points are so strongly autocorrelated the distribution would not be 
Gaussian. 
Also, all of the different samples could be combined into one 
big sample, and that sample would again have an approximately Gaussian 
distribution. And also if points were chosen, say, one one hundredth 
of a second apart throughout the total record length, then the 
150,000 points so obtained would have an approximately Gaussian 
distribution. 
Finally the distribution of every point on the whole record 
would be approximately Gaussian, and, since the record is continuous, 
this permits a computation as to how long a time out of the total 
twenty-five minutes the record will occupy a given range of pressure 
values. From equation (7.33) modified by a substitution of P(t,) for 
n (t,) and EpHmax Lor Bax? it is possible to compute the probability 
that a point will exceed a certain value. If the probability that 
the record will exceed the value Py is p(I) and if the probability 
that the record will exceed the value Pir is p(II), and if the value 
of Py is greater than the value of Prt then the probability that 
the value will lie between Py and P,, is (p(II) - p(I)). Also 
(p(II) - p(I)) multiplied by the length of the record (25 min), then 
gives that fraction of the total time of the record that the pressure © 
value will be between Py and Prt 
For the record under study, one scale division was equal to 
0.855 standard deviations. Therefore the probability that the re- 
cord would lie between the scale line for the mean of the record and 
the scale line one unit above was equal to 0.3034, and theoretically 
for 7.58 minutes out of the total 25 minutes, the graph of the wave 
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