Attribute data, definition

The ERD is an excellent tool to help understand and explore the conceptual data model. It is less useful, however, for documentation purposes. To address the documentation requirements, a data dictionary (DD) is used. The DD is a repository of aU data definitions for all the data in the system. A DD entry is required for each data flow and data store on every DFD and for each entity in the ERDs. Developing a DD entry typically begins with naming the data structure being described, either by flow name, file name, or entity type. The list of attributes follow, with a description of the attribute, the attribute type, and the maximum size of the attribute, in characters. The attribute type is specified (e.g., integer, text, date, or binary). In the case of a data file, the average size of the file is indicated. 

Continuous data can be converted to attribute data by applying an operational definition for the count or classification. A recorded dimension can be classified as meeting or not meeting the specification however, this conversion does not work in reverse. The measured dimensions tue unknown for a part... 

A.1.3.6.2.3 In the event that an ingredient with unknown acute toxicity is used in a mixture at a concentration >1%, and the mixture has not been classified based on testing of the mixture as a whole, the mixture cannot be attributed a definitive acute toxicity estimate, in this situation the mixture is ciassified based on the known ingredients oniy. (Note A statement that x percent of the mixture consists of ingredient(s) of unknown toxicity is required on the iabei and safety data sheet in such cases see Appendix C to this section, Aiiocation of Labei Eiements and Appendix D to this section. Safety Data Sheets.)... 

Note, however, that the frequency/malfunction relationship is not mutually exclusive and a specific mechanical problem cannot definitely be attributed to a unique frequency. While frequency is a very important piece of information with regard to isolating machinery malfunctions, it is only one part of the total picture. It is necessary to evaluate all data before arriving at a conclusion. 

Manley-Harris and Richards (Missoula, Montana) have compiled a comprehensive account of the dianhydrides of D-fructose and related compounds, more than 30 in all. These compounds, several of which are of importance in the sugar industry, have in the past presented significant problems in their chemical characterization. Their chemistry was surveyed as early as 1945 by McDonald in Volume 2 of this series, and discussed again in Volume 22 by Verstraeten. The current article furnishes detailed NMR data for each of the anhydrides, providing definitive reference data for accurate identification and correlation with earlier literature, where erroneous structural attributions are rather frequent. 

This definition can be described as analysis in the process and is closely related to the traditional role of analytical chemistry in process control. The classical scope of a process analytical method is it to supplement the control scheme of a manufacturing process with data from a process analyzer that directly measures chemical or physical attributes of the sample. 

The results of EPR studies of photoezcited triplets of model systems show that it is not possible to give generally applicable rules for the interpretation of the spectroscopic data. In a number of cases there appears to be a well-understood relationship between dimerization effects and dimer geometry. In most of the systems considered here that is not the case. It is not clear to what difference in make-up of the dimers this discrepancy must be attributed and this is an interesting point of further investigation. Evidently, as long as the data on fairly well characterized model systems are not fully understood it will be impossible to derive definitive conclusions concerning the structure of the special pair from data on its photoexcited triplet state. 

The remaining errors in the data are usually described as random, their properties ultimately attributable to the nature of our physical world. Random errors do not lend themselves easily to quantitative correction. However, certain aspects of random error exhibit a consistency of behavior in repeated trials under the same experimental conditions, which allows more probable values of the data elements to be obtained by averaging processes. The behavior of random phenomena is common to all experimental data and has given rise to the well-known branch of mathematical analysis known as statistics. Statistical quantities, unfortunately, cannot be assigned definite values. They can only be discussed in terms of probabilities. Because (random) uncertainties exist in all experimentally measured quantities, a restoration with all the possible constraints applied cannot yield an exact solution. The best that may be obtained in practice is the solution that is most probable. Actually, whether an error is classified as systematic or random depends on the extent of our knowledge of the data and the influences on them. All unaccounted errors are generally classified as part of the random component. Further knowledge determines many errors to be systematic that were previously classified as random. 

GC-tlS analysis. The complete list of the compounds detected in the effluent mixture is shown in Table 1. The comparison of the mass spectra with literature data [3,9] permitted the individuation of all the species indicated by two asterisks. As for the remaining species, the attribution of those indicated by one asterisk can be considered reasonably safe, although not definitive, since pure samples of such substances were not... 

The definitions above are consistent with Part 11. Per Part 11, paragraphs 22, 45, and 72 in the preamble refer to when data on transient memory become electronic records. In summary, the primary attribute to take into consideration for the above definitions is record retrievability. It is essential to take this into account when deciding the applicability of Part 11. 

For this definition, the primary attribute to take into consideration is whether the data is accessible once it is put into storage, not the retrievability of the record. This definition would consider data in transient memory to be an electronic record. 

DQIs are usually thought of as attributes of a laboratory measurement system. However, a broader definition of primary DQIs will enable us to assess the entire measurement system that includes not only the laboratory measurements but also the sampling design and field procedures. Such broad interpretation of the primary DQIs will allow us to evaluate all components of total error and with it the overall, not just the analytical, data quality. The DQI definitions (EPA, 1999a) presented in this chapter are interpreted in a manner that encompasses all qualitative and quantitative components of total error. 

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