Variance dominance

Jensen That is true. We know that there is a lot of dominance variance in intelligence, or you wouldn t get a high degree of inbreeding depression of IQ or g. It is higher than for many other polygenic traits. 

Gangestad You don t need a lot of dominance variance to get inbreeding depression on a trait, if most of the trait s genetic variance is due to mutations. With rare mutations there are very few double recessive mutations, and therefore, almost all of the variance caused by each mutation is merely additive, even if the effect of double recessives is nonadditive. So rare mutations can produce low dominance variance yet substantial inbreeding depression. 

DNA-F ingerprinting, genetischer Fingerabdruck DNA sequencer DNA-Sequenzierungsautomat doctor knife Abstreifmesser, Rakel doff (a bobbin) text Abnahme dominance variance Dominanzvarianz donate... 

PCA is a frequently used method which is applied to extract the systematic variance in a data matrix. It helps to obtain an oveiwiew over dominant patterns and major trends in the data. 

Using the first and second order terms in the variance equation gives exactly the same answer. For different conditions, say where one variable is not dominating the situation as above for the load, then the use of the variance equation with second order terms will be more effective. 

Now, it is of interest to determine if either the resistance to mass transfer term for the mobile phase or, the resistance to mass transfer term in the stationary phase dominate in the equation for the variance per unit length of a GC packed column. Consequently, taking the ratio of the two resistance to mass transfer terms (G)... 

This variance grows without bound with the size of the CCD and dictates that the smallest practical number of pixels should be used. A 2 x 2 array (or quadcell) with = i, is thus the configuration that dominates many existing slope sensors (Tyler and Fried, 1982). 

In future improvements in technology may mean that that read noise no longer is the dominant noise source, and Poisson noise arising from the quantum nature of light is in fact the limiting factor. In this case the variance of the centroid noise is equal to. 

Both types of symmetric displays exhibited in Figs. 32.9 and 32.10 have their merits. They are called symmetric because they produce equal variances in the scores and in the loadings. In the case when a = 3 = 1, we obtain that the variances along the horizontal and vertical axes are equal to the eigenvalues h associated to the dominant latent vectors. In the other case when a = P = 0.5, the variances are found to be equal to the singular values X. 

For measurements by AS, the errors of the isotope ratio will be dominated by counting statistics for each isotope. For measurements by TIMS or ICP-MS, the counting-statistic errors set a firm lower limit on the isotopic measurement errors, but more often than not contribute only a part of the total variance of the isotope-ratio measurements. For these techniques, other sources of (non-systematic) error include ... 

The competition model and solvent interaction model were at one time heatedly debated but current thinking maintains that under defined r iitions the two theories are equivalent, however, it is impossible to distinguish between then on the basis of experimental retention data alone [231,249]. Based on the measurement of solute and solvent activity coefficients it was concluded that both models operate alternately. At higher solvent B concentrations, the competition effect diminishes, since under these conditions the solute molecule can enter the Interfacial layer without displacing solvent molecules. The competition model, in its expanded form, is more general, and can be used to derive the principal results of the solvent interaction model as a special case. In essence, it seems that the end result is the same, only the tenet that surface adsorption or solvent association are the dominant retention interactions remain at variance. 

To construct the reference model, the interpretation system required routine process data collected over a period of several months. Cross-validation was applied to detect and remove outliers. Only data corresponding to normal process operations (that is, when top-grade product is made) were used in the model development. As stated earlier, the system ultimately involved two analysis approaches, both reduced-order models that capture dominant directions of variability in the data. A PLS analysis using two loadings explained about 60% of the variance in the measurements. A subsequent PCA analysis on the residuals showed that five principal components explain 90% of the residual variability. 

Additional insight is possible from Fig. 38b. Here we see that the magnitude of the explained variance accounted for by the second PC has noticeably increased after minute 70. This is consistent because, from process knowledge, it is known that removal of water is the primary event in the first part of the batch cycle, while polymerization dominates in the later part, explaining why the variance profile changes around the 70-minute point. 

As discussed in Sect. 6.1, the bias due to finite sampling is usually the dominant error in free energy calculations using FEP or NEW. In extreme cases, the simulation result can be precise (small variance) but inaccurate (large bias) [24, 32], In contrast to precision, assessing the systematic part (accuracy) of finite sampling error in FEP or NEW calculations is less straightforward, since these errors may be due to choices of boundary conditions or potential functions that limit the results systematically. 

In Eq. (7), CLOGP is the first term accepted in the step-wise MLR analysis and accounts for 60% of the variance in —log SQ. The other terms are accepted in the order E (8%), B (4%) and Xq 1 (8%). Clearly, lipophilicity is the dominant factor in the aqueous solubilities of these 58 crystalline substances. This is consistent with work of Yalkowski s group [3, 4, 20]. By inference, the other three factors together could be interpreted as a proxy for mp. 

Since driving force of variations of the volatilisation rate is influenced by the predominant mean sea surface temperature changes of it will influence the evolution of the volatilisation rate and, hence, the distribution of the substance. The influence of the wind speed is expected to increase in a warming climate with higher sea surface temperatures, as it was shown that for high sea surface temperatures the variance is dominated by wind speed changes. 

PCA [12, 16] is a multivariate statistics method frequently applied for the analysis of data tables obtained from environmental monitoring studies. It starts from the hypothesis that in the group of original data, there is a set of reduced factors or dominant components (sources of variation) which influence the observed data variance in an important way, and that these factors or components cannot be directly measured (they are hidden factors), since no specific sensors exist for them or, in other words, they cannot be experimentally observed. 

Note that as Re/, goes to infinity with Sc constant, both the turbulent energy spectrum and the scalar energy spectrum will be dominated by the energy-containing and inertial/inertial-convective sub-ranges. Thus, in this limit, the characteristic time scale for scalar variance dissipation defined by (3.55) becomes... 

Figure 4.13 Principal component analysis of the mean isotopic data for oceanic islands (courtesy of Vincent Salters). In the top left corner, the plane of the first two components (the Mantle Plane of Zindler et al, 1982) explains 93 percent of the variance. Component 1 is dominated by lead isotopes, component 2 by Sr and Nd isotopes. Other components are plotted for reference. In the top right corner, the Mantle Plane is viewed sideways along the direction of the second component, so the distance of each point to the plane can be easily seen. In the bottom left corner, it is viewed along the axis of the first component. The bottom right corner shows how little variance is left with components 3 and 4.

Distance measures were already discussed in Section 2.4. The most widely used distance measure for cluster analysis is the Euclidean distance. The Manhattan distance would be less dominated by far outlying objects since it is based on absolute rather than squared differences. The Minkowski distance is a generalization of both measures, and it allows adjusting the power of the distances along the coordinates. All these distance measures are not scale invariant. This means that variables with higher scale will have more influence to the distance measure than variables with smaller scale. If this effect is not wanted, the variables need to be scaled to equal variance. 

The goal of Q-mode FA is to determine the absolute abundance of the dominant components (i.e., physical or chemical properties) for environmental contaminants. It provides a description of the multivariate data set in terms of a few end members (associations or factors, usually orthogonal) that account for the variance within the data set. A factor score represents the importance of each variable in each end member. The set of scores for all factors makes up the factor score matrix. The importance of each variable in each end member is represented by a factor score, which is a unit vector in n (number of variables) dimensional space, with each element having a value between -1 and 1 and the... 

The data for the samples from the GF plot presented in Table III show a different picture. Although the variability in these samples is higher than that in the EC plots throughout the season, the situation found in the first sampling, where the dominant contribution to the variance was due to the irregular distribution of the herbicide within the laboratory samples did not persist. 

On the eighteenth day the analysis of variance and the standard deviation presented in Table III show that the between samples variation was dominant and remained so throughout the season. 

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