Known Algorithm to compute errors of given nature

Question

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.

ADDED

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.

Answer 1

Your objective is not quite clear, however, as per my understanding, if you're just trying to express the errors of the balloon B and C w.r.t A, you shall have many simple models to your disposal like mean squared error within the estimation period, root mean squared error, white noise standard deviation etc.

If the noise is random, you may try Berkson Error Model Or you may also express the error as a probability distribution.

You may try explaining "combine them suitably when I have a function explaining something"... that will help others explain things better, perhaps.