Known Algorithm to compute errors of given nature

I have three sets of data, measured by three different devices: A,B and C of air balloon whose fall is influenced by wind and each data sheet looks like;

A:

Longitude    Latitude    Altitude    Weight    Rotation(about main axis)   Time
...       ...           ...       ...       ...                         ...

B:

Longitude    Latitude    Altitude    Weight    Rotation(about main axis)   Time
...       ...           ...       ...       ...                         ...


and similarly for C.

Are there known standard techniques to compute the error in measurement of B and C with respect to A (considered standard)?.

Note that except time, all other parameter can both increase and decrease and longitude/latitude and rotation are only parameters that can be positive and negative.

The problem with regression is that I do not have independent/dependent variables. The method of error analysis I generally approach is calculate $Err_{x},Err_{y},Err_{z}$ and combine them suitably when I have a function explaining something. I do not have a function to propagate error from given quantities.

By combining using a suitable function, I mean : computing error of all quantities individually and propagating the error.

I am trying to measure accuracy of the devices B (let's disregard C for now) taking A as standard.

• originally asked at crossvalidated.SE . It's not purely statistics. So I moved here. Jul 17, 2013 at 10:55