The picture is very compelling, I think, although it does not confirm the
formal prediction.
Instead, the trend shows a marginally significant decrease in
the deviations of the egg data.
The Chisquare is 150.68 on 180 degrees of freedom, with
probability 0.9455. The trend is steadily opposite to the
usual (and specified)
direction, but somehow it looks right -- symbolic of
the moment's contrast to the preceding days.
Although there was no formal prediction made for a reaction of the EGG
network to the event, we do not want to
forget the heroic sacrifice that
brought down the fourth hijacked plane.
Exploratory analyses of the time prior to the crash itself suggest
a poignant correlation an extraordinary rise in the cumulative
deviation followed by a precipitous fall.
Exploratory Work by Independent Analysts
Dean Radin produced a variety of analyses of the September 11 events.
Some samples are presented here, with more in the
extended analysis page.
A paper with a detailed analysis of many aspects of the data gives
special attention to the
location of the eggs.
Dean's treatment of the low-level data is different from the GCP's
standard approach. Instead of a composite (Stouffer) Z across eggs,
he calculates the Z-score per egg and sums the squared
Z-scores and degrees of freedom across eggs. This responds to the
variability among the eggs while the standard analysis responds to
correlation among the eggs. This approach also mandates empirical
instead of theoretical variance for the Z-score calculations.
Dean uses sliding window smoothing or moving averages of the
data across time. This can make interpretation difficult because the
results depend very heavily on the choice of parameters such as the
window width and centering.
Because Dean also tries several sets of parameters to "optimize" the
presentation, there is a form of data selection, so any probability or
odds ratio that appears in the figures is very much an
overstatement. It is, moreover, very difficult to
compensate with the usual Bonferroni adjustment for multiple
analysis because of the uncertain number of analyses he does. Dean
believes that work of this kind is legitimate in the context
of good evidence from properly designed studies. I present the analyses
here with the caveat that they have no evidentiary value,
although as complements to formal analysis, they may lead to useful
questions. I should add that Dean says everything he tried showed
unexpected structure in the data from September 11.
The following graph shows results for a 6-hour sliding window
covering Sept 6 - 13. Dean identifies the
peak value in this graph as occurring at 9:10 AM, Sept 11.
However, the algorithm that he used for the sliding window averages the
data for the six hours preceding the plotted point. Thus, in terms of
the original, unsmoothed data, the "peak" weight of the averaging actually
occurs three hours earlier, at 06:10. (The "19's" on the x-axis indicate
19:00 on each day.)
In any case, the drop between this peak and the equally strong negative
peak about 7 hours later is extreme, corresponding to 6.5 sigma
(odds against chance of 29 billion to 1 if this were calculated for
an a priori prediction). Dean observes that "such large
changes will eventually occur by chance, of course, but this
particular change happened during an unprecedented event, suggesting
that this `spike' and `rebound' were not coincidental."
A permutation analysis shows that the likelihood of getting a 6.5
sigma drop in Z-scores (based on a 6-hour sliding window)
in one day, and within 8 hours or less (as observed) is p = 0.002.
The next figure shows the 2-tailed probabilities associated with the
smoothed Z-score as odds ratios. There is an extraordinary spike
near the time of the attacks, driven by large deviations that precede
the first plane crashing into the World Trade Center towers; its
weighted center is at 06:10, corresponding to the peak in the Z scores.
The second spike occurs roughly seven hours later, with the weighted
center at about 1:00 pm. The "0's" in the x-axis shows the start of each
day.
To help assure that there was no mistake in the processing,
this same figure was recreated using algorithmically generated
pseudo-random data instead of the real
data generated by the truly random eggs located in countries all around
the world. This figure speaks for itself.
Peter Bancel has taken another independent perspective, focusing on the
temporal development or autocorrelation of
the data to gain perspective on possible linkage
of the eggs' output over time. He explains
"Basically, what I do is do an autocorrelation of the sec-by-sec
Stouffer z's over regs, using Fourier techniques. The resulting
autocorrelations are then normalized to the Sqrt (of the number of data
pts-the lag). This gives a distribution of "z" values that should be
very closely N(0,1) distributed. I then visualize the result by taking
the cumulative sum, much as we do for a classic reg experiment.
"The large rise in the autocor 8-12 figure can be understood as coming from a
large excusion in the cumulative deviation of the z-scores (sec-by-sec
Stouffer's z - not the z^2) which occurs from 9:50 to 11:50. This
positive excursion as an isolated data set has a two-tailed-pvalue of
2 x 10-4 (z=3.71). So it's strong and it lasts for 1.9 hours. Placed
in the context of a 24 hours data window I guess a Bonferroni correction
would put the pval at 2.5 x 10-3."
A much more refined analysis of autocorrelation shows the relative
predictability of the device variance over lags up to four hours, second
by second, passing each autocorrelation window over the full 24 hour EDT day.
The next figure shows the data from Sept 11 in red, compared
with 60 surrounding days of August and September. The latter
show a cloud of essentially random traces, nearly all of which remain
within a 5% probability envelope. In stark contrast, the Sept 11
autocorrelation is consistently large over the first hour of lags,
continuing at a lower but still significant level for the second hour,
after which the cumulative deviation line becomes essentially flat
again. Besides the obvious difference from the comparison traces, an
indication of the likelihood is given by the fact that the cumulative
trace penetrates a one in a million envelope -- but see below.