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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 computationSun, 12 Oct 2008 04:24:32 -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/12/t1223807098134rhn5a0ryszgk.htm/, Retrieved Sun, 19 May 2024 14:00:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=15413, Retrieved Sun, 19 May 2024 14:00:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

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] [exercise 1.13] [2008-10-12 10:24:32] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P       [Exercise 1.13] [EX 1.13 Vr 1 Corr...] [2008-10-19 14:25:54] [0996801648c22721e57e03a7deb595f2]
Feedback Forum
2008-10-19 14:31:00 [Olivier Uyttendaele] [reply] 
Wanneer student het aantal geboortes aanpast, raakt hij aan de vraagstelling.
De parameter om de waarnemingen te verhogen is het aantal dagen.

Correcte oplossing op basis van model student;
http://www.freestatistics.org/blog/index.php?v=date/2008/Oct/19/t1224426405rl3y0rogx11gn27.htm

De resultaten zullen per berekening nog veranderen, toch zullen de schommelingen tussen de resultaten beperkt zijn
2008-10-19 19:33:40 [d98e91d35a68264f810b457e53f1edf1] [reply] 
De student geeft geen antwoord op de vraag of de de waarschijnlijkheid van 16.348% +60% mannelijke geboortes in het kleine ziekenhuis heel accuraat is. De student doet hiervoor wel 3x de berekeningen opnieuw, maar geeft verder geen antwoord.

De student heeft de 2de vraag verkeerd begrepen: het gaat steeds over het kleine ziekenhuis (dus enkel de resultaten van het kleine ziekenhuis moeten weergegeven worden) en geeft als antwoord dat in het grote ziekenhuis de resultaten het meest accuraat zijn.
De student heeft de foute parameter gekozen om de accuraatheid te vergroten. Uit de tekst volgt wel dat de student begrijpt dat het resultaat accurater wordt naarmate er meerdere geboortes zijn, maar hij/zij geeft aan dat het aantal geboortes per dag in het ziekenhuis op 1 jaar moet vergroot worden. Maar daarmee verandert de student de opgave. Eigenlijk moeten de meerdere geboortes over een langere periode bekeken worden.

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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 time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15413&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]2 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=15413&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15413&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.)
Number of simulated days365
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 Hospital8198
#Males births in Large Hospital8227
#Female births in Small Hospital2777
#Male births in Small Hospital2698
Probability of more than 60 % of male births in Large Hospital0.0794520547945206
Probability of more than 60 % of male births in Small Hospital0.150684931506849
#Days per Year when more than 60 % of male births occur in Large Hospital29
#Days per Year when more than 60 % of male births occur in Small Hospital55

\begin{tabular}{lllllllll}
\hline
Exercise 1.13 p. 14 (Introduction to Probability, 2nd ed.) \tabularnewline
Number of simulated days & 365 \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 & 8198 \tabularnewline
#Males births in Large Hospital & 8227 \tabularnewline
#Female births in Small Hospital & 2777 \tabularnewline
#Male births in Small Hospital & 2698 \tabularnewline
Probability of more than 60 % of male births in Large Hospital & 0.0794520547945206 \tabularnewline
Probability of more than 60 % of male births in Small Hospital & 0.150684931506849 \tabularnewline
#Days per Year when more than 60 % of male births occur in Large Hospital & 29 \tabularnewline
#Days per Year when more than 60 % of male births occur in Small Hospital & 55 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=15413&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]365[/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]8198[/C][/ROW]
[ROW][C]#Males births in Large Hospital[/C][C]8227[/C][/ROW]
[ROW][C]#Female births in Small Hospital[/C][C]2777[/C][/ROW]
[ROW][C]#Male births in Small Hospital[/C][C]2698[/C][/ROW]
[ROW][C]Probability of more than 60 % of male births in Large Hospital[/C][C]0.0794520547945206[/C][/ROW]
[C]Probability of more than 60 % of male births in Small Hospital[/C][C]0.150684931506849[/C][/ROW]
[ROW][C]#Days per Year when more than 60 % of male births occur in Large Hospital[/C][C]29[/C][/ROW]
[C]#Days per Year when more than 60 % of male births occur in Small Hospital[/C][C]55[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=15413&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=15413&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 days365
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 Hospital8198
#Males births in Large Hospital8227
#Female births in Small Hospital2777
#Male births in Small Hospital2698
Probability of more than 60 % of male births in Large Hospital0.0794520547945206
Probability of more than 60 % of male births in Small Hospital0.150684931506849
#Days per Year when more than 60 % of male births occur in Large Hospital29
#Days per Year when more than 60 % of male births occur in Small Hospital55


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 365 ; par2 = 45 ; par3 = 15 ; par4 = 0.6 ;
Parameters (R input):
par1 = 365 ; 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')