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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_babies.wasp
Title produced by softwareExercise 1.13
Date of computationMon, 13 Oct 2008 01:40:21 -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/t12238836661qier88rhoiu2la.htm/, Retrieved Sun, 19 May 2024 16:31:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15561, Retrieved Sun, 19 May 2024 16:31:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact234

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   P   [Exercise 1.13] [1.13] [2008-10-08 15:26:49] [6743688719638b0cb1c0a6e0bf433315]
F   P       [Exercise 1.13] [1.13 ex1] [2008-10-13 07:40:21] [9b05d7ef5dbcfba4217d280d9092f628] [Current]
Feedback Forum
2008-10-17 15:21:08 [Matthieu Blondeau] [reply] 
Dit is een correcte oplossing. Hoe groter het aantal observaties, hoe nauwkeuriger de uitkomsten (wet grote getallen). Dus door het aantal dagen van 365 naar 3650 te vergroten zal de oplossing nauwkeuriger zijn.
2008-10-17 15:22:15 [Matthieu Blondeau] [reply] 
De student heeft wel maar 1 simulatie verricht. Het is misschien aangeraden om er meerdere te doen om zeker te zijn van de bevindingen.
2008-10-19 15:14:05 [Natascha Meeus] [reply] 
De student vergroot de tijdspanne zodat het resultaat nauwkeuriger wordt. Dit is een goede redenering. Er hadden wel meerdere uitvoeringen van de berekening mogen gebeuren zodat er meerdere resultaten waren om te vergelijken.
2008-10-19 15:53:00 [Stéphanie Claes] [reply] 
Om de nauwkeurigheid na te gaan heeft de student bij de reproductie meteen ook gezien dat de parameter tijd diende aangepast te worden om een nauwkeurig resultaat te verkrijgen.
Als de student meerdere reproducties had uitgevoerd dan was dit nog duidelijker geweest.
2008-10-20 13:46:33 [Elias Van Deun] [reply] 
Hij heeft de enige juiste parameter veranderd. Daarmee laat hij zien dat hij afweet van de wet van de grote getallen. Alleen zou ik opteren om meer reproducties te maken om zo toch een nauwkeuriger resultaat te bekomen.

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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 time10 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15561&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15561&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15561&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 time10 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days3650
Expected number of births in Large Hospital45
Expected number of births in Small Hospital15
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital82328
#Males births in Large Hospital81922
#Female births in Small Hospital27169
#Male births in Small Hospital27581
Probability of more than 60 % of male births in Large Hospital0.0646575342465753
Probability of more than 60 % of male births in Small Hospital0.150410958904110
#Days per Year when more than 60 % of male births occur in Large Hospital23.6
#Days per Year when more than 60 % of male births occur in Small Hospital54.9

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 3650 \tabularnewline
Expected number of births in Large Hospital & 45 \tabularnewline
Expected number of births in Small Hospital & 15 \tabularnewline
Percentage of Male births per day(for which the probability is computed) & 0.6 \tabularnewline
#Females births in Large Hospital & 82328 \tabularnewline
#Males births in Large Hospital & 81922 \tabularnewline
#Female births in Small Hospital & 27169 \tabularnewline
#Male births in Small Hospital & 27581 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0646575342465753 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.150410958904110 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 23.6 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 54.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15561&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]3650[/C][/ROW]
[ROW][C]Expected number of births in Large Hospital[/C][C]45[/C][/ROW]
[ROW][C]Expected number of births in Small Hospital[/C][C]15[/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]82328[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]81922[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27169[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27581[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0646575342465753[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.150410958904110[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]23.6[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]54.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15561&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15561&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 days3650
Expected number of births in Large Hospital45
Expected number of births in Small Hospital15
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital82328
#Males births in Large Hospital81922
#Female births in Small Hospital27169
#Male births in Small Hospital27581
Probability of more than 60 % of male births in Large Hospital0.0646575342465753
Probability of more than 60 % of male births in Small Hospital0.150410958904110
#Days per Year when more than 60 % of male births occur in Large Hospital23.6
#Days per Year when more than 60 % of male births occur in Small Hospital54.9


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 3650 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 3650 ; par2 = 45 ; par3 = 15 ; 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')