Longitudinal data model with heavy skewed data I have 8 months of data for three groups (which belong to the same product) with three different measurements and one independent variable. I would like to perform a hierarchical multilevel model to check statistical differences over time (considering time as the repeated measure and measurements as co-factors). The distribution of none of measures is normal. The data is heavy skewed and tailed. These are not outliers and I can't simply remove them, and I prefer to stay away from transforming the data for the sake of easier interpretations (my audiences have no statistical background).
Is there any way I can perform the test on this data? Or is there any alternative approach?
I would greatly appreciate your help.

And Here is the plot of transformation Log+1


This is a case of zero inflated lognormal distribution/ data generating process, which is a common model for lifetime value. Take a look at github.com/google/lifetime_value/blob/master/README.md. In EDA you can plot the proportion of zeros per group and the boxplots on log1p scale. For modeling use the algorithms above and the machines/days as features; then you can use bootstrap or bayesian inference to test differences among machines/days.
For decision making it depends on your downstream use case if you care about mean spend, > 0 spend, or tail risk differences
