I've done some study. And made a couple of loops to compare the dbinom() and rbinom() Here are the results: The instructor only asked for 1000 trials so its not that accurate. but its close to it.
> for(x in c(1:10)) + {print(dbinom(x,10,.25)) } [1] 0.1877117 [1] 0.2815676 [1] 0.2502823 [1] 0.145998 [1] 0.0583992 [1] 0.016222 [1] 0.003089905 [1] 0.0003862381 [1] 2.861023e-05 [1] 9.536743e-07 > > results <- rbinom(1000, 10, .25) > for (x in c(1:10) ) + {d=sum(results == x) + df = (d/1000) + print(df) + } [1] 0.173 [1] 0.279 [1] 0.274 [1] 0.15 [1] 0.059 [1] 0.013 [1] 0.004 [1] 0 [1] 0 [1] 0 > Thanks for pointing me to the rbinom() function regards Brendan beetle2 wrote: > > Hi Guy's > I was wondering if someone could point me in the right direction. > > dbinom(10,1,0.25) > > I am using dbinom(10,1,0.25) to calculate the probabilty of 10 judges > choosing a certain brand x times. > > I was wondering how I would go about simulating 1000 trials of each x > value ? > > regards > Brendan > > -- View this message in context: http://www.nabble.com/Binomial-simulation-tp23106347p23118881.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.