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Authored by: PJ on Friday, April 12 2013 @ 04:41 PM EDT |
You are leaving out of the equation the simple
truth that most patents dn't make any money at
all. They are not used by anyone, and they are
not used to sue anyone, and no one buys them.
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Authored by: IMANAL_TOO on Friday, April 12 2013 @ 05:13 PM EDT |
You wrote "On average"
The average is a wide topic in itself and may be
the wrong tool here.
from http://en.wikipedia.org/wiki/Average
If you refer to the arithemtic mean, it is definitely the wrong
tool:
"Many different descriptive statistics can be chosen as a
measure of the central tendency of the data items. These include the arithmetic
mean, the median, and the mode. Other statistics, such as the standard deviation
and the range, are called measures of spread and describe how spread out the
data is.
The most common statistic is the arithmetic mean, but
depending on the nature of the data other types of central tendency may be more
appropriate. For example, the median is used most often when the distribution of
the values is skewed with a small number of very high or low values, as seen
with house prices or incomes. It is also used when extreme values are likely to
be anomalous or less reliable than the other values (e.g. as a result of
measurement error), because the median takes less account of extreme values than
the mean does."
and from http://en.wikipedia.org/wiki
/Arithmetic_mean
"While the arithmetic mean is often used to
report central tendencies, it is not a robust statistic, meaning that it is
greatly influenced by outliers. Notably, for skewed distributions, the
arithmetic mean may not accord with one's notion of "middle", and robust
statistics such as the median may be a better description of central
tendency."
See also http://en.wikipe
dia.org/wiki/Measure_of_central_tendency
and
http://en.wikipedia.org/wiki/
Geometric_mean
Averages can be a complex field as you also may
need to identify which underlying distribution your data has. That can be tough.
If patent return is random, you may have a Poisson distribution
http://en.wikipedia.org
/wiki/Poisson_distribution.
In probability theory and
statistics, the Poisson distribution (pronounced [pwasɔ̃]) is a
discrete probability distribution that expresses the probability of a given
number of events occurring in a fixed interval of time and/or space if these
events occur with a known average rate and independently of the time since the
last event.[1] The Poisson distribution can also be used for the number of
events in other specified intervals such as distance, area or
volume.
For instance, suppose someone typically gets 4 pieces of mail
per day on average. There will be, however, a certain spread: sometimes a little
more, sometimes a little less, once in a while nothing at all.[2] Given only the
average rate, for a certain period of observation (pieces of mail per day,
phonecalls per hour, etc.), and assuming that the process, or mix of processes,
that produces the event flow is essentially random, the Poisson distribution
specifies how likely it is that the count will be 3, or 5, or 10, or any other
number, during one period of observation. That is, it predicts the degree of
spread around a known average rate of occurrence.
Hard to tell,
but good luck!
BTW, all of these statistics can be calculated readily
using R, as found at www.r-project.org/
.
--- ______
IMANAL
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Authored by: Anonymous on Saturday, April 13 2013 @ 05:48 AM EDT |
I have begun to form the opinion that the whole mess gives an impression that
the objective might be to fatten the wallets of lawyers. [ Reply to This | Parent | # ]
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