frequency of large negative departures from the mean is not 
as great as the frequency of large positive departures from 
the mean. However, the departure from the Gaussian distribution 
is not so great that the resemblance to the Gaussian distribu- 
tion is lost. 
It is interesting to consider the probability that such 
a histogram composed of one hundred values chosen at random 
from a seven minute wave record could have come from a Gaussian 
distribution. The Chi-Square Test can be employed to determine 
this probability by standard statistical methods. The needed 
values computed by the methods described, for example, in Hoel 
[1947] are given in Table 15. 
Table 15. Chi Square Test of the upper histogram 
in figure 14. 
X, 9(X,) ee ta eS Shs De (f, - BoA 
2.30 .029 167 - .669 0.448 0.268 
eye 2091 5.24 -. 2237 0.056 0.011 
ibeald! R20 Ze = mate, 9 4.09 16.70 1.40 
ORbeCaes 39) 849.5 -1.51 2.28 On2a7 
0.00 -399 23.0 -3.96 W567 0.684 
0.583 .337 19.4 Bical 13.00 0.670 
Thy alte) S20 So aay) 2.26 Big dla 0.435 
au 7/3) .088 5.06 -3.06 9.39 1.854 
Peso) .028 TA(ERE -1.61 2.60 ee oualt 
2.89 .006 0.345 2655 0.26 0.754 
3.46 -OO1 0.058 -942 0.89 15.29 
(Chi)° = 23.1 
Less last term Fats 
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