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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 15:25:43 -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/t1223846857tmw1di9e1znl2g5.htm/, Retrieved Sun, 19 May 2024 16:09:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15553, Retrieved Sun, 19 May 2024 16:09:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact178

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 2: Result 1...] [2008-10-12 21:25:43] [96c9291ce335a5c9abba7b920811c2df] [Current]
Feedback Forum
2008-10-17 12:13:11 [Ciska Tanghe] [reply] 
De student ziet heel duidelijk het verband tussen het aantal dagen en het aantal geboortes. Naarmate het aantal dagen groter wordt, zal de accuraatheid van het resultaat groter worden.
2008-10-17 14:34:36 [Matthieu Blondeau] [reply] 
Hoe groter het aantal observaties, hoe nauwkeuriger de oplossing (Wet grote getallen). De student heeft dit hier ook toegepast, bijgevolg is dit een correcte oplossing.
2008-10-18 16:16:41 [Hidde Van Kerckhoven] [reply] 
Inderdaad, het aantal dagen is veranderd van 365 naar 3650... Dit is dan ook een correcte oplossing
2008-10-19 12:06:54 [Evelyn Ongena] [reply] 
De student heeft goed opgemerkt dat de juistheid van het percentage toeneemt wanneer we de resultaten voor een langere periode bekijken. De schommelingen zullen afnemen. Er is echter geen conclusie geschreven of mogelijke redenen gegeven waarom het wijzigen van deze parameter zou leiden tot een juister resultaat.
2008-10-19 14:27:16 [Dries Van Gheluwe] [reply] 
Goede oplossing van deze oefening, ik kan hier niets aan toevoegen
2008-10-19 14:40:11 [Kristof Augustyns] [reply] 
Zoals in de vorige vraag al te hebben aangehaald dat de simulated days moeten worden verhoogd om een zo nauwkeurig mogelijk resultaat te verkrijgen.
Maak er 3650 van i.p.v. 365 en men bekomt dan een inkrimping van de grafiek in horizontale zin.
Hier ziet men dus duidelijk dat de rode grafiek vanaf een 300 aantal simulated days meer en meer nauwkeuriger wordt.
Op deze oefening valt dus niets aan te merken, maar als men weet dat enkel de simulated days moeten worden aangepast van 365 --> 3650, is het niet moeilijk om hierop niet te falen.
2008-10-19 14:57:33 [Stijn Loomans] [reply] 
De student heeft goed begerepen wat hij moet aanpassen van parameter om een meer accurater oplossing te krijgen. Namelijk het aantal dagen. Heeft dit ook weer 2 keer berekend wat tot een accurate analyse lijd
2008-10-19 20:25:25 [256f97d8b7c07ed49f142eff724c6520] [reply] 
Door uw parameter tijd te vergroten, is de oplossing gebaseert op meer gegevens. 3650 is het grootste bereik dat je kan invoeren dus dit is de juiste oplossing. Je kan dit verschillende keren berekenen zo kan je aantonen dat met deze methode de oplossingen veel dichter bij elkaar liggen.

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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 time4 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15553&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15553&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 time4 seconds
R Server'George Udny Yule' @ 72.249.76.132






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 Hospital82265
#Males births in Large Hospital81985
#Female births in Small Hospital27460
#Male births in Small Hospital27290
Probability of more than 60 % of male births in Large Hospital0.0624657534246575
Probability of more than 60 % of male births in Small Hospital0.157534246575342
#Days per Year when more than 60 % of male births occur in Large Hospital22.8
#Days per Year when more than 60 % of male births occur in Small Hospital57.5

\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 & 82265 \tabularnewline
#Males births in Large Hospital & 81985 \tabularnewline
#Female births in Small Hospital & 27460 \tabularnewline
#Male births in Small Hospital & 27290 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0624657534246575 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.157534246575342 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 22.8 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 57.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15553&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]82265[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]81985[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27460[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27290[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0624657534246575[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.157534246575342[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]22.8[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]57.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15553&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 Hospital82265
#Males births in Large Hospital81985
#Female births in Small Hospital27460
#Male births in Small Hospital27290
Probability of more than 60 % of male births in Large Hospital0.0624657534246575
Probability of more than 60 % of male births in Small Hospital0.157534246575342
#Days per Year when more than 60 % of male births occur in Large Hospital22.8
#Days per Year when more than 60 % of male births occur in Small Hospital57.5


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')