NET Framework 4.7.2 using the C# programming language. ProUCL 5.2 has been developed in Microsoft. ProUCL 5.2 is compatible with Microsoft Office 10.0 and 11, and represents an upgrade of all previous versions of ProUCL. ProUCL: Statistical Software for Environmental Applications for Data Sets with and without Nondetect Observations. This requires balancing both objectives (coverage and accuracy) to select the most appropriate UCL method.Ĭitation: USEPA. The objective should be to not only control for Type I error, but also to protect against large Type II errors.

Although this philosophy ensures that the likelihood of one decision error will be small (i.e., Type I error, concluding a site is not contaminated when it is), such an overestimate can result in a high likelihood of the opposite decision error (i.e., Type II error, concluding a site is contaminated when it is not). Depending on the data, there are some UCL estimators in ProUCL (particularly Chebyshev and H) that can generate gross overestimates of the mean so that adequate coverage will almost certainly be achieved in these cases, but accuracy suffers. Historically, ProUCL has placed emphasis on achieving adequate coverage, but not on achieving an accurate estimate of the mean, in the sense of an upper bound for the mean that is as close as possible to the true mean while maintaining the desired coverage. The Technical Guide and Users Guide due to size are now downloaded separately from the program. Version 5.2 is the latest version of the software that has been updated to include improvements to the Technical Guide and the User Guide for clarity, code updates to correct for reported bugs, and several changes made to the decision logic for the recommendation of UCLs for this version. ProUCL version 5.2 is a comprehensive statistical software package with statistical methods and graphical tools to address many environmental sampling and statistical issues. ProUCL version 5.2.00 (5.2) is the latest update of the USEPA ProUCL statistical software package for analysis of environmental data sets with and without nondetect (ND) observations.

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