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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 10:25:20 -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/t1223915142nbr4ob3lm8r4wyi.htm/, Retrieved Sun, 19 May 2024 16:35:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15714, Retrieved Sun, 19 May 2024 16:35:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

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] [ex 1.13 - vraag 1] [2008-10-13 15:54:03] [1e82cb4c98d4057b5653dbe7a07f2cda]
F   P     [Exercise 1.13] [] [2008-10-13 16:21:16] [74be16979710d4c4e7c6647856088456]
F   P         [Exercise 1.13] [] [2008-10-13 16:25:20] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F R             [Exercise 1.13] [] [2008-10-13 16:30:59] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-10-19 12:08:31 [9142cf052ad32d043faa9486189092cf] [reply] 
De student heeft in deze berekening aangetoond dat hij de juiste parameter heeft veranderd en zo aangetoond dat hij weet hoe meer dagen er gebruikt worden voor de steekproef, hoe nauwkeuriger het antwoord wordt.

Maar het is belangrijk om de steekproef meerdere malen uit te voeren. Want telkens je de steekproef uitvoert met dezelfde parameters bekom je een ander resultaat. De resultaten wijken echter niet veel van elkaar af maar het verschil is wel lichtjes merkbaar. Dit is te verklaren doordat de computer telkens at random getallen uitkiest .Dus door de steekproef meerdere malen uit te voeren en daar een gemiddelde van te nemen wordt de oplossing meer accuraat.

Ook is het van belang om je eigen bevindingen neer te schrijven en niet enkel een tabel te kopiëren in je word document. Zo kan het overkomen dat je de opgave niet volledig begrepen hebt.

Het is dan ook een tip naar de volgende opdracht toe om een aantal steekproeven uit te voeren en een korte verklaring in word bij te voegen.

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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 time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15714&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]4 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=15714&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15714&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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 Hospital82078
#Males births in Large Hospital82172
#Female births in Small Hospital27319
#Male births in Small Hospital27431
Probability of more than 60 % of male births in Large Hospital0.0695890410958904
Probability of more than 60 % of male births in Small Hospital0.150684931506849
#Days per Year when more than 60 % of male births occur in Large Hospital25.4
#Days per Year when more than 60 % of male births occur in Small Hospital55

\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 & 82078 \tabularnewline
#Males births in Large Hospital & 82172 \tabularnewline
#Female births in Small Hospital & 27319 \tabularnewline
#Male births in Small Hospital & 27431 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0695890410958904 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.150684931506849 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 25.4 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 55 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15714&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]82078[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]82172[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27319[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27431[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0695890410958904[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.150684931506849[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]25.4[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]55[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15714&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15714&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 Hospital82078
#Males births in Large Hospital82172
#Female births in Small Hospital27319
#Male births in Small Hospital27431
Probability of more than 60 % of male births in Large Hospital0.0695890410958904
Probability of more than 60 % of male births in Small Hospital0.150684931506849
#Days per Year when more than 60 % of male births occur in Large Hospital25.4
#Days per Year when more than 60 % of male births occur in Small Hospital55


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')