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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 computationMon, 13 Oct 2008 13:10:30 -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/t12239254647svklqgdmrv1jcy.htm/, Retrieved Sun, 19 May 2024 14:56:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15964, Retrieved Sun, 19 May 2024 14:56:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

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] [Exercise 1.13 Lan...] [2008-10-13 19:10:30] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-10-17 12:32:00 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
Om de preciesie van de uitkomst te verhogen moet men volgens de 'wet van de grote getallen' een zo groot mogelijk aantal observaties uitvoeren. De student heeft daarom het aantal geobserveerde dagen van 365 naar 3650 gebracht. Door deze berekening te maken komt hij dus een preciesere uitkomst uit. Goed gedaan!
2008-10-17 13:06:51 [339a57d8a4d5d113e4804fc423e4a59e] [reply] 
Om de uitkomst precieser te maken, heeft de student het aantal observaties vergroot van 365 naar 3650. Volgens de wet van de grote getallen wordt een uitkomst inderdaad nauwkeuriger naarmate men het aantal simulaties vergroot. De student heeft dus een correcte bewerking gemaakt.

De geblogde bewerking werd anononiem gepost en is dus niet verifieerbaar.
2008-10-18 15:20:56 [Siem Van Opstal] [reply] 
dit is een correcte oplossing. door het aantal dagen te vergroten naar 10 jaar wordt de temmijn en zo ook het aantal observaties veel groter. door de wet van de grote getallen worden de resultaten nauwkeuriger. Ik heb 3 berekeningen gedaan met een termijn van 10 jaar en mijn resultaten waren 14.9; 15.2 en 15.7. Het verschil tussen de 3 blijft kleiner dan 1%, de nauwkeurigheid is dus groter geworden. http://www.freestatistics.org/blog/date/2008/Oct/18/t1224338959zf5zamm4xzs3vig.htm
http://www.freestatistics.org/blog/date/2008/Oct/18/t12243395427kaytpsb769bzkj.htm
http://www.freestatistics.org/blog/date/2008/Oct/18/t1224339681h1eum6fvzg5vqge.htm

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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 time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15964&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]5 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=15964&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15964&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






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 Hospital81928
#Males births in Large Hospital82322
#Female births in Small Hospital27481
#Male births in Small Hospital27269
Probability of more than 60 % of male births in Large Hospital0.0673972602739726
Probability of more than 60 % of male births in Small Hospital0.145753424657534
#Days per Year when more than 60 % of male births occur in Large Hospital24.6
#Days per Year when more than 60 % of male births occur in Small Hospital53.2

\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 & 81928 \tabularnewline
#Males births in Large Hospital & 82322 \tabularnewline
#Female births in Small Hospital & 27481 \tabularnewline
#Male births in Small Hospital & 27269 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0673972602739726 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.145753424657534 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 24.6 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 53.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15964&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]81928[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]82322[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27481[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27269[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0673972602739726[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.145753424657534[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]24.6[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]53.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15964&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15964&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 Hospital81928
#Males births in Large Hospital82322
#Female births in Small Hospital27481
#Male births in Small Hospital27269
Probability of more than 60 % of male births in Large Hospital0.0673972602739726
Probability of more than 60 % of male births in Small Hospital0.145753424657534
#Days per Year when more than 60 % of male births occur in Large Hospital24.6
#Days per Year when more than 60 % of male births occur in Small Hospital53.2


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')