0
Hallo,
Meine Kommilitonen und ich haben probleme mit Verttrauensintervalle und wie man da die obere und untere Schranke mithilfe Beta berechnet.
Konkret bei folgender Aufgabe haben wir probleme:
![](https://media.mathefragen.de/media/2022/1/28/585758bfa79f316c861dad98.png)
Mein anfangs Gedanke war, dass bei $p_0(0) $ die Trefferwahrscheinlichkeit $p=0$ sein muss, da ja durch:
![](data:image/png;base64,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)
schon der erste Treffer die obere Schranke bricht. Aber des macht irgendwie gar keinen Sinn und wenn k= 1 ist, weiß ich gar nicht wie man das rechnet.
Weil alle Beispiele dich ich gefunden habe, gehen von einer vorhandenen Verteilung aus, die ich ja eigt. gar nicht haben dürfte.
Wenn mir da jmd. helfen kann wäre ich sehr dankbar.
Meine Kommilitonen und ich haben probleme mit Verttrauensintervalle und wie man da die obere und untere Schranke mithilfe Beta berechnet.
Konkret bei folgender Aufgabe haben wir probleme:
![](https://media.mathefragen.de/media/2022/1/28/585758bfa79f316c861dad98.png)
Mein anfangs Gedanke war, dass bei $p_0(0) $ die Trefferwahrscheinlichkeit $p=0$ sein muss, da ja durch:
schon der erste Treffer die obere Schranke bricht. Aber des macht irgendwie gar keinen Sinn und wenn k= 1 ist, weiß ich gar nicht wie man das rechnet.
Weil alle Beispiele dich ich gefunden habe, gehen von einer vorhandenen Verteilung aus, die ich ja eigt. gar nicht haben dürfte.
Wenn mir da jmd. helfen kann wäre ich sehr dankbar.
Diese Frage melden
gefragt
carlos
Student, Punkte: 24
Student, Punkte: 24
Wie sind $p_o(k)$ und $p_u(k)$ definiert?
─
orbit
04.02.2022 um 15:01