Estimation of the Upper Confidence Limit on the Mean of Datasets with Count-Based Concentration Values |
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Authors: | William Brattin Timothy Barry Stiven Foster |
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Institution: | 1. SRC, Inc. , Denver , CO , USA;2. U.S. Environmental Protection Agency, Office of Policy, Economics, and Innovation , Washington , DC , USA;3. U.S. Environmental Protection Agency , Office of Solid Waste and Emergency Response , Washington , DC , USA |
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Abstract: | Mathematical approaches are not well established for calculating the upper confidence limit (UCL) of the mean of a set of concentration values that have been measured using a count-based analytical approach such as is commonly used for asbestos in air. This is because the uncertainty around the sample mean is determined not only by the authentic between-sample variation (sampling error), but also by random Poisson variation that occurs in the measurement of sample concentrations (measurement error). This report describes a computer-based application, referred to as CB-UCL, that supports the estimation of UCL values for asbestos and other count-based samples sets, with special attention to datasets with relatively small numbers of samples and relatively low counts (including datasets with all-zero count samples). Evaluation of the performance of the application with a range of test datasets indicates the application is useful for deriving UCL estimates for datasets of this type. |
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Keywords: | UCL asbestos CB-UCL |
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