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Abstract Details

Activity Number: 355
Type: Contributed
Date/Time: Tuesday, July 31, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Graphics
Abstract - #305922
Title: Dynamic Graphics for Exploring and Understanding Multiple Regression Diagnostics
Author(s): Mervyn Marasinghe*+ and Yihui Xie
Companies: Iowa State University and Iowa State University
Address: Department of Statistics, Ames, IA, 50011, United States
Keywords: dynamic graphics ; regression diagnostics ; multicollinearity ; model selection ; diagnostic plots ; case statistics

Regression techniques are extremely popular across many disciplines. Modern statistical software systems incorporate many of the diagnostics and static graphical tools currently available. However, practitioners still have to rely on conventional methods of examining several models and comparing the relevant diagnostics manually for making decisions such as model selection, analysis of multicollinearity or determining the effect of cases on model fit. In this paper, we describe several tools that incorporate well-known dynamic graphic techniques, that allow the user to examine and compare model fits using multiple criteria and eliminates the element of artificiality in model selection.

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