Process data, qualitative/quantitative

The use and benefits of regression analysis can be appreciable, particularly in the evaluation of process data. In these applications, processes having as many as fifty variables, which are continuously changing over months of operation, can be evaluated by this technique. For these, the daily log records for say 400 to 500 data points are analyzed through the selected model (usually linear as a first approximation) to determine the relative effects of each variable on the response. This analysis in many cases has led to qualitative and often to quantitative determination of key operating variables whose effect had been masked on individual data point comparisons by the simultaneous changes in other less important, but unknown, variables. 

Process data is either quantitative or qualitative in nature. Quantitative data, or variable data, is measured along a continuous scale (i.e., 1 to 60 seconds). Qualitative data, or attribute data, is measured in categories, like pass/fail, yes/no, blue/green, and so on. Both types of data have value, but usually variable data is preferred over attribute data because it tells you more about the process. 

Even in those cases where an aiialysis is qualitative, quantitative measures are employed in the processes associated with signal acquisition, data extraction, and data processing. The comparison of, say, a sample s infrared spectrum with a set of standard spectra contained in a pre-recorded database involves some quantitative measure of similarity in order to find and identify the best match. Differences in spectrometer performance, sample preparation methods, and the variability in sample composition due to impurities will all serve to make an exact match extremely unlikely. In quantitative analysis the variability in results may be even more evident. Within-laboratory tests amongst staff and inter-laboratory round-robin exercises often demonstrate the far from perfect nature of practical quantitative analysis. These experiments serve to confirm the need for analysts to appreciate the source of observed differences and to understand how such errors can be treated to obtain meaningful conclusions from the analysis. 

Toxicity and dose-response to an exogenous chemical are dependent upon the concentration of the toxicant at the site(s) of action (e.g. the target organ). The disposition of a chemical in an organism is dependent upon the processes of absorption, distribution, metabolism, and excretion, defined as toxicokinetic data. Qualitative and quantitative information on each of these processes would be useful in risk assessment. For autoimmune diseases, toxicokinetic data may be helpful in identifying the potential organ systems that are likely to be involved or the responsible metabolite. 

In a recent study we used a BCS with six classes, where the solubility was classified as either low or high in accordance with the cutoff values set by the FDA and the permeability was classified as low (FA < 20%), intermediate (20% < FA < 80%), or high (FA > 80%) [55], This classification was chosen because we believe it provides a better tool for absorption ranking of compounds in drug discovery than the stricter permeability classification provided by the FDA. Experimental determinations of the Caco-2 permeability and intrinsic solubility were performed in-house, and PLS in silico models based on PTSAs were derived. In comparison to the experimentally determined data, the combination of the two in silico models resulted in 87% of the compounds being sorted into the correct class. The compounds included in a reference test set given by the FDA were correctly sorted with an accuracy of 77%. To summarize, these results indicate that more sophisticated in silico models combining computational analysis of the solubility and permeability can successfully estimate the absorption process both qualitatively and quantitatively [55]. 

For reaction 20-1 mentioned above, carried out hypothetically in the vapor phase, the enthalpies have been estimated from thermodynamic data for the metals, M, in the series Mn2+, Fe2+,..., Cu2+, Zn2+. At the same time, from the spectra of the [M(H20)6]2 + and [MC14]2 " ions the values of A0 and A, have been evaluated and the differences between the two LFSE s calcu-J ated. Fig. 20-33 shows a comparison-between- these Two sets-of quantities — It is evident that the qualitative relationship is very close even though some quantitative discrepancies exist. The latter may well be due to inaccuracies in the AH values since these are obtained as net algebraic sums of the independently measured enthalpies of several processes. The qualitatively close agreement between the variation in the enthalpies and the LFSE difference justifies the conclusion that it is the variations in LFSE s that account for gross qualitative stability relations such as the fact that tetrahedral complexes of Co11 are relatively stable while those of Ni11 are not. 

Within the overall aim it is the task of quantitative safety analysis to ascertain the frequency or occurrence probability of undesired events leading to incidents. Safety analysis will, in the case of problematic results of qualitative analysis, necessarily inspire the question of whether it should be continued in quantitative form. The question arises in particular when new technical equipment and processes are used. Quantitative safety analysis starts with knowledge of the logic structure of the system to be examined, as has already been ascertained in the course of qualitative analysis. A condition for execution is the presence of sufficient data—information about the behavior of the individual system components and parts. The information must be arranged in such a way that reliability characteristics (failure probabilities, failure rates) and maintenance characteristics (rates of repairs) can be derived. It is only when it is certain that sufficient data are available that quantitative analysis is possible. 

Whilst the qualitative analysis of filler dispersion in polymer composites poses its own difficulties, quantitative evalnation of mixing in these systems creates further challenges. Firstly, to establish the spatial location or size distribution of the additive, a statistically representative number of particles must be examined, preferably from various fields of view within the specimen. Providing there is sufficient contrast between the phases, as is discussed later, automatic image analysis techniques can be applied to rapidly assimilate and process data. Secondly, additive particles frequently have an irregular geometry and may also be exposed in a two-dimensional array at sections other than their mid-point, (i.e., only the tips of the particles may be on view). Thirdly, there is the question of how to define mixing and express this numerically. 

Qualitatively, the effect of substituents upon the ease of oxide formation is that which would be expected for the electrophilic process 20a shown. Quantitatively, a limited range of data is available for perbenzoic acid oxidation of pyridine and its homologues and halogen derivatives in 50 per cent aqueous dioxan 20 at 25 . The second-order rate constants for 3- and 4-substituted pyridines fit a Hammett plot with p = — 2-35 and are also roughly linear with pKa Bulky 2-substituents cause deviations but not so markedly as in quaternization (see p. 190). 

The first of them to determine the LMA quantitatively and the second - the LF qualitatively Of course, limit of sensitivity of the LF channel depends on the rope type and on its state very close because the LF are detected by signal pulses exceeding over a noise level. The level is less for new ropes (especially for the locked coil ropes) than for multi-strand ropes used (especially for the ropes corroded). Even if a skilled and experienced operator interprets a record, this cannot exclude possible errors completely because of the evaluation subjectivity. Moreover it takes a lot of time for the interpretation. Some of flaw detector producers understand the problem and are intended to develop new instruments using data processing by a computer [6]. 

The measurement of the current for a redox process as a fiinction of an applied potential yields a voltaimnogram characteristic of the analyte of interest. The particular features, such as peak potentials, halfwave potentials, relative peak/wave height of a voltaimnogram give qualitative infonnation about the analyte electrochemistry within the sample being studied, whilst quantitative data can also be detennined. There is a wealth of voltaimnetric teclmiques, which are linked to the fonn of potential program and mode of current measurement adopted. Potential-step and potential-sweep... 

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