Diciamo che ho 1000 componenti e ho raccolto dati su quante volte questi registrano un errore e ogni volta che hanno registrato un errore, tengo anche traccia del tempo impiegato dal mio team per risolvere il problema. In breve, ho registrato il tempo di riparazione (in secondi) per ciascuno di questi 1000 componenti. I dati sono forniti alla fine di questa domanda.
Ho preso tutti questi valori e ho disegnato un grafico di Cullen e Frey in R usando descdist
dal fitdistrplus
pacchetto. La mia speranza era di capire se il tempo per riparare segue una distribuzione particolare. Ecco la trama con boot=500
per ottenere i valori bootstrap:
Vedo che questa trama mi sta dicendo che l'osservazione rientra nella distribuzione beta (o forse no, nel qual caso, cosa sta rivelando?) Ora, considerando che sono un architetto di sistema e non uno statistico, che cosa sta rivelando questa trama? ? (Sto cercando un'intuizione pratica del mondo reale dietro questi risultati).
MODIFICARE:
QQplot usando la qqPlot
funzione nel pacchetto car
. Per prima cosa ho stimato i parametri di forma e scala usando la fitdistr
funzione.
> fitdistr(Data$Duration, "weibull")
shape scale
3.783365e-01 5.273310e+03
(6.657644e-03) (3.396456e+02)
Quindi, ho fatto questo:
qqPlot(LB$Duration, distribution="weibull", shape=3.783365e-01, scale=5.273310e+03)
MODIFICA 2:
Aggiornamento con un QQplot lognormale.
Ecco i miei dati:
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