Square deviation

The ternary diagrams shown in Figure 22 and the selectivi-ties and distribution coefficients shown in Figure 23 indicate very good correlation of the ternary data with the UNIQUAC equation. More important, however, Table 5 shows calculated and experimental quarternary tie-line compositions for five of Henty s twenty measurements. The root-mean-squared deviations for all twenty measurements show excellent agreement between calculated and predicted quarternary equilibria. 

This subroutine also prints all the experimentally measured points, the estimated true values corresponding to each measured point, and the deviations between experimental and calculated points. Finally, root-mean-squared deviations are printed for the P-T-x-y measurements. 

A linear dependence approximately describes the results in a range of extraction times between 1 ps and 50 ps, and this extrapolates to a value of Ws not far from that observed for the 100 ps extractions. However, for the simulations with extraction times, tg > 50 ps, the work decreases more rapidly with l/tg, which indicates that the 100 ps extractions still have a significant frictional contribution. As additional evidence for this, we cite the statistical error in the set of extractions from different starting points (Fig. 2). As was shown by one of us in the context of free energy calculations[12], and more recently again by others specifically for the extraction process [1], the statistical error in the work and the frictional component of the work, Wp are related. For a simple system obeying the Fokker-Planck equation, both friction and mean square deviation are proportional to the rate, and... 

A typical molecular dynamics simulation comprises an equflibration and a production phase. The former is necessary, as the name imphes, to ensure that the system is in equilibrium before data acquisition starts. It is useful to check the time evolution of several simulation parameters such as temperature (which is directly connected to the kinetic energy), potential energy, total energy, density (when periodic boundary conditions with constant pressure are apphed), and their root-mean-square deviations. Having these and other variables constant at the end of the equilibration phase is the prerequisite for the statistically meaningful sampling of data in the following production phase. 

Example Crippen and Snow reported their success in developing a simplified potential for protein folding. In their model, single poin Ls rep resell t am in o acids. For th e avian pan creatic polypeptide, th c n ative structure is not at a poten tial m in imum. However, a global search fotin d that the most stable poten tial m in im urn h ad only a 1.8, An gstrom root-m ean-square deviation from thenative structu re. 

A fourth hierarchical method that is quite popular is Ward s method [Ward 1963]. This method merges those two clusters whose fusion minimises the information toss due to the fusion. Information loss is defined in terms of a function rvhich fdr each cluster i corresponds to the total sum of squared deviations from the mean of the cluster ... 

In order to examine whether this sequence gave a fold similar to the template, the corresponding peptide was synthesized and its structure experimentally determined by NMR methods. The result is shown in Figure 17.15 and compared to the design target whose main chain conformation is identical to that of the Zif 268 template. The folds are remarkably similar even though there are some differences in the loop region between the two p strands. The core of the molecule, which comprises seven hydrophobic side chains, is well-ordered whereas the termini are disordered. The root mean square deviation of the main chain atoms are 2.0 A for residues 3 to 26 and 1.0 A for residues 8 to 26. 

Now sum the squared deviations over all possible values of their (small) ranges and take the averages. These quantities can be interpreted as in Eqs. (2-63) and (2-64) ... 

The squared deviations between the calculated and actual responses are shown in Table 12.6c (see column labeled SSq). The AIC values are calculated according to Equation 11.30. The values are shown in Table 12.6c. It can be seen that the fit to the curves with a mean Emax and slope gives a lower AIC value. Therefore, this model is statistically preferable. It is also the most unambiguous model for simple competitive antagonism since it fulfills the criteria of parallel dextral displacement of dose-response curves with no diminution of maxima. The calculated curves are shown in Figure 12.7b. 

In analytical chemistry one of the most common statistical terms employed is the standard deviation of a population of observations. This is also called the root mean square deviation as it is the square root of the mean of the sum of the squares of the differences between the values and the mean of those values (this is expressed mathematically below) and is of particular value in connection with the normal distribution. 

A test of the accuracy of the approximate wave function 0 for the ground state has been given by Eckart3 by considering the mean square deviation from the exact eigenfunction lP0 ... 

It seems as if an energy value of sufficiently high accuracy has now been found for the helium problem, but we still do not know the actual form of the corresponding exact eigenfunction. In this connection, the mean square deviation e = J — W 2 (dx) and criteria of the Eckart type (Eq. III.27) are not very informative, since s may turn out to be exceedingly small, even if trial function... 

Variance The mean square of deviations, or errors, of a set of observations the sum of square deviations, or errors, of individual observations with respect to their arithmetic mean divided by the number of observations less one (degree of freedom) the square of the standard deviation, or standard error. 

Fig. 5.17. Time domain CARS of nitrogen under normal conditions. Points designate experimental data, solid line calculation with a = 6.0 A, b = 0.024, c = 0.0015. The insert depicts the dependences of the relative mean-square deviation on each of the parameters , b and c, the other two being fixed at their optimum values. The deviations are expressed as percentage of optimum parameters.

Sk and Sx are the mean square deviations of the variables log k and from their... 

Figure 3. A comparison of the aC backbone of PelC with PelE. The aCs which superimpose within a root-mean-square deviation of 1.5 A are shown in black and those, within 3.0 A are shown in dark gray. The remaining backbone regions are shown in light The largest structural differences occur in the loops capping one end of the parallel p as well as in those comprising the putative substrate binding groove.

One of the earliest interpretations of latent vectors is that of lines of closest fit [9]. Indeed, if the inertia along v, is maximal, then the inertia from all other directions perpendicular to v, must be minimal. This is similar to the regression criterion in orthogonal least squares regression which minimizes the sum of squared deviations which are perpendicular to the regression line (Section 8.2.11). In ordinary least squares regression one minimizes the sum of squared deviations from the regression line in the direction of the dependent measurement, which assumes that the independent measurement is without error. Similarly, the plane formed by v, and Vj is a plane of closest fit, in the sense that the sum of squared deviations perpendicularly to the plane is minimal. Since latent vectors v, contribute... 

One can hence think of (normal-mode composition factor) ej = ejaSja as the fractional involvement of atom j in normal mode a.The dimensionless vector eja also specifies the direction of the motion of atom j in the ot-th normal mode. Interestingly, the mode composition factors are also related to the magnitude of the atomic fluctuations. In a stationary state ) of a harmonic system, the mean square deviation (msd) of atom j from its equilibrium position may be expressed as a sum over modes of nonzero frequency ... 

---