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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 11:52:47 -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/t1223920426diicjny75xg0ale.htm/, Retrieved Sat, 18 May 2024 05:08:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15799, Retrieved Sat, 18 May 2024 05:08:57 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact144

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         [Exercise 1.13] [1.13 deel 1] [2008-10-13 17:52:47] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-10-15 15:17:03 [Simon Meeusen] [reply] 
Antwoord 1 is correct. Wanneer we het aantal dagen (Number of simulated days) vergroten van 365 tot 3650 krijgen we een accuratere waarschijnlijkheid.
Dit komt door de wet van de grote getallen, deze zorgt ervoor dat er minder schommelingen ontstaan in de resultaten.
2008-10-17 09:11:19 [Davy De Nef] [reply] 
De student heeft de opgave begrepen en perfect uitgevoerd.
2008-10-18 09:26:22 [Jeroen Michel] [reply] 
U veranderd inderdaad de correcte parameter van 365 dagen naar 3650 dagen. hierdoor krijgt u een accurater resultaat. Probeer de volgende keer misschien het aantal trekkingen te verhogen. Hierdoor kan u nog nauwkeurigere conclusies trekken en verschillen optekenen.
2008-10-19 14:14:57 [Natascha Meeus] [reply] 
De student heeft de vraag begrepen. De student heeft de tijdspanne vergroot, wat de kans op afwijkingen verkleint. De student had de berekeningen wel meerde malen kunnen uitvoeren om zo meerdere resultaten te bekomen om te kunnen vergelijken.

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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 time5 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15799&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15799&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15799&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 time5 seconds
R Server'George Udny Yule' @ 72.249.76.132






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 Hospital82055
#Males births in Large Hospital82195
#Female births in Small Hospital27413
#Male births in Small Hospital27337
Probability of more than 60 % of male births in Large Hospital0.0665753424657534
Probability of more than 60 % of male births in Small Hospital0.145205479452055
#Days per Year when more than 60 % of male births occur in Large Hospital24.3
#Days per Year when more than 60 % of male births occur in Small Hospital53

\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 & 82055 \tabularnewline
#Males births in Large Hospital & 82195 \tabularnewline
#Female births in Small Hospital & 27413 \tabularnewline
#Male births in Small Hospital & 27337 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0665753424657534 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.145205479452055 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 24.3 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 53 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15799&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]82055[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]82195[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27413[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27337[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0665753424657534[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.145205479452055[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]24.3[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]53[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15799&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15799&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 Hospital82055
#Males births in Large Hospital82195
#Female births in Small Hospital27413
#Male births in Small Hospital27337
Probability of more than 60 % of male births in Large Hospital0.0665753424657534
Probability of more than 60 % of male births in Small Hospital0.145205479452055
#Days per Year when more than 60 % of male births occur in Large Hospital24.3
#Days per Year when more than 60 % of male births occur in Small Hospital53


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')