Control charts or residual plots are very helpful to detect problems, and we have to look at them always to try to understand how well our model performs. It is important tp have a certain order ibn the X axis to succeed in the interpretation. In this case is in order by the value of the reference, but the order can be by date, by GH,....
There are different rules and we have to check them. One rule is that there must not be nine points or more in a row on the same side of the zero line, and this is what it happens in this case for a model where the Monitor functions show that there is a problem with the slope.
Look from left to right and see how more than nine points (red) in a row are over the zero line. Once corrected (yellow points) the distribution improves.
Well, well .... Yes, many effects can/will cause asymmetric residuals, and yes, such can often be "corrected". BTW what is the correction applied here (I assume a simple de-trending .. ;-) But regardless, I have always wondered WHY a symmetric residual distribution will relieve one from the demand of UNDERSTANDING the reason(s) BEHIND the original residual asymmetry. I can certainly point to many influencing factors in addition to 'straight' measurement errors ... many, e.g. sampling errors (which are anything BUT symmetrically distributed), sample mass reduction -, sample preparation errors ... and let's not forget model errors! The reality is that we are trying to deuce which factor (or factors) is/are responsible with only one residual plot at our hands ...
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