FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 15:32:20 +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/t1286897488thfrmxb43j6awvu.htm/, Retrieved Tue, 30 Apr 2024 14:23:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=82958, Retrieved Tue, 30 Apr 2024 14:23:23 +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 2] [2010-10-12 15:32:20] [bab9730fcc23dcda9f14e2a014bd4fbb] [Current]
Feedback Forum
2010-10-15 12:48:54 [] [reply] 
Het klopt dat als we het aantal gesimuleerde dagen verhogen dat de nauwkeurigheid van de kansberekening toeneemt, dit omdat het steekproefgemiddelde dichter bij het populatiegemiddelde gaat liggen en dus accurater is.

Toch is het niet zo dat als we in de formule voor de binomiale verdeling een grotere 'n' waarde invullen dat het resultaat nauwkeuriger gaat zijn, dit is enkel het geval als we proefondervindelijk te werk gaan en niet bij het gebruik van een formule. Daarom is het altijd nuttiger en accurater om de formule te gebruiken.
2010-10-15 12:57:14 [] [reply] 
Het klopt dat als we het aantal gesimuleerde dagen verhogen dat de nauwkeurigheid van de kansberekening toeneemt, dit omdat het steekproefgemiddelde dichter bij het populatiegemiddelde gaat liggen en dus accurater is.

Toch is het niet zo dat als we in de formule voor de binomiale verdeling een grotere 'n' waarde invullen dat het resultaat nauwkeuriger gaat zijn, dit is enkel het geval als we proefondervindelijk te werk gaan en niet bij het gebruik van een formule. Daarom is het altijd nuttiger en accurater om de formule te gebruiken.
2010-10-16 15:13:30 [Andries Achten] [reply] 
Dit klopt inderdaad. Dat door de 'n' in de formule te verhogen een beter resultaat wordt verkregen klopt uiteraard niet.

Post a new message

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82958&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=82958&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82958&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'George Udny Yule' @ 72.249.76.132






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days3285
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 Hospital73972
#Males births in Large Hospital73853
#Female births in Small Hospital24555
#Male births in Small Hospital24720
Probability of more than 60 % of male births in Large Hospital0.0687975646879756
Probability of more than 60 % of male births in Small Hospital0.159208523592085
#Days per Year when more than 60 % of male births occur in Large Hospital25.1111111111111
#Days per Year when more than 60 % of male births occur in Small Hospital58.1111111111111

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 3285 \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 & 73972 \tabularnewline
#Males births in Large Hospital & 73853 \tabularnewline
#Female births in Small Hospital & 24555 \tabularnewline
#Male births in Small Hospital & 24720 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0687975646879756 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.159208523592085 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 25.1111111111111 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 58.1111111111111 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=82958&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]3285[/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]73972[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]73853[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]24555[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]24720[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0687975646879756[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.159208523592085[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]25.1111111111111[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]58.1111111111111[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=82958&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=82958&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 days3285
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 Hospital73972
#Males births in Large Hospital73853
#Female births in Small Hospital24555
#Male births in Small Hospital24720
Probability of more than 60 % of male births in Large Hospital0.0687975646879756
Probability of more than 60 % of male births in Small Hospital0.159208523592085
#Days per Year when more than 60 % of male births occur in Large Hospital25.1111111111111
#Days per Year when more than 60 % of male births occur in Small Hospital58.1111111111111


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 3285 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 3285 ; 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')