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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_babies.wasp
Title produced by softwareExercise 1.13
Date of computationMon, 13 Oct 2008 04:15:39 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Oct/13/t1223893042n9bu6qpcrfzmgec.htm/, Retrieved Sun, 19 May 2024 16:34:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15621, Retrieved Sun, 19 May 2024 16:34:42 +0000
QR Codes:

Original text written by user:par 2 en par 3 gewijzigd
IsPrivate?No (this computation is public)
User-defined keywordssimulatie 2
Estimated Impact182

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Exercise 1.13] [Exercise 1.13 (Wo...] [2008-10-01 13:28:34] [b98453cac15ba1066b407e146608df68]
F R P   [Exercise 1.13] [1.13 Gr-kl 60-20 ...] [2008-10-08 18:22:56] [44ec60eb6065a3f81a5f756bd5af1faf]
F           [Exercise 1.13] [ex 1.13 simulatie 2] [2008-10-13 10:15:39] [4e6e942b02458d77e60e1b0e1044e71b] [Current]
Feedback Forum
2008-10-16 16:09:35 [Dana Molenberghs] [reply] 
Dit is fout, om de nauwkeurigheid te wijzigen moet je par 1 (het aantal dagen) veranderen. Het gaat hier om een simulatie van geboortes, dus elk jaar is anders. Wanneer je meer dagen neemt wordt er rekening gehouden met verschillende jaren en bekom je een nauwkeuriger antwoord.
2008-10-17 18:10:14 [Jan Van Riet] [reply] 
Dit is een foute redenering. Door het geschatte aantal geboortes in beide ziekenhuizen te vergroten verklein je het risico op grote afwijkingen, maar verstadiger is het om het aantal dagen te vergroten. Dit steunt op de wet van de grote getallen (waardoor het resultaat vanzelf constanter wordt).

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Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15621&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15621&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15621&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days365
Expected number of births in Large Hospital60
Expected number of births in Small Hospital20
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital10891
#Males births in Large Hospital11009
#Female births in Small Hospital3646
#Male births in Small Hospital3654
Probability of more than 60 % of male births in Large Hospital0.052054794520548
Probability of more than 60 % of male births in Small Hospital0.128767123287671
#Days per Year when more than 60 % of male births occur in Large Hospital19
#Days per Year when more than 60 % of male births occur in Small Hospital47

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 365 \tabularnewline
Expected number of births in Large Hospital & 60 \tabularnewline
Expected number of births in Small Hospital & 20 \tabularnewline
Percentage of Male births per day(for which the probability is computed) & 0.6 \tabularnewline
#Females births in Large Hospital & 10891 \tabularnewline
#Males births in Large Hospital & 11009 \tabularnewline
#Female births in Small Hospital & 3646 \tabularnewline
#Male births in Small Hospital & 3654 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.052054794520548 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.128767123287671 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 19 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15621&T=1

[TABLE]
[ROW][C]Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)[/C][/ROW]
[ROW][C]Number of simulated days[/C][C]365[/C][/ROW]
[ROW][C]Expected number of births in Large Hospital[/C][C]60[/C][/ROW]
[ROW][C]Expected number of births in Small Hospital[/C][C]20[/C][/ROW]
[ROW][C]Percentage of Male births per day(for which the probability is computed)[/C][C]0.6[/C][/ROW]
[ROW][C]#Females births in Large Hospital[/C][C]10891[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]11009[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]3646[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]3654[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.052054794520548[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.128767123287671[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]19[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15621&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15621&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days365
Expected number of births in Large Hospital60
Expected number of births in Small Hospital20
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital10891
#Males births in Large Hospital11009
#Female births in Small Hospital3646
#Male births in Small Hospital3654
Probability of more than 60 % of male births in Large Hospital0.052054794520548
Probability of more than 60 % of male births in Small Hospital0.128767123287671
#Days per Year when more than 60 % of male births occur in Large Hospital19
#Days per Year when more than 60 % of male births occur in Small Hospital47


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 60 ; par3 = 20 ; par4 = 0.6 ;
Parameters (R input):
par1 = 365 ; par2 = 60 ; par3 = 20 ; par4 = 0.6 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
numsuccessbig <- 0
numsuccesssmall <- 0
bighospital <- array(NA,dim=c(par1,par2))
smallhospital <- array(NA,dim=c(par1,par3))
bigprob <- array(NA,dim=par1)
smallprob <- array(NA,dim=par1)
for (i in 1:par1) {
bighospital[i,] <- sample(c('F','M'),par2,replace=TRUE)
if (as.matrix(table(bighospital[i,]))[2] > par4*par2) numsuccessbig = numsuccessbig + 1
bigprob[i] <- numsuccessbig/i
smallhospital[i,] <- sample(c('F','M'),par3,replace=TRUE)
if (as.matrix(table(smallhospital[i,]))[2] > par4*par3) numsuccesssmall = numsuccesssmall + 1
smallprob[i] <- numsuccesssmall/i
}
tbig <- as.matrix(table(bighospital))
tsmall <- as.matrix(table(smallhospital))
tbig
tsmall
numsuccessbig/par1
bigprob[par1]
numsuccesssmall/par1
smallprob[par1]
numsuccessbig/par1*365
bigprob[par1]*365
numsuccesssmall/par1*365
smallprob[par1]*365
bitmap(file='test1.png')
plot(bigprob,col=2,main='Probability in Large Hospital',xlab='#simulated days',ylab='probability')
dev.off()
bitmap(file='test2.png')
plot(smallprob,col=2,main='Probability in Small Hospital',xlab='#simulated days',ylab='probability')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Number of simulated days',header=TRUE)
a<-table.element(a,par1)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Expected number of births in Large Hospital',header=TRUE)
a<-table.element(a,par2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Expected number of births in Small Hospital',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Percentage of Male births per day
(for which the probability is computed)',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Females births in Large Hospital',header=TRUE)
a<-table.element(a,tbig[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Males births in Large Hospital',header=TRUE)
a<-table.element(a,tbig[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Female births in Small Hospital',header=TRUE)
a<-table.element(a,tsmall[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'#Male births in Small Hospital',header=TRUE)
a<-table.element(a,tsmall[2])
a<-table.row.end(a)
a<-table.row.start(a)
dum1 <- paste('Probability of more than', par4*100, sep=' ')
dum <- paste(dum1, '% of male births in Large Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, bigprob[par1])
a<-table.row.end(a)
dum <- paste(dum1, '% of male births in Small Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, smallprob[par1])
a<-table.row.end(a)
a<-table.row.start(a)
dum1 <- paste('#Days per Year when more than', par4*100, sep=' ')
dum <- paste(dum1, '% of male births occur in Large Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, bigprob[par1]*365)
a<-table.row.end(a)
dum <- paste(dum1, '% of male births occur in Small Hospital', sep=' ')
a<-table.element(a, dum, header=TRUE)
a<-table.element(a, smallprob[par1]*365)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')