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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 computationMon, 13 Oct 2008 02:43:54 -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/13/t1223887532foqu2ehaaeyxnx7.htm/, Retrieved Fri, 17 May 2024 23:17:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15577, Retrieved Fri, 17 May 2024 23:17:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact248

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] [Grafiek met groot...] [2008-10-13 08:43:54] [b5110a3ab194da7214bdf478e0a05dbd] [Current]
-   P     [Exercise 1.13] [verbetering oefen...] [2008-10-16 13:13:25] [74be16979710d4c4e7c6647856088456]
Feedback Forum
2008-10-16 13:17:37 [66991d38d6a4b2d9fe97b6c889f3689c] [reply] 
De student begrijpt zeer goed dat hoe meer steekproeven er gebeuren hoe correcter de oplossing (wet van de grote getallen). ze verandert echter de foute parameter om tot een corercter resultaat te komen. door het aantal geboortes te wijzigen, wijzigt ze de opgave.
hierbij vind u een link naar een correcte wijziging van de parameter om tot een meer accuraat resultaat te komen. http://www.freestatistics.org/blog/date/2008/Oct/16/t1224162852272628q5fj8ehlu.htm
het is jammer dat er geen links zijn die aantonen dat de bekomen resultaten heel verschillend zijn wanneer men geen parameters aanpast.
2008-10-17 10:28:34 [Ciska Tanghe] [reply] 
Eerst en vooral heeft de student geen berekeningen weergegeven van het resultaat zonder dat er parameters gewijzigd worden.
Daarnaast heeft de student de verkeerde parameter verandert. Om een nauwkeuriger resultaat te bekomen, moet het aantal dagen veranderd worden. Je verandert de opgave als je het aantal geboortes verandert.
2008-10-19 12:58:31 [9142cf052ad32d043faa9486189092cf] [reply] 
De student heeft de wet van de grote getallen wel door, maar heeft een wijziging aangebracht in de opgaven door het aantal geboortes aan te passen. Om een zo nauwkeurig mogelijke oplossing op de gestelde vraag te bekomen moet men de parameter van het aantal dagen veranderen. Hoe groter het aantal dagen hoe meer nauwkeuriger het resultaat (parameter number of simulated days: 3650).

De student begrijpt de wet van de grote getallen. Hoe groter de steekproef is hoe nauwkeuriger het antwoord. Toch is het van belang de steekproef meerdere malen uit te voeren. Want hoe meer steekproeven je gaat uitvoeren hoe nauwkeuriger je antwoord wordt.

Ook is het opvallend dat per steekproef (zonder de parameters te wijzigen) het resultaat een kleine afwijking vertoont t.o.v het vorige dit is te verklaren doordat de computer willekeurig getallen neemt uit de database en het dus ook mogelijk is om te maken te krijgen met een afwijkend jaar.

Het is dus ook heel spijtig dat de student maar een enkele berekening heeft gemaakt en zo niet kan zien dat je een verschillend resultaat uitkomt per steekproef zonder dat je een wijziging aanbrengt in de parameters.

De meest accurate oplossing zou dan zijn dat het resultaat ligt tussen het grootste en het kleinste getal van de verschillende steekproeven die je hebt uitgevoerd (verschillende steekproeven zonder parameters te wijzigen).

Tip: probeer in de toekomst meerdere steekproeven te doen en de opgaven niet te wijzigen.
2008-10-19 13:29:10 [Dwayne Dauchy] [reply] 
student geeft in haar oplossing de juiste analyse over de correctheid van een waarschijnlijkheid alleen heeft ze in haar berekening de verkeerde parameter verandert om dit aan te tonen. De berekeningen waarbij de parameters htzelfde blijven maar de waarchijnlijkheid veranert ontbreekt.

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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=15577&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=15577&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15577&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 Hospital110
Expected number of births in Small Hospital40
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital19981
#Males births in Large Hospital20169
#Female births in Small Hospital7280
#Male births in Small Hospital7320
Probability of more than 60 % of male births in Large Hospital0.0109589041095890
Probability of more than 60 % of male births in Small Hospital0.0904109589041096
#Days per Year when more than 60 % of male births occur in Large Hospital4
#Days per Year when more than 60 % of male births occur in Small Hospital33

\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 & 110 \tabularnewline
Expected number of births in Small Hospital & 40 \tabularnewline
Percentage of Male births per day(for which the probability is computed) & 0.6 \tabularnewline
#Females births in Large Hospital & 19981 \tabularnewline
#Males births in Large Hospital & 20169 \tabularnewline
#Female births in Small Hospital & 7280 \tabularnewline
#Male births in Small Hospital & 7320 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0109589041095890 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.0904109589041096 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 4 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 33 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15577&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]110[/C][/ROW]
[ROW][C]Expected number of births in Small Hospital[/C][C]40[/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]19981[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]20169[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]7280[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]7320[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0109589041095890[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.0904109589041096[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]4[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]33[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15577&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15577&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 Hospital110
Expected number of births in Small Hospital40
Percentage of Male births per day(for which the probability is computed)0.6
#Females births in Large Hospital19981
#Males births in Large Hospital20169
#Female births in Small Hospital7280
#Male births in Small Hospital7320
Probability of more than 60 % of male births in Large Hospital0.0109589041095890
Probability of more than 60 % of male births in Small Hospital0.0904109589041096
#Days per Year when more than 60 % of male births occur in Large Hospital4
#Days per Year when more than 60 % of male births occur in Small Hospital33


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 110 ; par3 = 40 ; par4 = 0.6 ;
Parameters (R input):
par1 = 365 ; par2 = 110 ; par3 = 40 ; 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')