# Similarity between observations and an ideal trend

I have a series of well known data showing, for example, the average weight by age in Europe.

Let's say yesterday I called a lot of people and I asked for their age and weight. The data coming from these observations roughly follows the same trend (i.e. average distance between observation and ideal values is very close to zero).

One year later I do the same, but for some reason the weight increases among older people (i.e. average distance between observation and ideal values increases).

How do I verify this kind phenomena? How do I compare that the observations I did in the past are "better" (i.e. more close to the ideal) than the one I did today?

I did this by calculating the average distance between observation and ideal value, but it's a formula I made up. Is there something well known for doing this?

Thank you!

• I see no reason why the average error wouldn't be a good indicator. You can also use the correlation coefficient, i.e. the ratio of the explained variance over the total variance. – Yves Daoust Mar 5 at 23:28