By Nagnibida N. I.
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Extra resources for A basis for the space of functions analytic in a disk
5 Completeness Completeness is a measure of whether all the data necessary to meet the project objectives have been collected. Completeness is calculated only after the rest of the PARCC parameters have been calculated or qualitatively evaluated to determine the validity of data. It is the ﬁnal and all-inclusive indicator of data usability. The DQI of completeness enables us to determine whether data of acceptable quality have been collected in sufﬁcient quantity to meet the project objectives.
3. Representativeness is also a measure of how accurately and precisely the data represent a sample after it has been collected and placed in a sample container. Proper sample preservation and handling in the ﬁeld and at the laboratory; the correct application of subsampling techniques during analysis; the use of proper preparation and analysis procedures—all of these are important in maintaining the ‘collected sample’ representativeness. The ultimate representativeness of a data collection design is predetermined during the planning phase of the project.
The planning team will select and state the decision error limits based on such considerations as regulatory guidance; natural matrix variability; practical constraints (cost and schedule); physical constraints; potential impacts on human health and the 33 34 Sampling and Analysis of Environmental Chemical Pollutants Step 6. Specify Limits on Decision Errors Case Study A Case Study B 1. What is a range of possible values of the parameter of interest where the consequences of decision errors are relatively minor (‘gray region’)?
A basis for the space of functions analytic in a disk by Nagnibida N. I.