**List of links to frequency distributions**

All these plots are "probability plots" whose horizontal axes are concentrations or log concentrations of Sb and whose vertical axes represent cumulative probabilities (normal or uniform). Straight lines represent coherent distributions, i.e., modes.

__14 points (Guinn's test bullets)__

The first two plots are the same as in the monograph, but
with lines added to show the possible presence of two modes. The normal
probability plot shows a good fit to the lower mode, which presumably represents
virgin ore with some effect from scrap ore, and a less-good fit to the upper
more, which presumably represents diluted scrap lead previously hardened with
high concentrations of Sb. The log-normal probability plot is the one that Larry
used for his probability calculations, with a single line through all the
points. That still seems to be a reasonable approach, far more so than trying to
fit a single line through the normal plot. Here we investigate whether it lends
itself to two lines, and conclude that it works better at the upper end than at
the lower end.

The last two plots are attempts to see whether a uniform
distribution (even spread between 0 and 1200 ppm of Sb) can explain the points.
The uniform plot shows that the points as a whole do not fit a single line.
Instead, the lower eight fit tightly to one line and the upper six somewhat more
loosely to another. The log-uniform plot also shows two lines, but with the
opposite tightness of fit. Interestingly, it appears that the uniform lines fit
as well or better than the normal lines.

Normal probability

Log-normal probability

Uniform probability

Log-uniform probability

__Upper points (of the 14 test bullets)__

The second of each pair of plots fits the upper mode better.
This leads us to think that the upper mode is logarithmic.

Normal probability

Log-normal probability

Uniform probability

Log-uniform probability

__Lower points (of the 14 test bullets)__

The lower points are definitely fit worse by log functions
than by linear functions. This we consider the lower points to be broadly
linear.

Normal probability

Log-normal probability

Uniform probability

Log-uniform probability

__12 quarters of three bullets (heterogeneity test)__

The interesting thing about these four plots is how similar
they are to each other. We suspect that the fundamental reason is the narrow
range of Sb in the 12 quarters—a factor of only
three vs. 50 to 60 for the 14 bullets. The narrower the range, the more easily
it can be fit by any distribution. The three lines on these plots are
strictly preliminary—there may be only two zones.

Normal probability

Log-normal probability

Uniform probability

Log-uniform probability

__16 points (Guinn's 14 plus unfired round and Walker fragment)__

The upper points are the same here as in the 14 points
because the two added have the lowest concentrations of Sb (15 and 17 ppm). The
extra points seem to make a cluster at the low end. Whether this represents a
mode remains unclear. The expanded set of lower points is still fit better by a
uniform function than by a normal one.

Normal probability

Log-normal probability

Uniform probability

Log-uniform probability

__Views of the uniform distribution__

How do real uniform distributions appear on the kinds of
probability plots discussed above? To address this question, we took 100 uniform
points (1, 1, 1, etc.), formed the cumulative distribution (1, 2, 3, etc.), and
plotted the latter against various probability scales on the vertical axis
(normal, log-normal, log-uniform). We did this for 100 points and 10 points. The
results showed that the normal probability plot appears S-shaped (for lack of a
better word), with a broad linear zone in the center and positive and negative
tails at the top and bottom, respectively. We were interested to note that the
actual distributions, at least part of which he have independently come to think
may may be uniform or nearly so, shows this lower tail, a linear zone in the
middle, and at least the beginnings of a positive tail at the upper end. The
log-normal plot of the uniform data appear quasi-exponential, with a steep tail
at the top. We do not see this kind of tail at the upper end of our actual
log-normal plots. A similar shape was seen for the log-uniform plot. Taken
together, these results reinforce our impression that the lower ends of our
distributions may be uniform (or normal), whereas the upper ends are logarithmic
(or log-normal).

100 points viewed on a normal
probability plot

100 points viewed on a log-normal
probability plot

100 points viewed on a log-uniform
plot

10 points viewed on a normal
probability plot

10 points viewed on a log-normal
probability plot

10 points viewed on a log-uniform
plot