Being that you wish to nitpick the method used in the subject test, I would
point out that what was described was a "method" NOT a "methodology"!!!!A
common mistake used even by those somewhat familiar with scientific testing.
If you simply figure out how much the engine used over the entire
Yes what the OZP was described was a method. But you apparently
completely missed the point I was attempting to make. He is using the
wrong methodology. Yes I did make the mistake of critiquing some of the
methods also when I should have stayed with the main point which is that
the OP is using wrong methodology. Attempting to make more than one
point per posting tends to confuse the dim twitted.
The idea of using statistical analysis to solve what is really an
accounting problem is the wrong methodology. Let me give an analogy.
lets suppose you go to the grocery store and you put 42 items in your
shopping cart. When you get to the checkout counter you tell the clerk
that you are going to pick 17 items from the cart and through scientific
statistical analysis of the 17 picked items you are going to determine
how much you will pay the store. The clerk will inform you that is the
wrong methodology (more likely the store will just call the cops). It
doesn't matter if you argue that you are going to use well established
statistical methods and that you are extremely knowledgeable in regards
to scientific testing - the store will still insist that is a ridiculous
way to approach the problem.
The application of statistical analysis to solve what is a basic
accounting problem is using the wrong methodology. In the OP's case the
specific methods used also happen to be suspect, but even if the
statistical methods weren't flawed the fact remains - he's not using the
Once one has abandoned what is a ridiculously unreliable methodology
for this problem then you can start to think about the specific methods
one might choose to use. For instance, since the OP appears to be
numerically challenged he could account for the oil consumed in the
following way: He could keep all the containers that the oil comes in.
Then at 3000 miles when he drains the oil he could carefully and
thoroughly extract the oil from the oil pan and oil filter and put the
used oil back in the original containers to the original level. After
42000 miles simply divide 42000 by the number of empty containers to
determine that miles per quart number that he is looking for. No record
keeping or paper work will be necessary. And my strong suspicion is he
would arrive at a substantially different number than he is now.
I recorded everything I found as precisely as possible. Nothing is
"missing" from the dataset.
However, one record was questionable and was therefore not included: my
very first properly-recorded check gave me a mileage of 2,200. This created
an anomalous spike (that did not occur at any other point in the test). I
therefore decided I'd done something wrong during that particular check,
and excluded it from the results.
It was left out because I didn't think to put it in.
Part of my reasoning for posting this to Usenet and BITOG was to solicit
others' opinions on the test, its methodology, and the report. These I have
received (some a bit irascibly), so thanks for that.
I have updated the PDF to account for the issues that have been brought up
here and in BITOG.
I had to clear my cache before the updated PDF would show up.
I put the fresh oil into a graduated container and pour it into the engine
from that. This way I can make certain I add exactly what the engine used.
If my measuring and chart are correct, then a reading of x-milliliters low
low ought to be exactly offset by the same amount added back in. And it is,
so far as I can see.
This methodology means that there is not a need to do a linearity
check, at least for this study.
I quoted what you wrote above at honda-tech.com . The threads are in
the tech/misc and Acura Integra sections.
Aside, for those trying to post to the BITOG forum: I applied to join
the BITOG forum almost a week ago and still have not been approved.
You are dissing a guy who has provided good information for me and
others for quite a while here. Lay off of him. He does more work to make
this newsgroup valuable than just about anyone else.
Please don't be discouraged by his dissing you. Every newsgoup has those
who want to criticize without offering their own work for review.
I appreciate you time and effort here. I know there are many more who do.
Another person heard from who is stupid and proud of it.
In case you haven't noticed I have been posting in response to idiots
who think this study is brilliant. The OP who posted the study gave me
the impression that maybe he wasn't of the same mind as you idiots are.
By "called-out", I meant I expected somebody to bring
up the issue of measurement accuracy when reading off
the dipstick. The entire work depends on that, of course.
And nobody but you brought it up. That doesn't say much
for BITOG, frankly.
I responded to that because it seemed to indicate he actually was
interested in why this methodology might be flawed.
The problem is actually much worse than just "the issue of measurement
accuracy when reading off the dipstick". Besides the measurements just
being slightly wrong, there are two additional problems that may
compound or significantly magnify the initial measurement errors.
1) If the first dipstick reading happens to for some reason have a
tendency to overestimate the amount of oil used, then the second
reading will perforce have a tendency to underestimate the amount of
oil used. This is because the first measurement determines how much
make up oil is added. If your measurement tells you that you used .65
quarts but you really used only .5 quarts then that extra .15 that is
added goes to the second measurement. If your first measurement is
wrong in one direction it will tend to make the second measurement
equally wrong in the opposite direction.
2) Averaging miles/quart for driving intervals of different lengths does
not give an accurate average. Lets take some example numbers to see why:
A- drive 1000 mi .5 quarts down on the dipstick = 2000 mi/qt
B- drive 1500 mi 1.2 quart down on the dipstick = 1250 mi/qt
Average of A and B = 1625 mi/qt
Now even if we pretend the measurements were dead nuts accurate what we
have is 1.7 quarts used in 2500 miles which comes to 1470 miles per
quart as the actual oil consumption. The 1625 is just a bogus number.
Calculating how much oil an engine uses isn't rocket science and
certainly doesn't require complicated charts and complex calculations. I
once owned a car that burned 600 miles per quart. I drove it for 250,000
miles and it used the same amount of oil during the whole time I had the
car. I could predict within 20 miles when the dipstick would exactly
reach the add line. It went about 800 miles on the first quart, then 600
miles thereafter. This was because when the oil was changed 5 quarts
brought it to slightly above the full line. Adding oil when it hit the
add mark brought it up to slightly below the full mark. Just like
clockwork over and over that pattern repeated. I used a little thicker
oil in summer but it didn't make a noticeable difference in the
An initial check is taken before each test sequence.
But you're only using two data points. I suspect that, as the dataset grows
ever larger, that the difference between your first method and your second
will lessen greatly, and will eventually disappear. That's why sample-size
is so critical to any sort of statistics.
No sorry doesn't at all work that way. You claim to be trying to
determine how much oil is being used on average. Your method arriving at
that number is grossly unreliable. However your data is too spotty to
actually estimate how inaccurate that method is for this data.
The correct method is easy. If you summed how much make up oil you
added in total plus how much less than full it was at the time of oil
change, you would get a number that represents the total consumption
over the entire 42000 miles. You then make the calculation on that total
consumption and total miles.
Try your method with the IRS. Tell them you are going to average
dollars/day for various periods of income that you selected by some
unknown criteria and in which some of them you earned a lot of money per
day and some periods not so much per day and that you will use that
average of those 17 dollars/day figures as an accurate measure of your
annual income. The idea is so absurd that you probably will even get an
IRS agent to laugh.
You don't have a statistical problem to solve - you have an accounting
problem and you are applying absurd accounting practices.
You make some fair points.
I respond by posting my raw data. They are in an Excel file, here:
The first two columns give the actual miles driven during each test, and
the actual observed amount of oil used during that test.
(It must be noted that, in order to maintain consistency with the
"reported" mileage, the oil amounts are slightly adjusted. i.e.: 0.622 is
actually 0.6; 0.597 is also actually 0.6.)
I know you chose extremes in your example in order to make a point, but
now, having /real/ numbers to work with, you do the arithmetic, and tell me
what you get.
Remember that doing it my way, I get 1663 mi/qt. Doing it your way
This Excel data does not even look like the same data as the PDF. For
example, in your PDF file you have what is labeled a first reading at
321,771 miles and a second reading at 323,206 miles. That is an
interval of 1435 miles. I don't see any interval in the Excel file that
is even close to 1435. I see another 1st reading at 310,440 and a second
reading at 311,635 which is an interval of 1195. But I see nothing in
the excel file that corresponds with that number either. I'm sorry I
don't know what to make of your data. I don't know if you have a lot of
typos or arithmetic errors or if something else is going on.
I already told you the data is too spotty to actually know what the oil
consumption might be to any reasonable degree of accuracy. You should be
able to determine the consumption in 3000 to 6000 miles with much more
confidence in the accuracy than you can get from working with this data.
Whatever you are doing and whatever your engine is doing appears to be
fairly consistent. That much can be inferred from looking at the data.
But it looks to me that it is quite likely that your results could be
You can't do it my way with that data. That's my point - GIGO (look it
up if you don't know)
so where is YOUR analysis, asshole? what - you don't have any? and you
can't do the stats? and you don't actually have a damned thing to say
other than whining loser bullshit? what a total non-surprise.
This would be a bad application for stats. I think the OP's study does
an excellent job of illustrating why statistical analysis can be an
exceptionally poor way to get a good answer. But what can you do - some
people are so misguided they will attempt to use statistical analysis to
tell them what day it is.
Are you trying to champion stupidity?
I have been saying it is really dumb to use statistical analysis for
determining something that is a basic accounting problem like
determining oil consumption. And the only response you come up with is
"why don't you show us the statistical analysis of oil consumption you
have done". Are you saying If I can't match your stupidity - I shouldn't
jim beam wrote:
I apparently don't have what it takes to be as dumb as you that is for
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