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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 computationFri, 10 Oct 2008 11:49:29 -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/10/t1223660995eh5h0tckgtjf7f3.htm/, Retrieved Sat, 18 May 2024 04:46:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15254, Retrieved Sat, 18 May 2024 04:46:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154

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] [] [2008-10-10 17:31:50] [74be16979710d4c4e7c6647856088456]
F   P       [Exercise 1.13] [] [2008-10-10 17:49:29] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-10-16 17:30:52 [Ken Van den Heuvel] [reply] 
Persoonlijk zou ik hier de periode gemaximaliseerd hebben en meer reproducties gedaan hebben. Zo verbeter je de nauwkeurigheid immers nog meer.
2008-10-18 15:29:51 [Ellen Smolders] [reply] 
De student heeft de correcte parameter aangepast, toch zou het beter zijn geweest indien deze in 3650 dagen had gewijzigd, dan zou hij het meest nauwkeurige resultaat hebben verkregen.
De parameter 'aantal dagen' werd veranderd doordat men inderdaad een veel neuwkeuriger en stabieler resultaat bekomt, wanneer men de aantal simulaties verhoogd.(= wet van grote getallen). We kunnen zien uit de berekeningen (referenties 2 en 3) dat de curve veel stabieler en 'vlakker' verloopt naarmate het aantal dagen verhoogt. Dit betekent dat de waarschijnlijkheidsgraad verkleint.
2008-10-19 09:27:33 [Carl Heselmans] [reply] 
Ik sluit mij aan bij mijn medestudenten. Meer kan ik hier niet meer op zeggen. Enkel je naam niet vergeten bij je berekening te zetten.
2008-10-20 18:21:23 [Martjin De Swert] [reply] 
Alles wat er over te zeggen valt, is al gezegd geweest.

correcte parameter is aangepast, MAAR aantal dagen had beter 3650 geweest om het nauwkeurigste resultaat te bekomen.

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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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15254&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15254&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15254&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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days2920
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 Hospital65734
#Males births in Large Hospital65666
#Female births in Small Hospital21846
#Male births in Small Hospital21954
Probability of more than 60 % of male births in Large Hospital0.0623287671232877
Probability of more than 60 % of male births in Small Hospital0.150342465753425
#Days per Year when more than 60 % of male births occur in Large Hospital22.75
#Days per Year when more than 60 % of male births occur in Small Hospital54.875

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 2920 \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 & 65734 \tabularnewline
#Males births in Large Hospital & 65666 \tabularnewline
#Female births in Small Hospital & 21846 \tabularnewline
#Male births in Small Hospital & 21954 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0623287671232877 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.150342465753425 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 22.75 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 54.875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15254&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]2920[/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]65734[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]65666[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]21846[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]21954[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0623287671232877[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.150342465753425[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]22.75[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]54.875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15254&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15254&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 days2920
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 Hospital65734
#Males births in Large Hospital65666
#Female births in Small Hospital21846
#Male births in Small Hospital21954
Probability of more than 60 % of male births in Large Hospital0.0623287671232877
Probability of more than 60 % of male births in Small Hospital0.150342465753425
#Days per Year when more than 60 % of male births occur in Large Hospital22.75
#Days per Year when more than 60 % of male births occur in Small Hospital54.875


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 2920 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 2920 ; 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')