Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
I INTRODUCTION
As part of the general program at the College of Marine Studies at the University of
Delaware, Dr. C. M. Pleass developed a system to measure the swimming velocities of
single-cell marine alga.
Having had a long time interest in psychoenergetic studies, Dr, Pleass
initiated a remote action (RA)
investigation with the marine alga, Dunaliella, using specialized
hardware.
As a consequence of his early effort and his promising results, SRI International
asked Dr. Pleass to conduct specific experiments to test a model of psychoenergetic
functioning that we
call Intuitive Data Sorting (IDS).
SRI
let a two-year subcontract to the
University of Delaware to achieve this goal.
The
first year’s task was to develop appropriate
statistics and protocols for a formal experiment that will be conducted during the second year
of the contract.
This report contains the results of the
first year’s effort.
If we assume that a valid psychoenergetic phenomenon exists in Dr.
Pleass’ experiments,
then there are two heuristic models that are discussed below, RA and IDS, that might describe
the mechanism.
(The proposed protocol will allow us to determine from the data whether this
assumption
is valid.)
A.
Remote Action
One hypothesis proposed by Dr. Pleass
is that (RA) accounts for the data.
(The
cells
are “forced” to conform to the “intent” of the participant in the usual cause-and-effect way.)
This hypothesis supposes that consciousness interacts with matter and, in particular, with living
systems.
As a plausibility argument included in his report to SRI, Dr. Pleass invokes one side
of a controversy that suggests that physical systems (micro and macroscopic), in the absence
of measurement, exist as mixtures of all their possible configurations simultaneously.
Although
this can be demonstrated to be true with quantum systems, two basic assumptions are required
to be true to extend the idea to the Dunaliella
cells:
•
Consciousness
is a contributing factor in the quantum measurement
process.
•
Living systems exist in “indefinite”
states and thus qualify as quantum
systems.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
The prevailing thought in physics today
is that neither of these assumptions are valid.
Although there are a few respected physicists who question the role of consciousness in the
physical world,
virtually none of them believes that macroscopic bodies exhibit measurable
quantum mechanical effects.
(There are circumstances in which macroscopic bodies exhibit
quantum effects—superconductivity for example, but these are very special cases.) A
different hypothesis, therefore,
will probably more appropriately account for the data.
B.
Intuitive Data Sorting
At SRI, we have been constructing a model to explain psychoenergetic data from a
different perspective.
Based upon an interpretation of an experiment that we conducted in
1979,
1 * we propose that an informational process, rather than a causal one,
is responsible for
certain putative RA phenomena.
It
is beyond the scope of this report to present a detailed
description of the model; rather, we
will provide a broad overview.
We propose that humans can make decisions (by psychoenergetic means) to take
advantage of the natural and unperturbed fluctuations of a system.
In the context of the
Delaware experiments, suppose that an individual
is asked to “make” the Dunaliella swim
faster.
Rather than “causing” the cells to swim faster, we suggest that the participant has
simply initiated the
trial by anticipating when the Dunaliella were going to swim faster as part
of their natural fluctuation in velocity.
Thus, the participant has capitalized upon natural
events, rather than “causing” anything to occur.
We
call this ability IDS.
We have been able to design the Delaware experiments in such a way as to distinguish
an IDS~mediated result from a causally mediated one.
By definition, causal effects, on the
average,
will effect the Dunaliella on a cell-by-cell basis.
For example, they are phototropic
(i.e M each cell
is attracted by
light).
The net effect of such a causal relationship
is that
velocity averaged over a large number of cells
will produce a very large
statistical effect.
Because any informational processes
(i.e., IDS), by definition, do not perturb systems
(at least
classically), averaging over a large number of cells can only reduce any observed statistical
effects.
Therefore,
if the effects that Dr.
Pleass observes are causal
(i.e., RA), then averaging
over a large number of cells will produce a strong result.
If the data are weaker as a function
of cell average
(but remain significant), then the effect
is likely to be informational
(i.e.,
IDS).
Dr.
Pleass* FY 1986 progress report
is contained in Appendix A.
The complete details
of the apparatus,
analysis, protocols, and early results, provided by Dr.
Pleass, can be found
*
References are listed at the end of this report.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
in the annex to Appendix A. A brief summary of the past experimental details are provided
below.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
II METHOD OF APPROACH
A,
Hardware
The Dunaliella are contained in a small test tube in a temperature-controlled, darkened
room.
Their velocities are measured by a standard laser Doppler technique, which
is sensitive
to the velocities of individual cells.
The laser Doppler apparatus allows for velocity
measurements to be averaged over any number of cells ranging from one to an arbitrarily
large number. A typical velocity for a single
cell
is of the order of 20 micrometers/sec.
The data from the laser Doppler device are accumulated, displayed, and retained on an
IBM PC.
All participant and monitor interactions with the experimental apparatus are
performed through the IBM PC.
B.
Statistics
The velocity data, like those for most living systems, have a large degree of variance, for
which there are a number of contributing factors, including:
•
Living systems inherently exhibit a low signal to noise ratio*
•
Dunaliellae are sensitive to environmental factors such as light and low
frequency electromagnetic radiation.
•
Dunaliellae appear to exhibit a 24-hour circadian rhythm.
The initial attempt to analyze this kind of data included a double difference technique.
Each
set of data involved
(1)
a putative RA period, during which the participant attempted to
modify the swimming velocities,
(2)
a control period immediately thereafter, and
(3) two
matching periods collected when the participant was absent,
i.e., pseudo-RA and
pseudo-control, respectively.
The total run score by this technique
is given by
V f ^control
I
VC vP
eud0 con 'ro1
- xP
eud0_RA
)
•
Run Score = Zj
( Xj
-
Xj
)
-
2j ' Xj
A
J
1=1
j=1
where the X’s are the velocity averages for each data point,
[j]
.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Although many data were collected and analyzed by this technique, the statistical
method proved to be unstable; reliable probability assessments of any effects, therefore, were
difficult to obtain.
The current method in use at the University of Delaware
is a modification
of the above procedure.
Run scores are accumulated for a particular participant, and the
resultant distribution
is
fit with a Gaussian curve.
Global controls are collected under
conditions that emulate the experimental conditions as closely as possible
(e.g., using the same
phase angle in the circadian cycle, the same RA/pseudo session spacing,
etc.).
These data
are also
fit with a Gaussian curve.
The
statistical significance of the RA session
is estimated
by the ratio of the variances between the periods of effort and the periods of global control.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
III RESULTS AND RECOMMENDATIONS
The subcontract with Delaware entails a two-year
effort; therefore, there are no new
experimental results to report as of this writing. We recommend that a number of points be
considered before initiating any formal trials.
A.
Protocol
Before any tests for mechanisms are warranted,
it must
first be demonstrated that there
is some valid psychoenergetic effect.
This obvious criterion affects the selection of
participants, the details of the participant-monitor interaction protocol, and the
statistics. We
suggest that one or more pilot series be conducted, using the final protocol and statistics to aid
in the selection of "good” participants.
As described in the introduction, the tests for mechanisms require a series of trials using
a known causal component
(e.g., a light source). We suggest that trials be conducted when a
participant
is attempting to modify the algal swimming velocities; these trials should be
counterbalanced by identical trials during which the participant
is replaced with a light source
adjacent to the
cells.
B.
Statistics
As part of our recommendations for methods of analysis, we include a detailed
description of a technique that
is commonly used in geophysics and that may be applicable to
the Delaware experiment.
1.
Background
Radar technology has provided a number of powerful techniques to perform time
series analysis upon “noisy” data similar to that described above. We will focus our attention
upon a technique/that
is in common use in geophysics.
In 1913, C. Chree introduced a method to correlate sunspot activity with terrestrial
magnetism. 2
His approach was a particularly powerful one and exists today under a number
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08 ; C|A-RDP96-Q07a9R00220.00300Q1-4
of different names
(e.g., epocn analysis, and signal averaging).
Signal averaging
is a widely
used technique in such diverse fields as brain research and radar; therefore, quantities such as
signal-to-noise ratios, system sensitivity, and sampling limitations are available in the literature.
When applied to research with the Dunaliella
cells, the technique contains one important
underlying assumption—namely, that there
is a time-stable (however small) component to the
cell velocities that may correlate with the participant’s effort.
To clarify the technique, we will
use an example from central nervous system (CNS) research.
To measure a visual evoked
response, the following steps are performed:
1.
A light
is flashed in a subject’s eyes
(the event).
2.
Occipital EEG
is measured every millisecond for 250 ms
(the time
series data).
3.
Steps
1 and 2 are repeated for approximately 100 flashes—the data
from each step are averaged into the preceding data.
If there
is no time-stable component of the occipital EEG to the light flashes, the
data will average to zero.
What
is found, of course,
is that in some fraction of the
population, a persistent signal survives the averaging.
That signal
is called the visual evoked
response.
To apply this technique to the Dunaliella experiment,
let us assume that we have
measured the cellular velocities, V, average over
[q]
cells (RA condition).
Further, assume
that we have
[n] number of events (similar to the light flash above)
consisting of single button
presses by the participant to initiate a trial consisting of [m]
velocity measurements.
Figure
1 shows a matrix of data that can be constructed from such a situation.
Vjkis the velocity measurement at time
[k]
for each button press
[j], and
tic (k =
l,m) represents the kth data point (after
all the data have been detrended).
As in the CNS
research example, we average the velocities across
all button presses for each data point to
produce column means
(the V-bars in this example).
The row means, V-bars,
will be
discussed below. A similar data matrix can be constructed when the apparatus
is completely
unattended
(the MCE condition). We must now determine
if the RA matrix
is significantly
different from the MCE matrix.
An Analysis of Variance (ANOVA) technique
is the
appropriate statistic to use to understand this situation.
An ANOVA can determine:
1.
If the RA-condition column means differ significantly from the
MCE-condition column means (an ANOVA interaction term).
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
2.
If the variation observed in the RA-condition column means
is
highly significant.
Button
Press
*2
.
.
.
l m-2
t m-1
i
Vn
.
.
.
V
.
.
.
Vjk
•
•
•
•
•
n
vnl
.
.
.
Vnm
Vi
V2
>
.
,
Vm
FIGURE
SIGNAL AVERAGING DATA MATRIX
The second case above
is particularly interesting.
If the number of button presses
is large, and
if the RA-condition means are highly significant
(i.e.,
- p < 10- 15), then there
is
strong evidence in favor of a causal interaction rather than an IDS process.
IDS
is not a
competing process
if a system possesses a nearly infinite signal-to-noise ratio—the case that
is
required
if item
(2) above
is true.
Under the condition that the ANOVA shows a significant interaction between the
RA and MCE conditions, then a second analysis
is imperative when working with Dunaliella.
The problem
is that the Vjks are not necessarily statistically independent and cannot be
considered random variables in the usual sense.
By ignoring possible persistent temporal
components to Vjk (i.e., the 24- hour circadian rhythm), Forbush et
al. 3 demonstrated that a
gross underestimate of the residual variance may occur leading to a highly inflated F-ratio
from the ANOVA.
In their test example in geomagnetic data, the inclusion of a correction
for a known temporal persistency reduced the F(26,3874) ratio from 4.96 to 1.16
(a p-value
increase from 10- 15 to 0.3)!
Forbush
et al. suggest that a variance correction must be made.
Although there are a number of different techniques to provide a variance correction, the
technique that Forbush et al.
suggest provides a quantitative estimate of a “coherence length”
beyond which a variance correction
is not needed.
Thus,
it
is possible to determine
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
experimentally
if the circadian rhythm (or any other periodicity)
is confounding the
interpretation of the data.
C.
IDS Considerations
It
is possible that the above technique will simplify the IDS aspect of the experiment.
Suppose a participant produces
[n] button presses each resulting in [m] data points (average
velocity over
[q]
cells each).
Suppose further that associated with such a data matrix
is an
identical MCE matrix (possibly collected just before to the participant’s
first button press).
For the purpose of an IDS analysis, we consider this situation as
[n] data points in which the
independent variables are
all equal to [m] and the dependent variable
is given by
V* - VjCm)
,
where
is the grand mean for the MCE matrix where the number of columns
is [m]
,
and Vj
is the row mean for the jth of the
[n] data points described above.
If we collect a number of such data sets by allowing [m] to vary, then using the IDS
formalism, the IDS and RA hypotheses will produce the curves that are shown in Figure 2.
ln(m)
FIGURE 2
PREDICTIVE CURVES FOR THE RA AND IDS HYPOTHESES
If our initial assumption
is incorrect
(i.e., that there are no psychoenergetic effects),
then the data will
lie along the MCE curve shown in Figure
2.
If, however, there
is a
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
psychoenergetic effect that can be described by an informational process, then the data
will lie
along a straight line with the same slope (-0.5)
as the MCE line, but with a significantly
different intercept.
Should the effect prove to be causal, then the data are expected to
lie
along a curve similar to the RA curve.
One advantage of working with a living system as an RA target,
is that,
in this case, a
known cause-and-effect relationship can act as an RA-like control.
If the shift of the
velocity distribution because of the phototropic behavior of the cells can be determined, then
the phototropic data as a function of [m] can be calculated in advance.
Thus,
if the phototropic data
lie along the predicted RA-like curve and the data
produced by a participant lie along the IDS curve, then we should have compelling evidence
in favor of the IDS hypothesis.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
IV CONCLUSIONS
By using the most powerful statistical techniques available, psychoenergetic effects
if
present, can be observed in this exceptionally noisy system.
If psychoenergetic effects can be
verified, we believe that the experiment conducted by Dr. Pleass constitutes one of the most
important tests of the IDS hypothesis.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
REFERENCES
1.
May, E, C., Humphrey, B.
S., and Hubbard, G.
S., "Electronic System Perturbation
Techniques,” Final Report, SRI International, Menlo Park, California (September 1980).
2.
Chree, C., "Some Phenomena of Sunspots and of Terrestrial Magnetism-Part II,” Phil.
Trans. Roy. Soc. London, Vol. 213A, pp. 245-277,
(1913).
3.
Forbush,
S. E., Pomerantz, M. A., Duggal,
S.
P., and
Tsao, C. H.,
“Statistical
Considerations in the Analysis of Solar Oscillations Data by the Superposed Epoch
Method,” Solar Physics, Vol.
84, pp. 113-122 (1983).
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Appendix
BIO LASER DOPPLER CONSCIOUSNESS RESEARCH
C. M. Pleass
College of Marine Studies
University of Delaware
Newark, Delaware
A Progress Report for 1985-1986
to
SRI International
Contract No. C-11498
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
BASELINE DATA
A study of the structure of 1985 baseline velocity data derived from cultures of the
motile marine alga Dunaliella has shown that any given data string may or may not exhibit
trend, abrupt mean shifts, periodicity, or any combination of these.
Examples are given in
the Annex.
Only the diurnal
(or circadian) rhythm
is consistently manifest.
Because
it
is long
compared to the duration of a typical set of psi runs,
it normally appears as trend in the data
strings that report the velocities.
Figures
1 and 2 show 24 hours of velocity and vector data
from Dunaliella.
The correlation between circadian phase angle, the expected magnitude of
the observed velocity, and the direction of motion
is evident.
Our 1986 baselines are ’’quieter” and rarely show abrupt mean
shifts or periodicity,
other than the trend that derives from the circadian rhythm.
This results from changes in
laboratory practice described in the section on microbiology.
PROTOCOL FOR AN IDS EXPERIMENT
A protocol has been developed for 1986-87 studies of the IDS hypothesis. The protocol
allows the participant (we choose to use the word participant, instead of the more usual term
operator, to try to encourage a synoptic view of the experiment) to determine the length of
the psi run, which
is marked on the evolving data string by using preprogrammed keys on the
computer keyboard.
The participant touches the key FI when they are ready to begin the psi
run, and the key F10 at the end.
The psi task has been to visualize the algae at the laser
beam crossover, and the variable
is the resolved value of the algal velocity. We ask
participants using this procedure to end their runs as abruptly as possible, the moment that an
intrusive thought diverts their attention.
They then read a dictionary while control data
evolves.
Figure 3 illustrates a hypothetical set of three runs.
Note that while the participants
are given a cue on the monitor at the end of the minimum control period B, they may be
engrossed in Webster’s definition of fescennine and, therefore, choose to delay their next run.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Absolute Velocity- 24 Hour Record
100 r-j—
i—r*r—
[—
i—
i—
i—r-j—
i—
i—
i—
i—
|—
i—
i—
i—
i—
|—
i—
i——
i—|
i
i
i
I-1
Velocity
„
msec" 1 xl 0-6
Time
in hours
FIGURE
l
Circadian Variation in Dunaliella in Swimming Velocity
o
During the First 24 Hours in Continuous 6328A Light
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
% moving
down
FIGURE 2
Circadian Variation in Dunaliella in Direction of Motion
o
in the First 24 Hours in Continuous 6328A Light
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
FIGURE 3
Format for PK86
"Oo
*r~
S-o
CL
to
Q-
II
II
H
C
CO
O
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
minimum
control
period.
B
-
2A
variable
extension
of
the
control
period
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Feedback
is provided at the end of each session,
in the form of a plot of V psi minus V
control for each run within the
set.
This procedure
is likely to result in run times varying from 30 seconds to 10 minutes,
with clustering around
1 to 4 minutes.
However, we want to start in this mode.
In the
1986-87 proposal there
is a flow chart of the proposed research and notes about future trials
with sequence lengths at equally spaced intervals of log (sequence length), and experiments in
which psi alternates with light.
STATISTICAL PROCESSING
Because trend in the data
is the norm,
statistical processing starts with detrending.
The
method used through most of 1984-85 involved double differencing and
is described in the
Annex.
In early 1986, a more straightforward technique was developed;
all data are regressed
onto a line, and the slope of this line
is then used to detrend each datum. Somewhat to our
surprise, detrending in this way does not blur the structure of plots of run scores (Figures 4
and 5).
After detrending, the data are processed in the simplest possible manner by using the
difference in the mean velocity during the psi and control periods as the ’’score”
for the
trial.
Because there will normally be a “score," even
if there
is no psi mediation, individual values
of [psi-control] do not relate exclusively to the null hypothesis that there
is no psi effect; they
are qualitative indicators.
Histograms of these scores are also inconclusive until they are
compared to equivalent histograms prepared from data strings that have no psi mediation at
all.
In 1986 we have chosen to take this "global control” data on the following day at exactly
the same circadian phase angle
(time).
The technique used
is to transfer the time markers
from the real psi and control data string to the global control string.
This global control data
accumulates while the experimenter
is doing other work in adjacent laboratories, blind to the
process.
One way of comparing accumulated experimental and global control data to see
if there
is evidence of a psi effect
is to describe Gaussians that best
fit the histograms, and then
compare the experimental to the global using a F test. We are presently moving away from
this approach because the distributions of experimental data are so non-normal, but
it
is
informative to use this approach to check that detrending by regression does not change
results originally obtained by detrending using double differencing.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
TIME
FIGURE 5
Standard Error Bars For Test 706 - Detrended Velocity
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Figure 6 records F tests on the output from these two different analyses.
Because the
data are not normally distributed the p values which result from the F test are only indicators,
but they are both low, and remarkably similar.
The PK86 data comes from much quieter baselines
(see microbiology) and the global
data are quite a good approximation to a normal distribution, with X2 = 0.44
(Figure 7).
The
experimental data
(Figure
8) have the characteristic non-normal distribution that we are
beginning to recognize as evidence of response to a stimulus.
Once again, the F test
is only
an indicator, but the numbers are quite encouraging.
EXPERIMENTAL
GLOBAL
2.64,
DF = 176/158
p <
(Statgraphics limits!)
Note that like the old WAVE data given in the Annex, the PK86 data have larger
variance in the experiment.
PK85 data
still seem to reflect one participant’s tendency to calm
the algae (Annex, page
16).
As the other individual data bases grow, we will be able to sort
this out.
Spontaneous variation in the duration of our 1985-86 psi runs has provided an
opportunity for a preliminary examination of the variation of the psi score with sequence
length.
Figures 9 through 12 show plots of PK85 and PK86 scores against sequence length
with the corresponding globals.
(Watch the varying scale on the ordinate caused by the
software used).
As yet, we do not feel we have enough data or enough spread in sequence
length to form even tentative conclusions:
that will be a task for 1986-87.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
GLOBAL CONTROL
PK85
F
=
EXPERIMENTAL
CASE
- PK85
data detrended and scored by double differencing:
^control
” Vpsi
^
"
^ Vpseudocontrol
”
Vpseudopsi^
F
=
1 .64,
204 DF
p
=
2x1
0"4
CASE
- PK85
data detrended by regression,
then scored
as:
V
- V
psi
control
F
= 1.707,
204 DF
p
=
7x1 O' 5
FIGURE
F Tests On Best Fit Gaussians From Histograms of Scores
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
ENTER THE NAME OF THE VARIABLE CONTAINING YOUR
DATA:
DELTA
NUMBER OF OBSERVATIONS =
158
<2 MISSING VALUES EXCLUDED)
SAMPLE AVERAGE
= 0.066076
SAMPLE VARIANCE = 26.755
SAMPLE STANDARD DEVIATION
= 5.1725
MINIMUM VALUE
= -12.353
MAXIMUM
=
15.167
RANGE = 27.52
LOWER AND UPPER QUARTILES = -3.1363 2.8868
INTERQUARTILE RANGE = 6.0231
MEDIAN = 0.029543
COEFF.
OF SKEWNESS = 0.11154
STANDARDIZED VALUE
= 0.57237
COEFF.
OF KURTOSIS = 3.0251
STANDARDIZED VALUE
= 0.064358
Press ENTER to continue.
f
r
e
q
u
e
n
c
Frequency Histogram
ESTIMATED PARAMETERS:
0.066076 5.1725
CH I *2 GOODNESS—OF—F I T STATISTIC *
15.147 WITH
PROBABILITY
OF
A LARGER VALUE
= 0.4409
15 DEGREES OF FREEDOM
A = V "psi
"
"
V "control
FIGURE 7
Global PK86 - Detrended By Regression
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
ENTER THE NAME OF THE VARIABLE CONTAINING YOUR DATA:
DELTA
NUMBER OF OBSERVATIONS
=
176
(O MISSING VALUES EXCLUDED)
SAMPLE AVERAGE - -0.031364
SAMPLE VARIANCE = 70.551
SAMPLE STANDARD DEVIATION
= 8.3995
MINIMUM VALUE
«= -50.165
MAXIMUM
= 49.546
RANGE = 99.71
LOWER AND UPPER QUARTILES - -3.5909 3.0301
INTERQUARTILE RANGE
= 6.6209
MEDIAN
= -0.083973
COEFF.
OF SKEWNESS = -0.48565
STANDARDIZED VALUE = -2.6303
COEFF.
OF KURTOSIS
-
18.356
STANDARDIZED VALUE = 41.535
Press ENTER to continue.
f
r
e
q
u
e
n
c
y
Frequency Histogram
ESTIMATED PARAMETERS:
-0.031364
CHI *2 G0QDNE5S-0F-F I T STATISTIC
PROBABILITY OF
A LARGER VALUE
=
8.3995
= 50.005 WITH
6 DEGREES OF FREEDOM
4.6902E—
A
control
FIGURE 8
Experimental PK86 - Detrended by Regression
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4
ACKNOWLEDGEMENTS
The
consistent support
of the McDonnell Foundation has enabled
this work. We
would also
like
to thank
our
colleagues
at
the
Princeton
School
of
Engineering and
Applied
Science
for
continued
inspiration,
and many
excellent
ideas, and
Ms. Anju
Joglekar,
Ms.
Lisa
Knight,
and
Mr.
Gary
Defibaugh
for
their
assistance
with
the
analytical work.
Approved For Release 2000/08/08
: CIA-RDP96-00789R002200030001-4