Average and subsample residuals

In order to understand better the performance of a model, different blind subsamples of a sample had been sent to a laboratory, so in some cases we have the lab values of four subsamples of a sample and in other cases two subsamples of a sample. There are two cases with only one subsample.

 

For every subsample we calculate the average for the lab, and the average for the predictions, to get the red dot residuals.

 

We have also the residual of every subsample vs its prediction and those are the gray dots.

 

The plot (with R) gives a nice information about the performance of the model and how the average performs better in most cases than the individual subsamples.

 

We can see the warning (2.SEL) and action limits (3.SEL), and how the predictions for the average fall into the warning limits.