It seems to be that the minimum number of observations needed to calculate variance is 2. I can see the logic behind this because by logic, there can not be variance for a single point. But on the other hands, I don't think 2 points are really enough to calculate variance in general. This is because you need to observe more data to understand how much variance there truly is.
Ex: if it rains 2 days but does not rain 1 day, the variance might tell me that there is a a lot of variability in rain, but more data would really be needed to be certain. This is actually why you need something like min 25-30 data points to calculate the variance.
So if this is true, how can we have variance in repeated measure longitudinal regression models when there is usually just 3-4 obs max per person? If you combine everything into one dataset, I think you might be able to calculate variance for whole population .... but how can you calculate individual variance for each person individually when you only have 3-4 obs? Isn't it like the rain example?