Fitness correlation

The first order plot for the isomerization reaction shows a good linear fit (correlation coefficient = 0.998) while there is... 

The time-series analysis results of Merz et were expressed in first-order empirical formulas for the most part. Forecasting expressions were developed for total oxidant, carbon monoxide, nitric oxide, and hydrocarbon. Fitting correlation coefficients varied from 0.547 to 0.659. As might be expected, the best results were obtained for the primary pollutants carbon monoxide and nitric oxide, and the lowest correlation was for oxidant. This model relates one pollutant to another, but does not relate emission to air quality. For primary pollutants, the model expresses the concentrations as a function of time. 

A more recent study for estimating Henry s law constants using the bond contribution method was provided by Meylan and Howard (1991). In this study, the authors updated and revised the method developed by Hine and Mookeijee (1975) based on new experimental data that have become available since 1975. Bond contribution values were determined for 59 chemical bonds based on known Henry s law constants for 345 organic compounds. A good statistical fit [correlation coefficient (r ) = 0.94] was obtained when the bond contribution values were regressed against known Henry s law constants for all compounds. For selected chemicals classes, r increased slightly to 0.97. 

Fig. 18 Top CD spectra. Oligomer 15 (solid line) oligomer 15 in the presence of 100 equiv of (-)-a-pinene (dotted line) and 100 equiv of (+)-a-pinene (dashed line) in a mixed solvent of 40% water in acetonitrile (by volume) at 295 K. [15]=4.2 pmol. Bottom Plot of IniCn for 15 against the solvent composition. The solid line is the least-squares linear fit (correlation coefficient=0.9987), and the dotted line is the extrapolation to 100% water. Error bars are from the nonlinear fitting of the data to...

Re-1870s evolution diagram for 16 iron meteorites from diverse chemical groups. The line is the best-fit correlation for the IIA and MB irons. Modified from Shen et al. (1996) using X = 1.666 x... 

Kamlet parameter that depends on expl compn (see article on Velocity of Detonation in this Vol). Their correlation is shown in Fig 17 Kamlet Finger (Ref 23) propose a somewhat simpler empirically-fitted correlation, namely, y/ZE1 = 0.887 >asp0° 4. It should be emphasized that this correlation, as well as the one in Fig. 17, are based on isentrope expansion calcns with all their inherent uncertainties as to the equation of state to be used for the expand-ign detonation products... 

Try, in succession, the first-order, second-order, and third-order plots until a straight-line plot is obtained. It turns out that the first-order plot gives an excellent fit (correlation coefficient = 0.9999), so a = 1. The slope is -8.718 x 10 4min ,... 

Densities at six temperatures accurate to four significant figures over the range of 20° to 100°C for this viscosity standard was extrapolated to higher temperatures using an excellent linear fit (correlation coefficient of 0.999998). 

The response of the system to SO is linear at him levels (ca. > 1 ppbv) but nonlinear at lower levels. This is a characteristic of the reaction system since the same behavior is exhibited by the liquid phase analysis system. Figure 7 shows this nonlinearity at low SO2 levels for the gas phase analyzer. A second-degree equation (e.g., for the data shown, Y = aX2+bX+c produces excellent fit, correlation coefficient > 0-999) and may be used for calculations. It should be noted that both the measures suggested above for improving the LOD actually result in an increase of the analyte concentration and thus do not involve an increased need for manipulation in the nonlinear response region. 

In a manner similar to that outlined above for Foxtail without surfactant, the TFMS compounds were separated into two groups consisting of only meta- or only para-substituted derivatives, and the corresponding data gathered in each herbicidal test series for the three weed types (in the presence and absence of Tween 80) were fitted to the Hansch equation. 4-SCH3 and 3-SCH3 data points were omitted from all the fits (see below). The H-substituted parent compound was omitted when the TFMS compounds were separated into two groups (see above). For comparison with Equations 10-18, the final best-fit correlation equations obtained for Foxtail in the presence of 0.1% Tween 80 are tabulated below. 

Houle D 1991 Genetic covariance of fitness correlates what genetic correlations are made of and why it matters. Evolution 45 630-648... 

Humphrey Parallel data were produced by Napoleon Chagnon on the Yanomamo. He showed a fitness correlation with the number of people that an individual had killed in battle. Men who had killed became sexually attractive. 

Z = corrugated plate length. Bubble column results fit correlation of Neme et al, Chem. Eng. Technol, 20, 297 (1997) for structured packing. Nst = Stanton number = kZ/D Npr = Eroude number = Usup/gz... 

Figure 5. Time-dependent anisotropies for labeled polyisoprene chains in dilute 2-pentanone solutions. The smooth curves through the data points are the best fits to the Hall-Helfand model for 22.8 C, -8.6 C, and -26.5 °C (bottom to top). The data at 35.1 °C is omitted for clarity. Semilog plots of the best fit correlation functions are shown in the inset. Note that all the correlation functions are quite non-exponential.

Figure 6. Arrhenius plot for dilute solutions of labeled polyisoprene in 2-pentanone. The activation energy calculated from the slope of the best fit line is 7.4 kJ/mole. On the vertical scale. T represents the 1/e point of the best fit correlation functions using the Hall-Helfand model. The data points represent results of independent experiments. The units for t and n are ps and centipoise, respectively.

That is, at flow rate 100, with n = 1, the cost doubles when the size doubles, and there no longer is economy of scale. Since the key size is where n= I, polynomial curve-fit correlations of costs should be avoided, since the size corresponding to n = 1 is not obvious. 

A series of non-equilibrium Tm-values were measured on samples recrystallised from the melt under standard conditions (the so-called Tm2-values, see 1.1.4) and a number of nonequilibrium literature values were used to look for an improved correlation between Tm/Tg relations. We tried to improve the results of such a relation by distinguishing different groups of polymers instead of looking for one relation for all types of polymers. Three groups of polymers offering the best fitting correlations, were selected finally ... 

An alternative approach to using traditional parametric statistical methods to calculate the significance of fitted correlations, would be to directly assess the model based on its ability to predict, rather than merely to assess how well the model fits the training set. When the quality of the model is assessed by the prediction of a test set, rather than the fit of the model to its training set, a statistic related to r or can be defined, and denoted q or q, to indicate that the quality measure is assessed in prediction. A q may be calculated by internal cross-validation techniques, or by the quality of predictions of an independent test set, in which case an upper-case is used. The equation to calculate q (or Q ) is shown in equation 9.3. 

The large number of variables, and the dependence of many of them upon conversion, usually means that it is necessary to compare experimera with predictions and to Iterate to values of the parameters which give best-fit correlation with experimental data. These control strategies are, therefore, specific to a particular emulsion polymerization reaction (i.e. a specific formulation and reaction conditions). While the principles of this approach can be applied to a range of emulsion copolymerizations, it is not possible to predict the optimum addition rate profiles directly and considerable experimental effort is required to establish optimum values of parameters for each system. 

By using the bound-liquid diffusivity data of Stamm (1963), it is possible to obtain the following least-squares, best-fit correlation for D, ... 

The various correlating equations (Wilson, Van Laar, etc.) have multicomponent forms, but the most versatile method for combining binaiy pair data is that of Chien and Null. This method permits use of the best-fit correlating method for each binaiy pair followed by combination of the binary data to give multi-component equilibrium distributions. 

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