Calibration (statistics)
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'''Calibration''' in [[statistics]] is a reverse process to [[linear regression|regression]]. The ''calibration problem'' is the use of known data on the observed relationship between a dependent variable and an independent variable to make estimates of other values of the independent variable from new observations of the dependent variable.
One example is that of dating objects, using observable evidence such as [[tree]] rings for [[dendrochronology]] or [[carbon-14]] for [[radiometric dating]]. The observation is [[causality|cause]]d by the age of the object being dated, rather than the reverse, and the aim is to use the method for estimating dates based on new observations.
The [[Operational definition|problem]] is whether the model used for relating known ages with observations should aim to minimise the error in the observation, or minimise the error in the date. The two approaches will produce different results, and the difference will increase if the model is then used for [[extrapolation]] at some distance from the known results.
==See also==
*[[Calibration]]
[[Category:Regression analysis]]
[[nl:Kalibreren]]