Degrees of freedom, significance

The above results apply to the ideal gas of molecules. The objects addressed in the context of molecular modeling of complex systems are known in the form of macroscopic samples, mostly in the condensed phase. Thus the intermolecular degrees of freedom significantly contribute to the thermodynamical and other properties due to intermolecular interactions. For taking these latter into account the Monte-Carlo (MC) or molecular dynamics (MD) techniques are applied to model systems containing from hundreds to thousands of molecules and correspondingly tens and hundreds of thousands of atoms. These two approaches represent two more modem contexts where a demand for efficient methods of calculation of molecular potential... 

Early studies showed tliat tire rates of ET are limited by solvation rates for certain barrierless electron transfer reactions. However, more recent studies showed tliat electron-transfer rates can far exceed tire rates of diffusional solvation, which indicate critical roles for intramolecular (high frequency) vibrational mode couplings and inertial solvation. The interiDlay between inter- and intramolecular degrees of freedom is particularly significant in tire Marcus inverted regime [45] (figure C3.2.12)). 

The distribution of the /-statistic (x — /ji)s is symmetrical about zero and is a function of the degrees of freedom. Limits assigned to the distance on either side of /x are called confidence limits. The percentage probability that /x lies within this interval is called the confidence level. The level of significance or error probability (100 — confidence level or 100 — a) is the percent probability that /X will lie outside the confidence interval, and represents the chances of being incorrect in stating that /X lies within the confidence interval. Values of t are in Table 2.27 for any desired degrees of freedom and various confidence levels. 

The value of fexp is compared with a critical value, f(a, v), as determined by the chosen significance level, a, the degrees of freedom for the sample, V, and whether the significance test is one-tailed or two-tailed. 

If Sa and sb are significantly different, however, then fexp must be calculated using equation 4.19. In this case, the degrees of freedom is calculated using the following imposing equation. 

To begin with, we must determine whether the variances for the two analyses are significantly different. This is done using an T-test as outlined in Example 4.18. Since no significant difference was found, a pooled standard deviation with 10 degrees of freedom is calculated... 

The value of fexp is then compared with a critical value, f(a, v), which is determined by the chosen significance level, a, the degrees of freedom for the sample, V, and whether the significance test is one-tailed or two-tailed. For paired data, the degrees of freedom is - 1. If fexp is greater than f(a, v), then the null hypothesis is rejected and the alternative hypothesis is accepted. If fexp is less than or equal to f(a, v), then the null hypothesis is retained, and a significant difference has not been demonstrated at the stated significance level. This is known as the paired f-test. 

This value of fexp is compared with the critical value for f(a, v), where the significance level is the same as that used in the ANOVA calculation, and the degrees of freedom is the same as that for the within-sample variance. Because we are interested in whether the larger of the two means is significantly greater than the other mean, the value of f(a, v) is that for a one-tail significance test. 

As stated earlier, the main motivation for using either PCA or PCA is to construct a low-dimensional representation of the original high-dimensional data. The notion behind this approach is that the effective (or essential, as some call it [33]) dimensionality of a molecular conformational space is significantly smaller than its full dimensionality (3N-6 degrees of freedom for an A-atom molecule). Following the PCA procedure, each new... 

In principle, energy landscapes are characterized by their local minima, which correspond to locally stable confonnations, and by the transition regions (barriers) that connect the minima. In small systems, which have only a few minima, it is possible to use a direct approach to identify all the local minima and thus to describe the entire potential energy surface. Such is the case for small reactive systems [9] and for the alanine dipeptide, which has only two significant degrees of freedom [50,51]. The direct approach becomes impractical, however, for larger systems with many degrees of freedom that are characterized by a multitude of local minima. 

A different problem of spacing, which is closely related to directional solidification but has some additional degrees of freedom, is electrocrystallization. We would like to mention at least one example of greater significance, namely the production of porous silicon [131], which is still not very well understood today. 

The simulation of a molecular system at a finite temperature requires the generation of a statistically significant set of points in phase space (so-called configurations), and the properties of a system can be obtained as averages over these points. For a many-particle system, the averaging usually involves integration over many degrees of freedom. 

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