Calibration in statistics is a reverse process to 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 caused 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 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.
Calibration is the process of modifying the input parameters to a groundwater model until the output from the model matches an observed set of data.
When a computed solution is imported to GMS, the point and flux residual errors are plotted on a set of calibration targets and a variety of plots can be generated showing overall calibrationstatistics.
In addition to the calibration targets next to the observation points, you can choose to display any of a number of statistical plots.
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