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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 computationSat, 03 Oct 2009 03:43:18 -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/2009/Oct/03/t125456307087ecw93o8wciqp8.htm/, Retrieved Sat, 04 May 2024 06:19:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=43471, Retrieved Sat, 04 May 2024 06:19:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsHSWWS1V1
Estimated Impact163

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] [Babies calculator q2] [2009-10-03 09:43:18] [0ac076d75ca34fca6c6c006e00f5ed6b] [Current]
-   P     [Exercise 1.13] [WS1 review1 vr2] [2009-10-08 10:51:13] [023d83ebdf42a2acf423907b4076e8a1]
Feedback Forum
2009-10-09 10:51:44 [Sofie Coppens] [reply] 
Hoe hoger het aantal dagen, hoe groter de nauwkeurigheid. De grafiek gaat zich steeds meer stabiliseren en er zijn dus minder schommelingen te zien.
Dus om nog nauwkeuriger te werk te gaan kan je kiezen voor 3650 dagen of door de r-code aan te passen (dan kan je nog meer dagen invullen). Het resultaat is nauwkeuriger als je langer simuleert maar je gaat nooit een exact antwoord krijgen.
2009-10-12 07:11:54 [eebb4efff92c79c7fb3158df2047b545] [reply] 
Uw analyse is correct, de uitvoering echter niet. De wet van de grote getallen (Law of Large Numbers) bepaalt immers dat hoe groter de n-waarde, in casu het aantal gesimuleerde dagen, hoe groter de nauwkeurig zal zijn. Het is dus essentieel dat u de n-waarde (het aantal gesimuleerde dagen) maximaliseert. In Chapter 1 Discrete Probability Distributions, van het pdf-document dat u terug vindt onder hoofdstuk 2 van deze cursus, zal u meer informatie vinden m.b.t. de theoretische uiteenzetting van deze wet.
2009-10-12 07:17:31 [Mathias Danneel] [reply] 
Uw analyse is correct, de uitvoering echter niet. De wet van de grote getallen (Law of Large Numbers) bepaalt immers dat hoe groter de n-waarde, in casu het aantal gesimuleerde dagen, hoe groter de nauwkeurig zal zijn. Het is dus essentieel dat u de n-waarde (het aantal gesimuleerde dagen) maximaliseert. In Chapter 1 Discrete Probability Distributions, van het pdf-document dat u terug vindt onder hoofdstuk 2 van deze cursus, zal u meer informatie vinden m.b.t. de theoretische uiteenzetting van deze wet.

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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 time4 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 & 4 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=43471&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]4 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=43471&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=43471&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 time4 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days1460
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 Hospital32980
#Males births in Large Hospital32720
#Female births in Small Hospital11095
#Male births in Small Hospital10805
Probability of more than 60 % of male births in Large Hospital0.0643835616438356
Probability of more than 60 % of male births in Small Hospital0.138356164383562
#Days per Year when more than 60 % of male births occur in Large Hospital23.5
#Days per Year when more than 60 % of male births occur in Small Hospital50.5

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 1460 \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 & 32980 \tabularnewline
#Males births in Large Hospital & 32720 \tabularnewline
#Female births in Small Hospital & 11095 \tabularnewline
#Male births in Small Hospital & 10805 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0643835616438356 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.138356164383562 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 23.5 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 50.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=43471&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]1460[/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]32980[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]32720[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]11095[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]10805[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0643835616438356[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.138356164383562[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]23.5[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]50.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=43471&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=43471&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 days1460
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 Hospital32980
#Males births in Large Hospital32720
#Female births in Small Hospital11095
#Male births in Small Hospital10805
Probability of more than 60 % of male births in Large Hospital0.0643835616438356
Probability of more than 60 % of male births in Small Hospital0.138356164383562
#Days per Year when more than 60 % of male births occur in Large Hospital23.5
#Days per Year when more than 60 % of male births occur in Small Hospital50.5


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1460 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 1460 ; 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')