JSM 2005 - Toronto

Abstract #302977

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 438
Type: Topic Contributed
Date/Time: Wednesday, August 10, 2005 : 2:00 PM to 3:50 PM
Sponsor: Section on Quality and Productivity
Abstract - #302977
Title: Avoiding Correlated Observations when Control-charting Hierarchically Structured Data
Author(s): Carl Pierchala*+ and Jyoti Surti
Companies: National Highway Traffic Safety Administration and National Highway Traffic Safety Administration
Address: 400 Seventh Street SW, Washington, DC, 20590,
Keywords: Correlated observations ; Data quality ; Hierarchical data ; Statistical process control ; Traffic crash data
Abstract:

We review a novel program of control charting for data quality that we have conducted at the National Highway Traffic Safety Administration for several years and describe some of the data quality problems that have been remedied. Then, we address a technical issue due to the hierarchical nature of traffic crash data. hat is, within a crash, there are one or more vehicles; and within a vehicle, there are one or more persons. Thus, observations at the vehicle-level or person-level may not be statistically independent, so control limits based on the usual assumption of independent observations may be too narrow. To deal with this issue, we have reformulated affected control charts in terms of crash-level data, where the independence assumption is more realistic. For example, we chart the monthly proportion of fatal crashes with no belted occupants, as opposed to the original charts for the monthly proportion of fatal crash occupants who are unbelted. In this paper, redefinitions are implemented for selected cases. Historical data are reanalyzed using the redefinitions and the results are contrasted against those from the original formulations.

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Revised March 2005