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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 05:54:24 -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/t1223553594vwrk0i90h4r5v1b.htm/, Retrieved Sun, 19 May 2024 14:39:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15082, Retrieved Sun, 19 May 2024 14:39:29 +0000
QR Codes:

Original text written by user:Hier heb ik het aantal geboortes vermeerderd tot 50 in een groot ziekenhuis en 20 in een klein.
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact229

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 wij...] [2008-10-09 11:54:24] [f24298b2e4c2a19d76cf4460ec5d2246] [Current]
Feedback Forum
2008-10-17 15:50:11 [Matthieu Blondeau] [reply] 
Deze oplossing klopt volgens mij niet. Hoe groter het aantal observaties hoe nauwkeuriger de oplossingen (wet grote getallen). Men moet dus het aantal observatiedagen veranderen om een nauwkeuriger resultaat te verkrijgen en niet het aantal geboortes.
Zie oplossing hieronder:
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL http://www.freestatistics.org/blog/date/2008/Oct/17/t1224258580icgmda5ys42dttw.htm, Retrieved Fri, 17 Oct 2008 15:49:44 +0000
2008-10-19 14:09:13 [Lindsay Heyndrickx] [reply] 
Hier heb ik inderdaad een fout gemaakt met het aantal geboortes te veranderen in plaats van het aantal dagen.
Hier heb ik de opgave veranderd en dit is geen juiste oplossing. Als je het aantal dagen verandert kom je op een veel nauwkeurigere oplossing.

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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 time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15082&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]1 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=15082&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15082&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 time1 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 Hospital50
Expected number of births in Small Hospital20
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital9075
#Males births in Large Hospital9175
#Female births in Small Hospital3620
#Male births in Small Hospital3680
Probability of more than 60 % of male births in Large Hospital0.073972602739726
Probability of more than 60 % of male births in Small Hospital0.147945205479452
#Days per Year when more than 60 % of male births occur in Large Hospital27
#Days per Year when more than 60 % of male births occur in Small Hospital54

\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 & 50 \tabularnewline
Expected number of births in Small Hospital & 20 \tabularnewline
Percentage of Male births per day(for which the probability is computed) & 0.6 \tabularnewline
#Females births in Large Hospital & 9075 \tabularnewline
#Males births in Large Hospital & 9175 \tabularnewline
#Female births in Small Hospital & 3620 \tabularnewline
#Male births in Small Hospital & 3680 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.073972602739726 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.147945205479452 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 27 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 54 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15082&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]50[/C][/ROW]
[ROW][C]Expected number of births in Small Hospital[/C][C]20[/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]9075[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]9175[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]3620[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]3680[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.073972602739726[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.147945205479452[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]27[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]54[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15082&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15082&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 Hospital50
Expected number of births in Small Hospital20
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital9075
#Males births in Large Hospital9175
#Female births in Small Hospital3620
#Male births in Small Hospital3680
Probability of more than 60 % of male births in Large Hospital0.073972602739726
Probability of more than 60 % of male births in Small Hospital0.147945205479452
#Days per Year when more than 60 % of male births occur in Large Hospital27
#Days per Year when more than 60 % of male births occur in Small Hospital54


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 50 ; par3 = 20 ; par4 = 0.6 ;
Parameters (R input):
par1 = 365 ; par2 = 50 ; par3 = 20 ; 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')