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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 computationSun, 12 Oct 2008 09:49:16 -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/12/t1223826786swt1mciwys1v1x5.htm/, Retrieved Sun, 19 May 2024 13:38:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15487, Retrieved Sun, 19 May 2024 13:38:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

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] [Vraag 1: Parameter] [2008-10-12 15:49:16] [00a0a665d7a07edd2e460056b0c0c354] [Current]
Feedback Forum
2008-10-15 15:38:22 [Gert De la Haye] [reply] 
juiste oplossing, als je op de grafieken gaat kijken kent deze na een tijdje bijna een constant verloop!
2008-10-17 09:48:28 [90714a39acc78a7b2ecd294ecc6b2864] [reply] 
De oplossing is correct evenals de conclusie. De studente begrijpt de vraagstelling volledig, dit merk je aan haar analyse, nl. de schatting wordt nauwkeuriger naarmate er meer simulaties worden uitgevoerd. Hierdoor heeft zij de juiste parameter veranderd. Eventueel kan je eerst nog bewijzen dat de berekening niet nauwkeurig is door verschillende herberekeningen maar dit lijkt mij een detail aangezien de oplossing en conclusie correct zijn.
2008-10-20 07:32:27 [Dorien Peeters] [reply] 
De studente heeft de opdracht goed begrepen. Ik heb ook net als deze student, het aantal dagen opgetrokken om zo een nauwkeuriger resultaat te verkrijgen. Je moet dus de paramter 'het aantal dagen' vernaderen in plaats van het aantal geboortes (indien je het aantal geboortes zou veranderen wijzig je de opgave). Naarmate je meer simulaties gebruikt wordt het resultaat ook nauwkeuriger.
2008-10-20 16:19:53 [Kim De Vos] [reply] 
De student heeft de opdracht begrepen en correct opgelost. Ze heeft het aantal dagen opgetrokken tot 3650 wat overeenstemt met 10jaar ipv 1 jaar.
Hierbij nam ze waar dat de waarschijnlijkheid 15.39% bedraagt, wat niet overeenstemt met 16.43%. Het resultaat is niet accuraat.
Ze had wel meerdere berekeningen mogen doorvoeren om haar resultaat nog met meerdere bewijzen te kunnen bijstaan.
2008-10-20 18:24:56 [Hans Van Rooy] [reply] 
Ze heeft het probleem goed begrepen en geeft een correcte oplossing voor het probleem.Eventueel kan ze nog enkele malen de berekening reproduceren om aan te tonen dat de accuraatheid van de oplossing heel laag ligt.

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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 time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15487&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]8 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=15487&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15487&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 time8 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 Hospital81755
#Males births in Large Hospital82495
#Female births in Small Hospital27388
#Male births in Small Hospital27362
Probability of more than 60 % of male births in Large Hospital0.0687671232876712
Probability of more than 60 % of male births in Small Hospital0.153972602739726
#Days per Year when more than 60 % of male births occur in Large Hospital25.1
#Days per Year when more than 60 % of male births occur in Small Hospital56.2

\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 & 81755 \tabularnewline
#Males births in Large Hospital & 82495 \tabularnewline
#Female births in Small Hospital & 27388 \tabularnewline
#Male births in Small Hospital & 27362 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0687671232876712 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.153972602739726 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 25.1 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 56.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15487&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]81755[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]82495[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27388[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27362[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0687671232876712[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.153972602739726[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]25.1[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]56.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15487&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15487&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 Hospital81755
#Males births in Large Hospital82495
#Female births in Small Hospital27388
#Male births in Small Hospital27362
Probability of more than 60 % of male births in Large Hospital0.0687671232876712
Probability of more than 60 % of male births in Small Hospital0.153972602739726
#Days per Year when more than 60 % of male births occur in Large Hospital25.1
#Days per Year when more than 60 % of male births occur in Small Hospital56.2


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')