Error difference

This analysis is applied to each operation at the particular level of the HTA being evaluated. In most cases the analysis is performed at the level of a step, for example. Open valve 27B. For each operation, the analyst considers the likelihood that one or more of the error types set out in classification in Figure 5.7 could occur. This decision is made on the basis of the information supplied by the PIF analysis, and the analyst s knowledge concerning the types of error likely to arise given the nature of the mental and physical demands of the task and the particular configuration of PIFs that exist in the situation. The different error categories are described in more detail below ... 

It is well to remember that the equations of this section deal with cases that differ fundamentally from that of Equation 10-3. This equation deals with the different errors in the result of a single measurement (that is, N or x), and the others are combinations of standard counting errors of different quantities that go to make up a complex datum that usually cannot be obtained in a single measurement. 

The same problem discussed in Examples 11.1 and 11.3 is taken to illustrate the ideas described in this section (Chen and Romagnoli, 1997). To evaluate the performance of the proposed approach under different error distributions, Monte Carlo simulations have again been performed on the four previous distributions. 

Now we want to have a closer look on the sitnation. We have to consider and avoid two different error possibilities. We want to exclude to think that the analyte is present where it is indeed not. This would be a false positive answer, a type I error. 

In session 9.1, we deal with the situation where 1 is subject to equal errors. In this session, we investigate the case where 1 is subject to different errors (Fig. 9.4). 

Each of these error components adds its own uncertainty to the total uncertainty budget of the analytical procedure. Therefore, the different error components are referred to as sources of uncertainty. Depending on the sources of uncertainty taken into account and thus on the conditions of the measurement, the overall MU will be different and another definition of MU will apply. This means that there is no single, straighforward definition of MU. It is rather a concept the interpretation of... 

As illustrated in Figure 5, the error of an analytical result for a specified analyte concentration is composed of different error components, forming together the ladder of errors ... 

By an argument based essentially on this sort of reasoning, W. Heisenberg could relate the error in position due to the measuring process, A, to the error in the momentum A (mu) when simultaneous determinations are desired. (The product of mass and velocity is called momentum. A signifies a difference, error or uncertainty.) A concise statement of the... 

As Liptak and Shields point out, accurate values of gas phase deprotonation and solvation energies are needed for reasonably accurate pA l values. An error of 1 pK unit results from an error in AG of 1.36 kcal mol-1 or 5.7 kJ mol-1, and an error of 0.5 pAa unit corresponds to an error in AG of only 2.9 kJ mol-1. For some purposes such an energy-difference error would be considered small, 1 kcal mol-1 or 4 kJ... 

From the remaining choices, eliminate any versions that make a different error, even if they correct the error in the prompt. This includes any versions that are grammatically correct but are unnecessarily wordy, ambiguous, or use unnecessarily complicated sentence structure. 

Model predictive control (MPC) was developed in the 1970s and 1980s to meet control challenges of refineries. The advantages of MPC are most evident when it is used as a multivariable controller integrated with an optimizer. The greatest MPC benefits are realized in applications with dead-time dominance, interactions, constraints, and the need for optimization. As opposed to a traditional control loop, where the controller responds to a difference (error) between the set point and measurement, the predictive controller uses a vector difference between the future trajectory of the set point and the predicted trajectory of the controlled variable as its input (Figure 2.52). 

The flame must dry, vaporize, and atomize the sample in a reproducible manner with respect to both space and time. Unlike titrimetric and gravimetric analysis, atomic absorption spectrometry is a secondary analytical technique. Concentrations are determined by comparing the absorbance values obtained for samples with those obtained for standards of known determinant concentrations. It is very important, therefore, that samples and standards are always atomized with the same efficiency to produce a cloud of atomic vapour of highly reproducible geometry. If samples and standards behave differently, errors will result. 

There are, of course, two ways in which a straight line can be fitted, one with and one without the intercept. Each generates different error sum of squares according to the model. The values of the coefficients and the errors are given in Table 2.4 for both datasets. Note that although the size of the term for the intercept for dataset B is larger dian dataset A, this does not in itself indicate significance, unless the replicate error is taken into account. 

It is noted that k is comparatively dominant in the first interval, while ks, reduces by nearly two decimal places from the first to third interval. As these extra terms are needed near the overlap region of the intervals, their presence indicate an interaction of the higher order modes of the system, before transiting to the next interval. Fig. 5.3 indicates that the first Hopf bifurcation occurs at Rccri = 51.934. Difference between this value and the other values given by Jackson (1987), Zebib (1987) and Morzynski Thiele (1993) is essentially due to different error sources of different methods accumulating to trigger vortex shedding in the wake. An accurate method... 

The value of a single measurement result may differ (and actually always differs) from the expected (real) value. The difference is a result of the occurrence of different errors. There are three basic types of errors ... 

Table 8.2 Table of different error function forms... 

Although this procedure does work it is by no means very satisfactory, as there are three different errors for the three different types of comparison. Further, it is not the most efficient possible. 

However, if one compares the values of the lattice parameter obtained when a different kind of a systematic error was assumed and accounted for in the data, the difference between the two is statistically significant (4.1583 vs. 4.1574 A for sample displacement and zero shift effects, respectively). This is expected given the different contribution from different errors as seen in Figure 5.19. Usually, both effects are present in experimental data. The refinement of two contributions simultaneously is, however, not feasible due to strong correlations between sample displacement and zero shift parameters as shown in Figure 5.21. 

Grammar-Usage-Mechanics weak mechanics/grammar/usage—more than 8 but fewer than 15 different errors in about 400 words... 

TABLE 2.4. Mean Estimates of Km (True Value = 1.00) for Data with Different Error Types... 

In Table 6 the different errors between experimental and calculated levels are reported for each of the 7F multiplet sublevels and for all the multiplets. We note that the total error is greatest for the phosphates. 

Figure 19-8 Conceptual basis of control charts. A, Frequency distributions of control observations for different error conditions. B, Display of control values representing those distributions when concentration is plotted versus time on a control chart.

The outcome C S M is considered to be a partial success of the model. Both the student response and the model response were in error, but they were different errors. In these cases, the model accurately predicted that the student lacked critical knowledge and would err. 

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