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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 14:32:43 -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/t12238438127trz1yub330ll8k.htm/, Retrieved Sun, 19 May 2024 14:40:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15540, Retrieved Sun, 19 May 2024 14:40:17 +0000
QR Codes:

Original text written by user:probability
IsPrivate?No (this computation is public)
User-defined keywordsgeen parameters gewijzigd
Estimated Impact165

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] [ex 1.13 simulatie 1] [2008-10-12 20:32:43] [852438d026c018c4307b916406f98c62] [Current]
-           [Exercise 1.13] [ex 1.13 simulatie 2] [2008-10-12 20:45:59] [ec1c727838a7caf353f22e99d242fe74]
Feedback Forum
2008-10-17 18:07:41 [Jan Van Riet] [reply] 
Dit is een correcte berekening. Alle parameters blijven ongewijzigd, met als gevolg dat de resultaten vrij hard schommelen, en nooit éénzelfde getal blijven.
2008-10-18 18:35:05 [Astrid Sniekers] [reply] 
Uitleg oplossing vraag 1:
De student heeft de vraag naar mijn mening niet goed begrepen. Hij of zij had de berekening met de ongewijzigde parameters (365, 45, 15 en 0.6) meerdere malen moeten uitvoeren. Enkel dan kon hij of zij besluiten dat de oplossing niet nauwkeurig is. Ook de wijziging die de student in de verschillende parameters doet, zijn niet correct. Hij of zij verandert namelijk de opdracht. Als we de opdracht mogen wijzigen, is het natuurlijk wel waar dat als we de parameter met het aantal geboortes in het kleine ziekenhuis verhogen de oplossing nauwkeuriger gaat worden. De student heeft spijtig genoeg niet gedacht om het aantal dagen te verhogen. Hij of zij had de berekening een aantal keer over een grotere tijdspanne (bv. 3650 dagen = 10 jaar) moeten uitvoeren. De wet van de grote getallen zegt namelijk dat hoe meer simulaties u doet, hoe nauwkeuriger uw resultaat wordt.

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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=15540&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=15540&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15540&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 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 Hospital8256
#Males births in Large Hospital8169
#Female births in Small Hospital2747
#Male births in Small Hospital2728
Probability of more than 60 % of male births in Large Hospital0.063013698630137
Probability of more than 60 % of male births in Small Hospital0.156164383561644
#Days per Year when more than 60 % of male births occur in Large Hospital23
#Days per Year when more than 60 % of male births occur in Small Hospital57

\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 & 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 & 8256 \tabularnewline
#Males births in Large Hospital & 8169 \tabularnewline
#Female births in Small Hospital & 2747 \tabularnewline
#Male births in Small Hospital & 2728 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.063013698630137 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.156164383561644 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 23 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 57 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15540&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]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]8256[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]8169[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]2747[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]2728[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.063013698630137[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.156164383561644[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]23[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]57[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15540&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15540&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 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 Hospital8256
#Males births in Large Hospital8169
#Female births in Small Hospital2747
#Male births in Small Hospital2728
Probability of more than 60 % of male births in Large Hospital0.063013698630137
Probability of more than 60 % of male births in Small Hospital0.156164383561644
#Days per Year when more than 60 % of male births occur in Large Hospital23
#Days per Year when more than 60 % of male births occur in Small Hospital57


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 365 ; 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')