Compare two sets of data

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. 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 standard techniques to compare the error 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 the 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 use is to calculate ${\rm Err}_{x}$, ${\rm Err}_{y}$, and ${\rm Err}_{z}$, and combine them suitably when I have a function explaining something. I do not have 'the function'. (I mean I do not have a function to use 'propagation of error'.)

Precisely, I am trying to measure accuracy of devices B and C

Side note: I could not find an appropriate tag.

Answer 1

I would suggest two approaches. First compare relative differences in all measures, and then use linear regression.

1. Compute relative differences, e.g. $(longitude_{B}-longitude_{A})/longitude_{A}$ for all variables at all time points (assuming the devices measured all variables at the same time points). Then look at the distribution and change over time of the differences.

2. If the differences increase over time (which might or might not be plausible) you could use linear regression with time as predictor and relative difference in e.g., longitude, as variable you want to explain. This could be done for each variable and pair of devices.