We present a new context for regression that requires few scope-limiting assumptions, and a corresponding collection of new methodological tools. Many of these tools use simple graphs, along with a well-developed theory, to discover information about the dependence of a response on the predictors. All the methods flow from a few key ideas concerning dimension reduction, understanding the role of the distribution of the predictors, and thinking about and using graphs in regression analysis. Regression graphics have developed rapidly over the past six years, and many new developments are in progress. The topics were selected to be immediately useful in applications, to set the stage for future study in the area and show its promise.
Regression analysis is one of the fundamental tools for the practicing statistician.
The traditional role of graphics in regression, at least with many predictors, has been peripheral, used mostly to judge model adequacy. Regression graphics moves graphs to the center of analysis. The new theory will be presented at a very general and intuitive level. We want to encourage to participants to use this approach in their own work and teaching. The methodology described is very general, and can be used in almost any regression problem.