Quahtative data

The mode is the most common value in the sample. The mode is easily found from a tabulated frequency distribution as the most frequent value. The mode provides a rapidly and easily found estimate of sample location and is unaffected by outliers. However, the mode is affected by chance variation in the shape of a sample s distribution and it may lie distant from the obvious centre of the distribution. Note that the mode is the only statistic to make sense of quahtative data, e.g. the modal (most frequent) technique used in the laboratory is infrared spectroscopy . The mean, median and mode have the same units as the variable under discussion. However, whether these statistics of location have the same or similar values for a given frequency distribution depends on the symmetry and shape of the distribution. If it is near symmetrical with a single peak, all three will be very similar if it is skewed or has more than one peak, their values will differ to a greater degree (see Fig. 40.3). 

Quahtative data describe an observation quantitative data use numbers. 

Bateman, K.R, KeUmann, M., Muenster, H., et al. (2009) Quantitative-quahtative data acquisition using a Benchtop Orbitrap mass spectrometer. Journal of the American Society for Mass Spectrometry, 20,1441-1450. 

Experimental values of X have been tabulated for a number of polymer-solvent systems (4,12). Unfortunately, they often turn out to be concentration and molecular weight dependent, reducing their practical utility. The Flory-Huggins theory quahtatively predicts several phenomena observed in solutions of polymers, including molecular weight effects, but it rarely provides a good quantitative fit of data. Considerable work has been done subsequentiy to modify and improve the theory (15,16). 

Even though the simple distillation process has no practical use as a method for separating mixtures, simple distillation residue curve maps have extremely usehil appHcations. These maps can be used to test the consistency of experimental azeotropic data (16,17,19) to predict the order and content of the cuts in batch distillation (20—22) and, in continuous distillation, to determine whether a given mixture is separable by distillation, identify feasible entrainers/solvents, predict the attainable product compositions, quaHtatively predict the composition profile shape, and synthesize the corresponding distillation sequences (16,23—30). By identifying the limited separations achievable by distillation, residue curve maps are also usehil in synthesizing separation sequences combining distillation with other methods. 

A key feature of this model is that no data for mixtures are required to apply the regular-solution equations because the solubiHty parameters are evaluated from pure-component data. Results based on these equations should be treated as only quaHtative. However, mixtures of nonpolar or slightly polar, nonassociating chemicals, can sometimes be modeled adequately (1,3,18). AppHcations of this model have been limited to hydrocarbons (qv) and a few gases associated with petroleum (qv) and natural gas (see Gas, natural) processiag, such as N2, H2, CO2, and H2S. Values for 5 and H can be found ia many references (1—3,7). 

As evidenced by the correlation of the BH model with the experimental data. Figure 2.4, the model is only in quahtative accord with the experiment. Clearly, the BH model cannot account for the breadth in the correlation of the rate constants for porton transfer with driving force. The origin of the discrepancy may lie in the single-mode namre of the BH model, which allows only for vibrational excitation in the low-frequency promoting mode. Excitation in the reactant and product modes of the vibration associated with the transferring proton is not taken into account in the BH model. Therefore, the discrepancy between experiment... 

Although these electrophysiological data indicate that nerve agents may have direct effects on the nervous system unrelated to AChE inhibition, the data do not provide a means of determining a dose conversion to an integrative whole-body endpoint such as lethality or quahtative/... 

A, but the experimental data of course include such effects. This may be viewed as an advantage, the electron correlation effects are implicitly taken into account in the parameterization, and we need not perform complicated calculations to improve deficiencies in the HF procedure. However, it becomes problematic when the HF wave function cannot describe the system even quahtatively correctly, as for example with biradicals and excited states. Additional flexibility can be introduced in the trial wave function by adding more Slater determinants, for example by means of a Cl procedure... 

Literature data is almost entirely for small equipment whose capacity and efficiency cannot be scaled up to corrunercial sizes, allhough it is of quahtative value. Extraction processes are sensitive because they operate with small density differences that are sensitive to temperature and the amount of solute transfer. They also are affected by interfacial tensions, the large changes in phase flow rates that corrrmonly occur, and even by the direction of mass transfer. For comparison, none of these factors is of major significance in vapor-liquid contacting. 

The reaction described by the data in Tables 2-1 and 2-2 is to be carried out in a PFR. The entering molar flow rate of A is 0.867 mol/s. Calculate the reactor volume necessary to achieve 80% conversion in a PFR. (a) First, use one of the integration formulas given in Appendix A.4 to determine the PFR reactor volume, (b) Next, shade the area in Figure 2-1 which when multiphed by FXo would give the PETl volume. (c) Make a quahtative sketch of the conversion, X, and the rate of reaction, —r, down the length (voliune) of the reactor. 

The information needed about the chemical kinetics of a reaction system is best determined in terms of the structure of general classes of such systems. By structure we mean quahtative and quantitative features that are common to large well-defined classes of systems. For the classes of complex reaction systems to be discussed in detail in this article, the structural approach leads to two related but independent results. First, descriptive models and analyses are developed that create a sound basis for understanding the macroscopic behavior of complex as well as simple dynamic systems. Second, these descriptive models and the procedures obtained from them lead to a new and powerful method for determining the rate parameters from experimental data. The structural analysis is best approached by a geometrical interpretation of the behavior of the reaction system. Such a description can be readily visualized. 

Figure 5-23 has been used to correlate furnace performance data for a multitude of industrial furnaces and combustors. Typical operational domains for a variety of fuel-fired industrial furnaces are summarized in Table 5-7. The WSCC approach (or speckled furnace model) is a classic contribution to furnace design methodology which was first due to Hottel [op. cit.]. The WSCC model provides a simple mace design template which leads to a host of more complex furnace models. These models include an obvious extension to a tanks-in-series model as well as multizone models utilizing empirical cold-flow velocity patterns. For more information on practical Furnace design models, reference is made to Hottel and Sarofim (op. cit.. Chap. 14). Quahtative aspects of process equipment have been treated in some detail elsewhere (Baukal, C. E., ed.. The JohnZink Combustion Handbook, CRC Press, Boca Raton, Fla., 2001). 

To obtain quahtative dynamic information, the usual approach involves the use of dynamic models that are based on physical intuition and/or the ease of the formulation. In this procedure, there is always the problem of overinterpretation of a limited data set and/or the possibility that the resulting physical picture is not unambiguous models can never be proven, just be ehminated. The so-called model-free approach completely describes the information on fast dynamic processes by just two parameters with model-independent significance, namely a generalized order parameter which is a measure of the spatial restriction of motion and an effective correlation time which is a measure of the rate of the motion. This model is reasonably well suited for the description of internal... 

ISPD International Society of Peritoneal Dialysis MRSA methicillin-resistant Staphylococcus aureus MRSE methicillin-resistant enterococcus NKF-K/DOQl National Kidney Foundation Kidney Disease Outcomes Quahtative Initiative PAN polyacrylonitrile PMMA polymethylmethacrylate PS polysulfone URR urea reduction ratio USRDS United States Renal Data System VRE vancomycin-resistant enterococcus WBC white blood cell (count)... 

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