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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 12:45:52 -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/t1223923665boi5qzplm0mx3jg.htm/, Retrieved Sun, 19 May 2024 13:32:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15901, Retrieved Sun, 19 May 2024 13:32:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165

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] [Ex 1.13 - 3650 days] [2008-10-13 18:45:52] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F           [Exercise 1.13] [Ex 1.13 - 365 days] [2008-10-13 18:49:11] [74be16979710d4c4e7c6647856088456]
-           [Exercise 1.13] [ex 1.13 - 80% pro...] [2008-10-13 19:27:26] [74be16979710d4c4e7c6647856088456]
F             [Exercise 1.13] [ex 1.13 - 80% boys] [2008-10-13 19:33:52] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-10-20 14:49:21 [Ine Coremans] [reply] 
De student heeft hier wel de juiste parameter aangepast, 3650 dagen ipv 365 dagen. Hij heeft deze echter niet een aantal keer gereproduceerd om na te kijken of door deze parameter aan te passen, het antwoord effectief nauwkeuriger wordt.
Hij geeft dit wel als antwoord, maar baseert zich op slechts één meting. Om dit na te gaan zijn meerdere metingen nodig. Zo kan je deze vergelijken met de oorspronkelijke 365 dagen en kijken of er verbetering is.
2008-10-20 15:45:43 [Kim De Vos] [reply] 
De student doet metingen met extremen, hierbij wordt duidelijk dat het resultaat 16.438% niet accuraat is. Om een duidelijker resultaat te bekomen dient de student de berekening meerder malen te produceren. Hierbij verkrijgt men een resultaat dat zich situeert tussen 11% en 16%
2008-10-20 16:25:19 [802532bf09ebf60737f960400127f461] [reply] 
De student heeft het probleem begrepen en heeft de juiste parameter gewijzigd. Enkel zou de student meerdere steekproeven moeten doen zodat het resultaat nauwkeuriger 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 time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15901&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]6 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=15901&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15901&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 time6 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 Hospital82191
#Males births in Large Hospital82059
#Female births in Small Hospital27322
#Male births in Small Hospital27428
Probability of more than 60 % of male births in Large Hospital0.06
Probability of more than 60 % of male births in Small Hospital0.144931506849315
#Days per Year when more than 60 % of male births occur in Large Hospital21.9
#Days per Year when more than 60 % of male births occur in Small Hospital52.9

\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 & 82191 \tabularnewline
#Males births in Large Hospital & 82059 \tabularnewline
#Female births in Small Hospital & 27322 \tabularnewline
#Male births in Small Hospital & 27428 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.06 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.144931506849315 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 21.9 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 52.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15901&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]82191[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]82059[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27322[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27428[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.06[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.144931506849315[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]21.9[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]52.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15901&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15901&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 Hospital82191
#Males births in Large Hospital82059
#Female births in Small Hospital27322
#Male births in Small Hospital27428
Probability of more than 60 % of male births in Large Hospital0.06
Probability of more than 60 % of male births in Small Hospital0.144931506849315
#Days per Year when more than 60 % of male births occur in Large Hospital21.9
#Days per Year when more than 60 % of male births occur in Small Hospital52.9


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; 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')