Multiple linear regression as a tool for data quality control and bottle interpolation and extrapolation.
The use of multiple linear regression (MLR) for data interpolation is presented
and put in relation to oceanic process as for example mixing. Using data
from the South Atlantic error estimates are given for various parameters
and different MLR schemes.
Finally further applications of the MLR method are presented: inferring missing parameters, deducing temporal changes like the increase of CO2 in the ocean, and calculating parameters on a world wide regular grid.