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Specific yield, also known as the drainable porosity, is a ratio, less than or equal to the effective porosity, indicating the volumetric fraction of the bulk aquifer volume that a given aquifer will yield when all the water is allowed to drain out of it under the forces of gravity:
The fraction of water held back in the aquifer is known as specific retention. Thus it can be said that porosity is the sum of specific yield and specific retention. Specific yield of soils differ from each other in the sense that some soil types have strong molecular attraction with the water held in their pores while others have less.
Often, data follows a normal distribution, and for such distributions, there is a direct relationship between the design margin (relative to a given specification limit) and the yield. By expressing the specification margin in terms of standard deviation (sigma), yield (Y) can be calculated according to this specification.
Specific storage or storativity: a measure of the amount of water a confined aquifer will give up for a certain change in head; Transmissivity The rate at which water is transmitted through whole thickness and unit width of an aquifer under a unit hydraulic gradient. It is equal to the hydraulic conductivity times the thickness of an aquifer;
Process yield is the complement of process fallout and is approximately equal to the area under the probability density function = / if the process output is approximately normally distributed. In the short term ("short sigma"), the relationships are:
The deviation of each data point is calculated by subtracting the mean of the data set from the individual data point. Mathematically, the deviation d of a data point x in a data set with respect to the mean m is given by the difference: =
This page was last edited on 8 February 2007, at 23:14 (UTC).; Text is available under the Creative Commons Attribution-ShareAlike 4.0 License; additional terms may apply.
If one makes the parametric assumption that the underlying distribution is a normal distribution, and has a sample set {X 1, ..., X n}, then confidence intervals and credible intervals may be used to estimate the population mean μ and population standard deviation σ of the underlying population, while prediction intervals may be used to estimate the value of the next sample variable, X n+1.