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<font face="Courier New">Heinz,<br>
<br>
I don't know if this will serve you, but you cn always approximate
the inverse of a function using spline interpolation. If you have
y(k) = f(x(k)) fo a range of values of x then you can interpolate
the data y(k) x(k) for a value yo to get an xo that approximates
finv(yo).<br>
<br>
Regards<br>
<br>
Federico Miyara<br>
<br>
<br>
<br>
</font><br>
<div class="moz-cite-prefix">On 17/05/2020 18:49, Heinz Nabielek
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:EE9179B4-8405-4552-B654-1E593AE51423@me.com">
<pre class="moz-quote-pre" wrap="">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
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