TABLE 6. NOISE SAMPLES CHOSEN, BY VARIOUS METHODS, AS BEING VERY CLOSELY GAUSSIAN. 



SAMPLE 

 NO. 



COMPUTED MOMENTS 



9i 



g 2 



X 2 



s 



MEAN 



STD. DEV. 



3l 



(SKEWNESS) 



S2 



(KURTOSIS) 



(var g,)& 



(var g 2 )^ 



30 



0. 00008 



0. 97868 



-0. 00198 



-0. 067 



0.85 



1.45 



14.7 



32 



0. 00458 



0. 97796 



0. 01884 



-0. 005 



0.36 



0.05 



2.8 



37 



0. 01141 



1. 00828 



0. 00793 



-0. 030 



0.13 



0.25 



3.9 



41 



0. 00690 



0. 99298 



0. 00965 



0.003 



0.22 



0.03 



2.7 



67 



0. 05984 



0. 99511 



-0. 01290 



0.014 



0.33 



0.18 





68 



0. 06427 



0. 98920 



-0. 01077 



0.030 



0.39 



0.52 



38 



83 



0. 02767 



0. 98493 



-0. 02185 



0.009 



0.56 



0.12 



5.4 



98 



0.04498 



0. 97240 



-0. 01331 



0.019 



0.24 



0.16 





104 



0. 00398 



0. 99926 



-0.01461 



0.003 



0.30 



0.03 



2.2 



112 



0. 04519 



1. 00764 



-0. 00009 



0.006 



0.00 



0.11 



42 



are very closely gaussian. The largest value of chi-square 

 occurs for sample no. 112, which has a very good shape; 

 however it does have a mean which is different from zero, 



thus causing the large \ 

 v 3 for the same reason. 



Sample no. 68 also has a large 



Cumulative probability graphs of the noise samples 

 can reveal if the curve has skewness or kurtosis and can 

 also reveal other deviations. However this method was not 

 used beyond plotting a few curves. The graphs would per- 

 haps require an overlay to estimate skewness and kurtosis 

 but, as before, the sample distribution needs to be stan- 

 dardized for each sample before the overlay can be applied. 



Analysis with the cumulative probability method was 

 not performed, as it was felt that it would not provide much 

 more information than the method of moments and the chi- 

 square test, and the estimates would not be as accurate as 

 those obtained by the method of moments. This method 

 does, however, give a better indication of whether a noise 

 sample is gaussian than does the overlay method on the XY 

 probability density curves. 



36 



