Abstract:Pipe loop studies are used to evaluate corrosion control treatment, and updated regulatory guidance will ensure that they remain important for water quality management. But the data they generate are difficult to analyze: non-linear time-trends, non-detects, extreme values, and autocorrelation are common features that make popular methods, such as the t- or rank-sum tests, poor descriptive models. Here, we propose a model for pipe loop data that accommodates many of these difficult-to-model characteristics: a … Show more
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