ufos and missing persons -- causal connections (4/n)

[MrPosti...@kymhorsell.com]

EXECUTIVE SUMMARY:
- We look again at UFO activity and missing persons, this time
long-term missing persons in Australia listed on a govt website.
- Adjusting for population changes in the US and AUS over the period
1930-2020 we find there is a strong and expected statistical link
between UFO activity and a chunk of the missing person data.
- We also show a strong likelihood of a causal connection by showing
the statistical model found on the middle 1/3 of the data also
predicts the relationship in the other 2/3 of the data, sight
unseen.
- Together with prev analysis of other sets of missing persons in the
US and UK we seem to have shown there is likely a global (or at the
very least the English-speaking part of it) and long-standing
pattern of UFO-related missing persons.
- In a future post we shall see almost no segment of the UFO data
(e.g. breaking sightings down by shape, color, time or location
seen) does NOT find a similar causal link.


We've previously looked at links between UFO activity and missing
persons in the US and UK.

While "links" were seen -- in the sense that years or regions of high
UFO activity (using sightings mostly in the US as a proxy for global
UFO activity) were also those of the higher numbers of missing persons
and contrariwise -- that doesn't constitute very good evidence of a
causal connection.

But we can start here to outline methods where causal connections can
be shown.

I'll again use the NUFORC database of sightings (mostly in the US, but
some in Canada and some in other parts of the world) as a proxy of
global UFO activity and we'll look at the Australian missing persons
data to show the "association" is not limited to just the N Hem.

The Australian govt has a website listing around 450 "long term
missing persons" going back to the 1930s. A missing person case ends
up on the list of there is otherwise no clue to the fate of the person
concerned and they have been missing for some time. Some, perhaps
many, of the cases likely involve mundane explanations.

But we can correlate our proxy for UFO activity against the
year-by-year data -- the year in which the victims were first noticed
to be missing -- to see if there is a strong statistical relationship.

We've done that before. But this time we'll add a twist.

We'll set up a "validating" procedure. We'll "hide" most of the data
from the statistical procedure, see if there is a high-probability
link between the proxy and missing person data in the middle 1/3 of
the dataset, and then check whether the years before that "training
set" and years after the training set both show the same relationship.
If they do then we have evidence of a causal relationship.

As we might recall the US Surgeon General's Report in the 1960s showed
the first scientific evidence for causality between smoking and lung
cancer by outlining types of statistical links. It was shown
following years of higher levels of smoking there were higher levels
of lung cancer, preceding years of higher levels of lung cancer there
were higher levels of smoking, and in regions of the US with higher
levels of smoking there were higher levels of lung cancer.

We are going to do much the same with UFO data and long-term missing
persons in Australia.

Because UFO sightings and missing persons are also affected by the
size of the population we will adjust the data to rates per bn
population. As the population of the US and Australia grew over the
years since the 1930s we will "deflate" both sets of numbers by the
current population size.

And another adjustment that is required happens on the UFO side. The
NUFORC started a web reporting form in Mar 2006 to capture a wider
and more immediate set of sightings data. By no coincidence that
followed the Nimitz incident in 2004. Unfortunately for statisticians
the character of reports after 2006 are much different from those
leading up to 2006. For one thing the annual totals of sightings after
they would be reported via the web form rose 10-fold. In addition,
some classifications for UFO appearance were added -- before 2006
certain "shapes" were not seen as a bona fide category while after
that time it turned out they were relatively common.

So these considerations require the UFO data to be manipulated to
offset the change at 2006.

In the simple "variance adjustment" scheme used here we simply find a
scaling factor for the pre 2006 numbers that makes the sample variance
of the pre 2006 data match the post 2006 data.

After all these adjustments the procedure then find the following:

**** train ****
beta in 0.00154104 +- 0.00131404 90% CI
P(beta>0.000000) = 0.987977
|r| > rc (0.377000 2-sided) at 5%; reject H0:not_connected
r2 = 0.25219767
model=[y = 0.00154104*x + 7.02939]
**** validate ****
train: 1977 1997 19.7088
back: 1930 1977 21.9323
fore: 2000 2020 18.8736

The first parameter spat out is the "beta" for the relevant time
series regression between UFO sightings per capita for each year
between 1977 and 1997 (the middle 1/3 of the dataset) and long-term
missing people in Australia. For each 1000 UFO's seen per mn
capita in the US an associated 1.5+-1.3 missing persons per mn
capita were noted in Australia. The stats show the link in this
section of the dataset is highly robust. The "p-value" for the T-test
says there is less than 2% probability this link is just by luck. The
Rank test shows there is only a 5% chance the order of years by UFO
sighting and the order by long-term missing persons could be so
similar, just by luck. Together we might argue there is only a tiny
chance -- perhaps as small as .05% -- that pattern could come up just
by luck.

The R2 also is interesting. At least in this middle "training" part of
the dataset UFO sightings vary year by year and predict about 25% of
the year by year missing persons data. It seems UFO's "explain" up to
1/4 of Australian long term missing persons.

But now the most interesting part. We have only shown the statistics
1/3 of the data. Did that predict the other 67% of the data? If we
have really captured a causal connection then it should.

The "validate" part of the output confirms there is a close match
between the patterns found in the middle third and the first and last
thirds of the dataset.

In the training portion -- data between 1977 and 1997 -- the model
predicts missing persons with an error of "19.8". This turns out to
mean an error within 70% of the actual value across that part of the data.

The remaining numbers show within the other 67% of the data the error
is the same or even less.

It seems there is almost no chance there is not a causal connection of
the type and proportion found in the training dataset.

UFO activity is likely and in some manner causally connected to
long-term missing persons in Australia.

In a subsequent post we will look at splitting the UFO data into
segments by shape, color, location and time of sighting and show
"most" segments show exactly the same causal links with Australian
long-term missing persons. The vast majority of the groups seem to be
linked, although it is ambiguous whether observed UFO activity in the
US is directed linked with abduction or the *prevention* of abductions
in Australia.

In most cases there is a -ve correlation between sightings of a
particular type and missing persons. But that doesn't mean much.

If a group of suspects is seen in one town only on days when there are
no burglaries in another town is it something the police should be
interested in or not?

--
"Nothing in life is to be feared, it is only to be understood.
Now is the time to understand more, so that we may fear less."
- Marie Curie

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The Debrief/Micah Hanks, 07 Apr 2022
Brazilian Senators are calling for a special session that aims to bring
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(UAP), according to an official Senate Requirement that was recently
filed.
Citing countries around the world that "have established research
commissions for these vehicles," the Requirement references initiatives
like those undertaken by the United States Dept of Defense in
recent years, and states that "a govtal opening is sought so that
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[Lawrence Solkoski]