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A pooled analysis is a statistical technique for combining the results of multiple epidemiological studies. It is one of three types of literature reviews frequently used in epidemiology, along with meta-analysis and traditional narrative reviews .
The grand mean or pooled mean is the average of the means of several subsamples, as long as the subsamples have the same number of data points. [1] For example, consider several lots, each containing several items. The items from each lot are sampled for a measure of some variable and the means of the measurements from each lot are computed ...
In statistics, pooled variance (also known as combined variance, composite variance, or overall variance, and written ) is a method for estimating variance of several different populations when the mean of each population may be different, but one may assume that the variance of each population is the same. The numerical estimate resulting from ...
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The red population has mean 100 and variance 100 (SD=10) while the blue population has mean 100 and variance 2500 (SD=50) where SD stands for Standard Deviation. In probability theory and statistics , variance is the expected value of the squared deviation from the mean of a random variable .
Intergovernmental risk pool is the use of the risk pool risk management technique commonly practiced by private insurance companies, but applied to public entities (e.g. made up of government agencies, school districts, county governments and municipalities) who come together to form a pool to provide protection against catastrophic risks such as floods or earthquakes.
Risk pooling is an important concept in supply chain management. [2] Risk pooling suggests that demand variability is reduced if one aggregates demand across locations because as demand is aggregated across different locations, it becomes more likely that high demand from one customer will be offset by low demand from another.
One key difference between the two statistics is that in the ICC, the data are centered and scaled using a pooled mean and standard deviation, whereas in the Pearson correlation, each variable is centered and scaled by its own mean and standard deviation. This pooled scaling for the ICC makes sense because all measurements are of the same ...