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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 computationSun, 12 Oct 2008 14:10:36 -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/t1223842396kq2flvtrz8wqx3i.htm/, Retrieved Tue, 28 May 2024 20:12:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15537, Retrieved Tue, 28 May 2024 20:12:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147

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] [Oplossing voor vr...] [2008-10-12 20:10:36] [d41d8cd98f00b204e9800998ecf8427e] [Current]
F   P       [Exercise 1.13] [Oplossing voor vr...] [2008-10-12 20:29:35] [74be16979710d4c4e7c6647856088456]
F R P         [Exercise 1.13] [Oplossing voor vr...] [2008-10-12 20:36:38] [1cd4837981622d2cad9c86253a547ea0]
Feedback Forum
2008-10-17 16:22:01 [681c55e73135f51ec9ab72d25c0ff036] [reply] 
Het antwoord is inderdaad correct.

Stel parameter staat op 360 dagen;
Als we dan een aantal simualaties doorlopen kunnen we opmerken dat de resultaten schommelen tussen de 10 en 17% dit is een redelijk grote schommeling.

Als we de parameter nu veranderen naar 3650 dagen en we voeren verschillende simulaties uit, kunnen we vaststellen dat de resulateten schommelen tussen de 13 en 14%. Dit is dus een veel kleinere schommeling dan we de parameter zetten op 365 dagen.

We kunnen dus concluderen dat hoe langer men het onderzoek uitvoerd, hoe nauwkeurig het resultaat zal zijn.

Het is wel belangrijk dat de student meer dan 1 simulatie uitvoerd en deze simulaties ook allemaal opneemt in zijn of haar oplossing.
2008-10-18 09:37:34 [8e2cc0b2ef568da46d009b2f601285b2] [reply] 
De student heeft de vraag correct opgelost. Vermits er maar 1 blog link in zijn document is opgenomen kan ik niet nagaan of de student zijn oplossingen meerdere malen getoetst heeft.

In het document is ook geen redenering opgenomen om tot deze oplossing te komen.

De oplossing is van een onbekende auteur waardoor niet nagegaan kan worden of de student de oorspronkelijke auteur is van deze berekening.
2008-10-18 11:31:17 [Pieter Broos] [reply] 
Student past correct de wet van de grote getallen toe. Ik zou wel aanraden om in de toekomst te bloggen onder je eigen naam en in je word document de oplossingen concreet te noteren.
2008-10-20 17:30:50 [Zeno Thoelen] [reply] 
2008-10-20 17:37:47 [a7e076854c32462fd499d2de3f6d4e86] [reply] 
Het probleem is correct opgelost.
De student zou meerdere malen de berekening moeten uitvoeren, meer dan 1 simulatie uitvoeren(uit de blog kunnen we niet opmaken als dit inderdaad is gebeurd).
De student moet niet vergeten om zijn naam te vermelden bij de oplossing anders hebben we te maken met een onbekende auteur. Op die manier bestaat er een onzekerheid over het feit dat de student de berekening zelf heeft gemaakt.

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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 time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15537&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]5 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=15537&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15537&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 time5 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 Hospital82313
#Males births in Large Hospital81937
#Female births in Small Hospital27192
#Male births in Small Hospital27558
Probability of more than 60 % of male births in Large Hospital0.064931506849315
Probability of more than 60 % of male births in Small Hospital0.165479452054795
#Days per Year when more than 60 % of male births occur in Large Hospital23.7
#Days per Year when more than 60 % of male births occur in Small Hospital60.4

\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 & 82313 \tabularnewline
#Males births in Large Hospital & 81937 \tabularnewline
#Female births in Small Hospital & 27192 \tabularnewline
#Male births in Small Hospital & 27558 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.064931506849315 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.165479452054795 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 23.7 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 60.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15537&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]82313[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]81937[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27192[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27558[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.064931506849315[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.165479452054795[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]23.7[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]60.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15537&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15537&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 Hospital82313
#Males births in Large Hospital81937
#Female births in Small Hospital27192
#Male births in Small Hospital27558
Probability of more than 60 % of male births in Large Hospital0.064931506849315
Probability of more than 60 % of male births in Small Hospital0.165479452054795
#Days per Year when more than 60 % of male births occur in Large Hospital23.7
#Days per Year when more than 60 % of male births occur in Small Hospital60.4


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')