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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 computationThu, 09 Oct 2008 06:42:29 -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/09/t1223556209m3axuwem7745nlj.htm/, Retrieved Sun, 19 May 2024 16:31:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15093, Retrieved Sun, 19 May 2024 16:31:06 +0000
QR Codes:

Original text written by user:Geboortes in ziekenhuis meer dan 60% jongens met gewijzigde parameter
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact197

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] [Oefening 1.13] [2008-10-09 12:42:29] [95d95b0e883740fcbc85e18ec42dcafb] [Current]
-   P       [Exercise 1.13] [Geboortes] [2008-10-19 12:26:33] [7d3039e6253bb5fb3b26df1537d500b4]
Feedback Forum
2008-10-19 13:06:05 [Stéphanie Claes] [reply] 
De student heeft het verwacht aantal geboortes gewijzigd (verhoogd)om zo het resultaat nauwkeuriger te maken, op deze manier wordt echter de opgave anders, dit is dus incorrect.
Om het resultaat nauwkeuriger te maken moet het aantal dagen verhoogt worden. Voor elke dag dat er een toevaltrekking is gaat het anders zijn. Een gemiddelde over deze trekkingen geeft een nauwkeuriger resultaat.

Onderstaande link geeft weer hoe dit geblogd had moeten worden. (Wijzigen aantal dagen in plaats van aantal geboortes)

http://www.freestatistics.org/blog/date/2008/Oct/19/t12244195163724f95krm7crha.htm
2008-10-19 13:54:40 [Stefanie Mertens] [reply] 
Om de nauwkeurigheid te bekijken mag je niet het aantal geboortes aanpassen maar het aantal dagen waarop je deze berekening laat lopen. als je 365 (1 jaar) vervangt door bv 3650(10 jaren) is de kans op toevalligheden in 1 jaar weggenomen en krijg je een accuratere oplossing. mvg
2008-10-20 17:09:57 [802532bf09ebf60737f960400127f461] [reply] 
De student moest niet het aantal geboortes per dag wijzigen, maar het aantal dagen veranderen van 365 in 3650. Zodat er minder afwijkingen zijn van het resultaat doordat de steekproef groter wordt.
2008-10-20 18:26:26 [a7e076854c32462fd499d2de3f6d4e86] [reply] 
Deze oplossing is niet correct.
Men mag het aantal geboortes niet wijzigen, enkel het aantal dagen moet aangepast worden (356 -> 3560). Op die manier wordt het resultaat nauwkeuriger. Men mag de opgave niet wijzigen.

Post a new message

Dataset

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15093&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]2 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=15093&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15093&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 time2 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 Hospital110
Expected number of births in Small Hospital40
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital20089
#Males births in Large Hospital20061
#Female births in Small Hospital7364
#Male births in Small Hospital7236
Probability of more than 60 % of male births in Large Hospital0.0164383561643836
Probability of more than 60 % of male births in Small Hospital0.0547945205479452
#Days per Year when more than 60 % of male births occur in Large Hospital6
#Days per Year when more than 60 % of male births occur in Small Hospital20

\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 & 110 \tabularnewline
Expected number of births in Small Hospital & 40 \tabularnewline
Percentage of Male births per day(for which the probability is computed) & 0.6 \tabularnewline
#Females births in Large Hospital & 20089 \tabularnewline
#Males births in Large Hospital & 20061 \tabularnewline
#Female births in Small Hospital & 7364 \tabularnewline
#Male births in Small Hospital & 7236 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0164383561643836 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.0547945205479452 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 6 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 20 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15093&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]110[/C][/ROW]
[ROW][C]Expected number of births in Small Hospital[/C][C]40[/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]20089[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]20061[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]7364[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]7236[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0164383561643836[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.0547945205479452[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]6[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]20[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15093&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15093&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 Hospital110
Expected number of births in Small Hospital40
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital20089
#Males births in Large Hospital20061
#Female births in Small Hospital7364
#Male births in Small Hospital7236
Probability of more than 60 % of male births in Large Hospital0.0164383561643836
Probability of more than 60 % of male births in Small Hospital0.0547945205479452
#Days per Year when more than 60 % of male births occur in Large Hospital6
#Days per Year when more than 60 % of male births occur in Small Hospital20


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.8 ;
Parameters (R input):
par1 = 365 ; par2 = 110 ; par3 = 40 ; 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')