.
The target population is graduate students,
university-level instructors who teach regression
analysis, and practicing statisticians who wish to learn the latest ideas in
using graphs to understand regression problems.
Regression analysis is one of the fundamental tools of the practicing
statistician. The traditional role of graphics in regression, at least with
many predictors, has really been peripheral, dealing mostly with questions of
model adequacy. Regression graphics moves graphs to the center of analysis.
This requires some new theory, but this theory can be presented at a very
general and intuitive level. Our hope is to encourage the participants to use
this approach in their own work, and particularly in their own teaching.