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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 computationTue, 12 Oct 2010 16:22:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Oct/12/t1286900497im2t67d9fx5ltl2.htm/, Retrieved Tue, 30 Apr 2024 12:20:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=82972, Retrieved Tue, 30 Apr 2024 12:20:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

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] [task 1] [2010-10-12 16:17:39] [8441f95c4a5787a301bc621ebc7904ca]
F   P     [Exercise 1.13] [task 2] [2010-10-12 16:22:32] [fff0a1ca5ad3b1801f382406d5a383a7] [Current]
Feedback Forum
2010-10-16 09:08:39 [347d11d64cf4ded9ba0714e7297d928b] [reply] 
Niet volledig (1/2).
Als je een kleiner aantal tests uitvoert en er zit een uitzondering bij kan die niet gecompenseerd worden. Daarom dat we de grote van de stochastische variabele verhogen omdat dit de nauwkeurigheid ten goede komt. En zoals je bij deze grafieken ziet gaan ze beduidend snel over naar een convergentie.
En je kan zeker de binomiaalverdeling gebruiken. Dit is zelfs beter omdat het geen last heeft van uitzonderingen en heeft ook geen probleem met eventuele afrondingen.
2010-10-18 18:19:43 [] [reply] 
Niet volledig (1/2)
Door de periode langer te maken, stijgt inderdaad de betrouwbaarheid. Dit komt omdat er een mogelijkheid bestaat dat er in de 1e simulatie een fout zit maar het is bijna onmogelijk dat je gedurende 10 jaar slechte metingen hebt. Hoe langer de periode, hoe meer de grafiek dus geconvergeerd wordt.
Wat je vergeten te zeggen bent, is dat je de formule van de binomiaal verdeling hier kan gebruiken. Deze formule is wetenschappelijker en nauwkeuriger. De formule kunnen we als volgt interpreteren: p = kans op de geboorte van een jongen en q = kans op de geboorte van een meisje. Beide zijn in dit voorbeeld 0,5. Verder hebben we x = 0,6 (hoeveel % jongens we willen hebben) en n = 45 (aantal geboortes in de grote ziekenhuizen)Dit zou op een kans van 6,7% komen voor de grote ziekenhuizen.

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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 time7 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 & 7 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82972&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]7 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=82972&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82972&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 time7 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






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 Hospital81745
#Males births in Large Hospital82505
#Female births in Small Hospital27521
#Male births in Small Hospital27229
Probability of more than 60 % of male births in Large Hospital0.0789041095890411
Probability of more than 60 % of male births in Small Hospital0.137534246575342
#Days per Year when more than 60 % of male births occur in Large Hospital28.8
#Days per Year when more than 60 % of male births occur in Small Hospital50.2

\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 & 81745 \tabularnewline
#Males births in Large Hospital & 82505 \tabularnewline
#Female births in Small Hospital & 27521 \tabularnewline
#Male births in Small Hospital & 27229 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0789041095890411 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.137534246575342 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 28.8 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 50.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82972&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]81745[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]82505[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]27521[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]27229[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0789041095890411[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.137534246575342[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]28.8[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]50.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=82972&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82972&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 Hospital81745
#Males births in Large Hospital82505
#Female births in Small Hospital27521
#Male births in Small Hospital27229
Probability of more than 60 % of male births in Large Hospital0.0789041095890411
Probability of more than 60 % of male births in Small Hospital0.137534246575342
#Days per Year when more than 60 % of male births occur in Large Hospital28.8
#Days per Year when more than 60 % of male births occur in Small Hospital50.2


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')