Compute score from variance or correlation coefficient?
Iím seeking inspiration in creating a scoring (figure of merit) function that can evaluate how close a set of parallel measurements are to a set of truth values. The truth values arenít a statistical mean per say, but rather target values being sought. A set of measurements together represent a measured state which is compared to a desired ďtruthĒ state. Each new measurement is independent of the previous one.
Iím neither predicting nor estimating states but rather evaluating the aggregate error of a set of events occurring at the same discrete time instant. Each event is in its own dimension/domain. So itís a little like seeking the correlation coefficient in a regression analysis but data isnít being fitted because truth data already exists, the data is at one time-point across a plurality of axes, and we just want to peek at the quality of the measurement.
I thought I could compute a sample variance using each dimensionís truth value as the sample mean, but I donít know if thatís mathematically sound.
Re: Compute score from variance or correlation coefficient?
I found a solution that appears to be working well. For those interested, read-on ...
I simply calculate the relative error of each measurement then use these to calculate the score as a weighted root-mean-square. This represents an "averaging" of the total error and the goal becomes to find the smallest possible score.