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Author*Unverified author*
R Software Modulerwasp_saundersetal71.wasp
Title produced by softwareSample Size
Date of computationMon, 09 Dec 2013 09:37:01 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Dec/09/t1386599883lkqgw50xeyue3d5.htm/, Retrieved Thu, 18 Apr 2024 22:58:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=231663, Retrieved Thu, 18 Apr 2024 22:58:25 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Sample Size] [] [2013-12-09 14:37:01] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\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 & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231663&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]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231663&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231663&T=0

As an alternative you can also use a 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'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Tabel 7.1: Steekproefomvang voor verschillende waarden van de populatieomvang op een betrouwbaarheidsniveau van 90%, een Proportie van 0.5 en een Power van 50%
Foutenmarge
Populatie5 %3 %2 %10 %
5042474929
10073889441
1509712513847
20011515817951
25013018821853
30014321525555
40016226132458
50017630038660
75019937652062
100021342962963
200023854691765
5000257653126467
10000263699144767
100000270746166368
1000000270751168868
10000000271751169168

\begin{tabular}{lllllllll}
\hline
Tabel 7.1: Steekproefomvang voor verschillende waarden van de populatieomvang op een betrouwbaarheidsniveau van  90%, een Proportie van  0.5 en een Power van  50% \tabularnewline
 & Foutenmarge \tabularnewline
Populatie & 5 % & 3 % & 2 % & 10 % \tabularnewline
 50 & 42 & 47 & 49 & 29 \tabularnewline
 100 & 73 & 88 & 94 & 41 \tabularnewline
 150 & 97 & 125 & 138 & 47 \tabularnewline
 200 & 115 & 158 & 179 & 51 \tabularnewline
 250 & 130 & 188 & 218 & 53 \tabularnewline
 300 & 143 & 215 & 255 & 55 \tabularnewline
 400 & 162 & 261 & 324 & 58 \tabularnewline
 500 & 176 & 300 & 386 & 60 \tabularnewline
 750 & 199 & 376 & 520 & 62 \tabularnewline
 1000 & 213 & 429 & 629 & 63 \tabularnewline
 2000 & 238 & 546 & 917 & 65 \tabularnewline
 5000 & 257 & 653 & 1264 & 67 \tabularnewline
 10000 & 263 & 699 & 1447 & 67 \tabularnewline
 100000 & 270 & 746 & 1663 & 68 \tabularnewline
 1000000 & 270 & 751 & 1688 & 68 \tabularnewline
 10000000 & 271 & 751 & 1691 & 68 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=231663&T=1

[TABLE]
[ROW][C]Tabel 7.1: Steekproefomvang voor verschillende waarden van de populatieomvang op een betrouwbaarheidsniveau van  90%, een Proportie van  0.5 en een Power van  50%[/C][/ROW]
[ROW][C][/C][C]Foutenmarge[/C][/ROW]
[ROW][C]Populatie[/C][C]5 %[/C][C]3 %[/C][C]2 %[/C][C]10 %[/C][/ROW]
 [ROW][C]50[/C][C]42[/C][C]47[/C][C]49[/C][C]29[/C][/ROW]
 [ROW][C]100[/C][C]73[/C][C]88[/C][C]94[/C][C]41[/C][/ROW]
 [ROW][C]150[/C][C]97[/C][C]125[/C][C]138[/C][C]47[/C][/ROW]
 [ROW][C]200[/C][C]115[/C][C]158[/C][C]179[/C][C]51[/C][/ROW]
 [ROW][C]250[/C][C]130[/C][C]188[/C][C]218[/C][C]53[/C][/ROW]
 [ROW][C]300[/C][C]143[/C][C]215[/C][C]255[/C][C]55[/C][/ROW]
 [ROW][C]400[/C][C]162[/C][C]261[/C][C]324[/C][C]58[/C][/ROW]
 [ROW][C]500[/C][C]176[/C][C]300[/C][C]386[/C][C]60[/C][/ROW]
 [ROW][C]750[/C][C]199[/C][C]376[/C][C]520[/C][C]62[/C][/ROW]
 [ROW][C]1000[/C][C]213[/C][C]429[/C][C]629[/C][C]63[/C][/ROW]
 [ROW][C]2000[/C][C]238[/C][C]546[/C][C]917[/C][C]65[/C][/ROW]
 [ROW][C]5000[/C][C]257[/C][C]653[/C][C]1264[/C][C]67[/C][/ROW]
 [ROW][C]10000[/C][C]263[/C][C]699[/C][C]1447[/C][C]67[/C][/ROW]
 [ROW][C]100000[/C][C]270[/C][C]746[/C][C]1663[/C][C]68[/C][/ROW]
 [ROW][C]1000000[/C][C]270[/C][C]751[/C][C]1688[/C][C]68[/C][/ROW]
 [ROW][C]10000000[/C][C]271[/C][C]751[/C][C]1691[/C][C]68[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=231663&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=231663&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Tabel 7.1: Steekproefomvang voor verschillende waarden van de populatieomvang op een betrouwbaarheidsniveau van 90%, een Proportie van 0.5 en een Power van 50%
Foutenmarge
Populatie5 %3 %2 %10 %
5042474929
10073889441
1509712513847
20011515817951
25013018821853
30014321525555
40016226132458
50017630038660
75019937652062
100021342962963
200023854691765
5000257653126467
10000263699144767
100000270746166368
1000000270751168868
10000000271751169168



Parameters (Session):
par1 = 0.05 ; par2 = 0.03 ; par3 = 0.02 ; par4 = 0.10 ; par5 = 0.90 ; par6 = 0.50 ; par7 = 0.50 ; par8 = 50 ;
Parameters (R input):
par1 = 0.05 ; par2 = 0.03 ; par3 = 0.02 ; par4 = 0.10 ; par5 = 0.90 ; par6 = 0.50 ; par7 = 0.50 ; par8 = 50 ;
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)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
par8 <- as.numeric(par8)
confidence <- par5
proportion <- par6
power <- par7
userpopsize <- par8
load(file='createtable')
compsize <- function(population,confidence,proportion,marginoferror,power)
{
par22 <- marginoferror*marginoferror
z <- abs(qnorm((1-confidence)/2)) + abs(qnorm(1-power))
dum <- z*z * proportion*(1-proportion)
size <- round(population * dum / (dum + (population-1) * par22))
return(size)
}
comprow <- function(population, confidence, proportion, marginoferror1, marginoferror2, marginoferror3, marginoferror4, power)
{
population <- as.integer(population) #this prevents scientific notation
row <- ''
row<-table.row.start(row)
row<-table.element(row,population,header=TRUE)
row<-table.element(row, compsize(population, confidence, proportion, marginoferror1, power))
row<-table.element(row, compsize(population, confidence, proportion, marginoferror2, power))
row<-table.element(row, compsize(population, confidence, proportion, marginoferror3, power))
row<-table.element(row, compsize(population, confidence, proportion, marginoferror4, power))
row<-table.row.end(row)
return(row)
}
npop <- array(NA, 200)
ppop <- array(NA, 200)
for (i in 1:200)
{
ppop[i] <- i * 100
npop[i] <- compsize(ppop[i], confidence, proportion, par1, power)
}
bitmap(file='test1.png')
dum <- paste(par1*100,'%')
plot(ppop,npop,col=2,main=paste('Steekproefomvang bij een Foutenmarge van ', dum), xlab='Populatieomvang', ylab='Steekproefomvang')
dev.off()
a<-table.start()
dum <- paste(confidence*100, '%, een Proportie van ',sep='')
dum <- paste(dum, proportion)
dum <- paste(dum, 'en een Power van ')
dum <- paste(dum, power*100)
dum <- paste(dum, '%',sep='')
a<-table.row.start(a)
a<-table.element(a,paste('Tabel 7.1: Steekproefomvang voor verschillende waarden van de populatieomvang op een betrouwbaarheidsniveau van ', dum),5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',1,TRUE)
a<-table.element(a,'Foutenmarge',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Populatie',1,TRUE)
a<-table.element(a, paste(par1*100,'%'),1,TRUE)
a<-table.element(a, paste(par2*100,'%'),1,TRUE)
a<-table.element(a, paste(par3*100,'%'),1,TRUE)
a<-table.element(a, paste(par4*100,'%'),1,TRUE)
a<-table.row.end(a)
a <- paste(a,comprow(userpopsize, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(100, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(150, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(200, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(250, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(300, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(400, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(500, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(750, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(1000, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(2000, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(5000, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(10000, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(100000, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(1000000, confidence, proportion, par1, par2, par3, par4, power))
a <- paste(a,comprow(10000000, confidence, proportion, par1, par2, par3, par4, power))
a<-table.end(a)
table.save(a,file='mytable.tab')