The dislocation vector is now being increased in regular steps (or lags) and each time a new scatter plot is made. The shape of the point cloud in the scatter plot is analysed and the moment of inertia around the 45 degree line is measured, as this is a good indicator for the spatial continuity. If the data is plotting in a straight line, than there is a lot of continuity and the data sets are in fact the same. This is a scatter plot and it captures the degree of variability of the data in space. ![]() The degree of variation in the property is examined in a crossplot between the new value v j and the old v i. The value v j at the new position is compared with the value of the original grid point v i. In the case when a gridded property is being used, the same sliding vector H is applied to all points, resulting in a directional bulkshift of the dataset. Sliding dataset statistics are performed by copying the original dataset. In first instance only one dataset is considered. The spatial continuity of the data is now investigated by applying geostatistical techniques (e.g. In the case of fuelless renewable technologies, fuel risk is zero and its correlation with fossil fuel costs is zero too. Finally, it is easy to see that σ p declines as ρ i, j falls below 1.0. This, in turn, allows higher risk/lower cost technologies into the optimal mix. This lowers σ p, since two of the three terms in Equation (3.2) reduce to zero. A pure fuelless, fixed-cost technology has σ i=0, or nearly so. fixed-cost, riskless) technology to a risky generating mix lowers expected portfolio cost at any level of risk, even if this technology costs more ( Awerbuch, 2005). More generally, portfolio risk falls with increasing diversity, as measured by an absence of correlation between portfolio components. Lower ρ among portfolio components creates greater diversity, which reduces portfolio risk σ p. The correlation coefficient, ρ, is a measure of diversity. Shimon Awerbuch, Spencer Yang, in Analytical Methods for Energy Diversity & Security, 2008 Correlation, diversity and risk Transitional metals (Ti, V, Cr, Mn, Fe, Co, Ni and Cu) show the most significant inter-elemental correlation coefficient, indicating their common geogenic sources present in the Gomati River Sediments. ![]() High correlation coefficient values are in bold.
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