Statistical Normalization
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See: Statistics, Normalization, Normalizing Constant, Standard Score, Normalization (Statistics).
References
2009
- http://en.wikipedia.org/wiki/Normalization_(statistics)
- In one usage in statistics, normalization is the process of removing statistical error in repeated measured data. A normalization is sometimes based on a property. Quantile normalization, for instance, is normalization based on the magnitude of the measures.
- In another usage in statistics, normalization refers to the division of multiple sets of data by a common variable in order to negate that variable's effect on the data, thus allowing underlying characteristics of the data sets to be compared: this allows data on different scales to be compared, by bringing them to a common scale. In terms of levels of measurement, these ratios only make sense for ratio measurements (where ratios of measurements are meaningful), not interval measurements (where only distances are meaningful, but not ratios).
- Parametric normalization frequently uses pivotal quantities – functions whose sampling distribution does not depend on the parameters – and particularly ancillary statistics – pivotal quantities that can be computed from observations, without knowing parameters.