Personally, I would expect an 8 cylinder to shed more than a 4, and a 12 cylinder more again.
As would I. More cylinders relative to displacement means the engine has a surface-area-to-volume ratio that would cause higher wear metal generation.
In other words, a 4.0 V8 should, ceteris paribus, have higher nominal wear metals than a 4.0L V6 or (theoretical) 4.0L four-cyl engine (which nobody builds for NVH reasons).
Not that I have any real gravitas to add to the discussion here, but I wanted take a side here with
@dnewton3. I do engines and data analysis for a living and while I'm not an SPC engineer, I'm fairly conversational in the language of the trade. My machine has JMP and Minitab on it and I know how to use them. I have done reliability studies and Weibull modeling with mixed-censored data. I never learned Matlab so I end up brute force processing data sets the hard way using JMP or Minitab or (Lord help me) Excel. It has sufficed for the last couple decades.
I like LSJr and I'm sure I'd get along with him personally. But knowing chemistry and oil doesn't mean you know data interpretation per se. It's one thing to know your own sample data (i.e. all the analyses SpeedDiagnostix has run) and to know what is typical for a given engine within your data. That's the macro view and I think is where Lake is probably most authoritative.
But the methods
@dnewton3 is talking about-- and the flaws he correctly identified in LSJr's approach-- are rooted in micro analysis. You aren't trying to pick an oil for all chevy trucks all over the world or all Volvos in Sweden. You are trying to pick an oil for YOUR vehicle-- a very tiny sample size. This is why Dnewton's approach focuses so much on baseline and analyzing your own data. Knowing what some oil does in someone else's duty cycle, burning someone else's local fuel, in someone else's car really doesn't offer you much in the way of conclusive data. Those drivers aren't you and your car isn't theirs.
This is why the macro view is of limited use. Your particular car or truck is just one engine within a massive dataset.
The micro view is more a "Case study" approach. Depth, not width. It's the difference between knowing the surgeon general's nutrition recommendations and knowing your own diet and exercise habits and exactly how your health is responding (or not) to those habits. It's the difference between reading the latest nutritional study vs knowing your own blood work in light of your own diet and activity routine.
One innovation that I'd like to see from oil analysis labs in their data presentation is a publication of percentiles within their data sets. Presumably the data is not normally distributed (normal distributions fail where there is a practical zero bound), so instead of mistakenly thinking in terms of "sigma" and standard deviations, I think a simple percentile would be most helpful and insightful. Heck, even decile-level resolution would likely be useful for many of us.
Let's say I send in a lab sample. Wouldn't it be neat to see a column in each wear metal row showing the percentile for the oil duration? Not just say, 8ppm iron in 4000 miles, but that your 8ppm in 4000 miles is the 73rd percentile for all vehicles in our data set with the same engine. Or the percentile in our data set of all vehicles in your ZIP code or state, or percentile of all vehicles in our data.
I'd love to see a normalized (per 1000mi) table of percentiles for each wear metal. This would go a long way towards putting the wear rates in context. So I have 8ppm of iron, is that normal? For my Toyota engine? For my area? For my oil life?
This would take that large macro-data set and help make it more relevant to the micro-trend your are trying to establish. This insight would let you, as the consumer of the UOA, know where your particular data falls within the context of all other engines, all others using your same engine, or all others using the same oil, etc etc. It addresses one of the main shortcomings of UOA by answering the "compared to what?" question in a useful way.
It's possible that the UOA that is normal for a Hemi might be alarmingly high for a GM 3800 or some other engine notorious for being "easy on oil." But what does "easy on oil" even mean? Well, it hints at what we really care about-- how is my engine and my oil doing compared to some other engine, some other oil, some other user?
If you only have micro, you forfeit the "compared to what" part and you're left basically with "compared to itself" which, while supremely valid for trending purposes, allowed very little comparison and context. But without the micro, your have no visibility on your particular data within the broader context.
This is why I think the provision of "percentiles" for key elements of the wear metal analysis would be super, super helpful. And why I feel like that absence is a real missed opportunity.