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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 computationTue, 14 Oct 2008 01:13:38 -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/14/t1223968752twbp26t550eompv.htm/, Retrieved Sun, 19 May 2024 16:33:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=16203, Retrieved Sun, 19 May 2024 16:33:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150

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] [baby boys small h...] [2008-10-14 07:13:38] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-10-15 15:15:52 [c4ccf1f44d59ce687616256b9e80d6b0] [reply] 
Deze student trekt op basis van één berekening de conclusie dat het bekomen resultaat het enige is. Door deze berekening meerdere malen uit te voeren zien we dat de resultaten kunnen verschillen. Zo bekomen we trouwens ook een beter zicht op het resultaat, want een schatting wordt nauwkeuriger naarmate er meerdere resultaten zijn.
2008-10-17 14:53:37 [Annelies Michiels] [reply] 
  2008-10-17 15:14:56 [Annelies Michiels] [reply] 
Ik kan niet echt veel feedback geven over je opdracht omdat je maar 1 moment hebt geblogt. Let erop dat je volgende opdracht meer blogt en de links ook opneemt in je taak.

Vraag 2
De eerste vraag heb je verkeerd begrepen denk ik. Je moest nagaan of de 16% in het voorbeeld een juiste oplossing was of niet. Het feit dat het kleine ziekenhuis een grotere kans heeft dan het grote ziekenhuis om meer dan 60% mannelijke geboorten te hebben op 1 dag was reeds bekend. Het was dus de bedoeling om het programma meerdere keren te laten lopen en elk resultaat te bloggen. Als je dit deed zou je merken dat het resultaat schommelt tussen de 13 en de 17%. Men kan dus concluderen dat 1 juiste oplossing niet bestaat.
We kunnen wel proberen de resultaten dichter bij elkaar te laten liggen door parameter 1 (het aantal dagen) te veranderen naar 3650. Als we het programma een aantal keer laten lopen kunnen we concluderen dat het resultaat dan zal schommelen tussen de 13 en 14%. Om de schommelingen zo klein mogelijk te maken moet men het programma dus over zoveel mogelijk jaren laten lopen.

Vraag 2
Je hebt in vraag 2 vraag 1 opgelost. In vraag 2 moest je nagaan wat er gebeurd als we het % veranderen naar 80%. Als we het programma weer een aantal keren laten lopen (elk resultaat bloggen)komen we tot de conclusie dat het resultaat ongeveer 0,2% is. In vraag 3 kom je inderdaad tot deze conclusie, maar omdat je het programma maar 1 keer hebt laten lopen kan je niet weten dat het resultaat schommelt.

Vraag 3
Hier moet worden nagegaan wat de kans is dat er minder dan 60% jongens worden geboren op 1 dag. Om tot dit resultaat te komen moeten we de R-Code veranderen:

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

Ook de tekst moet worden verandert in:

dum1 <- paste('Probability of less 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 less 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)

2008-10-17 16:47:19 [Gregory Van Overmeiren] [reply] 
Wel eerst en vooral, probeer er wat meer links bij te zetten, je hebt er slechts één waardoor het idd moeilijk is om feedback te geven en dit te reproduceren. Je geeft ook geen antwoord op vraag één nl welke parameter er veranderd moet worden (dit was het #dagen veranderen naar 3650 dagen). Zo gaan je berekeningen nauwkeuriger worden omdat je het bekijkt over een langere periode. (= wet van de grote getallen)
Je berekening (link) heeft volgens mij betrekking op vraag 2 want je hebt 80 % ingevuld. ook hier weer had je het aantal dagen moeten veranderen naar 3650 dagen voor een nauwkeuriger resultaat.Voor vraag 3 heb je ook de link vergeten en kan ik niet zien hoe je aan die aantal dagen komt.De student hierboven legt het perfect uit wat je moest aanpassen in de R-code.
Een kleine aanvulling : niet vergeten in de berekening van de derde vraag je dagen terug op 365 zetten omdat het gevraagd wordt in de opgave.
2008-10-19 13:38:53 [Nathalie Koulouris] [reply] 
De student weet hoe hij dit moet berekenen maar geeft geen verdere toelichting en trekt geen conclusie. Door de berekening een aantal keren uit te voeren kan je vaststellen dat het zelden gebeurt dat meer dan 80% van de geboortes jongens zijn. Als je de berekening een aantal keer uitvoert zal je zien dat je telkens een getal uitkomt dat zeer dicht bij nul 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 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=16203&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=16203&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16203&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)0.8
#Females births in Large Hospital8167
#Males births in Large Hospital8258
#Female births in Small Hospital2689
#Male births in Small Hospital2786
Probability of more than 80 % of male births in Large Hospital0
Probability of more than 80 % of male births in Small Hospital0.00273972602739726
#Days per Year when more than 80 % of male births occur in Large Hospital0
#Days per Year when more than 80 % of male births occur in Small Hospital1

\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) & 0.8 \tabularnewline
#Females births in Large Hospital & 8167 \tabularnewline
#Males births in Large Hospital & 8258 \tabularnewline
#Female births in Small Hospital & 2689 \tabularnewline
#Male births in Small Hospital & 2786 \tabularnewline
Probability of more than 80 % of male births in Large Hospital & 0 \tabularnewline
Probability of more than 80 % of male births in Small Hospital & 0.00273972602739726 \tabularnewline
#Days per Year when more than 80 % of male births occur in Large Hospital & 0 \tabularnewline
#Days per Year when more than 80 % of male births occur in Small Hospital & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=16203&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]0.8[/C][/ROW]
[ROW][C]#Females births in Large Hospital[/C][C]8167[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]8258[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]2689[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]2786[/C][/ROW]
[ROW][C]Probability of more than 80 % of male births in Large Hospital[/C][C]0[/C][/ROW]
[C]Probability of more than 80 % of male births in Small Hospital[/C][C]0.00273972602739726[/C][/ROW]
[ROW][C]#Days per Year when more than 80 % of male births occur in Large Hospital[/C][C]0[/C][/ROW]
[C]#Days per Year when more than 80 % of male births occur in Small Hospital[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=16203&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=16203&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)0.8
#Females births in Large Hospital8167
#Males births in Large Hospital8258
#Female births in Small Hospital2689
#Male births in Small Hospital2786
Probability of more than 80 % of male births in Large Hospital0
Probability of more than 80 % of male births in Small Hospital0.00273972602739726
#Days per Year when more than 80 % of male births occur in Large Hospital0
#Days per Year when more than 80 % of male births occur in Small Hospital1


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.8 ;
Parameters (R input):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.8 ;
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')