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