Data sets, comparison

Step 7 Comparison of the Structural Variety with Another Data Set... 

The objective of this study is to show how data sets of compounds for which dif-ferent biological activities have been determined can be studied. It will be shown how the use of a counter-propagation neural networb can lead to new insights [46]. The cmpha.si.s in this example is placed on the comparison of different network architectures and not on quantitative results. 

In Table III a comparison is made between calculated and observed chemical shift values of a variety of allenes that were not included in the data set of Table I,... 

This is an example of a paired data set since the acquisition of samples over an extended period introduces a substantial time-dependent change in the concentration of monensin. The comparison of the two methods must be done with the paired f-test, using the following null and two-tailed alternative hypotheses... 

A comparison between Gl, G2, G2(MP2) and G2(MP2,SVP) is shown in Table 5.2 for the reference G2 data set the mean absolute deviations in kcal/mol vary from 1.1 to 1.6 kcal/mol. There are other variations of tlie G2 metliods in use, for example involving DFT metliods for geometry optimization and frequency calculation or CCSD(T) instead of QCISD(T), with slightly varying performance and computational cost. The errors with the G2 method are comparable to those obtained directly from calculations at the CCSD(T)/cc-pVTZ level, at a significantly lower computational cost. ... 

Comparative analysis directly compares two or more data sets in order to detect changes in the operating condition of mechanical or process systems. This type of analysis is limited to the direct comparison of the time-domain or frequency-domain signature generated by a machine. The method does not determine the actual dynamics of the system. Typically, the following data are used for this purpose (1) baseline data, (2) known machine condition, or (3) industrial reference data. 

Reference or baseline data sets should be acquired for each machine-train or process system to be included in a predictive maintenance program when the machine is installed or after the first scheduled maintenance once the program is established. These data sets can be used as a reference or comparison for all future measurements. However, such data sets must be representative of the normal operating condition of each machine-train. Three criteria are critical to the proper use of baseline comparisons reset after maintenance, proper identification, and process envelope. 

Since variations in process variables, such as load, have a direct effect on the vibration energy and signature generated by a machine-train, the actual operating envelope for each baseline data set must also be clearly identified. If this step is omitted, direct comparison of other data to the baseline will be meaningless. The label feature in most vibration monitoring systems permits tagging the baseline data set with this additional information. 

FIGURE 11.14 Data set consisting of a control dose-response curve and curves obtained in the presence of three concentrations of antagonist. Panel a curves fit to individual logistic functions (Equation 11.29) each to its own maximum, K value, and slope. Panel b curves fit to the average maximum of the individual curves (common maximum) and average slope of the curves (common n) with only K fit individually. The F value for the comparison of the two models is 2.4, df = 12,18. This value is not significant at the 95% level. Therefore, there is no statistical support for the hypothesis that the more complex model of individual maxima and slopes is required to fit the data. In this case, a set of curves with common maximum and slope can be used to fit these data. 

Notice also, that PRESS, as defined, is not standardized to any absolute frame of reference. The more samples we have in our data set, the more errors there are to be squared and summed, and the larger PRESS is likely to be. Thus, PRESS is only useful for comparisons within a given data set. 

We stress that the latter parameter set serves only as a basis of comparison (cf. Table XXII), for we have found no evidence to claim any (even limited) generality for a (p) parameters. Table XXIII compares the fits with a (p) and a (A) parameters for data sets of Table VI having six or more points. 

Additional ionization equilibria involving ortho substituents have been reported by Charton (34) to follow eq. (1). The results of our analysis of the data for aqueous ionization of 2-substituted pyridinium ions, -substituted anilinium ions, and -substituted phenols are given in Table XXVIII. Comparison with the corresponding meta and para data set results is also included. 

Based on the above arguments, with peel strength measurements for the three SUV/Minivan tires. We will also include the elongation to break data for the Large Car-C tire even though the difference in wedge rubber modulus between the held and oven sets makes the comparison between these data sets somewhat questionable. 

Comparisons between data sets (r test, multiple range test, F test, simple ANOVA),... 

Notice that by the inclusion of Tg, the mean is much more strongly influenced than the median. The value of such comparisons lies in the automatic processing of large numbers of small data sets, in order to pick out the suspicious ones for manual inspection. (See also the next Section.)... 

ND 60.dat Fifteen columns that each contain 160 random numbers. To be used with MSD, HISTO, CORREL, SMOOTH to obtain a baseline, against which to compare real data sets the ruggedness of evaluations can be checked through comparisons with sets of random numbers. 

Natsoulis G, El Ghaoui L, Lanckriet GR, Tolley AM, Leroy F, Dunlea S, et al. Classification of a large microarray data set algorithm comparison and analysis of drug signatures. Genome Res 2005 15 724-36. 

It is not strictly required to use the same attributes in each data set. This allows the comparison of independent QDA results obtained by different laboratories or development departments in collaborative studies. Also within a single panel, individual panellists may work with personal lists of attributes. When the sensory attributes are chosen freely by the individual panellist one speaks of Free Choice Profiling. When each panellist uses such a personal list of attributes, it is likely that... 

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