Abstract #301240

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JSM 2002 Abstract #301240
Activity Number: 280
Type: Contributed
Date/Time: Wednesday, August 14, 2002 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and Marketing
Abstract - #301240
Title: Automotive Retail Registration Data Aberration Detection
Author(s): James Wendelberger*+
Affiliation(s): Urban Science Applications, Inc.
Address: 620 Los Pueblos, Los Alamos, New Mexico, 87544-2615, USA
Keywords: aberration ; outlier ; binomial ; incomplete beta function ; automotive data ; de-distortion
Abstract:

A retail automotive registration data aberration detection method is described. The procedure identifies retail automotive data, called aberrant data, which may have not properly had fleet data removed, data which may have been influenced by the proximity to the retail network outlet locations, data which may have been otherwise influenced by consumer behavior, or data that is extreme due simply to unknown and unmeasured random variations in data-influencing factors (lurking variables). The method has, as a null hypothesis, a binomial model for the data. The incomplete beta function is used to compute the attained significance level for the observed data. A computer program to implement the method is described. The method does not identify the cause of the aberration. The method identifies particular geographic units and make/segment/brand data for further scrutiny.

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