CONF=.95, N=1432, n=1 cdfbet("XY", n+1, N+1-n, CONF, 1-CONF) is doing it just fine. Correct and with high precision... h
On 18.05.2020, at 18:09, Tim Wescott <t...@wescottdesign.com> wrote: > > So you have \beta(x, n+1, N+1-n) = 0.95, and you want to solve for x? > > fsolve will do this for a single value of the confidence. Is that > sufficient? > > On Sun, 2020-05-17 at 23:49 +0200, Heinz Nabielek wrote: >> Dear SciLabers: >> >> can Scilab compute the inverse of the regularized Incomplete Beta >> Function? >> >> Example: in unbiased sampling in Austria with sample size N=1432, >> they detected n=1 infections. >> Therefore, expected infected fraction = 0.000698324. >> >> But this does not say much, because the sample size was small and the >> "success" was extremely small (fortunately). >> >> The standard procedure therefore is to derive the one-sided 95% upper >> confidence limit: >> CONF=0.95; N=1432; n=1: >> One-sided 95% upper confidence limit fraction = BETA.INV(CONF, n+1, >> N+1-n) = 0.003306121 >> >> How would I do that in Scilab? >> Heinz _______________________________________________ users mailing list users@lists.scilab.org http://lists.scilab.org/mailman/listinfo/users