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Speaker
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Transcript
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Hello, and welcome to my
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14.633
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3
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over the past 25 years, I
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have performed many studies and
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31.533
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7
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share with you a way of thinking
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about the distributions we
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49.366
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11
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motivated by precedent, ease of
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use, or empirically demonstrated
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05.666
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15
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about its processes. Further,
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when an excellent model fit is
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20.666
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19
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genesis of the distributions
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commonly used in parametric
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36.366
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23
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seen in the workplace as well as
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in the academic literature.
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51.400
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27
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literature, including textbooks
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and web based articles, as well
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07.166
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31
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reexamination that may fail to
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glean full value from the work.
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21.633
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35
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the exponential.
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Much is often made about the
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39.066
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39
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because they model fundamentally
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different system archetypes. In
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56.200
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43
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distribution does in fact, fit
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the lognormal data very well.
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The quality of the fit may also
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32.066
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48
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fits much better. And secondly,
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there's only a modest coincident
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55.333
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52
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the core process mechanisms
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these distributions represent
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11.600
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56
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analysis, but it provides a very
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familiar starting point for
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27.133
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60
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uncorrelated effects. Let's see
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if that is true.
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In order to create a good
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52.733
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65
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25,000. For the individual
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records, we'll use the random
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29.400
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06.333
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70
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71
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see that we did indeed obtain
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the normal distribution.
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Now let's consider the
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14.400
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76
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not able to imprint my brain
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with a sufficient knowledge of
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34.300
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80
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lognormal distribution are also
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very simple. As you can see, the
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50.233
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84
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this demonstration, we reuse the
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fluctuation data that were
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05.766
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88
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JSL scripting because I find it
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much more convenient for
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32.566
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92
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the number of records in each
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sample. Next, it extracts the
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53.200
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96
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products.
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The outer loop tracks the
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17.133
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101
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on the previous slide. The
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amplified product compensates
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33.700
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105
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distributions may be considered
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as generated secondarily from
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18.400
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110
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many similar internal processes
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is represented by its maximum
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35.000
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114
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to be Frechet distributed. The
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Weibull distribution represents
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50.466
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118
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processes that complete when any
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of multiple elements have
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08.766
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122
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using the Pareto distribution
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as the source. In this case, the
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27.600
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126
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absolute value of the normal
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distribution as the source.
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Now let's have a quick look at
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58.666
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131
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maximum is used.
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For the square root of the
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50.766
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136
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is not available, you can also
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see that the other common
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33.233
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46.033
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141
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value of the normal distribution
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quite well.
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Incidentally, Weibull
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28.066
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146
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distribution when its core
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behavior is substantially
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43.600
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150
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the four heme containing
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subunits mechanically interact
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59.166
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154
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up to now have all relied on
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independent samples. Professor
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15.766
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158
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extended to produce auto
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correlated data. Generation of
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32.100
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162
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sequence autocorrelation is
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about .75, yet the
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59.033
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02.300
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167
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the common survival
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distributions. You can see that
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26.400
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171
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good example of the relationship
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between real-world analytical
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42.000
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175
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commingle a single family
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residences with heavy industry.
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55.266
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179
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have similar features. The
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landowner must apply to the
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09.000
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183
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an opportunity to comment. Local
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officials then weigh the
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22.433
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187
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parties. This example is not
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approached as a demonstration
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36.633
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191
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processing time is 140 days. The
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fit is obviously imperfect, but
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52.733
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195
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distributed data results from
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processes yielding the combined
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08.400
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199
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ubiquitous, but the loglogistic
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is less frequently used. Without
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24.466
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203
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multistep process may be
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insufficient to impart log
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38.200
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207
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considered and the complexity
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of the underlying process should
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53.166
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211
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whether a process is
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substantially impacted by
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05.566
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215
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whether the cooperative element
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is connoted by positive terms such
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22.733
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219
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often been said, I would
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sincerely appreciate your
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35.033
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