Hello @roskaasia and welcome to the SAS Support Communities!
It's indeed the upper confidence limit for the geometric coefficient of variation which is causing the issue: According to the formulas in the documentation, SAS would try to compute
sqrt(exp(s**2/cinv(0.025,1))-1)
for your sample dataset, where (using your initial test data)
s=log(10.4/1.3)/sqrt(2)
But then exp(s**2/cinv(0.025,1))=exp(2201.5136...)=1.274...E956, which exceeds constant('big')=1.797...E308 by orders of magnitude, hence the overflow error.
With 3.1934 instead of 1.3 the exp(...) term is slightly below constant('big'): 1.786...E308.
I'd suggest that you compute mean and standard deviation of the log-transformed data and then transform back to the original scale, as described in the documentation linked above:
data logcheck;
set check;
if aval>0 then log_aval=log(aval);
run;
proc summary data=logcheck;
var log_aval;
output out=logstats(drop=_:) n=n mean=m std=s;
run;
data want(drop=m s);
set logstats;
geomm=exp(m);
gcv=sqrt(exp(s**2)-1);
run;
proc print data=want;
run;
You could also compute the other statistics (confidence intervals), if needed.
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